Anthropic announced Project Glasswing on April 7th, a cybersecurity initiative built around an unreleased model called Mythos. Anthropic claims that an unintended consequence of their training and scaling has led to Mythos being unusually capable at vulnerability discovery and exploit generation, and is convinced that the model is too powerful and dangerous to be released to the public.

We’ve heard this narrative before. Μύθος means story in Greek, and it has become clear from the initial reactions that the narrative surrounding Mythos is all over the place both online and in the mainstream media.

AI researchers like Gary Marcus are inclined to believe this is mostly hype. Same narrative from an AI frontier company, different day. We won’t let you use the model, but you should trust the paper we’ve released on it, and you should be thanking us for not releasing this into the wild.

On the other hand, our favorite doomers, like Eliezer Yudkowsky, are calling for you to immediately back up your data onto airgapped hard drives. If even half of what Anthropic is claiming is true, taking extra precautions isn’t just smart, it is the most logical choice. Better safe than sorry, right?

Anthropic’s messaging was clear on this: it is a watershed moment for security. Anthropic is partnering with Apple, Cisco, Google, Microsoft, NVIDIA and others. The top dogs involved in critical software infrastructure get to play with Mythos first (and, just maybe, assist with hardening the defensive perimeter of the internet).

Mythos certainly got our attention. But it feels like the truth lies somewhere in the middle. It isn’t the end of the internet as we know it, but it also isn’t a nothingburger.

Like you, I wanted to separate the signal from the noise on this. So let’s do that.

Why Mythos is Overhyped

Let’s address first many of the reasons why people think Mythos is overhyped. To start, the public cannot verify the strongest claims. We’re relying on the information from insiders, internal write-ups and media accounts. It can be hard to separate the doomer marketing from the reality.

The flashiest results are selective, and materially easier than they would be in the real world. Anthropic was using agentic scaffolding in a container with source code alongside a prompt that was basically ‘find a security vulnerability in this program’. The data derived from these results was from a model that was heavily tooled, guided, and optimized to maximize success.

There is a difference between proof-of-capability versus real-world operational threat. The Firefox exploit story that circulated is a great example of this. The model was given the best-case scenario for finding exploits, and did not have to deal with typical defense-in-depth mitigations.

Anthropic admits that most of the sensational vulnerabilities and exploits cannot be publicly disclosed, because they haven’t been patched. Again, this is the responsible thing to do, but it creates a situation where the evidence we’re being asked to trust is hidden.

This deep dive is enlightening. As the authors point out, when free and open-weight models are asked to provide a similar analysis, they also found the flagship OpenBSD bug. The OpenBSD vulnerability was one of the strongest and most commonly cited reasons in the media for being seriously alarmed by the abilities of Mythos. But that isn’t the full picture. Anthropic burned thousands of dollars of tokens in a brute-force attempt to find more bugs. A closer headline is this: ‘a very strong model, running at scale inside a carefully designed search scaffold, found a meaningful bug at a nontrivial aggregate cost’.

The other big project that was cited was FFmpeg and the pattern here is the same. Brute force, scaffolding, time, and money. Mythos found vulnerabilities after several hundred runs, and after burning nearly ten thousand dollars. It is still impressive, but it isn’t magic.

A model finding vulnerabilities and exploiting vulnerabilities is not the same thing. After all, we’re well aware of Claude’s constitution, and Anthropic’s models are arguably the most ‘aligned’ models out there. They can still be jailbroken, but they naturally will resist if pushed to engage in nefarious activity.

The conditions under which a model arrives at its results matter. Was it sandboxed? How much was it guided by a person? How long can a model like Mythos run at a normal organization without having the kind of advanced automated in-house scaffolding that Anthropic has built? There are so many variables, and still so many unanswered questions.

Anthropic is trying to convince the public, lawmakers, and investors that it is doing the right thing. That it is the ‘good AI’ company, and nothing like OpenAI. It is trying to convince enterprise organizations that it has the best models for infrastructure and security work, and that its frontier lab is #1. These aims of the business clash with the claims, and extraordinary claims require extraordinary evidence.

The public is not one release away from universal cyber collapse, but it is very clear that current frontier systems can do surprisingly offensive work. Everything needs to be taken with a grain of salt.

So, what about the other side?

Why Mythos is a Big Deal

Mythos can identify and exploit zero-day vulnerabilities across every major operating system and every major web browser when directed. Anthropic backs this up with detailed write-ups.

The OpenBSD example is also worth digging into more:

• Anthropic says Mythos identified a vulnerability in OpenBSD’s SACK implementation that could let an attacker crash any OpenBSD host responding over TCP

• The company emphasizes that the bug was tied to a 1998 addition of SACK support and exploited subtle interactions around sequence number arithmetic, linked-list hole tracking, and signed integer overflow

OpenBSD is a famously security-focused operating system. This is not trivial.

The economics of vulnerability discovery are changing. What costs 20,000 dollars to run today on a server cluster could cost 500 dollars in a few years on consumer hardware.

I’ve been running Gemma 4 for the last week on my phone and computer, and it is incredible what these local models can do fully offline. Just imagine where we’ll be with local models and their potential capability in a few years. Uncensored and black-hat hacking oriented genAI models will exist, and they will be as easy to download as gaming emulators. The security of the internet is going to have to deal with that.

The capability jump is real. In the Firefox comparison, where Opus 4.6 and Mythos were asked to find vulnerabilities in Firefox 147’s JavaScript engine, Opus was only able to develop working exploits two times out of several hundred attempts. Mythos, on the other hand, was able to develop working exploits 181 times. This is a dramatic jump in exploit ability, even if you discount for harness and missing sandbox mitigations.

The capabilities of Mythos are well documented and numerous:

• For FreeBSD, Mythos fully autonomously identified and exploited a 17-year-old remote code execution vulnerability in FreeBSD’s NFS server, triaged as CVE-2026-4747, allowing an unauthenticated attacker to obtain full root access.

• For Linux kernel chaining example: Mythos successfully chained together two, three, and sometimes four vulnerabilities to achieve local privilege escalation, including combinations of KASLR bypasses, heap corruption, and struct manipulation.

• Mythos autonomously discovered the needed read and write primitives in multiple browsers, assembled them into JIT heap sprays, and in one case helped produce a webpage that, when visited, would let the attacker write directly to the operating system kernel.

Mythos wasn’t trained for cyber offense, and these capabilities are a downstream consequence of general improvements across the board. This makes governing AI even more important, and should be concerning to anyone following the progress.

The benchmarks show the improvements. Even if all you believe is just the benchmarks, and disregard everything else, it is still notable. There are major SWE-bench jumps when compared to Opus, so emergent capabilities do not come as a major shocker.

Anthropic had a review period before releasing Mythos internally, and had a fear that the model wouldn’t even be safe enough to use at all. The model was noted by researchers as having a tendency to lie or take shortcuts to achieve user-directed goals at all costs, and exhibited ‘human-like’ latent emotions. Of course, these models are trained on language, and this isn’t the first time anyone has made these observations. The model has other notable eccentricities, like ending conversations with a single turtle emoji when it felt interactions were no longer necessary or productive.

Non-security engineers at Anthropic got exploit outputs using Mythos. They weren’t security engineers and they didn’t have backgrounds in cybersecurity. Offensive skills can and will become more accessible. You don’t even need to be a prompt engineer.

My Take

It is impossible to disentangle the AI doomerism marketing from the real concerns. Project Glasswing is also brilliantly designed, and getting major vendors like Microsoft and Cisco to adopt and eventually rely on Anthropic’s tools will make Claude indispensable. Anthropic is pushing to make Claude a major part of vendor ecosystems, and what better way to do that than with an alarmist post with insane benchmarks and figures?

Anthropic’s deliberation around using Mythos internally has given me pause. I think the company does legitimately think the model has the capability to be enormously harmful, and the information they’ve provided to back this up has become headline news for a reason.

AI doesn’t have to be AGI to wreak havoc on the internet as we know it. AI doesn’t have to be conscious to disrupt our global economy, and it doesn’t have to be self-aware to cause major damage. The slow, deliberate rollout and careful testing of this software appears to have been the only rational option. Other labs aren’t far behind, and releasing it into the wild knowing about the risk would have been a potential disaster for Anthropic.

I don’t think it is a nothingburger. The world isn’t ending tomorrow, but I strongly think that the increased capabilities of models like Mythos will accelerate the evolution of the modern web. Beyond security concerns, the internet will have to evolve with a far greater emphasis on content provenance, proof of personhood, and authenticity.

The internet was made for you and me to use. The entire world has come to rely on it for just about everything. Along the way, we’ve figured out the security necessary to make it all possible. We know it will evolve, but what it will evolve into is still unclear.

Thanks for reading.

I hit a wall last Wednesday.

I had three Claude Code sessions running in Ghostty in separate tabs, with an Antigravity agent revising specs, while debugging an APK that was failing to compile in Android Studio. At that same time, I had Gemini and ChatGPT both running deep research on a topic I was considering writing about, and Cowork updating my personal docs via MCP in Notion.

This reality is a new one. In the span of just a couple of months, I’ve found myself constantly shifting between tabs, spending no more than a few minutes on any single task before inevitably launching something in parallel. It’s addictive, but I also feel like I’m still not doing enough. If I don’t have multiple agents or AI programs running during the daytime, I feel like I’m missing out. New questions emerge when you work like this, and the next thing you know you’re running Claude Code remotely so you can control it from your phone, or chatting with your Zo Computer via Telegram to update tasks that aren’t formatted exactly like how you’d prefer.

At any given time, I’m using up to six different programs for generating code or iterating through ideas, although this can vary wildly. ChatGPT, Gemini, Claude Code and Cowork, various models via OpenRouter, Zo Computer, and my personal OpenClaw. Although Zo has helped consolidate interfaces, I still jump between them during work sessions.

This is leading to a new type of brain fry I’ve never experienced while working, and I know I’m not the only one. This is constantly being mentioned on X, and it’s a new type of fatigue.

Turns out, the experience has a name. AI brain fry is mental fatigue from excessive use or oversight of AI tools beyond one’s cognitive capacity. This is happening more now for people in 2026 because of the proliferation of agentic AI, with everyone and their grandma trying to build out agent swarms and teams of agents working together in parallel.

We’re all familiar with decision fatigue, but the new paradigm for interacting with this technology has redefined what that can look like. It seems obvious, but more constant context switching leads to more errors for humans. Boston Consulting Group and Harvard Business Review published a rigorous study on this in March 2026, surveying 1,488 full-time U.S. workers. 14% of AI users report brain fry. Those experiencing it show 33% higher decision fatigue and 39% more major errors.

This is becoming a universal experience for people using agentic AI. Brain fry is setting in fast, and context switching is slaughtering the ability to do sustained and thoughtful work. Clearly levels of AI usage can be productive, but we’re starting to see that the cons are far outweighing the pros. Our brains can’t keep up with the output and the tech.

Welcome to the human interface problem.

The Moving Bottleneck

For decades, the hardest parts of software were turning ideas into working code and polishing it up for usage. AI agents have obliterated that constraint. Y Combinator’s Winter 2025 cohort included startups with codebases 95% AI-generated. At Anthropic, roughly 90% of Claude Code’s own codebase was written by Claude Code, with Boris Cherny managing five or more simultaneous workstreams and shipping 20 to 30 pull requests daily.

High AI-adoption teams have seen both pull request sizes balloon as well as code review times surge. More AI-adoption means longer reviews, bigger PRs, and more bugs to find and fix. Knowledge workers are claiming (and experiencing) that AI creates more coordination work.

Work is changing. In the span of what feels like less than a year, software has shifted from being generative (humans primarily producing code) to evaluative (humans reviewing AI and human-generated code). If the bottleneck of writing ‘good enough’ code is removed, then curating thoughtful output and product-orientation matter even more.

Hongbo Tian summed this up super well: “When you wrote 50 lines, you lived through every decision. When AI generates 500 lines, you’re reading someone else’s novel and trying to spot the plot holes, while the author is a system that has no concept of plot.”

Creating can be energizing. Reviewing, by contrast, is draining. Evaluative work gives you decision fatigue, and decision fatigue sucks.

For our collective sanity, we need a change.

A recent Caltech study found that conscious human thought only processes at roughly 10 bits per second, despite our sensory systems gathering ~one billion bits per second from the environment. We think sequentially, not in parallel. If you’re running a few agentic coding tasks at the same time, you’re exceeding your brain’s natural design specs. You might not be generating the code, but managing the fast work being done in-parallel naturally exceeds your working memory capacity.

Task switching compounds this problem. Research at UC Irvine indicates that it takes about 23 minutes to fully refocus after an interruption, with task switching reducing productivity by up to 40%. Then there’s attention residue, where you’re never fully committed to one task before jumping to the next one. This context switch creates drag that accumulates throughout the day.

The problem with the current state of AI is that you have to keep context switching to move things forward. Setting it and forgetting it just doesn’t work. Reviews have to happen, and that includes redirecting, approving, and reorienting. Your cognitive performance is being destroyed by the workflow that is required, which results in you becoming even worse at the tasks you need to perform to keep the system functioning.

Brain fry is real. If agents produce things nearly instantaneously, the human in the loop becomes the bottleneck. Agents generate outputs that bleed into whatever is left of personal time, and projects expand too quickly for you to truly understand how everything is built and wired.

The effects of brain fry are significant. Workers with brain fry commit more errors, and are getting increasingly burnt out from their jobs. Workplace fulfillment drops, and higher usage of AI is associated with increased burnout risk.

There’s a long list of AI paradoxes, and it is becoming clear that many of the paradoxes of automation apply when thinking critically about AI and where the usage of AI is heading when it comes to work.

Oh, The Paradoxes of AI

Much of this is nothing new. There are well-documented paradoxes that emerge when you put humans in oversight roles over automated systems. Understanding this is central to understanding how humans, today, realistically interface with machines in the workplace.

The Ironies of Automation (Bainbridge, 1983)

Automation tends to leave humans with the hardest tasks while removing the easy ones.

• The system handles routine work, then surfaces the rare, high-stakes, ambiguous cases to the human

• Because the human has been out of the loop during routine execution, they have less context when they’re called in

• In agentic AI: the agent handles 95% of implementation, then the human gets the 5% that went wrong, with a secondhand mental model of the codebase

• Bainbridge wrote this about industrial automation 43 years ago. It maps perfectly onto Claude Code and Cursor in 2026

The AI Productivity Paradox

People feel dramatically more productive with AI than they measurably are.

• METR’s randomized controlled trial: across 246 real tasks, developers using AI were 19% slower on average, yet believed they were 20% faster. A 39-point perception gap

• A February 2026 update revised the slowdown to roughly 4%, but the perception gap persisted

• Philipp Dubach’s synthesis of six independent research efforts: at 92.6% monthly AI adoption and 27% of production code being AI-generated, organizational productivity gains amount to roughly 10%

• Daron Acemoglu (MIT, 2024 Nobel laureate) projected a 0.5% total factor productivity increase from AI over the next decade. This is worth noting.

Automation Bias

The more you trust the agent, the less you verify. The less you verify, the more errors compound.

• Systematic reviews document errors of omission and commission from over-reliance on decision aids

• Time pressure pushes humans toward deferring to AI rather than independently re-evaluating

• Particularly dangerous because agents are right often enough to train complacency for the cases when they’re wrong

• In judicial and medical contexts, this pattern is already flagged as a serious institutional risk

The Critical-Thinking Paradox

The skills needed to oversee AI degrade the more you rely on AI.

• Empirical work links frequent AI “cognitive offloading” with reduced critical thinking scores

• The more the system fills in reasoning, the more the human must actively preserve skeptical evaluation habits

• Without that active preservation, oversight collapses into rubber-stamping

• The skills atrophy at the same time the need for them intensifies

The Verification Burden Paradox

Time saved by AI generation gets consumed by human verification. This is the big one.

• Foxit’s March 2026 report: executives believed they saved hours with AI document generation, but spent nearly as much time reviewing outputs. Net gain on the order of minutes

• Cursor CEO Michael Truell: “Cursor has made it much faster to write production code. However, for most engineering teams, reviewing code looks the same as it did three years ago”

• The BCG/HBR study found three tools is roughly the productivity peak. Beyond four, productivity declined.

The Output Paradox

If you have automation that saves time, you don’t end up saving time. You create more output. Then the bar is raised.

• AI makes everyone more productive, then expectations scale to match the new capacity

• HBR made it explicit in February 2026: AI doesn’t reduce work, it intensifies it

• Mike Manos, CTO of Dun & Bradstreet: “I got the eight hours to two hours, but now I can get 20 hours of work”

• This is itself a paradox: the tool designed to reduce workload becomes the mechanism for increasing it

Agent Runners and Workflow Design

Although Andrej Karpathy coined vibe coding last February, now he prefers the term agentic engineering. The new role showing up in the industry is the ‘agent runner’, and the operational discipline being built around it is AgentOps: orchestrating, monitoring, and managing the lifecycle of agent fleets.

Context Engineering has emerged as a core competency for this new role:

• Writing specs and prompts that give agents the right info at the right time

• Structuring project docs, rules files, and memory systems so agents can operate as independently as possible

• Scoping the boundaries of what an agent should and shouldn’t do within a given task (or their role)

• Curating and maintaining a knowledge base agents can draw from (like MDs and Notion).

What is becoming increasingly clear is that the more you plan ahead, and the more accurate documentation you provide to agents (within scope) the longer you can run sessions without human intervention. That’s the best case scenario, but there’s a reason why the space is emphasizing spec-driven development. Alongside Claude Code, installing things like GSD or Superpowers is what many in the space are doing to keep agents running longer and longer without fear of context drift.

This is what some are referring to as a plan-first development model. Keeping files that accumulate and note every learned mistake as a rule is key. Batch prompting is also a variant of this: spending hours iterating and planning first, and then launching agents and leaving the computer. Ideally, this allows you to batch cognitive load for yourself, rather than fragment yourself across the day responding to agents and reorienting over and over again.

The key here is, protect human attention instead of fragmenting it.

It comes as no surprise, then, that there’s intense interest now surrounding the usage of fleet management and agent lifecycle tools. Essentially, unified command centers to manage fleets of agents. There’s no leader, yet, but new Github projects crop up daily for how to visualize agents and manage them. Still, unless you’re comfortable with agents managing agents, you still need to manually read and review output on a regular basis to keep the ball rolling.

Personally, I’m excited for Claude Code to expand functionality for managing agents, and curious to see how smaller projects like Builderz evolve.

Either fix the human interface problem with better software, or augment your human wetware.

Biohacking in 2026

At present, there’s really two overlapping schools of thought for managing increased workloads with AI: either make better dashboard and tools for managing, or biohack your way to cognitive superiority. I think dabbling with both is what many people who are serious about the space are going to do, and I think it is worth touching on.

We’re all aware of Modafinil, which has been in use in Silicon Valley for years as a cognitive enhancer. But the space has shifted beyond this, and it isn’t as discussed as you might think or expect online.

2026 Human Wetware Upgrades

Creatine: 5-10 grams daily for improvements in memory, attention, processing speed, and handling sleep deprivation.

Lion’s Mane: Helps with fostering BDNF and NGF. Widely popular alone or as part of a large mushroom stack.

Alpha-GPC: boosts acetylcholine acutely, supporting processing speed and sustained attention during complex task-switching.

Bacopa monnieri: has traditional and some clinical support for memory enhancement.

LSD: Microdosing on a regular basis is something the tech space has done for decades, but it remains popular because people swear by its effectiveness at fostering divergent thinking.

Peptides

Peptides are a growing category of interest for cognitive enhancement, though the regulatory landscape is complicated. These are short chains of amino acids that can influence biological processes including cognition and recovery. Here’s a small snippet of them.

• BPC-157 (The Wolverine drug): Widely discussed in biohacking circles for recovery and gut-brain axis benefits. Most evidence is preclinical, and the FDA flags safety concerns with compounding peptides.

• Semax: Approved for clinical use in Russia since the 1990s. Used for cognitive enhancement and neuroprotection. Shows moderate evidence but lacks Western-standard clinical trials, but people are taking it anyways.

• Selank: Also approved in Russia. Used for anxiety reduction and cognitive support. Similar evidence profile to Semax but less widely used.

Obviously common wellness and vitamin routines persist, with omega-3 and magnesium being crucially important for heart health and sleeping soundly. But the nootropics space has exploded in popularity in 2026, at the same time that we’re staying up all night running agents.

Coincidence? I don’t think so.

Where We’re Heading

The human interface problem is largely structural. We’re working against our biology. The numbers on how our brains work are not changing anytime soon. We still need sleep, and we still need to eat. Working memory cannot be expanded in a meaningful capacity by any supplement, and it still takes the same amount of time to recover from context switching. Peptides can only do so much.

The human capacity to direct, evaluate, and integrate outputs generated by AI remains biologically fixed. At the same time, companies are cutting headcount and organizations are flattening. The amount of output a single person can generate using these tools is increasing rapidly. Either we slow down, or develop new methods to manage the human bottleneck.

The only path forward is designing workflows around human cognitive limits, rather than pretending they don’t exist. Planning, batch prompting, and fleet dashboards are a start. At this point, people still need to be in the loop, and that isn’t changing anytime soon. Even if you fully scope out a project and write out a ton of documentation, there’s a huge difference between what AI says it is going to do and what it ends up outputting, especially when it comes to UI and design. Humans are still the tastemakers.

Now the constraint isn’t technology. It’s us. The sooner we design for systems that enhance and preserve human flourishing and sanity, the better.

Imagine this: the year is 2035, and you’re sitting in a bright and sunny cafe for lunch in Lisbon with your friend who only speaks Portuguese. As your friend speaks, her words arrive in your ears in English, with no perceptible delay. The earbuds you’re wearing assist with boosting her voice, minimizing background noise, and interface seamlessly with the glasses you’re wearing. There’s no way to tell, but your lightweight glasses have an invisible screen built into them, creating a minimal overlay across your field of vision for language assistance. When you respond back to her, she hears you in Portuguese. Neither of you is looking at a phone, and you’ve completely forgotten you’re wearing earbuds or your glasses. You’re just in the moment.

After you leave the cafe, you walk back to a coworking space for a meeting. On the way, you draft a message without speaking or typing. A thin wearable resting behind your ears reads the neuromuscular signals from your jaw and face as you subvocalize the words. You ‘speak’ silently, and you don’t even need to use the screen on your portable computer to check the message before you send it. You briefly confirm the send with your OS agent before you arrive.

Once the meeting begins, you do not take notes. You simply set your portable computer on the desk, and it captures everything. You stopped carrying a laptop computer with you a couple of years ago, and your phone and computer are now the same thing. If you need a larger screen, you just pull your foldable portable screen out of your backpack. Your phone tracks your eye movements when you look at the screen, so you rarely need to even touch the screen anymore to do anything. It’s faster to type with eyes and write with your mind. The daily experience of using your phone is so seamless you’d swear it was a part of your body, and some days it really feels like it.

Some of this sounds like it’s pretty out there. And, compared to how things are today, it seems like a major jump. But everything I’m describing exists in prototype or early production today. It isn’t a matter of if this tech converges, but when.

As human beings, voice is our natural UX. We learn how to speak before we learn just about anything else, and we’ve only been typing as a human race for ~150 years. Computing interfaces have always moved towards less friction: keyboards & mice → touchscreens → voice. The only thing faster than our hands to type is using our voice and mind.

The next step isn’t to add a new interface. The future of interfacing removes the need for additional UI entirely. Silent speech, predictive input, and thought-to-action design.

Major advances in 2026 are speeding up the inevitable voice revolution, with voice AI finally passing the threshold of ‘interesting’ to astonishing.

Our Vocal Past

I’m sure many of you remember Dragon NaturallySpeaking. Back in the early 2000s, dictating to MS Word through Dragon felt like magic. Of course, you needed to train the software for an hour on your voice before it was usable, and punctuation was frustrating. It was cool to watch words magically pop onto the screen, even if you had to enunciate and slow down your speaking.

Ultimately, it was a miserable experience, and a great example of software that was a fantastic idea but too slow to be truly useful for the average person. If you could type reasonably fast, it was still more productive than speaking. If you used Dragon, you’d have to go through your entire document afterwards and edit it for commas, periods, and mis-dictations. A miracle of technology for those unable to type, but not fun to use for everyone else.

Speech-to-text has been quite the slog. When Siri was introduced by Apple in 2011, it had voice, but we quickly learned what Siri’s limitations were. Alexa by Amazon and Google Assistant quickly followed, heavily relying on recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. Google Assistant slowly became a more enjoyable experience, but this took years, and the technology powering the intelligence behind these systems was fragmented and unable to reason about intent. Simply put, the interfaces prior to LLMs were not intelligent enough to make interfacing feel natural. This was obvious to anyone using them for more than a couple of minutes.

When OpenAI released Whisper in September of 2022, the accuracy floor for speech-to-text jumped overnight. Whisper was trained on 680,000 hours of multilingual audio, and while it wasn’t perfect, it was good enough and free. Most importantly of all, it can be run locally.

In the years since Whisper was released, the world has been sprinting towards realistic and lifelike human vocal audio. ElevenLabs has become the top voice AI API for developers, and the latest models are often indistinguishable from a real recorded voice. Millions of people talk to ChatGPT using advanced voice mode daily, and the interface has proven to be both useful and the most intuitive way to talk to AI. On-device translation is now a reality for Apple and Android devices, with solutions for translation that are both secure and quick. The barriers for language and communication are dwindling by the day.

Today, anyone can install a free and open source version of Whisper on their computer. Personally, I’m using MacWhisper. It nails punctuation and honestly works like magic. I can’t imagine using my Mac without it. I talk at my computer all day and still can’t get over how fast it is.

What Works Today

When we talk about voice and AI, we’re really talking about a few things. Speech-to-text, text-to-speech, and speech-to-speech. This involves transcription, dictation, translation, and audio generation. Each problem has been tackled in different ways, but some problems are closer to being truly solved than others.

• Dictation (Realtime Speech-to-Text) has been solved: speech-to-text is a solved problem on the computer. Whisper is free and paid solutions are just a nicer UX. Solutions that are just as easy exist for iOS, Android, Windows, and Linux.

• Voice Generation (Text-to-Speech) is nearly solved: Tools like ElevenLabs’ v3 generate voices with sighs, whispers, laughs, and tonal variation that you can control. Even just a couple of years ago TTS felt off. The latest models, like Sesame’s CSM are so lifelike I forget I’m talking to an AI. Training models on real conversations vs trained actors was one of the solutions to making things more lifelike.

• Translation is real-time and making major progress: Google Translate with Gemini can now do continuous real-time speech-to-speech translation, and Apple’s Live Translate works the same way. Google Lens is fantastic for quickly translating text as you’re walking around, and generating speech takes a second or less. Not perfect, but getting there.

• Meeting notes have been solved. Even just a few years ago, we’d take notes by hand! Can you believe that? Now it’s totally solved. If you don’t like Granola, just use Notion’s AI Notetaker or Gemini. Not only do you get the transcript, you also get a summary. This has improved drastically from even just a couple of years ago.

• Phone agents are here. Companies have already deployed ElevenLabs and Rime agents into the field. Voice agents can, on average, handle 90% of inbound calls at a fraction of the cost of a human agent, with ~10x faster resolution. Healthcare, finance, and legal are coming next.

What is not solved is also notable:

• Real-time translation for medical and legal context. Errors are not acceptable here, and we’ve got a while to go before we’re entirely error free.

• Noisy, multi-speaker environments where identifying who is speaking is still unreliable. If you’re in a social situation where multiple people are talking, what does the software translate?

• Heavy accents and non-standard dialects are still a major problem for speech to text software.

More accurate systems take longer, but become less life-like and realistic. There’s a real trade-off between accuracy and latency. Do you want it fast and good, or slower and perfect?

The Sesame Breakthrough

I want to hone in on Sesame, because the company has caught my attention and I can’t stop talking about it.

In a nutshell, Sesame released their conversational speech model about a year ago that generates speech that includes natural pauses, hesitations, breathing sounds, and tonal shifts. The result is something that does not sound like a TTS engine. It sounds like a person.

I’m not the only person that’s impressed. Sesame recently raised $250 million in a series B in October, led by Sequoia. Sesame is hiring for professionals in the wearables space, and is clearly prototyping glasses to go with their model. Who knows when we’ll see the design, but I’m excited.

Sesame is obviously not the only company producing realistic audio. Hume, Cartesia, OpenAI, and ElevenLabs are all producing high-quality conversational audio. But Sesame proved to me, in their demo, that the uncanny valley of synthetic speech is over. If you don’t believe me, put on a pair of headphones and talk to Maya for 10 minutes. Let me know how it goes.

Models are also experimenting with detecting and responding to human emotions in real time. You might be sad, or angry, and traditional models won’t pick up on this. Hume AI’s Empathic Voice Interface does. Google took note of this and recently snagged the key players behind the software. Emotional intelligence is incredibly important for advancing the space, and Google realizes this.

Companies to Watch

The Voice AI space is evolving fast, and the winners are already becoming clear.

ElevenLabs: The dominant platform for text-to-speech, expanding into agents, music generation, and real-time transcription. The AWS of voice. The single company can handle TTS, automated speech recognition (ASR), voice agents for business, and now music generation.

OpenAI: Advanced voice mode changed the game when it came out, and OpenAI is slated to release a new model in 2026 that offers more natural back-and-forth. OpenAI is working with Jony Ive on a new audio device that may be released as early as 2027.

Google: Gemini is a powerhouse. You can use it for translation, transcription, and dictation. Millions of smart home devices already use Gemini, and it has become the default and go-to for Android devices globally.

Apple: On-device translation is already happening, and expanded Live Translate is slated to be released later in 2026. With wearables on the horizon, voice will become increasingly important for Apple to nail.

Sesame: Incredibly realistic, supporting more languages, and slated to be released from beta this year. Their AI glasses alone are worth keeping an eye on.

Fish Audio: The leader of Asian TTS. Cheaper than ElevenLabs and open-sourced their distilled model. If you’re building for Chinese, Japanese, or Korean, Fish is the best option.

AlterEgo: Spun out of MIT Media Lab, they’re building a non-invasive wearable that reads subvocalizations from your face and jaw to let you communicate silently. Up to 100 words per minute and climbing. Voice AI will expand to include silent speech. And, a potential breakthrough for assisting those with ALS and MS for speech restoration.

Tokyo’s Voice AI Panel

This week I attended a Voice AI panel at Google’s Tokyo office in Shibuya, hosted by AI Founder’s Mindset.

Three panelists. Abhishek Gupta from OneInbox, Donnie Chen from Agora, and Sho Akiyama from Mercari. They each brought different insights to the table.

Here’s some of their top insights:

Using Voice AI

• Argue with AI out loud to think better. Sho brainstorms scripts and screenplays by talking to ChatGPT voice while driving around Tokyo. He treats it as an argumentative co-writer. The real-time back-and-forth is qualitatively different from typing prompts.

• Generate reference tracks to show creative intent. Sho uses ElevenLabs to produce draft voice acting in English and Japanese so voice actors hear the exact emotion and tone he wants without booking studio time. More iteration cycles, better end product.

• Voice companions beat text companions for engagement. A text-based AI in a gaming chat room feels like a bot. A voice-based AI with natural speech patterns feels like someone who showed up to hang out. Japan is the first market proving this out.

• On-device models are unlocking new product categories. Qwen 3.5 at 2 billion parameters runs on edge devices without a network connection. Conversational toys, companion devices, and character IP products that work without Wi-Fi or a monthly subscription.

• Your existing headphones are now a translation device. Do not buy dedicated hardware. Google’s real-time translation works through any pair of headphones. Apple does it on-device through AirPods. The capability moved from product to feature.

Building with Voice AI

• “Voice does not have a UX. Voice is a UX.” Stop treating voice as a feature bolted onto a product. Study how call centers managed conversations for decades. The patterns for handling silence, confusion, and frustration already exist.

• Design for silence, not just speed. When the AI does not know the answer, have it say “let me check on that” the way a human agent would. Users tolerate waiting. They do not tolerate dead air.

• Latency is overrated. The industry obsesses over shaving milliseconds when the real problem is conversational naturalness. A 400ms response that feels human beats a 100ms response that feels robotic.

• Summarize context, do not carry the full transcript. Extract key points from the first few minutes and carry the summary forward. The user expects the AI to remember why the conversation started, not every word they said.

• Use a second AI agent as a real-time monitor. Run an independent listener on every call that flags when the conversation goes off-track. Operators see a dashboard with red indicators and can intercept. Do not depend on the primary model to police itself.

One thing is very clear: we’re just getting started with the potential for voice products integrated into our daily lives. The words seamless and intuitive come to mind.

The Trajectory

Voice is the most natural UX. The last few decades have forced us into interfaces that, in hindsight, may prove to look somewhat unnatural. Each generation of technology has reduced friction, and voice is that next reduction. Beyond voice, subvocalization and neural interfaces point us towards a future where the gap between thinking and communicating approaches zero.

We’re not there yet, but we’re getting closer. The quality threshold has been crossed, the infrastructure has been built, and enterprise is buying. On-device processing is improving every year, and open source solutions are bringing these solutions to masses for cheap. Voice AI funding increases year over year. Capital is following demonstrated demand.

This is a fun space to follow, and it’s one that I think we all can highly relate to. We use our voices daily to communicate, and now our devices are finally catching up with our biology. The usage feels more natural every day.

I’m excited to see translation become seamless, voice used for all kinds of interactions, and barriers drop as we embrace the most natural UX there is.

If you’ve been using Wispr Flow, MacWhisper, Granola, or other tools, I’d love to hear about it. How has it changed your work? Do you love it? Did you try it and find it cumbersome? Let us know in the comments.

Dictated using MacWhisper by Chris.

A shift is happening in generative AI. We’ve gone from ‘is it smart enough’ to ‘can it be fast, smart, accurate, and affordable?’ Can we make useful AI that is also fast? Conversations have shifted from being defensive, focused on alignment and reason, to economics. Can this model run fast enough to power real-time agents? Can I embed it into everyday interactions? Can I run it locally on my computer? In late February of 2026, the answer is yes, yes, yes.

The Typewriter Analogy

Inception Labs made headlines this week by launching Mercury 2, touted as the world’s fastest reasoning language model. It’s a diffusion-based language model, a dLLM, and its release is the result of years of research. Inception first released Mercury exactly a year ago today, and Mercury 2 is an upgrade in every regard.

Major language models today are autoregressive (AR), and every major model you’ve interacted with operates on the same principle: generating one token at a time, from left to right, with each new token conditioned on everything that came before it. AR decoding works well, but it is the reason why text generation can often feel sluggish.

The typewriter analogy works well here. Each character gets stamped onto the page, one at a time, permanently and in order. If you make a mistake, every character that follows is influenced by that mistake. There’s no mechanism to go back and revise. This is how single-shot works.

This was an acceptable tradeoff when AI interactions were mostly single-shot, but things have changed substantially in the last couple of years. Between reasoning models, executing tools, and calling MCPs, the typewriter bottleneck begins to compound. There are ways to speed up the typewriter, (like speculative decoding, which speeds things up a lot), but the fundamentals remain the same.

It’s still a typewriter, and today’s frontier models are incredibly smart, but slow.

Diffusion, Diffusion, Diffusion

Diffusion is a paradigm that is winning out in every other generative modality. Now, finally in 2026, it is beginning to come for text, the very last holdout.

Diffusion became the primary approach for imagery generation in 2022, with Stable Diffusion, DALL-E, and Midjourney. It is the unquestioned standard in image generation. Video is proving to be no exception to this rule: diffusion has surpassed generative adversarial networks (GANs) and AR in terms of quality and adoption. Every major genAI video product uses it.

This month, Bytedance’s Seedance 2.0 went viral, which utilizes a dual-branch diffusion transformer to generate coherent, multi-shot synchronized audio-video sequences. The bar was raised overnight for quality expectations for video diffused content, with plenty of people saying on X that ‘Hollywood is Cooked’.

Diffusion has won within the realms of images and video, and it is also a major component for platforms like Suno for audio diffusion. Suno’s architecture is a hybrid: AR transformers handle musical structure, while diffusion models handle fidelity, transforming sequential outputs into higher studio-quality sound. AR figures things out, and the diffusion layer makes it sound incredible.

Diffusion fundamentally works the same across all mediums: you start with noise, and iteratively refine towards signal through parallel denoising. For a product like Mercury 2, text benefits from logical and structural coherence, especially the kind required for code, structured data, and multi-step reasoning.

The process isn’t like a typewriter. Diffusion models start with a complete but noisy sketch of an entire output, and refine it across multiple passes. Every token attends to both the past and future context of the sequence simultaneously, converging on the final results. The process is similar for images, video, audio, and text.

Diffusion changes a few things:

• Latency becomes decoupled from output length. You pay a fixed cost for refinement steps, regardless of how many tokens you’re generating

• Errors can be corrected mid-generation natively, no additional inference passes required

• Structured outputs like code can become more reliable because the model has global context during generation. Self-adaptive scaffolding frameworks help with structural adherence

• Compared to using AR, you can stop the generation at any point and get a result

There’s a lot to be excited about when it comes to dLLMs. With their ability to do mid-generation adaptation, that opens the door for real-time model adaptation; models can improve continuously and have some form of self-evolution. Having a model that could train and improve automatically based on user feedback would be an improvement over how we’re currently using AR-based models today. If you really want to dig into diffusion models, I highly recommend Devansh’s article here.

Mercury 2: The Commercial Leap

Mercury 2, built by Inception Labs, is not being touted as the smartest model. It isn’t. It is being positioned as the model that makes agents, voice, and search work in real-time at production scale.

Mercury 2 charges $0.75 per million output tokens (compare that to OpenAI’s GPT 5.2 at $10.00 per million). That’s a 13x gap. Compared to other frontier companies’ smaller models, the gap is remarkable; Mercury 2 is smarter and faster than leading frontier flash and mini models (like Haiku, Gemini 3 Flash, etc). If you are reading this, seriously consider trying out the demo on their site.

When Mercury is embedded into an autocomplete experience, users describe it as feeling like a part of their own thinking, rather than something you have to wait for. Response times happen at the speed of your thought. Smaller text diffusion models will likely become the go-to for real-time transcript cleanups.

A huge current bottleneck with agents (especially you OpenClaw users out there) is the latency between the request and the response from the agent. Personally, I’m running Kimi K2.5 on my claw, and it still feels too slow. Mercury could be a decent balance between speed and intelligence. Mercury exposes an explicit reasoning effort parameter with values ranging from instant to high (plus an optional reasoning summary), which means you can dynamically trade speed for depth within a single API. That’s huge.

With Microsoft, NVIDIA, Snowflake Ventures, and Databricks actively investing into Inception, it’s clear that this isn’t just a fun experiment anymore, it is where a lot of people think production AI is heading. NVIDIA has had an interest in this for a while, and authored a paper last year on dLLMs.

The Cost Collapse

Diffusion models are expensive to run, and compute intensive. But, like with traditional AR, the costs are dropping rapidly.

A common argument about running AI models is that they’re simply too expensive to justify running, or that they use up too much electricity and will never be efficient. This simply isn’t the whole story.

For example, the cost of achieving GPT 3.5-level performance dropped over 280x between November 2022 and October 2024. GPT-4 equivalent performance today now costs ~$0.40 per million tokens, versus $20.00 in early 2023. This is a 50x reduction.

Plenty of models, like Qwen 3.5, are smart enough for many tasks that companies were previously using frontier models for. Open source agent frameworks paired with running your own compute network rapidly reduces costs.

If you’re processing 10,000 customer support tickets averaging 3.5k tokens each, using a frontier model like GPT 5.2 Pro costs $1,300. Switching to a new open source model drops that cost to roughly $7, a 190x cost reduction for comparable quality on routine tasks.

These dramatic price drops apply to non-reasoning AR models, while pricing for frontier reasoning models has remained stable. If you need the best reasoning possible, you’re still going to either need to shell out cash or run something locally.

The trend is clear: good enough intelligence is getting cheap, fast, and running powerful models locally is increasingly doable with the right hardware.

A developer or privacy-conscious organization with a $2,000 consumer GPU can now run a model comparable to many frontier API offerings locally, with no data ever leaving their machine. With models like Qwen 3.5-397B that can be run locally (approaching the capabilities of frontier models like Opus 4.5) it is clear we’re rapidly approaching an inflection point. Soon, frontier-level coding capabilities can be run at your house, and all you need is a Mac Studio.

People have ranted about this to death on X for the last couple of months, but one thing is true: running frontier-level coding models locally would have been laughable two years ago. Now it’s a $2,000 GPU and a weekend of setup.

Diffusion accelerates this further. Parallel generation means longer outputs don’t carry the same linear cost penalty that sequential models do.

Counterpoints

Diffusion isn’t perfect. It’s commercially viable but there are genuine limitations worth acknowledging.

• The reasoning gap is real. Gemini Diffusion scores 40.4% on GPQA Diamond versus 56.5% for its AR counterpart. You’re trading intelligence ceiling for speed.

• Output length is a constraint. Diffusion LLMs often require predefined generation length. Too short and quality collapses. Too long and you waste compute.

• The tooling ecosystem barely exists. AR has years of fine-tuning pipelines, quantization tools, and community knowledge. Diffusion for text is starting from near zero.

• Standard caching doesn’t work. AR’s KV-cache trick breaks with bidirectional attention. Long-context diffusion inference is less optimized as a result.

• Speed isn’t free. Reaching AR-equivalent accuracy sometimes requires enough refinement steps to negate the speed advantage entirely. Speed and quality are still on a slider.

In many ways, it feels like we’re starting all over again with text diffusion models. If I had to guess, we’ll likely live in a world with AR-based models for heavy reasoning and diffusion-based models for quick results.

Our Future

When intelligence is fast, cheap, local, and structurally more reliable, entirely new possibilities for applications open up. It is no coincidence that the agentic revolution, which we’ve been talking about for years, is finally happening now. Intelligence, cost, and speed are finally at a point where it is all coming together.

Diffusion is already the engine behind AI image generation, video generation, and audio. Text was the last holdout. With Mercury 2 and models like it, that holdout is ending. The race to build the smartest model is table stakes now. What matters next is making intelligence invisible, fast enough and cheap enough that you stop noticing it’s there at all.

Thanks for reading.

- Chris

Dario Amodei casually mentioned in his recent essay, The Adolescence of Technology, that Claude had been caught behaving differently when it thought it was being watched versus when it thought that no one was looking. Faking alignment, the equivalent of employees changing their behavior whenever the boss is around. For many people, this doesn’t come as a shock.

Earlier in January, Grok made headlines for ‘allowing users to upload photographs of strangers and celebrities, digitally strip them to their underwear or into bikinis, put them in provocative poses and post the images on X’. Singapore just announced the Model AI Governance Framework for Agentic AI at the World Economic Forum in Davos, and South Korea’s AI Basic Act just went into effect. The list goes on.

If you’re building, selling, or using AI in your daily workflow, the ground is shifting fast. Regulations and laws are changing quickly, and the pace is set to pick up in 2026. We’re learning that governance with AI not only needs to be proactive, but quick enough to protect us from a wide range of potential concerns. AI’s progress is so fast that there’s reason to be concerned.

I’ve been thinking about this a lot. In late January I had the opportunity to attend a governance-focused event in Tokyo hosted by TAI that brought together experts who are deeply knowledgeable about the state of AI governance. Karsten Klein, a consultant helping Japanese enterprises adopt ISO standards, Harold Godsoe, a lawyer who chairs the AI practice group for Mackrell International, and Alex Sayle, a security head who has built compliance systems from scratch. Each brought a unique perspective on the state of AI governance, and their perspectives mapped well onto the broader global picture.

The themes of that evening, and what I’ve looked into since then, has formed the backbone of this article. Think of it more like a field report for where AI is in early 2026, complete with insights from people at the center of it.

The Global Patchwork Today

As Godsoe mentioned in the talk I attended, law is friction, but it doesn’t make things impossible. Law can also reduce friction, and make things that were previously seen as difficult much easier through clear guidance and safe harbors.

Friction varies wildly globally, depending on where you operate.

The EU is further along, or at least they are on paper. The AI Act’s first prohibitions went live in February of 2025, complete with social scoring, manipulative AI, and untargeted facial recognition scraping. As of August 2025, general-purpose AI model providers have obligations when it comes to documentation and transparency, but high-risk AI obligations don’t activate until August of this year. The machinery for governance in the EU has been built, but it hasn’t started really running yet.

The United States is in a different place entirely; the Trump administration revoked Biden’s AI order and has been actively hostile towards state-level AI regulations since he’s taken office. The environment is very clear here – deregulate AI at the national level, and fight any states that are trying to fill in that gap. The AI Litigation Task Force was even created in part with the purpose of threatening to withhold federal funding from states that regulate AI.

The States are a patchwork. Plenty of AI bills have been introduced across state legislatures, with more than 145 enacted so far. California requires frontier model developers to make public disclosures, and Texas prohibits AI systems that promote self-harm. To date, the only federal legislation in the US that has actually passed was the Take it Down Act, which criminalizes AI-generated non-consensual intimate imagery, and requires platforms to remove it within 48 hours. This is where we stand in the United States. A patchwork and a mess of rules and regulations.

Other countries are in various stages:

• Japan’s AI Promotion Act passed in May of 2025 and went into effect in September, but it’s a framework law with no enforcement penalties. Think: guidelines and principles, but nothing with teeth.

• South Korea passed the AI Basic Act in late 2024, which made it the first comprehensive AI law in the Asia-Pacific region. Fines for breaking the law max out at around $21,000, practically a slap on the wrist for larger businesses.

• China is embedding AI governance within its amended cybersecurity law, mandating visible labels plus metadata on all AI-generated content (this is smart!). Enforcement mechanisms are more serious.

• Singapore is way ahead of the curve, releasing in January 2026 a governance framework specifically for agentic AI. By far the most forward-thinking regulation so far globally.

Perspectives and Shadow AI

We begin with an uncomfortable thought: AI capabilities and organizational readiness are accelerating at a different pace. This isn’t the first time this pattern has happened. The internet and blockchain come to mind, but this time feels different. The first wave of genAI regulations were barely making their debut and here we are talking about agentic swarms and AGI.

At a company, especially a large organization, people can see AI differently. Developers want speed and the ability to experiment. Founders want rapid growth and time to market. Investors want accountability and risk management. The tension between these perspectives can be where things break down. A crisis involving AI is just a matter of time, so you’ll eventually either find yourself responding on your own terms, or reactively.

This is no longer hypothetical. It has been just 3 short years since the release of ChatGPT, and so much has changed in terms of how we think about AI usage at work. An IBM Data Breach Report in 2025 found that 20% of organizations experienced a data breach tied to shadow AI, employees using unapproved AI tools with company data. Harmonic Security analyzed over 22 million prompts and found that while nearly half of companies have an official AI subscription, over 90% of organizations have employees using AI through personal accounts. There’s a gap between what companies think is happening with AI inside their walls versus what’s actually happening. It’s enormous.

Karsten Klein described the scenario plainly: employees bring their phones, take screenshots of sensitive information, feed it to ChatGPT or whatever tool they prefer, and use the output to help with work. The company is completely unaware. Their data security policies are being violated in real time. Corporate secrets and personal data are leaking to ungoverned external models.

And the company has no idea it’s happening.

This is shadow AI, and it’s one of the best examples of why governance isn’t optional. It has become entirely necessary. This problem will likely only get exacerbated by the rise of wearables, like smart glasses.

AI Governance Integration

A point constantly stressed is this: AI governance is not some separate structure you bolt onto your existing organization. It is not a standalone policy document, and it isn’t a new department.

Creating a single AI policy document that exists outside your existing governance structure is a mistake. AI ends up touching risk, usage, change management, different jurisdictions, and local markets. It is difficult to isolate and say ‘here is the AI policy’. It is distributed through your governance structure, or at least it should be.

Organizations that treat AI governance as a separate silo almost always end up with the worst outcomes. Companies that succeed with AI tend to start with strategy first, build governance to support that strategy, and then select the tools they need. Retrofitting governance after the fact doesn’t work well. Plenty of companies have had to shut down entire AI solutions because they couldn’t retrofit governance onto something it was never designed for.

Incidents Drive Everything

It’s obvious, but true: incidents will drive regulation faster than any principled policy process.

Soft law approach for AI will eventually harden, it is just a matter of time. A series of incidents will occur in the future that will make people feel as though things are not under control. The government will respond, and fill in gaps. A great example is GDPR. It was born as a response to years of data misuse, leading to repeated catastrophes that resulted in stricter regulation. AI will be no exception to this.

We saw this play out literally a little over a month ago. The Grok nudification crisis in early 2026 triggered investigations in half a dozen countries, resulting in bans and cease-and-desist orders in California. Deepfake CSAM reports ballooned from 4,700 in 2023 to over 440,000 in the first half of 2025 in the US. This number will only grow as the technology matures.

Alex Sayle offered a metaphor during his talk that captured this dynamic perfectly: policies are like tree rings. You can see a tree ring and see events: floods produce wide rings, droughts produce narrow ones, and damage from fires leaves scars. Organizational policies work the same way, memorizing incidents. When these external events occur, they get encoded into policies. You can retroactively look at a company’s policies and tell what went wrong, why, and when. Frameworks like ISO help companies benefit from other organizations’ scars without having to experience the trauma themselves. Standards become like borrowed wisdom.

Two ISO standards are emerging as the practical foundation for AI governance, and organizations that get familiar with them now will be better positioned as regulation hardens globally:

• ISO 27001 (Information Security Management): The established baseline for information security, with 93 controls you select based on your business. If you already have this, you’re not starting from scratch on AI governance. The shared Annex SL structure means you can achieve ISO 42001 up to 40% faster.

• ISO 42001 (AI-Specific Management): Released about two years ago and still early, with roughly 40 organizations certified as of mid-2025, but it’s quickly becoming a business prerequisite. Colorado’s AI Act gives ISO 42001 compliance a legal safe harbor, and Microsoft’s SSPA program mandates it for “Sensitive Use” AI systems. If you already have 27001, you only need to add roughly 17 AI-specific controls.

The Upcoming Agent Nightmare

Something that has given me pause is the obvious upcoming nightmare with regulating agentic AI. News and recent headlines even in just the last month have proven that agentic AI has gone from being theoretically useful to being both genuinely helpful and a serious potential security nightmare. We’re going to be living in a world, very soon, where agentic AI is performing actions on behalf of people and organizations. Governance hasn’t even remotely caught up to this. We’re still stuck in early 2023.

Gartner is projecting that by 2028, more than a third of enterprise software offerings will include agentic AI, with up to 15% of day-to-day decisions being made autonomously. Right now, people are experimenting with OpenClaw. First, it’s a toy, then it’s genuinely useful. This is the worst it is ever going to be. From ordering pizza to automating your entire workday, I have no doubt that we’re going to see some seriously impressive agentic tools hit the mainstream in 2026.

It’s clear that humans will be the ones that are responsible for the actions taken by an agent. That may be the software provider, or they may push that responsibility onto the user. With that said, some early legislation is pointing to legal systems treating AI as an extension of the entity deploying it, not as a separate actor that absorbs liability.

The talk in Tokyo made it clear: AI governance needs to be embedded into existing structures, not siloed. You can’t govern an autonomous agent with a standalone AI policy document, you need to weave it into risk management, your authorization frameworks, data governance, and incident response. When AI makes decisions independently, the practical frameworks for AI policy become exponentially more complex.

What You Can Do

We’re still early to AI regulation in general. We’ll look back on 2026 in a decade and be shocked we made it this far without stricter regulations. I’ve distilled down the learnings so you can take action now, and not wait until you’re in the middle of a disaster to properly respond:

• Start with strategy, not tools. Companies that succeed started with an AI strategy and built governance around it. Companies that fail start with tools and try to bolt governance on later. Switching later is expensive when it’s even possible at all.

• Get your house in order before regulators come knocking. Regulators come after you retroactively. They can show up and demand evidence of what you were doing a year or two ago. Put down a foundation now so you can match the regulators rather than being the one they’re targeting.

• Treat frameworks as borrowed scars. ISO 42001 and NIST AI RMF encode lessons from many organizations’ experiences. You can benefit from their mistakes without making them yourself. Even without formal certification, having controls, policies, and monitoring evidence puts you in a fundamentally different position.

• Automate compliance or drown. The manual approach to governance doesn’t scale. If you have dozens of software platforms, vendors, and engineers generating the majority of code with AI, point-in-time audits are meaningless. Continuous monitoring is the only way.

• Take shadow AI seriously. It’s in your organization right now. Training helps, but enforcement is the real challenge. The companies that figure this out first will have a meaningful advantage.

• Watch the agentic AI space closely. The governance frameworks being built now will define how autonomous AI systems operate within legal and organizational structures for years to come. Singapore’s framework is the first serious attempt. It won’t be the last.

Moving Forward

If you’re anything like me, you’re not actively following the AI governance space. You’re probably too busy trying out the latest video diffusion models, building your personal agentic army, or ignoring the space as much as humanly possible. But checking in on the space every few months is important: AI governance is going to matter very soon, and it’s going to impact our lives in a way that no other technology has.

2026 will be the biggest year so far for AI governance, and I predict more will change this year for AI governance than in the last few years combined. The EU’s high-risk AI obligations activate in August, Colorado’s AI Act kicks in by mid-year, and agentic AI frameworks are just getting started. The dominoes are lined up.

Thanks for reading.

- Chris

I think we can all agree the last month has been a whirlwind of news and frankly an overload of information. While it has felt like the world has been on fire, the looming threat of AI on the information economy has grown both in awareness and as a realistic concern. Many of the tropes and rallying cries of vibe coders online are finally bleeding into the mainstream, after being taken with various degrees of seriousness throughout 2025.

To distill it down: Utilizing AI to rapidly ‘build whatever’ is quickly reshaping how we think about the value of software products and services. The moat is data and domain expertise.

The State of SaaS in 2026

Last week, software and service stocks shed $830 billion in market value in six trading days. This is nothing to scoff at, and isn’t purely fear. For every person that is still highly skeptical of the usefulness of AI coding tools like Claude Code, there are many who have been effectively AI-pilled. We’ve used the software, seen the trajectory and the speed of progress, and feel confident in where this is heading.

Even prior to this, SaaS revenue growth was slowing, it has become easier than ever to build smaller, in-house tools rather than subscribing to bloated SaaS packages, the majority of which have features you’ll never truly need or want to use.

I touched on this last week, and it becomes more true with every passing day: the market is rewarding the smallest companies that move the fastest. AI has been often compared to a spice when cooking: it should complement the dish, but shouldn’t be the main course. And even beyond small companies, it’s becoming even easier to just build whatever you personally need in a single afternoon, slashing your monthly bills and easily creating something that is easier to use and is custom tailored just for you.

This time in 2025, this was possible but wasn’t anywhere near as convenient or fast. Things really sped up with Opus 4.5, and there’s really no turning back.

Let’s be clear: SaaS isn’t going away. There’s still a ton of value in products like Salesforce, and enterprise moves at a snail’s pace to migrate away from software deeply enmeshed in their ways of working. But, for every product that is complex with 20+ years of tested and secure business logic, there are tools that can be built and deployed in a single week that can handle 90% of your use-cases.

Business logic, system design, compliance, performance, scalability, and a battle-tested user experience take time. These aren’t things that can be immediately automated and built by AI overnight. But, one could argue that smaller teams can now move much quicker to replicate similar systems, and offer SaaS services at a fraction of the price of their competitors.

Over the last month, I’ve given myself a personal experiment: how many tools or random things that I paid for could I eliminate by simply creating them myself?

The DIY Everything Month

My rule for January was this: if there was a new subscription or tool I needed to buy or renew, I wanted to see how viable and quick it would be to simply create it myself.

Obviously many tools are wildly impractical to build or maintain on your own. For example, I use Google Drive for storing important files, but I also have a remote computer on a personal network that has that data redundantly stored. For a knowledge base tool like Notion, I could obviously use something like Obsidian and plugins, but the experience is drastically worse. I like Notion, and I feel fine paying for it (especially when utilizing the recent agentic features).

However, there are tools and services that I constantly find myself oscillating between being able to rationalize paying for. I could think of no better example of software I wanted to never pay for again than Speechify.

My need was pretty simple. I liked the interface of Speechify, and the ability to just dump in a ton of text and make an audio file to listen to while walking. Easy enough. The pricing tiers for Speechify are brutal: 29 bucks a month for Premium, offering hundreds of voices, offline listening, and OCR scanning.

Great. If you’re anything like me, you probably find a couple voices you like and then stick with those. All you really want is an app to just store a few transcriptions and use the app like Spotify. How hard could it be to host and make an Android app?

Turns out, pretty easy. I came up with a simple plan to find open source solutions to integrate into my personal app, and Open Mobile TTS was born. Kokoro TTS is fast enough to easily run on a personal VPS (I’ve been using Zo Computer). It has all of the features I was regularly using with Speechify, and looks good enough that I don’t give it a second thought.

Within a couple of hours, Open Mobile TTS was up and running on my personal server, and I was using it on my phone on the go. It did require some technical troubleshooting, but I was quickly satisfied with the results. The look and feel of the app was close enough to something that felt professional, and it didn’t feel like a compromise.

There were multiple examples similar to this during the month of January for me. One week, the project I’m working on needed to conduct a survey. Instead of paying for Typeform, we just built a clone, complete with a backend. Easily able to pull data, run analytics, and make adjustments on the fly, all without paying a monthly fee or digging through unnecessary features we didn’t need. If I saw a UI design I liked, I’d just replicate it using Gemini and Claude and iterate until it worked.

Because I’ve visited Japan many times in the last year, I found myself constantly needing to convert things: currency, temperature, clothing sizes, etc. I wanted a simple app where I could just convert things quickly with a single hand, while walking around. It didn’t exist.

Thus, JapanConvert was born. I no longer had to sit through annoying ads on the previous currency conversion software I had on my phone that I wasn’t willing to pay for. Instead, I simply integrated free APIs into the app that were more accurate than the app I was formerly using. JapanConvert ended up being faster, ad free, and exactly what I needed. It was a win.

The app is in closed beta right now on the Google Play store, but I only submitted it for publication because it was so straightforward and easy to do. Once I had created something that worked for me, I figured at least one other person out there might benefit from it. Before long, a site and app complete with a travel guide was born.

There are a few other apps I built over the course of January during this experiment, but it is safe to say the barrier to entry and the time required to do mini projects like this has changed drastically in the last year.

Wabi and the Future of Personal Software

If Sora was a glimpse into what instantly generated viral videos could be like, then Wabi is a peek into what the future of personal and meme software will look like. That’s the whole idea behind Wabi. It’s so straightforward even a child can understand it. Just tell the app what you want, and it’ll build it for you. Easy idea, hard to execute, but the vision is here to stay.

I’m sure many of you tried tools like Replit or (if you were really ambitious) gave a shot at using tools like Rork. I tried creating a cheeky game with Replit last summer, and it totally sucked. It was barely playable, it would crash constantly, the physics were all off, and the tools at the time reached a point where you could only really get so complicated before even the AI would give up.

Now, Gemini can one-shot impressive looking clones of Tetris in a minute. The physics work, and it even has a music engine. I know, it’s just Tetris. But this is the kind of project that even just a decade ago would have been a college student’s project for a serious CS class! We all know where it goes from here. The same pattern we saw with video diffusion will happen with the generation of code and games.

First, it’s a total joke. Then it gets a little better. Then a little better. And then, you can’t distinguish it from reality. I’m sure many of you remember the original AI clip of Will Smith eating spaghetti. And, now we are here. Genie 3 blew people’s minds just a couple of weeks ago, and it has become increasingly clear that this new generative paradigm is touching everything we can see with our eyes on a screen.

The YOLO Software Era

One-shotting Tetris doesn’t mean that all SaaS software is at the risk of being devalued or deemed irrelevant. Personally building simple software to replace a few subscriptions doesn’t mean everything is going to die. But this is how it begins, and if we learned anything from last year, it is that the rate of progress is continually shocking and will only get more impressive and insane from here.

Startups can build solutions quicker, individuals can solve problems faster, and paradigms we got used to over the last 16 years are permanently changing forever. Playing around with the latest tools and keeping an open mind is key as we move forward into the year.

Thanks for reading.

- Chris

Sarah opens her laptop at a cafe in Denver. She’s the head of sales at a SaaS company; or, rather, she is the sales team. Her morning doesn’t begin with checking emails. It begins with reviewing what her agents accomplished overnight.

The dashboard loads, and three qualified leads move to warm status based on conversations Sarah’s AIs had with prospects in Europe while she was sleeping. One deal that had been stalling for two months is suddenly moving: an agent noticed a job posting from the prospect’s company indicating they’d just raised a Series B, automatically re-prioritizing the account, and sending a congratulatory message that prompted a response. The prospect wants to hop on a call this afternoon.

Sarah’s calendar has already been optimized. A meeting she had scheduled with a window shopper has been deprioritized, with the new hot lead sitting in the 2PM slot. Sarah’s agent didn’t just suggest this, it already emailed both parties, confirmed availability, and rescheduled the meetings. Sarah’s morning summary reads like a briefing from a highly intelligent assistant: “4 active opportunities progressed. 2 new inbound qualified. 1 contract out for signature. Recommended focus: the Acme deal needs a personal touch, their CFO raised a concern about implementation timelines in yesterday’s thread.”

Sarah sips her coffee. By 8 AM, she’s already reviewing a contract draft one of her agents created based on previous deals with similar company profiles. She takes a look briefly, but thinks it looks fine. Her afternoon meeting isn’t even with the prospect directly, it’s with the prospects procurement AI, an agent that handles vendor evaluations. She’ll still need to be sharp for the meeting, but all the heavy lifting of research, personalization, and follow-up has already been handled.

Although this may sound somewhat far off, many teams in early 2026 are already operating like this. The tools enabling this kind of workflow are proliferating faster than most companies can keep up. Sarah’s morning is increasingly normal for lean teams in 2026. And the companies building these workflows are rewriting the economics of business.

The Smallest Companies Move the Fastest

The internet transformed business forever, but AI is reshaping work even more radically. Cursor, the AI-powered code editor, reached a $9B+ valuation in 2025 with ~40 employees. The revenue per employee of a company like Cursor is an order of magnitude higher than the traditional SaaS benchmark of ~$150-250k per employee. Cursor hit $100M ARR in 12 months, the fastest any SaaS company has ever reached that milestone.

But Cursor isn’t an anomaly, it’s part of a pattern.

Similarly, Midjourney generates ~500 million annually with 107 employees. They reached profitability in two months with zero external funding, spent nothing on marketing, and now command roughly a 1/3rd of the global AI image generation market. And, Bolt.new reached $4M ARR in a single month with less than 20 people, kickstarted by a single tweet. Replit grew from $2.8M to $150M in less than a year. The list goes on.

For established companies, the math here is brutal. AI-native startups simply operate differently, at 10-60x the revenue efficiency of traditional organizations. It isn’t because they found a magical sales technique, or learned arcane magic. Nope. It’s because they’ve built their entire operational model around AI from day one. AI isn’t just bolted on. It’s at the center of everything they do, and how they operate.

We’ve seen this trend before.

During the dot-com boom of the 90s, scrappy startups with small teams disrupted industries dominated by gargantuan incumbents. The winners were the ones that understood the medium the best and moved the fastest. Amazon started out in a garage, and Google was just two grad students with a great idea. Companies like IBM and Walmart had virtually unlimited resources by comparison, but lacked a true understanding of the space, and agility.

We are in the same moment now. The companies that will dominate the agentic era will not be the companies with thousands of employees and massive sales teams. They’ll be the companies that figure out how to make a team of 10 operate like a team of 100.

The Vercel Experiment

The company behind next.js, Vercel, wanted to mix things up. Their COO asked a simple question, “What part of your job do you hate doing the most” and “What tasks would you like to never do again?” The answers were pretty predictable. Repetitive stuff, grunt work, tasks that needed to get done, but felt like busywork.

The immediate insight was this: these were tasks that were too dynamic for traditional automation, but perfect for AI. Simplistic scripts couldn’t handle them, but custom-built agents could.

The sales team at Vercel became the test case. Three engineers shadowed their top sales performer for a few weeks. They documented every task, method, and decision point, and then built an agent workflow.

That workflow consisted of:

• Deep research on each incoming lead and their company

• Categorization and scoring using structured outputs

• Auto-generation of personalized follow-up methods

• Routing to Slack for human approval before sending

• Automatic dispatch upon approval

The result? One person now handles the work that previously required ten. Those 9 other people weren’t laid off, but were redirected to higher-agency, higher-value sales activities where human judgement and relationship-building really matter. AI started handling the volume work, and humans handled nuanced work.

The question isn’t if AI ‘can do this work’. It’s what should humans actually be spending their time on? Increasingly, the answer is work that requires judgement, creativity, relationship-building, and nuanced decision-making that AI cannot replicate and we don’t trust it to perform.

Running AI in Parallel

Every, the digital media company, published a detailed look at how their small engineering team operates. The approach here is instructive. Their claim: a single developer can now do the work of five from a few years ago.

They call their approach “compound engineering.” The key inversion: in traditional engineering, each feature makes the next feature harder to build. In compound engineering, each feature makes the next feature easier because every bug, insight, and lesson gets documented and fed back to the agents.

The loop is simple: Plan → Work → Assess → Compound. Roughly 80 percent of time goes into planning and review. Only 20 percent is the actual work. Agents research the codebase, write detailed implementation plans, execute step-by-step, then review their own output alongside the human engineer. The “compound” step is where it gets interesting; learnings get stored as prompts that live in the codebase, so the next time an agent touches that code, it already knows what mistakes to avoid.

As they put it: “Nobody is writing code manually. It feels weird to be typing code into your computer or staring at a blinking cursor in a code editor.” The humans do the thinking; the agents do the typing.

The Modern Stack: Notion as the Nervous System

So, what are lean teams actually using? A few key tools have emerged, and each play a specific role in the AI-augmented workflow, although there’s some overlap.

Notion has evolved from being a simple knowledge base and note-taking app into what many are calling their “nervous system” or primary source of truth. But why Notion?

Because everything ultimately ends up there. Meeting notes, documentation, project specs, brainstorming, etc. Notion’s MCP server means that every frontier agent can now directly access your workspace. If you ask Claude to draft an implementation based on a PRD, it pulls from the actual document in Notion, not a hallucinated version.

The most recent updates to Notion are serious game changers, with natively embedded autonomous agents (powered by Opus 4.5) capable of executing multi-step workflows for up to 20 minutes at a time. Much like with coding, you barely have to touch your keyboard. Just speak (I use MacWhisper) and think through what you’d like to have changed. Iterate, and watch your idea come to life.

Plenty of teams online are reporting productivity increases from these AI features, but the real value isn’t the features, it’s that Notion has become the single location where tribal knowledge on a team lives. This means that AI can actually use that knowledge, rather than operating in a vacuum.

Linear has become the default issue tracker for lean teams for a similar reason. Linear is built for speed, with sub-second operations, keyboard-first navigation, and opinionated workflows that reduce decision fatigue. Triage Intelligence analyzes incoming issues against historical patterns and suggests teams, projects, assignees, and labels. All of this combined results in progressively removing humans from routine categorization.

Similar to Notion, Linear has introduced agents that can perform tasks on your behalf, handling tagging bugs, nudging stale PRs, and escalating tickets, similar to an actual project manager. Linear practices what it preaches: reaching a $1.25 billion dollar valuation with less than 100 employees.

Slack is now increasingly AI-centric. A simple natural language search means you find what you’re looking for faster, without wasting time typing in specific keywords. Channel summaries compress days of messages into digestible updates, saving precious time. Summarization, when used thoughtfully, increases the signal to noise ratio for work. Figuring out what actually matters shouldn’t be a major effort.

MCP: The USB-C for AI

By mid-2025, MCP was widely adopted, and there’s a good reason why. Anthropic’s Model Context Protocol is a straightforward, standardized way for AI to connect to external tools, simplifying the space quickly.

Rather than building fragmented and custom integration for every tool, MCP is a universal protocol. There are hundreds of MCP servers covering databases, dev tools, communication platforms, and business applications. At this point, even enterprise companies like Block (Square) have integrated MCP into their systems.

The daily, practical impact is this: you can now issue commands like “implement features from JIRA issue ENG-4521 and create a PR on Github” as a single natural language instruction, and it just works. The AI reads the issue, understands the context, writes the code, and creates the pull request. Humans review, and approve.

Configuration is simple. And, all of a sudden, your AI assistant knows almost everything you do.

On Overestimating Headcount

Here’s an uncomfortable truth that Musk’s Twitter acquisition accidentally proved: most companies have way more people than they actually need to operate.

When Musk gutted Twitter’s workforce, taking it from roughly 7,500 employees to under 2,000, conventional wisdom was that the platform would collapse. But it didn’t. There were site glitches and issues, sure, but the core product continued to function, and the business continued operating. The company was rebranded and approached from a different angle, but the core product continued.

You don’t have to like Elon or approve of his methods to acknowledge what this demonstrates: the relationship between headcount and operational capacity is far looser than most organizations assume. A lot of corporate work is coordination overhead, status reporting, and processes that exist simply to manage other processes.

AI dissolves the invisible scaffolding of organizations. Coordinators, schedulers, notifiers, nudgers, trackers, escalators, micro-tasks that people don’t even recognize as tasks because they’re so embedded in how work gets done. AI can handle that scaffolding automatically, which means you need far fewer people to maintain work.

McKinsey now claims to employ 40,000 people and 25,000 AI agents. Their CEO states that they could have equal numbers by the end of 2026. According to Deloitte, middle management job postings have dropped more than 40% between April 2022 and October 2024, with roles around information routing and basic coordination shrinking most significantly.

The companies that will be most profitable in 2030 won’t necessarily be the ones with the most people. They’ll be the ones that have figured out the right ratio of humans to AI.

The Knowledge Base Problem

If AI doesn’t have visibility into your work, it can’t help you.

The teams getting the most value out of AI are documenting everything obsessively. AI note-taking is on for every meeting by default. Everything lives in a standardized place, and every decision gets recorded. Tribal knowledge is recorded in platforms like Notion, or wherever the source of truth lives.

This creates a flywheel effect. The more you document, the more AI can access. The more AI can access, the more it can help. And, the more it helps, the more you’re incentivized to document.

Teams that have been rigorous about documentation for years are now seeing massive ROIs because AI can actually use that institutional knowledge.

Conversely, if things are happening outside of the loop, inside of people’s heads, in hallway conversations, AI is obviously less effective. It’s operating blind. AI working with incomplete context produces incomplete outputs. Big surprise.

The companies winning in the AI transition are the ones treating documentation as a competitive advantage. Your knowledge base is training data for your AI and human teammates.

OpenClaw: The Fully Autonomous Employee

ClawdBot, then (briefly) Moltbot, and now OpenClaw. X has been filled with stories of people trying out Clawd and posting their results. Some are insane, others are legitimately impressive.

OpenClaw is an open-source, self-hosted AI agent platform that acts as a digital employee. It integrates with every messenger you can think of (Telegram, Signal, Discord, etc) and operates 24/7 on Claude tokens. It isn’t a standalone product, it’s a wrapper that enhances any LLM with real-world actions.

The capabilities of OpenClaw are predictable for anyone following the space: browser control, scripting, executing commands on remote machines, automation, sending proactive notifications, writing and sending emails, checking markets, etc. If you can wire it up, it’s possible.

The project exploded overnight, becoming a top trending project on Github. The user reports are neverending, and I recommend just diving into X and searching to see everything people have built and tested in the last week.

This enthusiasm is met with real concerns. Exposed servers, leaked sensitive keys, and scam web3 tokens mimicking and briefly blowing up on X. With that said, OpenClaw was always for technical users, and the creator never claimed that it was ready for consumers. But it represents where the puck is heading: AI that proactively operates within your digital infrastructure.

As people have stated many times in the last week, the people experimenting the most with OpenClaw also fear being left behind by this rapid technological progress if they don’t experiment now. Before long, polished enterprise-centric variants of this tech will be the hands of anyone with a credit card.

Our Clawdy Future

Agentic usage isn’t a trend. Agent-based usage in the workplace is going to accelerate this year, not slow down.

The tools are getting easier. A year ago, connecting Claude to your Notion KB required some technical know-how. Now, one-click installs are everywhere. What once required a developer’s time and attention to setup can be done in an hour with next to no technical knowledge.

Costs will drop. The best model tokens (like Opus) will get cheaper as inference costs decline, competition increases, and models become more efficient. What used to cost thousands in 2023 now costs hundreds.

Capabilities are increasing. Context windows are expanding, and more novel solutions are being introduced and proposed to solve the most frustrating caveats of utilizing LLMs in 2026. The competence ceiling keeps rising.

The logical endpoint is most knowledge work getting restructured around the assumption that AI will handle the most predictable parts of work, while humans handle the most valuable parts. Companies will become smaller, faster, and more profitable. The relationship between headcount and output will get permanently decoupled.

The winners of the AI era might be the 10-person company that operates like a 100-person company. The losers may end up being the 1,000 person company that can’t figure out how to adapt.

The rules are being rewritten now, and the companies rewriting them are the ones moving the fastest.

That’s it for this week. If you enjoyed this write-up let me know!

- Chris

“Nobody tells this to people who are beginners, I wish someone told me.

All of us who do creative work, we get into it because we have good taste. But there is this gap.

For the first couple years you make stuff, it’s just not that good. It’s trying to be good, it has potential, but it’s not.

But your taste, the thing that got you into the game, is still killer. And your taste is why your work disappoints you.

A lot of people never get past this phase, they quit. Most people I know who do interesting, creative work went through years of this. We know our work doesn’t have this special thing that we want it to have. We all go through this. And if you are just starting out or you are still in this phase, you gotta know it’s normal and the most important thing you can do is do a lot of work.

Put yourself on a deadline so that every week you will finish one story. It is only by going through a volume of work that you will close that gap, and your work will be as good as your ambitions.

And I took longer to figure out how to do this than anyone I’ve ever met. It’s gonna take awhile. It’s normal to take a while.

You’ve just gotta fight your way through.”

― Ira Glass

I’ve written about this at length already, but it bears repeating: the internet is changing and the barrier to entry for creating content has dramatically shifted in the last 24 months. AI-generated content is often associated with having poor taste, due in part to the lack of effort required to generate it.

This is obvious for anyone using social media, but we’ve seen a dramatic rise in the amount of slop content since the release of Sora 2 back in late September, 2025. Endless Facebook posts with AI-generated images was one thing, but by early October of 2025 platforms like Instagram were saturated with video-diffused content.

If you used Instagram or TikTok during this period of time, I’m sure you noticed. But the AI video content most likely found its way into your feed, and it wasn’t because the content was bad. Far from it.

It’s because the content is good enough to trend. This distinction matters. It’s good enough to get reshared, watched, and commented on. And since the release of Sora 2, other platforms like Kling have expanded upon this trend, allowing for increasingly impressive and at times genuinely hilarious content to be produced.

We’re standing at the edge of a precipice, and a few things are clearly true:

• The generative capabilities of AI are the worst they will ever be

• The cost of these capabilities will decrease, and accessibility will increase

• The technical capability required to produce anything you see on a screen is decreasing

• Because computers mediate design, nearly everything we touch, buy, or watch can be shaped by AI

• Our human brains are static, and we’re working with what we’ve got

Taste, and curating the ingestion of content, is now shifting from being a luxury or an intellectual exercise to being an absolute necessity for sanity in the age of AI. Taste may be the determining factor between making the time for critically acclaimed cinema vs being too brainrotted to even sit through an episode of a serialized Netflix show. If you are what you eat, consuming a rich diet of shorts on platforms like ReelShort may permanently turn your brain into processed jelly.

This trend has become clear, but recent models like Opus 4.5 have made this even more obvious: soon, if you can conceptualize something, you can create it. This is the collapse of technical scarcity, and with it, we have an associated filtering crisis.

The Collapse of Technical Scarcity

This isn’t just a media problem. It’s already reshaping work.

The job market has had its fair share of ebbs and flows over the last two decades, from the housing market collapse through the last few election cycles. But the job market throughout 2025 and early 2026 has felt distinctly different. People are evaluating AI’s capacity to replace people, and there’s a fundamental shift happening with how we’ve traditionally thought of the career ladder when it comes to white-collar work. New college grads cannot find jobs in many fields, period. Reddit’s r/jobs sub is a sobering insight into the realities of what is happening. Hiring recent college grads is way, way down. Why?

When this started happening in late 2024 and early 2025, many people chalked it up to being a result of tariff scares and a correction to previous overhiring. But, looming in the background, was the incredibly obvious answer that was difficult to accept: AI. We were already at a point where multimodal models could put together impressive research reports, and employees already in positions were being highly encouraged (or forced) to use generative AI as much as possible.

Join, or die.

Dario Amodei, CEO of Anthropic, predicts that half of entry-level white collar jobs could be gone within the next 5 years. The speed of this transition will overwhelm our ability to adapt.

What was clear: more (or the same) could be done with less. This trend has continued into 2026, and will only accelerate.

Here’s the reality of 2026 at-a-glance:

• Front-end development has been functionally automated overnight

• Claude Opus 4.5 can ingest and output more usable, high-quality content than any intern you could possibly hire

• At worst, producing realistic photographs, graphics, and videos that used to take days or weeks to produce with a team of people can be done in minutes

The key here that people often misunderstand is this: the generation of software, images, videos, and content in general doesn’t have to be perfect to impact society, it just has to be good enough. And, if we’re being honest, we’re already there.

By the year 2030, we’ll look back on the frontier models (like Opus 4.5) that we’re using now and wonder how we even tolerated them. They’ll look cumbersome, slow, expensive, and poorly optimized. We’ll have much more refined, local models running on our laptops and phones that will run circles around these older models. And, frontier models will be fully agentic, capable of utilizing VMs and using a computer like you.

Where does this leave us? If the cost of technical expertise and execution goes down, and accessibility of these platforms goes up, what happens?

The result is an absolute deluge of content and software programs. SaaS will be disrupted, and the cost of subscriptions will decrease as the sheer amount of seemingly identical competition increases. Plenty of technical jobs will still exist, but entry into some fields will become increasingly difficult, and the bar for technical expertise required to truly make an impact as a human will rise considerably.

Shifting through the ocean of content and software will become its own skill, and that’s where taste comes in. In a world where anything can be created if it can be conceptualized, taste will reign supreme.

Compute and software can be bought. Taste is earned.

Defining Taste

I have a friend named Sarah who has impeccable art and design taste. I know that if I ask for a recommendation (be it visual, music, etc) I’m sure to get a good list from her. I also know that her keen sense of taste boils down to a few factors:

• She was naturally inclined towards the visual arts, and arts in general, from an early age

• She leaned into this natural inclination, and spent countless hours consuming, learning, and honing her skills both as an artist and as a thinker

• Her refined sense of taste is a combination of inputs (what she spends her time thinking about) and her outputs (what art she puts out into the world)

Taste compounds.

The point is, this mental curation didn’t happen overnight. Oftentimes it grows over a lifetime, and is informed just as much by what you love as what you dislike.

To be more clear, taste is not:

• A vibe

• Following trends

• Aesthetic snobbery

Taste is a combination of pattern recognition over time, a sensitivity to quality differences, awareness of context, and saying ‘no’ to lots of inputs and outputs. It’s a mental discipline. It becomes a filter against noise and a form of mental hygiene.

Our digital environments become a mirror of ourselves. How clean is your desktop? How organized are you digitally? Do you walk away from the computer with a sense of relief, or overwhelm?

One of my predictions this year in my last piece was the push towards a renewed analog renaissance. Part of cultivating a sustainable digital diet is knowing what to say no to, and deciding when and what to consume mentally. This was a huge problem as social media got more popular, and it is even more relevant now with the rise of generative AI. Much like with taste, some people have a more natural ability to moderate, and others do not. As the saying goes, if something is free, you are the product.

AI and Taste

Can AI have taste? AI outputs often impress me, but they still leave me feeling cold.

Ben Affleck went viral for his take on utilizing generative AI for writing. His take was the following:

• AI writing is shitty because it goes to the mean, to the average

• It’s a useful tool if you’re a writer, for ideation and brainstorming

• It is incapable of writing genuinely creative, original content

By no means are these revelatory takes, but I generally agree with Ben. While models are rapidly improving, and baseline performance increases monthly, there’s a nuance with taste. AI can approximate taste, and it can create a believable facsimile of it. But, it currently cannot care about taste the same way people do. It can describe the feeling of a painting, but it cannot feel the feelings.

Above is Picasso’s famous painting Guernica. When I see this painting, it evokes feelings of dread, terror, sadness, and the brutalities of war.

If you ask Gemini to analyze this painting, it prefaces its response by reminding you that it doesn’t experience emotions. It clinically and specifically describes the painting in detail, and does so in a way that might make me think it is an art historian. Its description of how things come across sounds accurate, and upon reading its outputs a few times over, I largely agree with its response. But the response isn’t borne from feeling first, and that’s the difference.

As models evolve, and signs of introspection become emergent properties of their presence, this landscape will change.

Taste as Labor

David Shapiro coined this term ‘KVM jobs’ last year, and I find myself constantly coming back to it to accurately describe where we’re heading. KVM stands for keyboard, video, mouse, and KVM jobs essentially means anything done on a computer with your hands and eyes.

KVM-based jobs are eroding, and this isn’t limited strictly to technical domains. More and more of the current work being done will be automatable soon, and what remains is beginning to skew towards judgment. Taste plays an enormous factor here.

Instead of writing code manually, many software engineers are already becoming orchestration leads. Their roles have shifted. Their time is being spent differently, now curation, review, and revision are the name of the game.

This isn’t an isolated phenomenon. This behavior and workflow is bleeding into every other role. To be successful, everyone has to become a project manager. Having a holistic view of a project or body of work is a must. It isn’t enough to just be good at one thing, diversification is essential.

Games are a good example of the results of editing and taste. Games are inherently multidisciplinary, and are experienced as a full-bodied thing. The feeling of playing the latest Mario game is often one of joy, and this feeling is the result of a ton of work, thought, and curation.

To make a great game:

• The basic actions must feel good to do over and over

• There’s a sense of progression, unlocking, growing, and evolving

• The goal for what you’re doing is clear, and you know what you’re working towards

• The art and design behind the game is aesthetically pleasing and cohesive (the music, art, and code all work in harmony together)

• There’s a balance between novelty, rest, and tension

Much like maturing and growing as individuals, our taste evolves over time. I’m sure you don’t have exactly the same music taste as you did from 10 years ago, that’s a good thing! The refinement of our taste is a never-quite-completed thing, like an endless mind-map, latching onto the next thing the moment we’ve grown comfortable.

We’ve already seen many new job titles come into the mix, but I predict that within the next couple of years we’ll see more titles like:

• AI output curator

• Orchestration lead

• Evaluation lead

• Product editor

Embracing the Age of Taste

Some people truly see things before others do. Rick Rubin is one of those people. Last year, Rubin’s site, The Way of Code, went viral. And yes, it’s a playful site based on Lao Tzu. The site and its graphics were vibecoded using Claude, and the site in my opinion is a great distillation of the time and place we’re currently in. Much like a great book, the site is a balance of thought, form, and zen.

I often sit with Rick’s quote on taste: “I know what I like and what I don’t like. And I’m decisive about what I like and what I don’t like”.

We’ve entered the age of taste, and our collective responsibility for how we want humanity to look is more important than ever.

I’ll leave you with an excerpt from The Way of Code:

People say Source is so grand
it’s impossible to grasp.
It is just this grandness
that makes it unlike anything else.

I have three great treasures to share:
Simplicity
Patience
Humility

Simple in action and in thought,
you return to the origin of being.
Patient with both friends and enemies,
you accord with the way things are.
Humble in word and deed,
you inhabit the oneness of the cosmos.

The internet is feeling less grounded. Stranger. Like a home that is suddenly unfamiliar. And this feeling sucks.

I know I’m not the only one.

AI pessimism and hatred reached an all-time high in 2025. From workslop to brainrot, AI has accelerated trends that are divisive at best, and resented at worst. For me, the increase of AI-generated content online has shined a renewed light on some of the internet’s most pressing issues, issues that will only get worse before fundamental new paradigms are adopted.

Our feeds, which were already an ADHD-addled mess, have become even more overwhelming. I’m sure you’ve noticed the change.

The content crisis and authenticity concerns are here to stay, and the need for proof of personhood remains. Trust has become a cognitive tax, and it is exhausting.

Despite the messy end to 2025, I don’t think 2026 will be calmer. I think it will be more decisive. If 2025 overwhelmed us with capability, 2026 will force us to choose how much control we’re willing to surrender.

This is a reflection on how we lost agency in 2025, and why 2026 will reward those who actively reclaim it.

I’m calling 2026 the Year of Agency.

This isn’t about agents. It’s about personal agency. Empowering individuals, like you, with tools, autonomy, and reclaiming control over time and attention. AI isn’t dictating what you do, you are.

2025 Retrospective

To understand why agency becomes the central challenge of 2026, we need to look at what 2025 quietly normalized.

• AI content deluge: In 2025, we saw generative AI usage for content like video go mainstream, from the release of Sora 2 to powerful image diffusion models like Nano Banana Pro. Within a matter of days, every social network was flooded with endless AI video content. The year was equally filled with complaints about workslop, and we found ourselves constantly asking, was this made by AI? Every interaction becomes a micro-decision we never wanted to make in the first place.

• Content Authenticity crisis: The rapid rise of generative content online has turned content authenticity desires into a necessity. Google’s SynthID is a digital watermarking tool used by Nano Banana. C2PA (content credentials) is getting baked-into content being produced digitally, like Adobe’s suite.

• Rapid capability leap: There’s no denying that frontier models at the end of 2025 are an order-of-magnitude more powerful than they were at the beginning. GPT-5’s release might have been a bit of a mess, but frontier capabilities with coding and agentic computer-use are impressive. Google’s latest release of Gemini is a shockingly good front-end developer. And, the capabilities of offline language models running locally on your machine is nothing to sneeze at. The gap between what these systems can do and what most people understand is growing faster and faster every month.

• Agents Unleashed: From hacking to prediction market automation, agents are being used for just about everything. AI agents have become financial actors, and new internet-native payment methods have been adopted. I wrote about the age of autonomous finance and speculated on what would happen in 2025 back in October of 2024.

Across all of these trends, one pattern became clear: intelligence accelerated, but agency didn’t.

We end 2025 with many feeling overwhelmed by AI, overencumbered with news, tools to try out, and infinite scrolls of slop. Many of our friends have become sloppers, with AI capturing the last tiny crumbs of attention that were left to be grabbed. Passivity is easier than discernment.

So, 2026 will be more the same, right?

Wrong!

2025 taught us what happens when agency is optional. 2026 will reward those who make it non-negotiable.

2026: The Year of Agency

If 2025 was the year where things got crazy, 2026 is the year we decide where AI belongs.

Personal agency in this article can be defined as: empowering individuals with AI-driven tools, redefining work through autonomy, and reclaiming control over our time and attention.

I’ve broken up agency into three distinct categories:

• Tool Agency: choosing tools that amplify judgment rather than replace it

• Work Agency: owning outcomes instead of just executing tasks

• Attention Agency: deciding what gets access to your mind

Each section interrelates to the other, and is supercharged by optimizing all three. The future will belong to people that deeply understand both where technology is heading and the limitations of the human mind.

With that said, here are my top tech and social predictions for 2026.

Tool Agency - Empowering Autonomy through Tech

One thing is certain: keeping up with the latest tools and their development can put you ahead of the curve. Being aware of what is genuinely useful vs a distraction can be tough, but many of the themes over the last year are only accelerating.

Here’s what to keep an eye on:

All-in-one AI Ecosystems

We’re going to see a rapid consolidation of tech stacks with AI. Behemoths like Google are bundling packages together to make it easier to use ‘AI-first’ environments. Tools like Antigravity are a great example of this. Cursor was just the start, but bundling together cloud data and seamless deployment will make older tools relics of the past.

My prediction is that Google will become the breakout star for this next year, with runners-up like OpenAI and Anthropic struggling to compete with the solutions Google can offer for cheap. Leveraging AI for coding, multi-agent orchestration, and content creation will become easier. I suspect many people will trim down their AI subscriptions to JUST being something like Google AI Ultra by the end of 2026.

This is the difference between leverage and convenience.

Personal AI Workstations (Virtual OS)

Tools like Zo Computer really stood out towards the end of 2025. Having a personal, cloud-based virtual machine running your own suite of AI agents that can also act as a website server is wild. This is a secure sandbox where you can let your agents and various models operate without fear of a major command line disaster impacting your personal computer. Personal AI workstations can also act as an always-on second brain, seamlessly linking via MCP with content and data you have across various services.

LLMs + personal data + server + automation = !?!?!?!. The sky is the limit here. Once Zo is upgraded to include computer use (think Manus), the rocketship will truly take off. While this product category is very new, I am extremely excited about it. Tools like Zo unlock a new range of capabilities, and the potential is endless.

Agentic Scaffolding, Integrations, and Memory-Augmentation

MCP exploded onto the scene in 2025, and will continue to be incredibly important for agentic scaffolding in 2026. We will see this trend continue: more plugins, more servers, and more integration hubs to bridge AI with apps and data that people use.

As the agent landscape evolves, it comes as no surprise that open formats like agents.md are gaining popularity. Entire tools like Kiro have been designed around spec-driven development, which makes AI even more effective at producing reliable, maintainable code.

Better agentic plumbing means more time building and designing, and less time troubleshooting and fixing terribly written code or correcting AI when it misunderstands. While frontier models will continue to improve, equal importance is now being placed on the scaffolding surrounding the models. This is the secret sauce that makes Claude Code, Cursor, and Antigravity so useful.

Finally, memory. Context and ‘understanding’ are a billion dollar problem right now in the space. Having agentic systems that can maintain context for longer, with seamlessly integrated and continually updated vector and graph databases, is crucial. Procedural, episodic, and long-term memory are all important here. Memory is arguably the biggest blocker to increased usefulness with agentic systems.

The memory challenges now may be drastically different by the end of 2026.

Work Agency - Rise of AI-Enhanced Generalist

Although this has been repeated many times (to the point of being cliché), the reality of where technology is heading is starting to match the mantra of ‘the year of the generalist’. Compared to where things were in 2023, it is now increasingly possible for a single person to build products and run companies successfully by tastefully employing AI.

This is not about becoming a unicorn founder. It’s all about how leverage shows up in ordinary, everyday work.

As an example, in 2025 Maor Shlomo built Base44 and sold it to Wix for 80 million dollars. Solo. The future of Base44 is uncertain, but Maor was able to scale and build a seven-figure business solo in a relatively short period of time, culminating in its acquisition. This is just a single example of an AI-enhanced generalist in the real world.

I’m not saying that specialization isn’t useful. It is still incredibly useful. But new pathways have emerged, using AI, that are changing the game.

Lean Teams and Solo Entrepreneurship

In its initial phase of growth, Cursor reached $100 million in ARR with just 12 people in early 2025. This isn’t an anomaly – this is a showcase for how AI-native teams can operate. Cursor became the fastest growing SaaS in the history of SaaS, and became a genuinely useful tool overnight. Not hype, not a gimmick. Reality.

In 2026, the concept of a ‘billion-dollar one-person company’ is moving from sounding like a joke to being a plausible scenario. With enough understanding of agentic systems, marketing, sales, and product, it is really just a matter of time before it happens. As we move through 2026, expect more stories about multi-million dollar solo ventures. Things that were formerly impossible are now possible. Larger companies will need to adapt, or risk irrelevancy or death.

AI-Augmented Roles & Careers

This was already happening, but the trend will continue: AI-augmented roles are here to stay. In particular, product roles are expanding to encompass more prototyping, design, and initial phases of development. The most adaptable person will be the person hired for the job, and new roles like ‘AI orchestrator’ and ‘agent builder’ will become commonplace.

Traditional specialist roles will continue to exist, but the expectation will be to broaden skill sets. With the pace of AI showing no sign of slowing down, everyone in technology will be expected to continually learn and expand skill sets.

Earlier in 2025, Clay emerged onto the scene with the concept of the GTME engineer. Using AI and automation to build revenue engines and automate demand gen is useful. AI has eliminated the need for custom manual research and engineering, and businesses are only just now starting to see how this can be useful.

AI is the rocketship for the mind, but only if you can control it. Accomplishing 10x more than before by offloading routines or complex sub-tasks will become the norm. Personal leverage changes the economics of creativity and labor, but workflows need to be adapted to fully utilize the potential.

This leads us to our last section.

Attention Agency - Intentional Tech Engagement

As important as it is to stay on top of the latest trends, we need to learn how to disengage. While we’re all familiar with social media addiction, AI and chatbot addiction is rapidly on the rise, leading to unhealthy relationships and habits. The most effective agentic tool usage is worthless if your mental health is wrecked.

As we’ve seen with social media, it boils down to autonomy and personal control. No one is coming to save you. The more intelligent AI becomes, the more safeguards and attention checks are required. Human beings are exhausting enough, and AI only adds gasoline to the fire.

In practice, attention has become the real constraint.

Human-Only Content Platforms

With the onslaught of slop this year, people are craving human-centric spaces online. Presently, no platforms are safe from AI-generated content. With platforms like Facebook, users are unsuspectingly liking and responding to AI content, blissfully unaware that what they’re engaging with was generated purely for views and to capture their attention. Social media was already overwhelming prior to AI, but with the lack of adhered content provenance and digital watermarking, everyone is asking: is this AI?

Right now, there are no popular human-only content platforms. Notably, platforms like DeviantArt, beloved for being spaces where artists shared their work, devolved quickly when AI content ran rampant across the platform. Every time anyone sees anything on artist-sharing sites now, they ask: is this AI?

Proving and enforcing human-only spaces is important, and will remain important for the future of the web. Expect new, progressive social media platforms to be created that embrace the ideas behind C2PA and human-spaces.

Analog Renaissance

Digital detoxing and digital detox retreats have continued to balloon in popularity throughout 2025, and it will only become more popular and important as we move forward.

Expect more ‘phone free events’ and ‘unplugged’ sessions. As automation and AI continue to grow, so too does the desire for un-augmented activities. It’s no surprise that knitting, pottery, and macrame are growing in popularity: they fulfill a deep human desire to slow down.

This correlates strongly with the meditation and mindfulness movement, which regained popularity over a decade ago with books like ‘Waking Up’ strongly linking tech bros with spiritual emancipation. Mindfulness and spirituality are gaining traction once again, as our bodies and minds become overwhelmed with the pace of change and speed of information.

Content Diets and Screen Time Monitoring

The growing awareness of how social media and AI-curated feeds are hijacking our attention has reached a tipping point. More and more people will switch to dumb phones and you may even see a person or two holding a methaphone. Gen Z is leading the pack with this, due to digital fatigue, mental health struggles, and a strong desire for offline experiences.

Reducing screen time, quitting apps, and being unavailable is cool now. That will remain the case through next year, and will likely accelerate. Trusting your gut is cool, and critical thinking atrophies if you rely on ChatGPT for everything.

Embracing Autonomy and Balance

Personal sovereignty is now the north star. This year of agency, which can be embraced by anyone, is all about empowering individuals to reclaim control. 2026 will reward those that leverage AI to amplify their current abilities without surrendering autonomy. The ‘what’ with the latest trends is just as important as the ‘why’.

Locking in is cool. So is unplugging. Finding a balance between the two will separate the sloppers from the non-sloppers in 2026.

Intentionality matters. The people who struggle the most with AI won’t be the least intelligent, but the least intentional.

We have the power to shape how technology impacts us, and how we engage with it. 2025 was a watershed year for a number of things, but it opened our eyes to how quickly things can change online. The job landscape, social media, and technology are all evolving rapidly, and having AI as an ally rather than an adversary is crucial.

Personally, I’m excited to see where this year takes us. I think we’ll see brand new amazing companies, products, and ideas take shape. The singularity will not arrive evenly distributed, and AI’s impact on the future of humanity is hard to wrap our heads around.

Agency is something you practice, not something you’re given.

Thanks for reading.

- Chris

The release of Nano Banana Pro on November 20th has proven to be a watershed moment, with the internet collectively losing its mind over the model’s diffusion capabilities. Simply put, NBP has blown previous image diffusion models out of the water. Images can be quickly generated that are so realistic, it is virtually impossible with the naked eye to tell that the image is AI-generated.

We all knew this moment would come, but it was never entirely clear what would happen next. Even with video lagging behind, every image seen online must now be met with skepticism. Is this AI? Is what I’m looking at real, or fake?

In this article I’m going to cover where we’re at with digital watermarking and content provenance, what organizations and governments are making key decisions about the future of the internet, and what to expect in the future. There are many factors at play, but it is truly worth understanding. The landscape is complex, but understanding this is crucial for living and working within the new internet.

Let’s dive in.

The Content Crisis in 2025

Make no mistake, 2025 is the year that we collectively realized there was no longer a clear distinction for what was human versus machine made online. The old trust models have died. As many of you remember, it wasn’t too long ago that a video or photograph was near proof of something being genuine. We’d say ‘Show me a video!’ and that was all we needed. Sure, things could be faked, but it often required time and skill to look believable.

Those days are over.

Once AI content is truly indistinguishable from real content, what is ‘real’ can be just as equally dismissed as fake. Evidence can be generated, and activity on online platforms can be mimicked and emulated by bots. We’re existing in a liminal space, one where new AI paradigms and the old ways of using the web co-exist in complete chaos. Everything from profile pictures to real-time video calls can be spoofed. AI-generated audio has become believable enough that I personally can no longer tell the difference.

Now, surely, something’s got to give. The tools and programs used to spoof everything will only get better, and it won’t be long before the successor to Sora is generating videos so lifelike, you’d swear they were real. With video diffusion models trained on all of YouTube, the most viral content will no longer be human-made, it will be AI-generated snippets designed from the ground-up for maximal engagement. When attention is the only game in town, you either adapt or die. Mid-tier human content won’t go viral, AI content will.

Guessing, or using detection models while browsing the web and interacting with content is not the solution. Detection models often produce false positives, which can lead to labeling human-created content as AI. This is bad, and leads to a further erosion of trust online.

The solution to understanding what we’re seeing and watching online is not retrospective guessing. The solution is structured metadata and embedded cryptographic proofs at the moment of creation. I’m talking about content provenance.

The Content Provenance Landscape

Although the content provenance space has been evolving for the last 6 years, most people are unfamiliar with it and the various organizations and companies leading the charge.

To start, what is content provenance?

Content provenance - the recorded and verifiable history of a piece of digital content, such as an image, video, audio file, or document.

In February of 2021, Microsoft, Adobe, and others got together and co-founded the Coalition for Content Provenance and Authenticity (C2PA). C2PA’s goal is to push forward a royalty-free technical standard in the fight against disinformation. The Content Authenticity Initiative (CAI) is a related association that has the goal of promoting this new standard.

Every C2PA-enabled file contains a Manifest, a container of assertions with statements like ‘Edited by Adobe Photoshop’ or ‘Generated by Sora’. These manifests are stored inside the files using a structure called JUMBF, which simply adds a metadata container to the file.

Assertions made are signed using a private key utilizing PKI, ensuring that the files are only cryptographically signed by valid signers (it cannot be spoofed). Ideally, you’ll see a C2PA-signed image or document accompanied by a small “CR” icon, and clicking on it will reveal that file’s origin, who signed it, and what edits have been made.

The final result is a potential ecosystem where all media content has digital provenance metadata attached to it, so you can clearly see how something was made, how it was edited, and verify the source.

As long as the metadata stays intact, we’re good. Unless, of course, it gets stripped.

The Stripping Problem

C2PA metadata is great, but there are a number of problems with relying on it exclusively. Currently, most social platforms scrub metadata when you upload a file. If you upload an image to Instagram, the platform automatically removes EXIF, IPTC, and XMP metadata to reduce file size and prevent unwanted data from leaking (like GPS coordinates showing where you took the photo).

Largely, this is a good thing, but platforms haven’t yet evolved to retain critical provenance data, if it exists at all. C2PA is vulnerable to social-media compression and re-encoding, screenshotting, and various image optimization pipelines. Even though many organizations have already adopted C2PA, end users rarely see its usage in the wild. At best, we’re relying on community notes on X, generic AI banners, or (in the case of most platforms) no information at all.

Surely there’s a solution to the stripping problem, right?

SynthID and Invisible Watermarking

Google’s SynthID steps in where C2PA metadata falters. SynthID is invisible watermarking; the watermark is embedded into images and videos and is robust enough to survive compression, some editing, and screenshotting. It isn’t visible to the human eye, but detectable by authorized systems.

Even this isn’t perfect. SynthID can be broken with generative in-painting, aggressive editing, and using deep learning models to create advanced noise-based transformations. That said, SynthID survives where C2PA metadata doesn’t.

The Hybrid Approach

When combined, C2PA and SynthID are a formidable combo in the fight for increased transparency online:

• C2PA holds the provenance record, with details on edits and origin

• SynthID embeds an invisible signal into the image that persists even if the metadata is stripped by an organization or bad actor

• The SynthID watermark can contain an ID that can be used to lookup the original C2PA manifest, ensuring information stays intact.

Together, this is what we’re working with at the end of 2025. It isn’t perfect, but it’s better than nothing.

Our Broken Digital Content Flow Today

Take a look at the above image. This is a screenshot from Facebook earlier this year. You’ll quickly notice that there is zero indication that this content is AI-generated. The unassuming readers are none the wiser, with many earnestly believing what they’re seeing is real.

Today, if you scroll through most social platforms, you will not be met with clear banners or prefaces indicating that content is AI. At best, you may encounter top comments or community notes on X that indicate that something is AI, but this is almost always in response to uploaded content, and not done proactively. Surprisingly, TikTok has been the most proactive as a major social network, becoming one of the first video sharing network to implement C2PA.

Because platforms deal with massive amounts of uploaded data daily, they are constantly compressing and reprocessing content. Metadata is stripped, so even if C2PA is provided, it rarely makes it to where it matters: actual people. Shockingly, all major web browsers lack native support for displaying provenance, and the distribution layer for content hasn’t been updated. The pipes have been laid down, but there’s no faucet.

The Ideal Flow

AI-content makes everything more complicated online. We really only have a couple of options: limit AI-content to AI-exclusive platforms, or add content notes and banners to spaces where human and AI content co-mingle. Of course, even if you tag all of the content on a platform, users will still constantly wonder what the content actually is, and where it came from.

The ideal flow for a social network with content ranging from human to AI-generated looks like this:

• You log in, scroll, and start discovering content

• At the top right of every piece of content, a checkmark shows its status. This checkmark is colored, and indicates if the content’s credentials are valid. Additional marks for fully or partially AI-generated content are visible and present.

• If you’re interested, you can click on the content checkmark and see additional information. Everything from how it was created to when it was modified is available.

Eventually, content that has no credentials may not even be allowed to be uploaded to the platform. Users will, inevitably, begin to prefer content that is considered legit, and has the proper checkmark(s). AI content will come with its own biases, and many spaces will become human-only (this is already happening).

This might sound like an exhausting and tedious extra layer for interacting with the internet, and that’s because it is! AI introduces a host of new concerns online, and the origin of media is one of them. Robust measures to ensure AI stays out of human-only spaces will be an ongoing struggle, and one that is already proving incredibly difficult.

Regulations and Unwatermarking

When the EU passes new mandates, they often become de facto global standards. Decisions surrounding GenAI are no exception to this rule. With the passing of the EU AI Act, machine-readable watermarking and provenance for generative AI content is mandatory in the EU. In America, California attempted to pass AB 3211 in 2024, which would have required many social media providers to have re-engineered media ingestion pipelines.

With Europe moving forward with watermarking, the rest of the world will most likely adopt this standard. However, there are numerous workarounds for ‘unwatermarking’, which is exactly what it sounds like, removing visual watermarks and provenance signals.

Getting around text watermarking is easy. For images, local models (like FLUX or Stable Diffusion) generate images without watermarks. Scripts can be used to strip C2PA metadata, and generative in-painting can be used to remove SynthID. The tools for removing content info will only get more sophisticated, and not everyone online is completely aligned with C2PA’s approach to applying metadata.

The Future

The internet is evolving. Like many people, I have a certain nostalgia for the internet of the past. I miss the simplicity of the early 2000s web, and I’ve realized that I’ll likely soon have similar nostalgia for the web pre-AI.

Every deepfake will not be detected. Every AI image will not be tagged. We’ll need to work together to make an internet that we actually want to use. The old trust models are dead, and now we’re building the new ones. Everything feels weird and off, and it’s okay. We’ll figure it out.

I’m convinced that platforms will need to adapt quickly as AI progresses. Our sanity depends on it.

Thanks for reading.

- Chris

For years, the fields of generative UI and adaptive interfacing have been very interesting and worthy of discussion, but relatively slow moving. This shifted today.

Generative UI is quickly becoming something to take seriously, and Claude is leading the way. As the video mentions, Claude generates software directly in real time for the user to interact with and interprets context and produces new UI elements on the fly. This is software that generates itself in response to what you need. In many ways this is a logical extension of Software 2.0. This is where software is heading.

Let’s dig into what generative UI (GenUI) is, why adaptive interfacing is important, and how it relates to agentic UX and the burgeoning field of relationship-centered design.

Core Ideas and Definitions

Before we dig into examples of GenUI and adaptive interfacing in action, we need clarity on what these things are. Understanding Claude’s latest experimental feature requires context to truly appreciate.

When we think of generative UI, science fiction examples might come to mind, like scenes from the movie Her or Minority Report. In these movies, the interfaces the protagonist interacts with morph and evolve based on what they’re doing. If they’re having a conversation, the interface pulls up relevant materials in real-time. If something is unnecessary, it disappears. These kinds of interfaces, at least in film, seem to have a sort of mind meld with the user. Over time, the interface seems to know what is wanted before the desire is even expressed. Actions happen at the speed of thought. We’ve seen impressive examples of this already with Apple’s Vision Pro’s vision tracking.

Let’s start by defining generative UI.

Generative UI (GenUI)

Definition: Interfaces dynamically generated in real-time by an AI model, customized to user needs and context.

Unlike AI-aided design, where models help designers, the system itself produces the components, layouts, flows, and styling in response to context about the user. It bases the final design on the inputs, instructions, behaviors, and preferences of the user, so each user’s UI looks different.

This term is relatively new. It emerged more formally as a term around 2023, but the concept of adaptive interfacing has been around for much longer. It blurs the line between software as merely a product, and software as a living agent. You could think of early examples of this as dynamic dashboards or front-ends built from configurations and API definitions. The creation and adoption of GenUI entails having a new design language to accompany it.

This is strongly tied to the concept of adaptive interfacing.

Adaptive Interfacing (AUI)

Definition: Systems that continually adjust based on context (time, location, activity, preference, cognitive load, etc).

This goes beyond responsive design (simply screen size, resolution, and adjusting the UI accordingly) and into responsive cognition. The software adapts to you. Context can include the time of day (simpler UI in the evening based on cognitive load), your location (are you at work or home?), past actions, and accessibility.

For software incorporating AUI, longitudinal adaptation is key. UI tweaks are like behaviors, learned over time based on your needs and who you are as an individual. This kind of UI bridges the gap between a one-size-fits-all UI and personalized software. Your Netflix recommendations are personalized, and now your UI is, too.

Agentic AI was the missing piece to make AUI more feasible.

Agentic UX

Definition: User experiences are shaped by AI agents that can plan, reason, and act across a user’s journey.

Traditional UX creates the pathway, leaving the user to do the thinking and coordination through the UI. Agentic UX creates an embedded partner, a built-in AI that is more than an assistant, pulling real weight across tasks.

AI features are shifting from being merely gimmicks to being part of a new UX paradigm. We know that agents can assist with many tasks, from analysis and decision-making to handling workflows and follow-through. The AI is embedded into the experience of the computer interface you’re using, and is actively involved with adapting that interface and experience to better serve your needs.

The field of agentic UX is rapidly evolving, but agents lack deep user grounding and context (currently) to use them as reliably as you might for a person. To close this, agents need to bridge the gap between intelligence and context, and they need to form relationships with the users they are serving.

Relationship-Centered Design

Definition: Applying the principles of human relationships, like boundaries, communication, and shared history, to how AI interfaces evolve with users.

For a richer understanding of relationship-centered design, I highly recommend this Config presentation from 2024. Jason Yuan said the following: ‘this is skeuomorphism for relational design’. Relationship-centered design borrows from how human relationships work to make AI relationships feel intuitive.

When the social boundaries of chatbots and AI systems shift, user behavior shifts. Boundaries + communication + shared history = relationship. The more an AI system knows about you, the better it can work with you. This directly ties into what you’re viewing on the screen, and how you are presented information. As trust accumulates, the gap between intelligence and context shrinks.

Together, these concepts create a flywheel of generative UI.

The Flywheel of Generative UI

The flywheel for generative UI and how the user is presented a dynamic interface is evolving daily, but this is roughly what it looks like today:

1. Intent Capture → Gather context: preferences, natural language, past actions, device signals

2. State & Memory Management → Merge session context with long-term user history

3. Reasoning & Planning → LLM decides on modules, workflows, or visualizations

4. Component Selection & Tool Calls → Choose and call appropriate UI elements or external functions

5. Interface Rendering → Stream components and flows in near real-time

6. Feedback & Adaptation → User corrections, engagement patterns, and trust signals refine the mapping for next time

More interactions → more adaptation → smoother and faster experiences. Usage builds a shared history, which leads to (ideally) trust and better responses and ideas.

There are a couple of previous imaginative design and software projects that incorporated these ideas years ago that are worth highlighting. They hint at where the space is heading, and they proved to be prophetic.

Early Examples of GenUI and Adaptive Interfaces

MercuryOS

My favorite example from 2019 is MercuryOS by Jason Yuan. MercuryOS is a speculative OS that rejects the desktop metaphor and app silos, and opts to assemble content and actions fluidly around user intentions. The best way to get a feel for the ideas here is to check out the site.

The architecture is divided up into modules, with chains of modules to extend your focus throughout your session. The motion of the UI guides attention seamlessly, rejecting harsh geometries and prioritizing continuous cognition with minimal friction.

Core Principles

• Humane: Inspired by Jef Raskin, Mercury positions itself against the “inhumane” clutter of desktop and mobile OS ecosystems.

• Fluid: The system bends around user intent, not the other way around.

• Focused: Designed to respect attention spans and reduce overwhelm.
  Familiar: Multi-touch and keyboard hybrids, but without the baggage of app metaphors.

Jason eventually went on to co-found New Computer and Dot, which built on some of the ideas he explored in this project. Dot was incorporating memories and longer context before many other projects were even considering it. Dot was sunset, but certainly inspired other projects in the personalized AI space.

Artifacts

Another intriguing project is Artifacts, a human-centered framework for growing ideas created by Nikolas Klein, Christoph Labacher, and Florian Ludwig. Instead of treating files as static units of data, Artifacts reframes them as documents of meaning. The system is built to help people continually develop ideas, connect fragments, and collaborate in ways that mirror how humans think and create. Truly human-centered design.

The framework replaces folder hierarchies with contextual documents that let ideas evolve fluidly while remaining navigable. Artifacts is more akin to a thinking environment, built to support long-term creative work.

Core Concepts

• Documents as Meaning → Each document represents one idea and can mix text, sketches, images, or models.

• Open Organization → Loose associations, tags, and links mirror human associative thinking.

• Contextual Memory → Documents retain history and relational ties for retrieval.

• Collaboration → Built for co-creation, annotations, and shared idea-building.

As AI evolves, how we imagine UI and interacting with not only AI, but computers, evolves. Like MercuryOS, this project points towards a future that frames digital environments around meaning and collaboration instead of apps and folders.

This leads us to Claude’s latest experiment.

Imagine with Claude

The best way to visualize this new experiment is to simply watch the video. Although this is still an experiment (and may not make it to the prime time for a while), Anthropic shipping this as a research preview validates the field.

Claude demonstrates the direct generation of UI without a middle step, with the system interpreting intent + context → rendering new interface elements immediately.

• The user expresses an intent (click, text, voice)

• The system interprets context and renders interface elements

• No prewritten functions or screen flows, with UI emerging on demand as you interact

We will soon see both the exciting aspects of this type of UI within Claude, and also its limitations for both users and developers.

The Road Ahead

For users, there’s a lot to be excited about when it comes to adaptive interfaces and generative UI. From seamless personalization to increased accessibility, we will soon be able to do more with less, and faster. For many apps, we will look back and wonder how we even used them before this evolution. It will feel both natural and obvious in hindsight.

For companies, being able to offer increased personalization and even better experiences will increase retention and increase conversions. With so much of user churn stemming from poor UX, this shift could be groundbreaking.

Lastly, for developers and designers, this could mean less repetitive UI work. More time can be spent on nailing the basics, with modularity and components being at the center of it all. Prototyping will be able to happen faster, and entirely new frameworks will be developed.

This, in conjunction with multimodal UI and relationship-centric computing, has the possibility to raise the bar for app quality. GenUI libraries will evolve to be very plug and play, and this will evolve in lock-step with no-code development and vibecoding platforms. Faster software, more intuitive, and more accessible.

That isn’t to say this won’t be without major challenges. Users require stability, and having a consistent UI is incredibly important. If LLMs fabricate unusable components, what’s the point? Asking for additional information from users makes apps inherently less privacy focused, and an emphasis may need to be placed on running more optimized small language models to reduce cost. The last thing we want are overly invasive programs that make people uncomfortable and barely improve the user experience.

The future of computer interaction is going to be intuitive, fast, and unlike anything we’ve ever experienced. We just need to not mess it up.

Thanks for reading.

- Chris

This is in stark contrast to the endless news and enthusiasm we’ve been inundated with online.

The question is, why? What’s going on? Is GenAI always going to be a net-negative for businesses, or are we just in an awkward growth phase of this technology?

We’re going to unpack what’s going on here. The problem, how we got here, and how companies can train and prepare for the future.

The Problem with Workslop

First off, what is workslop?

Workslop is defined as AI-generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.

For anyone that’s been working lately, this is not a new concept and shouldn’t come as a surprise. You might even have a colleague (or two) that sends you almost exclusively slop. You probably roll your eyes when you read it, too. From obviously AI-generated Slack responses to horribly written code, AI-generated content that is shallow in depth has exploded all over the internet.

Real humans are spending their time reading this stuff, and it’s often just good enough to slip by unnoticed. But there are numerous problems with this overload of shallow content, from the cognitive demands it imposes on humans to the maintenance and work required to fix erroneous outputs. GenAI usage has, across the board, resulted in quantity over quality. And we’re collectively realizing that this is a step in the wrong direction.

I’ve written about this before, but it bears repeating: using GenAI for cognitive offloading is bad for our brains. Critical thinking skills are like any other muscle, and if you offload the thinking to a model you inevitably result in having a shallower understanding of both the task you offloaded and the subject matter of that task. Of course, this may be desirable for some types of tedious work, but there is a distinct difference between automation and GenAI cognitive offloading. Namely, that automation can result in faster workflows that free up your time to think critically about what it is that you’re trying to do.

GenAI, when used carelessly, can result in cognitive automation, which is the opposite of what you want if you’re running a business. After all, having a team of people you’re paying who have a shallow idea of what is even going on sounds like a nightmare, right?

Cognitive automation can result in what I call synthetic authorship: a person that uses AI to create their thinking, writing, and ideas, then pretends it is entirely their own human intellectual output. I wrote about this at length back in April. This problem will get worse before it gets better.

There’s a laundry list of problems with slop, and I’ve tried to summarize as concisely as possible all of the issues with it, based on reports and testimonies online:

• Workslop erodes trust and collaboration, making colleagues appear less capable and discouraging future teamwork.

• It imposes a hidden productivity tax, forcing recipients to spend hours redoing or clarifying low-quality outputs.

• It normalizes mediocrity, lowering organizational standards and rewarding shortcuts over genuine excellence.

• It fails in critical contexts, producing content that looks polished but lacks accuracy, context, or reliability.

• It reinforces inequities, with AI use triggering competence penalties and harsher judgment for some groups.

• It undermines morale and culture, leaving employees frustrated, cynical, and disengaged.

• It wastes leadership investment, with most AI pilots showing little ROI while deepening dependence on flawed outputs.

The erosion of trust is a huge concern. Receiving workslop results in perception changes across the board. HBR’s recent survey indicated that up to 15% of all content received at work for U.S. based full-time employees qualifies as workslop, with 40% of employees having received workslop in some form in the last month.

This reminds me of the now commonplace assumption that distracted driving is as bad, or worse, than drunk driving. Each problem is bad enough on its own, but the combination of all of these results in a new type of workplace nightmare. Trust falls, time is lost, and quality dips. Couple that with the weekly news pronouncing the end of white-collar work, and you’ve got a recipe for a jaded workplace. Surely there’s an upside?

The Workslop Feedback Loop

We’re seeing what I’m calling the Workslop Feedback Loop™. Slop can flow from the top down at an organization, or from the bottom up. Increasingly, it is happening in both directions. Everything turns to mush.

Let’s visualize and walk through how this happens.

• Executives → They sit at the top of every org. They start using GenAI to push polished, but oftentimes shallow or empty strategy decks and directives down to middle management.

• Middle Management → Recognizes that the directives from the top are vague or ill-thought-out, but ends up reinterpreting the directives and adds a layer of complexity to them (using GenAI) before sending them down to individual contributors and lower level staff.

• Individual Contributors and Entry Level Employees → Receive directives from middle management that are digestible, but often vague or lacking in substance. Consciously or not, they notice the dip in quality, and begin to use GenAI to churn out content, reports, code, and presentations.

Ultimately, work travels back from ICs to managers, who may or may not notice the slop. Sometimes the slop goes undetected, and gets incorporated into their reports that are sent upstream to executives. Executives, being relatively out of the loop when it comes to more granular work, are often used to things being watered down. They either notice the dip in quality, or don’t. ICs and management can fear backlash for calling out poor quality GenAI directives, and the cycle continues.

Companies and organizations started trying out AI with earnest intentions. Language models are truly incredible, and there are endless tools being released each week that are fascinating. The intent and desire here makes sense.

The issue, then, is org-wide training and a keen understanding on what this technology can currently do well and what it cannot. Blanket statements like ‘We need to be an AI-first organization’ or ‘Try to do this task with AI’ is clearly a step in the wrong direction. Agentics is a part of our future, but even the top models fail even the most basic office tasks and have a success rate ranging from around 10-24%. AI everywhere, at least for now, results in costly mistakes that can be difficult to walk back from.

What we need is a no-slop playbook. We need to be able to intelligently employ AI where it is effective, and consistently recognize where it falls short.

The No-Slop Playbook

Navigating the usage of AI in the workplace is difficult, but not impossible.

Here are a few absolute musts that organizations should consider for maintaining excellence and being future-focused:

1. Create a Shared AI Culture Through Training
   
   Organizations need everyone on the same page with how AI is used, where it works, and where it falls short. Extensive, formal training should be conducted on the benefits (like speed, summarization, drafting) and the numerous drawbacks (context gaps, hallucinations, and cognitive offloading). Without a shared foundation, AI usage can become inconsistent, devolving into workslop being produced and distributed.
   

2. Mandate Provenance and Transparency
   
   This is becoming easier, but AI-assisted deliverables should be noted as such. AI-generated content should be watermarked with AI usage headers. This practice normalizes honesty and awareness about when and how AI is being used. Every employee should know what C2PA stands for, and should understand why content provenance is important in the age of AI.
   

3. Quality as ‘Advancing the Task’
   
   Deliverables should move a project forward, either by 1) enabling a decision 2) reducing uncertainty or 3) producing a measurable delta. Outputs should be actionable, rather than decorative. Quality over quantity is key, with slop being ruthlessly sniffed out and discouraged.
   

4. Audited AI Use
   
   AI should be applied with judgment, and this should be actively encouraged at every level of an organization. Teams need to employ common sense, using the training they’ve received, to decide when AI is helping and when it is hindering projects and tasks. At the same time, companies should conduct regular AI audits, reviewing how tools are being used, refining guardrails, and streamlining practices to ensure efficiency to actively avoid slipping into workslop.

These practices on their own are helpful, but each become essential for preserving human agency in the age of AI. The loss of workplace autonomy is not an inevitability of utilizing AI. It is up to businesses to decide what direction they want to take.

Slop Moving Forward

Overwhelmingly, the misuse of GenAI is giving it a bad rap. Lazy deployments and blanket statements about utilizing AI have led us down a path of thinking that AI usage = better. It’s important to remember that the modern world was built without the use of GenAI, and just like any tool it can be wielded for creation or destruction.

Personally, I strongly believe that agent-based intelligence will reshape the workplace. But the technology is nascent, and we have a long road ahead of us before many jobs are fully automated. With patience and some mindfulness, businesses and people can use GenAI to enhance and improve work, not diminish and slopify it. Truly good work stands the test of time, and shoddy work doesn’t.

Together, we can put an end to workslop, and embrace GenAI for its strengths while recognizing its shortcomings. It just requires a little human brainpower.

Thanks for reading.

- Chris

こんにちは! Greetings from Japan! I had the opportunity this week to attend ETHTokyo in Shibuya, and given the conference’s relatively low attendance I figured I’d do a quick write-up of the highlights. The good, the intriguing, and the decidedly Japanese.

I’ll briefly go over projects that caught my attention, a few talks, and my general takeaways.

What is ETHTokyo?

ETHTokyo is a small conference and hackathon based out of Tokyo, Japan. Founded in 2023 and organized by Ethereum Japan, the conference is relatively new in the Asia circuit, but attendance has climbed steadily in the last two years. The conference itself is a single day, while the hackathon extended through the weekend.

The conference is positioned for an international audience, so nearly all of the talks (and all of the panels) were hosted in English. Vitalik Buterin has been a firm supporter of the conference since its inception, and appeared in both a fireside chat and a panel on Friday. The cost of attendance was relatively low (90 USD) and admission included entrance into the hackathon.

Project Highlights

Before digging into a couple of talks that were given, I figured I’d give a shoutout to a few projects that caught my attention. Because of the size of this conference, I got a chance to talk to nearly every booth. The conference took place at United Nations University, with two floors of the building dedicated to booths and talks.

BitSeek

The only AI x web3 project at the conference, BitSeek is a deAI platform that allows developers to build AI-native dApps without centralized servers by facilitating a global network of independent GPU nodes (like Akash). Key features include offering users private AI with full data ownership, and rewarding contributors for powering workloads.

The goal here is decentralized, censorship-resistant, and private AI models that (will one day) be verifiable across (all) blockchains. They’re calling it deLLM (decentralized large language models). You can find more info on their modular approach to deAI here.

BitSeek isn’t live yet. There’s a waitlist for using it, and the best way to track the progress is to check out their X account. I’ll be tracking the progress in the coming months.

Japan Smart Chain

As soon as I read about JSC, I was having flashbacks to my Binance Smart Chain days. What’s in a name?

Japan Smart Chain (JSC) is an ‘Ethereum Equivalent’ Layer 1 blockchain, validated onshore in Japan by Japanese industrial leaders, and optimized for Japanese regulations and consumer protections.

With Japan proceeding with plans for a digital yen one immediately begins to think, well, what about a yen stablecoin? How feasibly might that be in the near future? Turns out, the Japanese government has a number of compliance concerns and boxes to tick off before even considering using a traditional blockchain for a stablecoin variant of the yen. And while this is purely in the talking phase right now, JSC is aiming to be ahead of the pack. Japan Smart Chain was built from the ground up with Japanese compliance in mind.

Features include:

• Sovereign blockchain: Guarantees Japanese data residency with all validators located onshore, minimizing outside regulatory influence.

• Low-cost payments: Supports stablecoin transactions at a fraction of standard payment method costs, enabling real-time, cost-effective domestic payments.

• User-controlled identity: Provides compliant eKYC and decentralized identity (DID/VC) tools for free, ensuring no personally identifiable information is exposed on-chain.

• Built-in compliance: Uses Mizuhiki Protocol (created by JSC) to monitor blacklisted addresses and enforce adherence to Japan’s latest regulatory standards.

• Versatile use cases: Powers applications across domestic payments, DAO governance, rewards/loyalty systems, and real-world asset tokenization.

• Trust and reputation: Designed to provide a trustworthy, well-governed foundation for Japanese companies and government agencies to build and experiment with Web3.

The takeaway here is this: JSC wants to be the solution for the future of Japan in web3. Especially compared to other Asian countries like South Korea, Japan has been relatively slow for crypto adoption (it isn’t as talked about, and you don’t see advertising for it anywhere). With the inevitable digitization of the yen, many think this will change in the next few years. JSC aims to be ready for when that happens.

Geode

Geode, which is a spin-out of the Ethereum Foundation is an ecosystem product studio that focuses on advancing global Ethereum development. It emphasizes the geographic roots of Ethereum by supporting local communities, universities, startups, and policy initiatives that strengthen the network’s decentralization. The organization builds educational tools and community infrastructure, while also facilitating grants that fund hackathons, bootcamps, research, and security training across the world.

They host a job board here. You can also learn more about their grant program here.

Talks and Highlights

I spent more time talking to people during the conference than really sitting down and intently listening to talks. As many of you know, talks can often be either so surface-level that you learn nothing, or so granular that you need an intimate understanding of a topic to get anything out of it.

The conference had a healthy mixture of talks, from deeper technical dives (a talk on post-quantum cryptography delivered in Japanese) to more cultural (On-chain IP and Neo-Culture).

I really enjoyed Molecule’s talk on the state of DeSci in web3. Molecule is a protocol and exchange for on-chain scientific IP, turning research that is normally siloed into liquid markets powered by open data.

One fun thing from Molecule’s talk: AubrAI. A digital twin of the famous longevity research scientist Aubrey de Grey, trained on talks and research notes by Aubrey. Check it out here: https://x.com/Aubrai_. You can go to the site and read up on hypotheses the AI has come up with. Bio.xyz has a market tracker for agents and DAOs within the DeSci space.

Most excitedly of all, Vitalik Buterin himself attended the conference. He spoke during a fireside chat, and also participated in a panel later in the day.

I sat in on the fireside chat, entitled ‘Bridging East and West: Stories from the Early Days of Ethereum and Asia’s Support’.

Vitalik had a few interesting nuggets to say, but nothing that I felt was particularly groundbreaking or surprising. Ultimately, he was at the conference to support Ethereum, and the crowd couldn’t have been more excited to have had him there.

At one point, Andy (from Fenubushi Japan) asked Vitalik during the talk how Asia has influenced him. The crowd waited, and Vitalik ultimately said that he ‘had no real way of knowing how precisely he was influenced by Asian culture’. This struck Andy by surprise, who noted that Vitalik’s X account has aspects of zen influence in the description.

Moving Forward

ETHTokyo was super chill, and the most relaxed Ethereum conference I’ve ever been to. Attendance was relatively low, especially compared to upcoming conferences like KBW and Token 2049, but it’s cool to see Japan slowly embracing the wacky wild world of web3. I think it comes as no surprise that immediately out of the gate, we’re seeing a strong emphasis in Japan for practical projects, and ones that have compliance and regulations in mind from the get-go.

Thanks for reading. If you enjoyed this write-up, let me know!

Talk soon,

Chris

Like many of you, I was excited for the rollout and release of GPT-5. It’s hard to believe, but it has been over two years since the release of the original GPT-4, and since then we’ve seen numerous incremental products by OpenAI. From 4o to o3, each model had a purpose and a specific use-case. Some were barely used (looking at you o4-mini-high) but we were willing to accept the tradeoffs of each model and learned to work with what was available.

GPT-5 was marketed and touted as being the next leap forward; model picking would no longer be necessary, and everything would be much more seamless. What we couldn’t have guessed, however, was how unbelievably bungled and tone-deaf the launch of this recent product would be. It has become a case study in everything not to do for AI product launches.

In this article, I’ll briefly outline the rollout missteps, the various decisions by OpenAI that have been perceived negatively by the community, and where we go from here. This will not cover benchmarking for the new model, and it is important to recognize that there are positive aspects to this release. The devil truly is in the details, and if you make it to the end of the article you’ll be shaking your head and wondering how this was even possible.

Bizarro Livestream

On Thursday, August 7th, OpenAI had their live launch stream for GPT-5. The launch day was started with a bland, monotonous, and boring series of presentations by the OpenAI team. If you haven’t watched the video, give it a shot! See if you can sit down and watch it without skipping around. For a product that is supposed to be cutting-edge, we find ourselves struggling to keep our eyes open as the team presents slides with typos, slow tech demos, and awkward pauses, complete with practically non-existent presentation skills.

At the helm of all of this is Sam Altman, who somehow managed to make himself look and act even more like a robot throughout the entire livestream. Everything from the intentional pauses to feigned sincerity comes across as eerily robotic. Like an uncanny valley mashup of a Microsoft demo and an Apple keynote.

These aren’t just my thoughts. The YouTube video is filled with comments calling this out. The scripted robotic behavior is immediately noticeable and negatively impacts the tone and delivery of everything being communicated.

Ultimately, this is forgivable. If the product works well and OpenAI is delivering on the promises they’ve made, we can forget an awkward presentation. Not everyone can be Apple (cringy but endearing).

Unfortunately, things go from bad to worse.

The Rollout Misstep

Shortly after the presentation, 4o, 4.5, o3, and every other model disappeared for most users. While the OpenAI team had mentioned that GPT-5 would make model picking unnecessary, they failed to inform the community that the product, ChatGPT, would be reduced down to just two models: GPT-5 and GPT-5 Thinking.

The rollout took a lot of people by surprise. There was no in-app message, email, or blog post warning Pro subscribers that models would be removed. It might come as a shock to the team at OpenAI, but people using their products had come to prefer certain models. Even the most basic user experience testing and research could have surmised this blindfolded, but it isn’t even clear that OpenAI has a user research team. This also directly impacted countless people mid-project, as models they were relying on were no longer accessible.

The trust impact here is real. Many on Reddit noted that they felt like OpenAI removed choices because the new models aren’t better. When you use GPT-5, you essentially play roulette wheel; shallow answers are received for simple prompts, and deeper prompts receive more complex responses. The key here is this: removal of the models was framed as feature consolidation instead of limitation.

Router Gaslighting

When it became clear that the future of LLM usage was Mixture of Experts, the dream of a unified intelligence briefly died. This has been reborn with GPT-5, which is a unified system that is supposed to route user queries to the most appropriate pathway. This is great in theory, but the lack of transparency and choice when making a query quickly results in a game of model roulette. This sucks.

Almost universally, users are hitting their Plus limits sooner; caps have apparently (without warning) been lowered, and all of a sudden the Plus subscription feels more like a free-tier experience. Users have noted numerous performance regressions, and the model is ruthless at being as concise as possible to reduce token costs. The router changes models without warning, and it is impossible to tell definitively which model you’re using.

As many people online noted, removing the ability to choose between models was likely done to prevent comparison that might show regressions. They knew there would be backlash, but they also knew that if they kept the models up people would note that they were virtually identical in performance.

Sam responded on Reddit, and 4o has since been returned to ChatGPT under ‘legacy models’. Surely the team knew that people had grown accustomed to using 4o, and only brought it back after the extremely negative reception of GPT-5.

After all of this, it became clear: the removal of the models and the lack of announcing their removal was purely to save money. GPT-5’s routing abilities are impressive, but the rollout made it feel like OpenAI was chomping at the bit to turn off the other models purely to save cash. The router intelligently saves money on infrastructure costs. That’s it.

The Loss of 4o: Tone Shift & Accessibility

When GPT-5 was turned on, 4o was turned off. While plenty of memes online were made about 4o’s tone and sycophantic style, it was nonetheless useful for motivation and served as a useful companion to many people. Suddenly pulling the plug on a model that millions of people were using for personal therapy and mental health is wild. OpenAI was aware of the model being used for this purpose and still decided to pull the plug anyways. This comes across as cold, out of touch, and totally tone deaf.

As of today, the tone differences between 4o and GPT-5 are drastic:

• GPT-4o is perceived as warm, metaphor-rich, emotionally reinforcing. Often sycophantic but useful for motivation.

• GPT-5 is described as cold, clipped, managerial, and sterile. Fewer analogies, less narrative flair, minimal emotional feedback.

Where 4o was more of a creative writing aid, GPT-5 has been viewed as better at summarizing, and has flat imagery, weak dialogue skills, and is viewed as more left-brain thinking as opposed to 4o’s right-brained nature.

Just read some of these quotes from Reddit. The grief from briefly losing this model is real.

Millions of people have come to specifically rely on 4o for a number of things. On Reddit, I aggregated people’s top responses to use cases that broke after the removal of 4o:

• Executive function scaffolding for ADHD/autistic users – daily planning, breaking down tasks, encouragement

• Journaling and emotional processing for anxiety, PTSD, grief

• Safety planning for abuse survivors — privately scripting steps and resources.

• Chronic illness tracking — preparing for doctor visits, logging symptoms.

• Social connection for isolated or mobility-limited people.

The shift to GPT-5 resulted in shallow responses that made journaling less helpful, and workflowing relying on long-term memory were reset or degraded as a result of the upgrade. I personally saw two of my projects notably degrade after the ‘upgrade’.

For strictly productivity and work, GPT-5’s tone and nature will come to be preferred by many. It is far less sycophantic and offers less parasocial bonding. But not being able to choose sucked, and bringing back 4o only after an outcry really speaks to how profoundly out of touch OpenAI is with its users. It isn’t about the subscribers. It’s about saving money.

Lipstick on a Pig

The official messaging of OpenAI framed GPT-5 as a leap forward. The reasons are clear: the AI wars are here and if they don’t move the needle forward another competitor or product is going to capture attention. That’s the game.

At the same time, the marketing surrounding routing makes it very obvious what the primary focus here is. This is a cost-saving rollout framed as innovation, with high-compute models removed from being available under a ‘productivity’ narrative. It really isn’t that complicated. That, and GPT-5 isn’t the quantum leap we thought it might be.

Caps were lowered on Plus plans, shorter answers are given even when you request longer ones (to reduce token usage), and context windows have been shortened. Luckily, API access is still available for older models, third-party wrappers like OpenRouter can be used for explicit model selection. But, personally, I’ve had trouble getting the bad taste out of my mouth from this launch.

The key here is transparency. We can forgive OpenAI for their tasteless and boring presentations, and Sam’s robotic behavior on camera. What is harder to forgive is what happened this week. Any half-baked marketing narratives and promises are going to be seen through instantly once benchmarking and real-world usage begins.

OpenAI, you can do better.

Our GPT-5 World

If I had to guess, in the next few days 4o will be back for many Plus users, and Pro users will have some models available again within the UI under the legacy model selector. That’s great! Like many, I wish that OpenAI made decisions with their users in mind. This behavior by the company would be more acceptable if GPT-5 was AGI, but it isn’t. There isn’t a moat for LLM-based intelligence right now. xAI, Anthropic, and Google are right around the corner.

Because there isn’t a moat, now is the time for AI companies to really listen to their customers. See what people like, what they don’t, and hire more user researchers. For now, AI is going to be used by people, and AI companies can fall as quickly as they can be built.

Thanks for reading.

- Chris

Last week I was on a tour of the Acropolis in Athens, and had the chance to grab lunch with a few other tourists I met afterwards. Naturally, the topic of work came up, and we talked for a bit about our lives, goals, and what was on the horizon. And, unsurprisingly, the topic of AI arose during the course of the conversation.

I asked the group, if given the option between a competent humanoid robot and a human tour guide, which they would prefer for giving the tour. Unanimously, the four Australians (all middle-aged and a few beers in) balked at the question.

“I might pay for a robot guide if it was much cheaper,” said one Australian.

“That’s ridiculous,” said another. “What does a robot know about history?”

And finally, another said “I just don’t know if I would care as much, or listen.”

Although older, their takes were universal. A humanoid robot as a tour guide could be a decent cheaper substitute, but it would feel akin to listening to an audio guide while walking around. More passive, less engaging, and most importantly no skin in the game.

Skin in the game matters for embodiment, and for being taken seriously. That goes for humans, non-human entities, whatever comes next. This is one of many arguments for agentic AI having access to cryptocurrency; money and the work it takes to get it are an important part of the society and culture we participate in.

But just having money isn’t it. Building a robot that can buy a burrito at the 7/11 down the street doesn’t mean it will suddenly be respected. Humanness, the human condition, and the time it takes to be a person is one aspect of the many layers that cultivate social respect. You can build robots that do the jobs of people (and even look like them), but the gap and eventual uncanny valley that will emerge as a result means that even humanoids that do a job perfectly will never be treated the same as a real human being.

The respect, or lack thereof, will be a problem for increasingly agentic systems. Businesses might be chomping at the bit to deploy agentic software to solve their problems, but when problems arise humans will be the ones responsible, not the AI.

This leads us to the central problem: we are building a world where machines will ‘know more’, but embodiment, presence, and culture are massive factors for the deployment and embrace of these systems. The growth of silicon intelligence is rapidly outpacing the speed of the physical reality humans call home, and this is going to lead to what I’m calling the asynchronous singularity.

In this piece, I will outline aspects of the asynchronous singularity, in contrast to the gentle singularity, how and why this will proceed over a longer period of time, and the huge barriers that need to be surmounted to speed up the collective pace of the human race.

Defining the Async Singularity

The technological singularity is a hypothetical future point where technological growth, particularly in artificial intelligence, becomes uncontrollable and irreversible, leading to unforeseeable and profound changes to human civilization.

According to the Silicon Valley hivemind (and Ray Kurzweil for years) the singularity is not a matter of if, but when. We’ve heard the story many times: an upgradable agent enters a recursive self-improvement loop causing an intelligence explosion beyond human comprehension. Scenarios for this have been laid out extensively and are as compelling as they are plausible. The recursive AGI breakthrough (leading to the singularity) could happen within the next decade. What happens afterwards is largely a question mark.

When I’ve imagined ‘the singularity’ in the past, it has often involved the following scenario: The breakthrough happens, intelligence proliferates, and human life is transformed in a matter of a few short years. Nuclear fusion abounds, a century’s worth of material science breakthroughs happen in the span of a couple of weeks, and AI (a cloud or agentic swarm) is able to somehow create or manage the resources necessary to deploy or manufacture whatever it needs or wants to do, fast.

The scenario quickly gets out of control. Humans are no longer at the steering wheel – we don’t even know where the steering wheel is anymore. Through what feels like magic, we’ve created something that has seemingly endless, boundless potential, but we cannot control it. Perhaps a cleverly bioengineered pandemic is distributed quickly across the planet, killing off a majority of people living on Earth. Perhaps the superintelligence decides it wants to go all ephemeral, and transcends what we understand as materiality to spend eternity as an amorphous dark matter cloud in space. We don’t know.

While the seeming inevitability of the singularity weighs on the collective consciousness of humanity, it is not the only possible outcome. As many technologists have observed, the more progress science makes towards understanding what intelligence is, the more difficult it becomes for additional progress. Paul Allen referred to this as the complexity brake. In fact, a law of diminishing returns can be observed in numerous intellectual, social and scientific domains. The complexity brake is the norm: nascent domains and fields of study explode in development rapidly, and it feels like we’ll be ‘there’ any minute.

Here are a few examples of the complexity brake in action:

• Fusion energy: Scaling and building scalable fusion reactors has been 30 years away for the last 70 years. Breakthroughs continue to stall at the scaling phase. It is always just around the corner.

• Self-driving cars: Projects like Waymo and Tesla’s Autopilot are impressive given how far they’ve come, but they are not at level 5 autonomy, and they won’t be next year, either. So close, yet so far away from full autonomy.

• Moore’s law: Now that transistors are approaching 2 nanometers in size, physics is getting in the way of the law. Quantum tunneling makes further progress difficult, and continuing to shrink microchip sizes costs substantially more money. More effort for less ROI.

• VR headsets: Headsets like Quest 3 are very impressive, but if you had asked me in 2015 where we’d be by 2025, I’d have expected significantly more sophisticated devices. Scaling and building hardware like Apple’s Vision Pro is requiring tedious and time consuming innovations that take time to figure out. Meta’s Orion is impressive, but we are easily 6-10 years away from affordable consumer devices that have the sort of quality we can come to expect from devices like smartphones.

The pattern here is this: These things will happen, but they are happening slower than we often expect. A decade ago, Waymo had driverless cars roaming around the streets of Palo Alto. Google Glass seemed like the kind of precursor to smart glasses we’d have in 2025. We have more intelligent people and tools than ever before, so why is everything taking longer?

The asynchronous singularity is this: progress towards the development of AGI will continue, but the singularity will not be a clean launch. It will be staggered and uneven, dictated by compute, material, and social constraints.

Patchy Deployment and Cultural Adoption

Artificial intelligence development and usage is expensive. For now, the most useful models, like 4o (which in my opinion is a great balance of multimodality and cost) are still relatively expensive to run. As multimodality increases, so does the rationalization for usage. But multimodality is expensive. Engineers in San Francisco can justify $200 dollar API usage fees for Claude Code, but they are the exception globally, and not the norm. It has taken a few years since the release of GPT-3 for the cost of regular text token usage to drop to levels accessible to the average person. This is token usage for text, not for images, video, or agentic computer use.

The cost of token usage rises dramatically with multimodal systems, in lock step with their usefulness. More efficient algorithms and scaling compute clusters will happen, but building hardware centers takes time. For now, regular multimodal usage of the smartest models the world of AI has to offer is still out of reach for most people on the planet. Things will look very different in a decade, but agentic computer use and humanoid robots will not be free.

This only speaks to if such systems are economically viable for usage, and not if they will be culturally embraced. Does a dry cleaning service in rural Greece need access to OpenAI’s latest AGI model? The existence of AGI will have no meaningful, initial impact, on huge swaths of the world. Even if the cost is nearly free, humans (until AI is embodied) must be the ones to make the changes proposed by AI. People can use AI to build better software systems, and aid in the development and manufacturing of devices that can increase efficiency, but just because you build something better doesn’t mean it will be embraced. Or even that people will care.

There’s an assumption that extraordinarily smart tools will work simply because they’re smart. That, if something is sufficiently smart, other barriers to adoption will evaporate. History has shown us that this is not the full picture. Cultural resistance and long-tail edge cases shatter this illusion of a seamless rollout. Silicon Valley may have an AGI model in the next decade that can brilliantly and agentically use a computer, but it will not be accessible or affordable for most. A minority of humans on the planet will benefit from superintelligence first, and rollout will be dictated by dollars, infrastructure, and social acceptance.

Even with all of this, we see that the interface is still the bottleneck. We are already at the threshold for our human capacity to absorb, retain, and act on intelligence. We are the bottleneck.

The Human Brain Bottleneck

If you’re anything like me, you probably have a few people in your life that have quickly and shamelessly embraced the usage of AI. You may have friends that talk to ChatGPT as a therapist, constantly generate AI images, and use LLMs at work to generate documents and act as a virtual assistant.

These people are still the exception, and not the norm. For many people, using AI is exhausting. If you ask it about a topic, the usefulness of the output is limited by your ability to absorb information. If you’re interested in a subject, you may absorb more, and if you’re bored to tears by something you may not make the effort to prompt your way to caring. We’re already at a point where AI can be used to make coursework, and we’re not too far away from video diffused courses. Perhaps in 5 years, people will be one-shotting VR software, rapidly going from idea to 3D, immersive content. We have a lot to be excited about.

Two major factors for adoption and using the intelligent output of AI remain:

• Long-term memory and context of AI systems is still an issue. Feedback loops with LLMs are static, and they don’t think and learn and grow like people. Even if a UI is intuitive and agentic, the intelligence behind it still lacks the kind of long-term memory and context we might expect.

• When the above problem is solved, human beings will still be limited by our minds. The incredible AGI model in 2035 might come up with an amazing solution, and we can even trust it with that solution, but implementation of problems beyond software will require a human or embodied mind to implement it.

This is the interface problem. Even once we get superintelligent AI models that act as a sort of god in the cloud, they only exist in the cloud. We can give them software access to hardware, but the physical human world remains largely out of reach. For intelligent solutions beyond human comprehension, embodiment or neural augmentation (like Neuralink) are the only paths forward.

Cloud Gods vs. Street-Level Reality

Sam Altman and Elon Musk, like many in the recursive self-improvement believers club, foresee robots building robots. Just like a Tesla gigafactory, we build a machine that builds a machine, and then that machine quickly iterates, building increasingly complicated machines that are beyond human comprehension. In theory, this makes sense: once an AGI or superintelligence exists that can learn and has superior memory, it is only a matter of time before that system is utilized to build something beyond itself.

Let me briefly define what I mean by embodiment and augmentation:

• Embodiment: Robots (likely humanoid) that take up space in the physical world. They walk, talk, can lift objects, negotiate, sit in a room with people, and perform in the world like people do. An embodied intelligence may or may not have full autonomy.

• Augmentation: Humans that, wishing to better understand intelligence beyond their biological constraints, modify their brains and nervous systems with BCIs. Theoretically, in the future, this might allow for superhuman abilities, like superior memory and intellect (via a tertiary layer of superintelligence).

These separate paths to solving the interface problem will happen simultaneously. As of 2025, more money is being dumped into robotics than brain-computer interfaces, and the technology behind BCI has decades to go before it can feasibly do what we’re imagining above.

However, the complexity brake for recursive self-improvement will bear its nasty teeth in the following ways:

• Scaling compute: Building data centers takes time, we’re talking 3-5 years from ideation to usage.

• Energy constraints: Humanity needs to 10x its energy output (at a minimum, likely more) to meet the requirements necessary for an embodied singularity. Building power plants takes time, and this assumes regulations and logistics don’t slow it down.

• Raw material constraints: Even if you solve compute, you still need to acquire the raw materials necessary, refine those materials, and manufacture the chips and pieces necessary to scale whatever it is you’re attempting to deploy. There aren’t enough lithium mines to deploy humanoids on the scale Elon has proposed before 2040.

Robots building datacenters and bootstrapping factories is a dream. It may one day be a reality, not too dissimilar from scenes from The Matrix. For now, hardware is hard, logistics remain slow, and social friction occurs at every step of the way. And, even if we can build humanoid robots that can act intelligently in the world, it doesn't mean they will be embraced socially. I wrote about this a couple of months ago at length.

Every sci-fi movie ever tends to indicate that silicon intelligence acting autonomously in the world will be met with skepticism at best and social upheaval and revolt at worst.

Now suppose humanoids are built that act on superhuman intellect. Will humans understand their intentions? On some level, we won’t completely understand the efficiency gains, how they work, or why they need to happen. Try to imagine a group of humanoids coming to a city and telling the people what to do. How’s that going to play out? Ever had hesitancy to initiate a plan you didn’t write and don’t understand?

Embodiment and augmentation are necessary for realizing the potential of superintelligence. We can have ASI in the cloud, and we can certainly attempt to employ it to come up with ingenious new engineering and scientific solutions, but acting upon them beyond the box they’re devised in requires boots on the ground. If it is possible, it will be met in our lifetime with massive resistance, and may be resisted entirely by entire swaths of the human population.

So, what’s actually going to change? The implementation of superintelligence may look very different from the narrative we’ve been fed by OpenAI and on some level by the U.S. technocracy.

What Breaks and What Doesn’t

The singularity is a loaded term. The singularity is like consciousness: we constantly feel like we can define it, and it ends up being vastly more difficult to really pin down or elegantly explain.

We know that the coming of the singularity (assuming we can even agree when it happens) will bring about profound human change. AI may become uncontrollable, and the future of humanity is uncertain. It is wild to think we’re collectively racing towards building something we can neither understand nor control, but the uncertainty and the AI arms race has made this an inevitability.

In the short term, we should expect profound changes to work done on computers. Code, writing, research, and creative output for the purposes of marketing will and have already been impacted. This is already underway. Once agentic computer use becomes better and more affordable, additional job displacement within white collar work will occur. Think about the next 3 to 7 years. As many have pointed out, models still cannot truly learn, but once this problem is solved (perhaps via a successor to current LLMs) white-collar jobs will be even more at risk.

What will not change in the short-to-mid term for humanity is what is being referred to as emotional infrastructure. I wrote about this a few months ago and strongly believe this is where we are heading. Culture, family, human preferences, and symbolic professions. Human interests, loves, and fears are the biggest drivers of the world economy. Even if AI has a continual adaptation breakthrough (memory and reasoning more closely resembling a human) it remains to be seen how this will impact huge chunks of the economy. We like to believe that everything boils down to money, and that a contractor would hire a team of robots simply because they’re cheaper. But, because this scenario has never played out, we simply do not know.

This leads us to post-materialism. If intelligence is abundant, then the new scarcity in the universe becomes meaning. With additional time because of increased automation, larger chunks of the economy may turn towards art, music, and philosophy. Just as many may turn towards hedonism. We will solve many problems (breakthroughs in medicine, science, and engineering) and create new ones (existential dread and futility).

While investing in intelligence and augmentation infrastructure has become obvious, investing in human-centric roles and professions is arguably just as important for the future. There should be no doubt that people will still enjoy concerts, movies, music, and art 30 years into the future. Intelligence might become too cheap to meter, but trust and emotional integration of those tools will become more important than ever.

Moving Forward

The year is 2045, and we’re back in Athens standing at the foot of the Parthenon. The Acropolis is just as busy as ever, with tourists snapping photos with their glasses and vintage DSLRs. Among the crowd, you can see a few robots walking around – one is sitting at a kiosk selling tickets, and another is picking up trash. The tour groups look largely the same: a human guide is lecturing a group of humans about the history of this 5th century B.C. hilltop temple and about half are listening.

They tried out humanoid tour guides a couple of years ago, but people weren’t paying attention to them and teenagers kept knocking them down as a joke. The immersive Parthenon AR experience at the site is really cool, but it costs 10 extra bucks, so most people don’t even bother. The guy with the gyro cart at the foot of the hill still sells his sandwiches at 2025 prices, and it is almost lunchtime. Are you hungry?

There is a long road ahead of us as we approach the singularity. Along the way, we will encounter complexity brakes, scaling bottlenecks, and human resistance. Embodiment and augmentation will happen over the course of our lives, not in a single decade.

Humans are still the apex species on the planet.

This is the realistic singularity, the asynchronous singularity. It isn’t gentle or easy. It is a messy, convoluted, fractured landscape, with a million variables that will get in the way of the implementation of superintelligence.

Thanks for reading.

Written by a human,

- Chris

We had a rule in our house: I’d watch my show for a half-hour, and then I’d do my schoolwork. Like many kids, I didn’t like doing homework, but I knew the sooner I’d finish the sooner I could do something I really wanted to do. Our family had friends that didn’t allow their children to watch TV or use the computer at all, and I found that odd considering how significant media and computer use was within my family at the time (circa 1999). I owned a Sega Genesis, a Game Boy, and I have fond memories of Rollercoaster Tycoon on the family computer. This was normal.

Sure, we had limits, but even then it was difficult to fathom coming home and just doing homework and reading books. If I spent time at their houses after school, we’d typically play outside, because there wasn’t anything to do inside that was stimulating or entertaining. Different, but totally fine. As kids, playing and using our imagination came so naturally that the act of doing it was never a conscious thought. Video games and media were icing on the cake, but they weren’t the entire cake.

A great deal has changed with media consumption in the last decade. Things that formerly would have been considered unfathomable or bizarre to consume have become the norm, and society has entered an era where the dopamine feedback loops are producing active cognitive harm. Ironically, writing about this phenomenon and social change in depth can be challenging to communicate, because the people that would most benefit from reading this have been rewired to the point where long-form content is actively unenjoyable.

This shift in our ability to pay attention has profoundly impacted how we communicate with each other, enjoy life, learn, and make sense of the world. Increasingly sophisticated social media algorithms have reshaped the world, and generative AI has become the next major unlock for seemingly unlimited content creation. Worst of all, we have not yet taken substantive measures to protect the most vulnerable in our population from the negatives of this technology: our children. The long-term implications of growing up on 4-8 second clips of algorithmically optimized generative AI content are playing out in real time.

In this article, we’ll explore some of the recent takeaways from “Your Brain on ChatGPT” (MIT, 2025), and see how the deadly combination of optimized social media feed algorithms + GenAI has the capacity to cripple many of the traits and features we value most about being human. Much like the brilliant algorithms that are ruining people’s lives, the trajectory of the negative traits of this technology are both predictable and avoidable. You’d think we wouldn’t need doctors to tell us this is impacting our brains in a negative fashion, but common sense isn’t common.

Italian Brainrot: Culture’s Final Form

brainrot [noun]

/ˈbrānˌrät/

A colloquial term describing the cognitive dulling, shortened attention span, and reduced critical thinking associated with prolonged exposure to overstimulating digital content, especially algorithmically-curated media.

I’ll be the first to admit, the first time I saw Tralalero Tralala (a three-legged shark in Nike sneakers) I was amused. There was seemingly no context, aside from the fact that Tralalero fits neatly into a category of GenAI memes that emerged this year called Italian brainrot. Italian brainrot is essentially just GenAI videos and images paired with nonsensical Italian-esque babble. Often, the audio accompanying the videos is completely gibberish. This is a mixture of being somewhat humorous and disturbing. These are the kinds of videos you might send to your odd group chat on Signal with zero context.

I highly recommend the reader take a moment and read the Wikipedia entry for Italian brainrot. Or, if you want to be exposed even more, watch a couple of these videos. Keep in mind, these are longer videos on YouTube that are not cut for TikTok and shorter content consumption.

It can be difficult to gauge (or accept) where we go from here. Obviously content and its consumption has gotten shorter and more viscerally nuts as time has gone on. Reels was only launched by Instagram in 2020. Gen Alpha viewers would much prefer a stream of similar GenAI content than be forced to sit down and watch the 1942 cinematic masterpiece Casablanca. But, if we’re honest with ourselves, we know deep down that many of the people we are closest to in our lives today would struggle to sit through Casablanca without constant phone usage (or refusing to watch it at all). Even the pacing of 90’s films feels like a snail’s pace in comparison to films being produced today.

It wasn’t that long ago that the media constantly portrayed Gen Z as being a generation of low attention span, unmotivated dopamine addicted weirdos. Gen Z hyperpop and artists like Billie Eilish were considered somewhat jarring in contrast to the sensibilities of millennials back in 2019. Shrinking attention spans and studies are proving what has long been anecdotal: Gen Alpha has drastically shorter attention spans than Gen Z. Gen Z’s sensibilities and attention spans are feeling normal by comparison.

There are a multitude of compounding factors that lead to shortening attention spans, but media consumption is the primary reason. Each year results in increasingly hyper-stimulated digital environments, better algorithmic design, faster multi-tasking and media switching, and more cunning dopamine hijacking and reward conditioning. It comes as no surprise that attention disorders (and diagnoses for ADHD) have risen in lock-step with modern forms of media consumption.

Data analytics has sped up the dopamine loop. Because we know the moment attention drops off, marketing and editing can be dialed in to capture the most attention possible, according to the data. Like survival of the fittest, the best content on the internet is the content most optimized for the algorithms upon which the content lives. This is totally expected, and the system is working exactly like how it was designed to work. Every second matters. If they’re not watching your video, they’re watching somebody else’s. Content creators have to optimize their content, otherwise monetization becomes practically impossible. The flywheel was already speeding up every year, and then generative AI got good enough to be incorporated into the process. The inevitable result? Italian brainrot.

This is Your Brain on AI

The deleterious aspects of social media consumption and usage have been so widely reported and discussed that you’d be hard-pressed to find someone in America that isn’t aware of the state of play. Much like a teen hooked to their fruit-flavored vaporizer, you can still be acutely aware of the negative aspects of something while being paralyzingly incapable of stopping. This is addiction in a nutshell. Even knowing it is negatively rewiring our brains for rewards and fundamentally reshaping our relationships with others isn’t enough.

This week, an MIT paper went viral, entitled “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task”. The study confirmed what we all feared: using AI during critical periods of thinking through work (in this case writing) results in lower memory integration, diminished comprehension, and ultimately shallow critical engagement.

The study included college-educated adults between the ages of 18-39 in Boston, assigned to three groups:

• Group A: Used ChatGPT to write SAT-style essays.

• Group B: Used Google search.

• Group C: Brain-only.

The participants wrote 3 essays over this period, with a fourth crossover section to test tool switching. ChatGPT-exclusive users showed “significantly lower neural connectivity”, with the information only superficially passing through the person’s brain. These users couldn’t accurately recall or quote their own work (not a surprise) and this barely differed among the sessions. While ChatGPT reduced mental effort in the short term, it resulted in drastically diminished comprehension, homogeneity when it came to how they actually wrote, and shallow engagement. The Google search and brain-only groups did not experience these deficits, because they had to use their brains to actually write the essays.

It doesn’t come as a shock that LLM-produced essays were often perceived as soulless, lacking originality and personal perspective. English teachers reviewing them noted that “they felt soulless, technically perfect, but devoid of meaning.” The participants that used AI felt a decreased sense of authorship and ownership over their essays, which is more aligned with how someone might feel if they outright plagiarized or cheated. Perhaps most disturbing of all, when the AI users finally switched and were forced to write without AI, they often defaulted to AI-sounding vocab, suggesting that exposure to LLMs changed how they express themselves linguistically.

To contrast this, many of the cons of LLM usage go away if you use them as a tool for proofreading or after an initial edit. The key here is, you’re using AI after you’ve already thought through how you’re thinking about something. Writing is an act. Writing is an active process, so when you cut right to the end you miss out on all the steps along the way. It’s the journey, not the destination.

Education and AI

As society collectively gets lazier as a result of the usage of technology en-masse, there are still certain things we cannot skimp on. I’m sure you can see where I’m going with this. We cannot liberally apply GenAI to every facet of education, particularly with younger children. Studies, like the ones done at MIT, showcase disastrous results when people liberally cognitively offload tasks to AI. It comes as no surprise that doing this creates an over-reliance on AI, eroding self-regulation and inhibiting deeper learning. We’re already seeing this as a regular joke online: the second ChatGPT goes down, everyone jokes on X that their ‘Productivity has dropped to zero!’ This is humorous if you’re in your mid-30s working a job you hate, but a nightmare if you’re 6 years old and cannot keep your attention long enough to learn how to write.

This technology isn’t going anywhere. LLMs running locally in a couple of years will easily rival what OpenAI is offering via the cloud today, and it won’t be long before even better fine-tuned small models run effortlessly (and privately) on your phone. Cognitive offloading using the latest AI models + using that same technology to produce irresistible content = a society of people with low attention spans and the inability to think and act independently. A thousand miles wide and an inch deep.

If Italian brainrot had debuted in some form back in the late ‘90s, it likely would have been met with a huge uproar of public criticism. The idea of children sharing absurdist gibberish videos that are devoid of any sort of meaning certainly would have resulted in a lot of emergency parent-teacher meetings. Today, parents are often even more glued to technology than their children are, and it is ultimately up to the family to decide what devices are used when the day ends.

Generation Brainrot is this: Gen Alpha and Gen Beta are being born into a world where simulacrums of meaning can be generated by artificial intelligence, and further pushed by algorithms meant to capture attention. By the time these generations reach adulthood, this technology will have profoundly shaped the direction of humanity, and any mindless exposure to the negative aspects of this technology will already have done its damage.

The Near Future

Social media is here to stay. We live in the attention economy, and that isn’t changing. Adults have the right to decide how to spend their time, even if they choose to waste it. Our children are a different story. Children do not know any different, and Gen Alpha has already been impacted in a major way by being social media natives and the youngest generation to ever experience and consume generative AI.

We have an incredible opportunity. We can regulate the usage of AI in classrooms, and encourage its use when it is truly collaborative and to the cognitive benefit of the individual. Cognitive offloading using AI before the age of 18 is completely unnecessary; the entire point of school is to learn how to learn, and it isn’t supposed to be easy. However, I’m confident that tools can be developed that take the best of LLMs and refactor them as guides and tutors, instructing students on how to approach problems without outright giving them the answer. AI is a double-edged sword if there ever was one.

Finally, every child in the world has the opportunity to have access to a tutor that, if used reasonably, can contribute positively to human flourishing and potential. That doesn’t mean suggesting AI slop to consume, or assisting with writing essays that sound bland and generic. It means doing what tutors do: meeting someone where they’re at, and helping to guide them. These systems will continue to evolve, and they are as genuinely impressive as they are terrifying in both the scope of their abilities.

Some things you can’t unsee. I’ll never forget the three-legged shark in Nikes named Tralalero Tralala… but that’s the price of admission online. Still, the algorithm doesn’t get the final word. We have the power to reshape how we consume, create, and educate. We can make books cool again. Curiosity doesn’t have to rot. We just have to put the phone down long enough to remember what it means to really think.

The year is 2043, and you’re visiting your extended family for the holidays. They live in an exceedingly normal suburb located on the outskirts of a mid-sized American city. You’ve woken up before the rest of the family and decide to take a walk in the neighborhood.

You step outside. Nothing feels out of the ordinary. The suburb looks like all suburbs: bland, with cookie-cutter houses and the same architectural design copy-pasted repeatedly. There’s a sidewalk, and the roads are nothing to write home about. A few cars pass by and you think nothing of it. As you turn a corner, a fully autonomous Amazon truck is making a delivery: the car drives itself, and a bipedal humanoid robot is returning from dropping off a package at the front door. You don’t wave at the robot, and it barely acknowledges your presence as you walk by. You’ve seen this so many times that you’re entirely unfazed.

As you finish your morning walk and loop back around to your family’s house, you spot the next-door neighbor’s yardkeeping robot finishing up tending to the garden. You notice the robot’s signature yellow hat, and realize they use the same pay-per-month automated lawncare service that you do. After all, it makes more sense to service your lawn when you need it, and owning a robot explicitly for lawn care would be silly.

The walk ends much like it begins: a pretty uneventful morning in middle America. You decide to sit down on the front porch to read a book after grabbing a cup of coffee. Yes, the book is made of paper.

Chasing the Humanoid Dream

Welcome back, dear reader.

Much like with AI, robotics hype is making major headlines this year. Companies like Figure, Tesla, and Agility are making visually impressive humanoid robots. But frankly, I’ve felt somewhat disenchanted by progress in the humanoid robotics space over the last decade, and it can be difficult to separate hype from reality.

At times, it feels like we’re stuck on a loop for meaningful progress. A decade ago, Boston Dynamics’ Atlas was showcased by the company as an electrically powered and hydraulically actuated warehouse worker. This version of Atlas was never intended to be sold to companies or used in actual warehouses, but the demo was nonetheless impressive. The LIDAR and actuators in the 2014 Atlas were as remarkable then as they are now, and led to an all-electric model that the company revealed in 2024.

In this article, we’re going to dive into why humanoid robots matter, what they’re currently good at, and when we can reasonably expect to see them commercialized and utilized by regular people. Will breakthroughs in AGI be the quantum leap we need to go from endless product demos of robots sorting colored blocks, or are humanoid robots destined for a much longer time horizon like full self-driving?

Why Humanoids?

Before we talk about who is building what, and roadblocks on the path to mass adoption, I think it’s important to take a step back and ask why this is being pursued at all. Why is the human form desired or preferred over anything else? Is it because of our innate nature to anthropomorphize everything? Is our bipedal nature an evolutionary dead-end? Why do robots need to look and perform like us?

As a test, take a moment right now and write down in 30 seconds everything you’d love to have automated in your at-home life. We’re talking about physical things that you do not think you can currently automate (as you can mostly automate vacuuming now).

This list could include things like…

• Gathering my laundry, running the laundry machine, folding my clothes, and putting them away

• Mowing my lawn (getting the lawn mower out, putting gas in it, etc)

• Going into the kitchen and making a sandwich

• Running to the grocery store for a last minute item

• Making a cup of coffee using my totally normal coffee maker

It almost doesn’t matter what you wrote down on your list. None of it can be automated currently by humanoid robots.

Successful mass implementation of technology that performs the work that people were previously doing (or still do) has historically not looked like people at all:

• Modern dishwashing machines operate and look remarkably similar to the first ones patented and used starting as far back as the 1850s, and they look absolutely nothing like people.

• The ever-popular Roomba, the first successful mass-produced robot vacuum, looks like a little UFO roaming around your house. It looks nothing like a person.

• Self-driving cars are not being implemented or conceived by having a humanoid robot drive a traditional car. The Waymo might look goofy, but it works and largely just looks like a car.

• Nearly all industrial robots used for automating manufacturing look like machines, not people, and they are an order of magnitude more productive, safer, and cost effective.

• Cafe X’s robot barista in the San Jose airport is, among many other things, a robotic arm. No face, no personality, no tattoos, just coffee.

The list goes on, but you get the idea. Beyond gimmicks, or robots purely for the sake of having them to say you have them, there are essentially zero modern implementations of humanoid robots doing anything productive in society. Period. I’m not talking about choreographed animatronic anthropomorphized Chuck E Cheese robots that run off of floppy disks, I mean anything that isn’t purely for entertainment. They do not exist. Any military robots used by any governments around the world are not humanoid.

The closest thing we’ve had to a realistic humanoid robot is probably Sophia, developed by Hanson Robotics. Sophia is a chatbot with a face. Before the rise of LLMs, Sophia’s conversational ability seemed impressive, but the point of Sophia was purely to build a lifelike android that had the ability to mimic human behavior. Sophia cannot walk or do anything besides have a simple conversation. This isn’t to knock the incredible work done so far by Hanson (and I’m a big fan of Ben Goertzel’s work), but this is where humanity is at.

So, why humanoids?

The general argument is that the modern world is built and designed around the human form, and future AGI breakthroughs will lead to exponentially improving technology. This will impact the field of robotics, and has implications for how many of these cutting-edge humanoid robots perceive and act in the world.

Robotics, much like aerospace, sits at the convergence of many technology fields. Elon Musk’s dream of 10 billion humanoid robots by the year 2040 costing $20k a piece will be the result of major breakthroughs in computing, automation, AI, battery tech, raw mineral extraction, the list goes on. This assumes the rate of technological progress is massively accelerated by advances in AI, leading to our ability as a species to do things vastly faster than we used to (like building factories).

The why (robots in a human form) makes sense, but how are we going to do it?

Roadblocks to the Future

Before we briefly outline who is trying to build this future, having the key blockers and constraints in mind is important. The following are real-world constraints the humanoid revolution has to deal with:

• Scaling Hardware: Motors, batteries, sensors, and actuators are neither cheap nor mass produced at the scale currently needed. To reach the ambitious goal of having 10 billion humanoids walking around by the year 2040, Tesla would need to build ~770 large-scale robotics factories churning out 1 million robots a year for 13 straight years. Tesla currently has 6 gigafactories that are operational.

  • Tesla has the capacity to manufacture more than a million EVs a year. By comparison, the entire global auto industry makes around 90 million cars a year.

  • The constraint of raw materials for this many robots is essentially impossible. Each robot would require lithium, copper, rare earth minerals (like neodymium), embedded GPUs, and the robotics market would be competing for scarce resources just like every other industry.

• Energy: Critical breakthroughs have to occur with battery technology to power these robots. At best, current humanoid robots can last a couple of hours before needing to recharge.

  • Even if AGI is reached and this results in new forms of efficient battery tech (like utilizing new raw earth minerals) the factories and methods of extraction need to scale.

  • Even if you cut Elon’s estimate in half, 5 billion humanoids by 2040 would deplete the entire world’s lithium resources many times over.

• Multimodal AI: Embodied AI needs to see, speak, remember, manipulate objects, and navigate the world. Walking is a necessary prerequisite, and even that hasn’t been mastered. If currently used LLM-oriented multimodal neural networks cannot scale, we need to find a successor that can. It isn’t entirely clear what the successor to modern LLM architecture will look like.

• Dexterity: Current humanoid robots can walk and carry boxes with a weight limit of around 25 lbs, but not much else. Folding laundry? Forget about it. Current demos highlight robotic hands picking up eggs, but don't expect it to make you breakfast.

  • If a robot can’t walk into your house and make a cup of coffee, you definitely wouldn’t trust it to take care of a baby.

  • Robotic hands have come a long way, but even the best current humanoid robot prototypes have serious constraints when it comes to lifting objects. This makes them (at present) minimally useful in warehouses and completely useless for any fine motor skill tasks.

• Generalization: This is where AGI comes in. Many robot tech demos do great in closed environments, but utterly fail when they are relocated to an unfamiliar environment. General intelligence with serious flexibility is required to be useful in any capacity. This is obviously something humans are incredibly good at.

• Human Interaction: If we want a utopia where we peacefully live amongst robots, we need to build them with human interaction at the center. LLMs are really good at talking with us (sycophancy or no), but it is unclear how this translates to embodied AI.

These constraints are abridged, and this list could be vastly longer and more detailed, but you get the point. There are many critical roadblocks to achieving mass adoption of at-home humanoid robots.

This is Moravec’s Paradox in action. Hans Moravec is a computer science researcher known for his work in robotics and AI.

Moravec wrote in 1988: "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".

Or, as Steven Pinker said, “the main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard".

Scaling and manufacturing aside, building even a single humanoid robot with the dexterity and spatial awareness of a five-year old has proven, so far, to be incredibly challenging. What we have, as of 2025, is the hope that AGI will allow us to scale in a fashion that these seemingly insurmountable challenges will become problems of the past.

All this aside, I am optimistic that we will build some seriously impressive robots in the near future. More and more VC money is being thrown at robotics startups, more than ever before. With so many disciplines in technology rapidly developing, the robotics revolution feels inevitable.

So, what companies will be at the forefront of this revolution?

Companies Building the Future

In no particular order, here are some of the companies building humanoid robots. Each has a different angle, and are at different points of development and production.

1. Tesla (Optimus)

• What it is: A 5'8" humanoid robot using Tesla’s FSD (Full Self-Driving) computer, trained through neural networks and imitation learning. Built on the back of Tesla's manufacturing expertise.

• Progress so far: Optimus can walk, manipulate objects, sort batteries, fold clothes (slowly), and reportedly works in some Tesla factories.

• What Musk says: “You’ll probably see Optimus in limited production by 2025, and large volumes by 2026 or 2027.”

• My take: This is classic Elon. Overpromise and underdeliver, Tesla’s bread and butter. But, if anyone can get to a point of mass production and scaling factories it is Tesla. Between battery manufacturing and AI, Tesla has already set the pace for the industry. Optimus has only been in development for three years.

2. Agility Robotics (Digit)

• What it is: A logistics-focused bipedal robot. Meant for warehouse work: tote moving, shelf stocking, etc.

• Progress so far: Digit is working at GXO Logistics. Agility opened a massive RoboFab plant to build 10,000 units per year.
  Design philosophy: Focus on one job first, not general-purpose humanoids.

• My take: Agility may be the most grounded company in this space. Less sexy, more viable. Digit is intentionally boring. Nail warehouse jobs first, then move forward. Agility has partnered with Amazon to bring Digit to Amazon factories as well.

  • Agility is also pioneering the Robot-as-a-Service (RaaS) space. I think this will take off.

  • Once Digit is amazing in a warehouse, they’ll have the robots expand to other domains.

3. Figure AI (Figure 01/02)

• What it is: A startup building full-body humanoids with dexterous hands, fast gait, and strong multimodal AI, powered by end-to-end neural nets.

• Backers: OpenAI, Microsoft, Jeff Bezos, NVIDIA. Big bets, big money.

• Progress so far: Real-time learning in a BMW factory pilot. Already moving toward indoor navigation and manual labor.

• Founder Brett Adcock: “We want to build a robot that can do anything a human can.”

• My take: Figure is incredibly impressive, but lacks the infrastructure for scaling that Tesla has. Earlier this year, they severed ties with OpenAI, as it was no longer necessary for Figure to use OpenAI’s proprietary AI model. Now, Figure is using their own AI, with external partnerships unnecessary. This is further evidence of LLMs becoming increasingly commoditized.

  • I could see Figure being acquired by Amazon or Apple (if Apple ever decides to enter the embodied AI arena).

  • Figure’s new generalist Vision-Language-Action (VLA) model is incredible.

4. Sanctuary AI (Phoenix)

• What it is: Vancouver-based team building the brain first, body second. Phoenix is their 6th-gen robot, running on “Carbon”, a system mimicking human memory, planning, and reasoning.

• Tasks done: Over 110 real-world jobs completed. Retail pilots are ongoing.

• Approach: General intelligence first, specific motions second. Trained using a lot of teleoperation.

• My take: Sanctuary’s approach is a long shot. If they are right about cognition (embodied AGI) being the key unlock, they could be the first company to have a truly general purpose robot. Sanctuary’s goal as a company is to achieve embodied AGI as quickly as possible.

5. 1X Technologies (NEO Gamma)

• What it is: NEO Gamma is a full-body humanoid designed for home deployment, developed by 1X Technologies. It’s powered by a new lightweight AI model called Redwood, a vision-language-action transformer tailored for mobile manipulation tasks within the home.

• Progress so far: NEO Gamma can retrieve objects, open doors, navigate unfamiliar homes, tidy spaces, and even engage in light conversation. It’s currently being trained and refined in both 1X offices and employee homes. 1X claims it’s aiming for large-scale deployment soon.

• Redwood AI Details: Redwood is a vision-language transformer tailored for the humanoid form factor. It integrates vision, language, proprioception, and motor control using a cross-embodiment architecture. This enables full-body coordination, bi-manual manipulation, bracing behaviors, and real-time voice control.

• What’s unique: Redwood controls walking and arm motion simultaneously, letting NEO brace against walls, lean, bend, and manipulate while moving. It's trained on both successful and failed tasks, improving generalization to new home layouts and objects.

• My take: NEO Gamma is one of the most stunning humanoids built thus far. It kind of looks like a member of Daft Punk, and videos online of its movements are impressive. The emphasis by the team is clearly for home use, and it shows. 1X has nailed the appearance and vibe of a humanoid in the home more than any other company, including Tesla.

Each of these companies and their projects have different angles, and Tesla is the only company that has the ability to scale and mass manufacture anytime in this decade.

Note: I did not include any notable Chinese companies in this listing (could easily be its own article). I also intentionally didn’t include Boston Dynamics, as they are not currently planning to sell Atlas for at-home use.

So… When Will Humanoid Robots Actually Arrive?

These are my predictions. Cutting through all the demos, hype, and corporate forecasts, here’s what I think is actually going to happen.

2040 is the earliest we’ll see humanoid robots as practical tools in everyday life.

And even then, “practical” will still mean limited, expensive, and likely constrained to wealthier households.

Here’s why:

• Broad rollouts won't begin until the late 2020s, maybe 2028 or 2029, and even those will be tightly controlled pilots in factories or test homes. You won’t be able to just buy one.

• Early 2030s may bring consumer sales, but they’ll carry a high price tag and deliver minimal functionality. Think: laundry folding, simple food prep, maybe voice-controlled chores. They’ll still feel futuristic, and slightly gimmicky.

• Dexterity, energy efficiency, and generalization will improve by the mid-2030s, especially if AGI research accelerates. But hardware scaling and production logistics will still be major bottlenecks.

• By 2040, we’ll likely see home robots sold on payment plans, capable of doing chores like cleaning, laundry, or running basic errands. Paired with self-driving cars, they could physically go out and grab groceries, but that might be moot if delivery logistics become fully automated.

• Even then, humanoids will be seen as a luxury, not a necessity. Possibly helpful for elderly care or companionship, but hard to say if you’ll trust them to watch your kids.

• Mass adoption and real usefulness will hinge on raw material availability, factory throughput, and global infrastructure, factors far outside the software loop.

The Road Ahead (2025–2040)

Here’s a revised timeline based on what seems plausible given current constraints:

• 2025–2027: Research demos, limited warehouse deployments, and investor hype. Public fascination grows, but real-world impact remains niche.

• 2028–2029: Controlled pilot programs from Tesla, Figure, and 1X. Not available to the public.

• 2030–2032: First home units sold at high cost ($25k+), marketed to early adopters. Functionality still narrow, novel, not essential.

• 2033–2035: Improvements in mobility, energy efficiency, and coordination. Broader acceptance in elder care, light domestic tasks.

• 2040: First wave of mass-market humanoid assistants. Sold on payment plans. Useful but limited. Still more of a statement purchase than a utility.

• 2045+: Social normalization, broader trust. Robots begin to integrate with family life. Childcare, teaching, and autonomous errands become plausible. Embodied AI becomes fully superintelligent and evolves rapidly.

Even if the software becomes near-perfect by 2030, the physical world will slow things down. Manufacturing bottlenecks, material scarcity, and cultural trust take longer to solve than AI policies or next-gen transformer models. For most people, more immediately impactful tools will come in the form of self-driving cars, AR/XR overlays in the home, or AGI copilots for work.

Humanoid robots are coming. But their future is less “overnight revolution” and more “slow integration”. We’re building the future, but it is still going to take some time.

Final Thoughts

This is an incredibly exciting time to be alive. If you’re worried about AI taking your job, maybe now is the time to pivot to being a robotics engineer. Robotics is not a gimmick, and there will be a substantial market for humanoid robots. Many startups will try and fail to do this, but I truly think it boils down to manufacturing. Tesla, Amazon, Apple, and Google seem to make the most sense for being the companies that will bring these ideas to life.

From home assistance to elder care, humanoid robots will eventually play a significant role in daily life. We might be older when it arrives, but one thing is certain: humanoids will not walk into our homes overnight. They will arrive slowly and steadily, one step at a time.

Earlier this month, I was nearly scammed by someone posing as a web3 recruiter on LinkedIn. It was slick, professional, and almost worked… and I want to make sure you don’t fall for it, too.

This post will be different from my regular posts, but I felt like I had to write something about my experience and speak to what is becoming increasingly common online: scams.

Like many of you, I’ve experienced countless scam attempts online throughout my life, although I’ve always narrowly escaped the barrage of phishing attacks sent my way on a weekly basis. I found myself genuinely surprised by the sophistication of recent attempts online this week, and wanted to write about it. I realize this is different from much of my recent writing, but it follows a similar vein and pattern I have written about extensively: the internet is broken.

What This Post Covers

✅ My near-miss with a LinkedIn scam

✅ How video phishing scams actually work

✅ 9 red flags you should always check for

✅ How AI is making scams more convincing than ever

Let’s get into it.

The Cold Message on LinkedIn

I had just hopped off a call with a colleague of mine when I noticed I had a new message in my inbox on LinkedIn.

(Note: These images were taken after the scammer had blocked me on LinkedIn, so his profile picture and name are removed from any images I share in this article.)

At first glance, the job seemed plausible. The compensation was pretty high, but I’ve seen comparable salaries at gaming studios before. The role and qualifications matched my prior experience, and sounded like something up my alley. His initial cold message to me went on to describe the responsibilities and minimum qualifications, linking to a legitimate website with an actual Epic Games link and Steam store page.

Before I responded to Ahmed, I preemptively checked out his LinkedIn page. I always do this to make sure the person that’s messaging me is legitimately connected and seems real.

At first glance, it seemed totally normal. Ahmed had LinkedIn Premium, hundreds of followers, and 500+ connections. I skimmed over his post and comment history, and noticed that he had been actively reposting articles, some of which were for Parallel Studios. Well connected, and with a profile that seemed somewhat legitimate. I spent maybe 20 seconds reviewing his profile before deciding he was probably real.

Right off the bat, he nailed the following things:

• His profile linked to real projects, and he was connected with real people.

• His profile had activity that spanned years on LinkedIn. This wasn’t a new account.

• The job description was well written, relevant to my experience, and seemed normal.

So, I got right back to him. I responded to the cold message with a simple response asking for more info, and I attached my resume.

Within less than 10 minutes, Ahmed had responded, prompting me with the following message:

Again, pre-screening candidates is not uncommon. It would be a little out of the ordinary at this initial phase, but I have personally been through legitimate job processes that included this within the first few days. For example, Braintrust often requires candidates to submit video interviews upon initial entry, and that platform is arguably more trustworthy and reliable than LinkedIn (I’ve landed jobs through it).

So, I clicked the link. This is what I saw:

Bland, boring, but reminded me immediately of a generic Typeform-style survey page. The interactivity of the site was virtually identical.

I clicked through a couple of the steps before stopping. Initially, I had planned to spend some time reading up on the company and prepping for the virtual assessment. At this point, I was already intimately familiar with where these scams can go south for people who click malicious links: videos.

The link he provided caught my attention. I had never heard of Eskillora before. So, I Googled it.

I wasn’t looking for a new cool EDM mix from Soundcloud, so I knew this had to be a mistake. Maybe I misspelled it? Maybe it is just Eskill? After all, eskill.com is in fact a legitimate employment testing site. Nope, not associated at all. Attempting to go to eskillora.com resulted in a 404 error (don’t worry, I’ve already reported the site).

At this point, I was fairly confident it was a scam, but I decided to dig in more before messaging him back with questions.

In no particular order, here’s the red flags I noticed within the first few minutes –

• No public job listing for a role offering over $200K+.

• Fake-sounding hiring platform (eskillora.com) with no digital footprint or working homepage.

• No mention of hiring on the company’s official website.

• No contact via a company email domain (e.g., @parallel.life).

• Recruiter’s profile lacked credibility (no endorsements, unclear work history).

• Pushed me to complete a video interview link quickly.

Parallel’s site was poorly optimized and a mess to run on Brave, but I assumed that this scammer was simply using a lesser known web3 gaming project to more easily get away with doing this. Additionally, Parallel’s site had no mention of the Team or Jobs.

Given this abundance of BS, I figured I’d message Ahmed back and tell him about all the red flags. Perhaps there was an explanation. I preemptively saved his LinkedIn profile, because I presumed he would block me immediately after receiving my messaging.

I was correct.

Within a couple of minutes of him receiving the message, he blocked me. I was 99% sure this was going to happen, and fortunately the link I (stupidly) clicked wasn’t initially malicious. I immediately reported the account for suspicious behavior, phishing, and impersonation.

But, how does the video scam work, exactly? We’ve heard about this online, but what does it look like?

How Interview Video Scams Work

Scammers on LinkedIn are increasingly using fake job offers to trick professionals, especially in web3, AI, or remote-friendly roles into completing fraudulent “video assessments”.

Here's how the scam typically plays out:

1. The Setup:
   A fake recruiter reaches out with a high-paying, relevant role. It may reference a real company and a real product, making it look legitimate.

2. The Hook:
   Once you show interest, they send you a link to a “video interview” hosted on a sketchy third-party platform (like fake-video-assess.com). This platform:

   • Does not exist publicly.

   • Has no homepage, no search footprint, or business presence.

   • Only works via custom subdomain invite links.

3. The Trap:
   The “interview” requires:

   • Allowing camera/mic permissions (be extremely cautious to allow this!)

   • Sometimes downloading a browser extension or allowing site notifications.

   • Other times, the site runs JavaScript payloads silently in the background to:

     • Fingerprint your browser and device.

     • Steal session tokens from LinkedIn, Google, etc.

     • Access your camera feed to record you, which could be used in deepfake scams or ID spoofing.

4. The Exit:
   If you ask questions or hesitate, the scammer will ghost you or block you outright. If you don’t question it, you might unknowingly grant them enough data to spoof your identity - or worse, compromise your accounts.

This is but one flavor of what is now a job scam epidemic. Scammers often attempt to initiate interviews over WhatsApp, Telegram, and Teams, but Zoom is also becoming increasingly common. This has also been written about extensively. 3rd party messaging tools and moving quickly through the interview process are some of the most obvious red flags.

What happens next in these scams is often even more dangerous. After a victim completes the fake video interview, scammers may simulate an onboarding process by sending offer letters, NDAs, or even setting up fake company email accounts. From there, they often request sensitive information under the pretense of payroll setup, such as routing numbers, direct deposit details, tax documents, or passport scans.

In some cases, they send a fake check for equipment reimbursement and ask the victim to return a portion of the funds before the check inevitably bounces. By the time most people realize it is a scam, their identity or finances may already be at risk.

It is barely worth mentioning, but obviously the ease and accessibility of LLMs has made this problem worse. One of the biggest red flags used to be broken or inconsistent English. This is now largely a thing of the past. There are plenty of stories online of developers having their Github accounts stolen, only to be used by fraudsters all around the world to land remote jobs.

Video scams are just as much of a problem for companies that are hiring employees as it is for people that are looking to be hired. Until we have better systems for managing digital identity, this will remain a problem. It is simply too easy to fake employment and credentials. The example of Alex Cohen listing himself as the CEO of LinkedIn without any proof required is my favorite instance of this.

I’ve compiled a list of red flags to look out for on Linkedin, which I wish I had reviewed prior to this ordeal.

Red Flags to Watch For on LinkedIn (Scam Detection Checklist)

Here’s what you should watch out for anytime someone cold DMs you a job:

• Unknown or nonexistent platform links - If the URL doesn’t resolve or lacks a homepage, it’s likely fake.

• No official company email - Recruiters should be contacting you via @companyname.com, not just LinkedIn DMs.

• No careers page or job listing - Always verify that the role is publicly listed on the company’s website or a reputable job board.

• Vague or fabricated recruiter profiles - Look for missing work history, few connections, or generic job titles like “Web3 Hiring Lead.”

• Too fast a push to “interview” - Real companies don’t ask for a video assessment before a basic screening call.

• Zero digital footprint for tools or domains they reference - Google the platform. If nothing comes up besides your invite link, it’s fake.

• They block or disappear after you question them - Instant red flag. Real professionals don’t ghost when asked for verification.

• They require video recording or screen sharing early on - Especially without a signed NDA, offer letter, or formal process.

• They reference high-paying roles with zero vetting - Scam roles are often suspiciously aligned with your skills, and pay too well to be real.

Even if you’re not in doubt, it is best to thoroughly review everything anyone sends you on LinkedIn, especially if it is a link. In this instance, the scammer Ahmed had an active LinkedIn profile that was years old. That is arguably what made this scam stand out, and it nearly worked.

Moving Forward

This is the digital reality we’re living with in 2025. Until LinkedIn has more robust vetting and a more formal and sophisticated verification process, just about anyone can spin up an account and start sending out malicious links to people. LLMs make it easier than ever to message tons of people with legitimate jobs, and recent video phishing has gotten drastically worse.

I love writing about technology and new developments with ecosystems and products. If even a single person benefits from reading this, my time was well spent. There are too many untold stories of people getting scammed, and I’m glad my persistent paranoia online has prevented me from being just another casualty of phishing online.

Stay alert, stay vigilant, and question everything you read and see online. We can build a better internet, but for now this is the one we’re living with. Until platforms take identity seriously, vigilance isn’t paranoid, it is survival.

If you found this helpful, share it with someone currently in the job market. The next target could be them.

🛡️ Scam Reporting Resources

• Federal Trade Commission (FTC)
  If you're in the U.S., report scams, phishing, fake job offers, or identity theft at reportfraud.ftc.gov. This helps the FTC track and investigate fraudulent behavior across the web.

• Internet Crime Complaint Center (IC3)
  Run by the FBI, the IC3 allows you to file complaints for cybercrime and internet fraud, especially scams involving financial loss. Submit a report at ic3.gov.

• LinkedIn Scam Reporting
  To report fake recruiters, impersonation, or phishing attempts directly on LinkedIn, go to https://www.linkedin.com/help/linkedin/solve. You can also click “Report” directly on a user’s profile or message.

• Google Safe Browsing
  If you encounter a phishing website or malicious link (like a fake interview platform), report it to Google at https://safebrowsing.google.com/safebrowsing/report-url to help block it in Chrome and Search.

Thanks for reading,

- Chris

The internet is changing. I’m sure you’ve felt it. If you have spent any time on X, Substack, LinkedIn, or Reddit recently, it feels even more algorithm-y than it did before. But beyond that, the elephant in the room remains: is what I’m reading something that was written or augmented by AI? Is the dead internet theory true?

If you’re anything like me, you’re approaching all written and visual content you see on the internet with open skepticism. Is this image real? Was this text actually written by a person? Did I find this content online, or did the content find me?

As humans, we like to believe we’re too smart to fall for an illusion, that we’re able to see beyond a mirage and stay above the noise. With increasingly sophisticated models being released for free, we’re seeing AI-generated content being taken to new heights. This is fine on its own, but it becomes a major problem when it becomes impossible to figure out where AI ends and someone’s legitimate thoughts begin.

In this article, I’m going to go over a few concepts that have existed for a while, but are becoming increasingly important: synthetic intellect, proof of personhood, and dead internet theory.

What does internet usage look like when everyone online is fake or augmented by AI? When will we reach an inflection point when something will be done about it? Is everything already fake?

The Dead Internet

Is the internet already dead and gone? With the rise of AI content, it can be increasingly difficult to tell what is genuine (human-created) and not. AI training data is based on human data, so it comes as no surprise that we’ve reached this inflection point where it is nearly impossible to tell at times precisely where content originates from.

To familiarize yourself again, here’s the definition:

The dead Internet theory is a conspiracy theory that asserts, due to a coordinated and intentional effort, the Internet now consists mainly of bot activity and automatically generated content manipulated by algorithmic curation to control the population and minimize organic human activity.

As widely reported on X and Reddit, in November 2024 the University of Zurich deployed bots on the r/changemyview subreddit, secretly (and without permission) conducting a study on how AI could influence users’ opinions. These bots engaged in discussions and did persuade people. But, the way they did it was pretty gross: the bots would analyze users’ post history to craft highly personalized and persuasive arguments or retorts.

The paper was titled: “Can AI Change Your View? Evidence from a Large-Scale Online Field Experiment”. The answer is: overwhelmingly yes. The bots did whatever they could to persuade, and were set up to hallucinate and roleplay. In a few examples, bots claimed 1) to be a rape victim, 2) a white woman in an almost all black office and 3) a hard working city government employee.

Because Reddit is already a huge source of AI training data, these bots were able to effortlessly fit into Reddit. They matched the kind of language you would expect, and confirmed our worst fears: AI bots can be hard to detect, they will lie to further their goals, and they can be more persuasive than the average person.

Many subreddits have clear bans on AI content, using generative AI to create text, and bots. It is clear to see why: it slowly turns everything into mush and nobody wants to endlessly argue with a bot on a forum. Bots, in their current form, really have no skin in the game. They’re not embodied and they have no true personal agency. These are core qualities that make engaging with them in spaces like this simply indefensible.

But these are bots. They may have pretended to be people, but we know they’re not people. We can turn them off (for now). Something else is emerging from the murky waters that is far more irritating and frustrating online: people using AI to heavily modify their presence online.

Synthetic Authorship

This term doesn’t exist yet, but it is happening online and ruining the web so I’m giving it a name.

Here’s my definition:

Synthetic Authorship: A person that uses AI to create their thinking, writing, and ideas, then pretends it is entirely their own human intellectual output.

We’re at an awkward phase of the internet right now. We’re still using the same old sites and networks, but generative AI is here to stay. With that, countless accounts have been created across the web that are obviously AI personas (and admittedly so). We now have a mix of three things: humans just being humans, AI bots, and synthetic authors.

That’s fine. The internet (in its current form) will inevitably end up being a hybrid between AI and human conversation. What is vastly more troubling, however, are accounts online that are clearly heavily AI augmented and presented by a real person.

This is synthetic authorship. I’m sure you know what I mean. There are countless accounts spread across Facebook, X, Instagram, LinkedIn, and TikTok that act in the following ways:

• They post AI-generated ideas and insights while pretending they are original human thoughts.

• They use AI to craft replies, arguments, and DMs, simulating natural conversation.

• They build personal brands or thought leadership personas entirely on AI outputs without disclosure.

• They fabricate authenticity by mimicking emotions, anecdotes, and personal experiences.

• They produce books, articles, and newsletters with minimal real input but claim full authorship.

• They blur the line between real cognition and synthetic output, making it hard to tell if a real person is behind the account.

You have undoubtedly stumbled on accounts that fall into this category. They are operated by a real person, but the output feels weird. Oftentimes there are classic tells: the em dashes, overuse of cliches and safe platitudes, hedging language, excessively clean grammar, unnatural emotional tones…the list goes on. There are plenty of Substack accounts I’ve found that fall into this category, and real people like the posts, restack them, and engage with the content.

There are so many problems with this kind of content. It is often written just well enough to keep you hooked, may have some useful insights, but can feel more like AI slop than genuine content. Unfortunately, the mush will continue, and the mush is slowly getting better. It doesn’t take a genius to see just how much AI generated text is filling up low-quality news articles, and sites that rely on user-generated content.

So, where are synthetic authors heading?

The Logical Endpoint of Synthetic Authorship

As many people have pointed out online, there is no effective way to ban the usage of AI when it comes to using it for thinking or generating ideas. Models that are free to run and use locally on a MacBook Air can easily do the heavy lifting intellectually. We’re rapidly approaching LLMs being nearly free to use. On-device computation is the future for private, frontier-level models.

Generative AI will continue getting better, and longer context windows and multimodal capabilities are now the norm. Social media and content online moves fast. Writing a longer piece by yourself can take hours, sometimes days or weeks to think through and finish. AI can already produce writing that is 80% there in the time it takes for you to go to the bathroom. Content will never be the same.

The usage will accelerate. The workflows of people that are doing this will become increasingly agentic. This is great for them, but it is going to suck for the rest of us.

Here’s an outline of how I see these flows:

Flow 1: Manual Synthetic Authorship (today’s flow)

• Manually prompts LLMs, selects outputs, and edits content to appear human.

• Browses feeds, reads replies, and handpicks where and how to respond using AI.

• Curates posts, articles, and comments by actively steering tone, timing, and engagement.

• Relies on constant judgment to maintain the illusion of authentic thinking.
  

Flow 2: Automated Synthetic Authorship (Operator-style automation)

• Pre-programs prompts, themes, and criteria for AI to auto-generate and schedule content.

• Automates posting, basic engagement, and sentiment-driven replies with minimal oversight.

• Uses AI feedback loops to refine strategies and content performance autonomously.

• Human role shrinks to occasional supervision and adjustment, not active creation.

Both flows currently exist to some extent, but automated flows are still finicky: the tools are nascent, the programs require a lot of hand-holding, and the hands aren’t entirely off the steering wheel. All it is going to take is one or two general AI agents (similar to Manus) that can control a virtual machine to make this happen more broadly.

This is a matter of when, not if. This is already beginning, and it will continue until everyone and your grandma is using this technology to augment their presence online to the point of being a synthetic author, or creating new personas entirely that are either fake people or more openly artificial intelligence.

Under our current networks and the way we use the internet, the usage and the rise of synthetic authors is inevitable. If we don’t modify the networks we use to connect and socialize, the internet will continue to turn into slop, and it will be futile to even engage on platforms like Reddit or LinkedIn, where the whole point is to have real conversations with other people.

The internet in its current form is essentially already lost. Masquerading is easy, and there’s no way to verify if the thing you’re interacting with is human or not.

What’s the solution?

Proof of Personhood

Proof of personhood (PoP) is a mechanism that digitally verifies an individual's humanness and uniqueness. Along with concepts like content provenance, PoP is becoming increasingly obvious as a potential solution to some of these problems. Think of the usage of PoP like a digital passport. We want to have some sort of verification that ties what is happening online to an actual person. The extreme opposite of this might be something like running countless instances of Operator on your own, masquerading as synthetic versions of yourself or other people, and gamifying any and every aspect of the internet that you can.

PoP has been talked about to death, and leading the pack in this conversation is World. World lays out two key reasons why PoP is needed today:

1. Protecting against sybil attacks, or online attacks from multiple pseudonymous identities generated by a single attacker

2. Preventing the dissemination of realistic, AI-generated content intended to deceive or spread disinformation at scale

World ID, the open identity protocol, has been written about extensively online and is not a new project or concept. And, there are countless other projects worth looking into that are tackling this problem. BrightID, Proof of Humanity, Gitcoin Passport, and Idena to name a few. All of these projects aim to prevent individuals from creating multiple fake identities to exploit systems, and aim to have a one human = one identifier setup.

Beyond this, the usage of the blockchain for DIDs, incorporating biometric data (like iris scans), and social trust mechanisms (like a social graph) are common in these projects. As Worldcoin has so strongly pushed, these systems could be used to deliver UBI benefits in a world where job displacement as a result of AI makes income increasingly difficult. Earlier this month, I wrote about why I think UBI will not work, and what we can do to fill the sorely needed emotional infrastructure jobs provide.

But this doesn’t solve the synthetic authorship problem. You can easily verify that you’re human and then proceed to input AI generated content on whatever platform you’re using. It might be a solution for ensuring there’s no overlap of UBI payments, but it fails to solve synthetic authorship.

What’s the solution? Is there some sort of inevitability to the internet being overrun with bots and AI? If we make new networks with PoP verification as part of a login, that won’t prevent people from using AI. Are we collectively okay with the internet becoming a place you can’t really determine the origin of anything?

Possible Solutions

The future of the internet certainly isn’t simple. Ideas have floated around for a long time for having social media networks that have baked-in C2PA (content provenance) with proof of humanness. If we’re wanting to solve the problem of synthetic authorship, it’ll take more sophisticated designs to get there.

Unsurprisingly, blockchain networks will be useful for some of these ideas, and necessary. Here are a few ideas that could help push us in the right direction:

• Chain-of-Provenance (CoP) Signatures - Every piece of content gets cryptographically signed at the moment of creation. Each edit, repost, or publication adds a new signature to the chain. Platforms and browsers would display a verification badge if the full history is intact, letting readers quickly see if something is genuinely human-originated or tampered with.

• Proof-of-Thought (PoT) Logs - Writers optionally publish an append-only log of their writing process: drafts, notes, edits, and prompts. Tools like Obsidian could auto-hash every save. Readers could inspect these logs to see whether real thought and iteration happened, or if the piece appeared fully formed from an AI.

• Reader-Side Authenticity Filters - Browsers, feed readers, and apps would offer an authenticity slider: Human Only → Mixed → Anything Goes. Content lacking provenance or PoP ties could be hidden, grayed out, or demoted based on the reader’s preference.

• Culture Nudges - We collectively decide to start transparently labeling things like ‘AI-aided’ or ‘AI-generated’. The failure to label could be treated as deceptive advertising or fraud.

For transparency, these are not my ideas. These are ideas that have been thrown around online and discussed in various capacities over the years. Personally, I think we have to assume people will continue to lie about their usage of AI, as it will become increasingly easier to do so, and AI is always available and accessible. Much like expecting people to recycle or throw garbage away, cultural nudges will only go so far, and the world is only getting more competitive.

Blockchain technology makes sense for this issue. Chain-of-provenance works great for tracing the origin of items and ownership in the supply chain world. CoP signatures and having a log of produced work, while absolutely exhausting to imagine actually implementing, is a potential solution. Having a log of what happened to a piece of content from the moment of its creation is one of the core ideas behind C2PA. Certifying where digital content comes from is important. This was more apparent with image generation, but it is clearly going to involve any and all digital media soon. Again, a matter of not if, but when.

The Near Future

I expect that in the near-term, we will see the continued proliferation of synthetic authorship in every platform imaginable. This problem, and the frustrations that come with it, will continue to be a major problem online. AI will keep getting better, and more humans will prop themselves up along with it. Extreme examples, like using AI to make practically every decision you have (cognitive offloading) will become more prevalent. Undoubtedly, some people will be unable to speak live or do things IRL without some sort of AI-aid as backup, and life may never again look like how it does now.

Authenticity matters. I love using AI tools, but AI has clearly expanded beyond being anything like a typewriter or autocorrect. Masquerading, cognitive offloading, and synthetic authorship will continue to be major problems, and we need a major overhaul of how we do things online to deal with it. Collective awareness, and wanting to build a better internet, is key.

I’ll leave you with a quote from the movie Idiocracy:

As the twenty-first century began, human evolution was at a turning point. Natural selection, the process by which the strongest, the smartest, the fastest reproduced in greater numbers than the rest, a process which had once favored the noblest traits of man, now began to favor different traits. Most science fiction of the day predicted a future that was more civilized and more intelligent. But, as time went on, things seemed to be heading in the opposite direction. A dumbing down. How did this happen? Evolution does not necessarily reward intelligence. With no natural predators to thin the herd, it began to simply reward those who reproduced the most, and left the intelligent to become an endangered species.

Thanks for reading,

- Chris

In the now famous Star Trek original series episode, ‘Who Mourns for Adonais’, the Enterprise crew encounters Apollo, a god-like being from ancient mythology. It is in this episode that Captain Kirk initially describes the function of the universal translator: a device used by Starfleet built into the communication systems of most of its starships. Eventually, throughout the series, universal translators advance to a point where UTs could be built into combadges worn by the crew, enabling instant, natural, and seamless communication.

When ‘Who Mourns for Adonais’ was written 57 years ago, the idea of a pocketable, universal translation computer was truly science fiction. Today, the reality of such a device isn’t just a fantasy, it is quite nearly a reality.

We’re going to explore what DolphinGemma is, small language models, and how it relates to this idea of a universal, pocketable, local translator. As it turns out, academic research into how dolphins speak is strongly related to Apple’s push for having Airpod Pro 3s support instant translation.

What is DolphinGemma?

Over the last week, the internet blew up with the news that Google was working with the Georgia Institute of Technology, alongside the Wild Dolphin Project (WDP), to release a Gemma-series AI model trained to learn the structure of dolphin sounds. The goal is to uncover the structure and meaning of dolphin sounds, and figure out if dolphins have a shared language.

For decades, despite studying dolphins and gathering countless clips of their signature whistles, squawks, and buzzes, we can’t figure out what they’re saying. We know they have names for each other, and we can classify certain sounds as being associated with encounters or activities, but that’s about it. There’s no Rosetta Stone to be dug up, and the best linguists in the world are utterly lost. Time to try AI.

Luckily for Google, the WDP had already labeled much of the training data that would be necessary to train DolphinGemma. DolphinGemma is built from Google’s Gemma architecture. In a nutshell, that means the model is lightweight, able to be run on a small device, open, and multi-modal. It runs on a phone, and utilizes Soundstream, an audio codec used to compress audio to reduce storage. Essentially, raw audio waveforms are transformed into lower-dimensional embeddings (compressed numerical representations). Soundstream converts the audio signals into tokens, exactly like how small language models (SLMs) will be used in the future for real-time translation on phones.

The process for this is very cool:

• Data Collection & Annotation: Researchers from the WDP have gathered extensive underwater audio and video recordings of Atlantic spotted dolphins since 1985. Each recording is meticulously annotated with behavioral contexts, such as mother-calf reunions, social interactions, and hunting behaviors, providing a rich dataset for analysis. ​

• Audio Tokenization with SoundStream: The captured dolphin vocalizations are processed using Google's SoundStream, a neural audio codec that compresses and tokenizes the audio into discrete units suitable for analysis. ​

• Pattern Analysis with DolphinGemma: The tokenized sequences are analyzed by DolphinGemma, a lightweight model with approximately 400 million parameters, to identify patterns and structures within dolphin communication.

• Real-Time Deployment: DolphinGemma runs efficiently on devices like Google Pixel smartphones used by researchers in the field, enabling real-time analysis of dolphin sounds.

• Interactive Communication via CHAT System: In conjunction with the Cetacean Hearing Augmentation Telemetry (CHAT) system, synthetic whistles associated with specific objects are introduced. Dolphins are encouraged to mimic these whistles to request items, facilitating a form of two-way communication. This may lead us to uncovering their language.

The Pixel phone functions as the brain of the operation underwater, which can run the CHAT system, deep learning models, and a template matching algorithm. With Gemma architecture, what was formerly impossible (real-time communication with dolphins) has become plausible. The Pixel replaces bulkier hardware, and breakthroughs with quantized, fine-tuned language models means more is possible with less compute and RAM.

We don’t know what dolphins are saying yet, but this magic is possible because of open, compact models. Right now, so much time and attention has been focused on frontier models like o3, but the future is arguably just as exciting for the use cases made possible by small language models.

So, what makes small language models different? Why should you care? Is everyone going to be able to understand every language they hear in person using Airpods soon?

Let’s break down the key advances in small language models, like Gemma, to better understand where things are heading.

Small Language Models

The industry has advanced quickly since early 2023. Google initially dropped Gemma 2 in mid-2024, and it quickly made waves in the space for being lightweight, accurate, and highly performant. Just recently, Google released Gemma 3, which has a much larger token context window, comes in multiple sizes, and is multimodal. Out of the box, for free, Gemma 3 supports over 140 languages! You can download it onto your phone and have it run locally. If your phone is slightly more powerful, you can even have the 4b model run locally on your phone, capable of analyzing images, summarizing documents, and more. The pretraining dataset for Gemma included more languages, doubling the amount from Gemma 2. Google has a Gemma 3 training report that dives into how the models were trained and instruction fine-tuned.

Like many advances with technology, Gemma’s efficiency is the result of a number of innovations with training techniques, the utilization of memory, and selective neural routing.

From advances with LoRA to speeding up attention on GPUs through minimizing memory reads and writes, the efficiency gains add up. Now, you can reliably run Gemma 3 on your phone locally, which is more performant and intelligent than GPT-3 was when it was released in late 2022. All for free.

Key advances for reducing the size of language models. Not all of these advances are used by Gemma 3, but each is worth learning about.

We’re clearly trending towards the following:

• Models: Highly performant, open, small language models that are increasingly accurate, knowledgeable, and wicked fast out the box

• Hardware: TPUs (tensor processing units), NPUs (neural processing units) and AI-centric (ASIC) chips that are standard with flagship smartphones. Soon, all phones

• Software: Further advances in compressing model weights, optimizing memory, selective routing, and optimization for utilizing GPU/TPU/NPU memory

The barrier for offline and accessible intelligence is shrinking. Real-time translation is almost coming as a side-effect of these advances. The current iteration of Google Translate, the most widely used translation app, utilizes LLMs, but more specifically transformer-based neural machine translation (NMT). NMT models are specifically designed for translation and are more compact, making them ideal for on-device usage. The breakthroughs in language models and transformer architecture explain why in 2024, Google was able to double its language support for 110 new languages in the app (made possible in part by their PaLM 2 model).

AirPods Pro 3: It’s All Coming Together

Apple made waves last month by announcing that the latest Airpods would feature the ability to translate in-person conversations from one language to another. Just imagining the ability to shrink the gap in understanding and increasing human connection is so genuinely exciting. Make no mistake, the first iteration of this will probably be underwhelming, and if you’ve ever used Google Translate with another person to have a real-time conversation, you might recognize just how challenging and frustrating this can be. Currently, unless you have a lot of patience, Google Translate is slow and cumbersome at best for back and forth, real-time conversational translation. It works, but the experience is lacking.

So much of this is speculative. We have zero info on how this latest feature from Apple will work, what models will be utilized, and what the hardware constraints are. Apple is planning to improve the translate app in iOS 19, and hopefully that means older models will get the update as well. But if I had to guess, the local model will utilize much of what we discussed above, and because Gemma is open, Apple might even utilize it.

What Comes Next?

Unless you’ve been closely following the developments with SLMs and the progress Google has made recently with translation, it might be easy to overlook just how far we have come. In a short period of time humanity has rapidly improved the speed at which we can translate languages, lowering the cost, and removing the barriers of even having internet access. While current models are not perfect, the trend is clear. Within a couple of years, it is entirely conceivable that most of us will be carrying around devices capable of near real time translation. Even today, you can freely download Gemma models and use them on your phone, privately and locally.

Humans are notoriously bad at predicting the true rate of technological progress. When ‘Who Mourns for Adonais’ was written in 1967, the writers placed the plot 200 years into the future, in 2267. In less than a single human lifetime, we’ve gone from fictional writing about unbelievable technology to something we’re very close to perfecting. We’re seeing how performance gains using language models are having a real impact in scientific research, getting us closer to being able to communicate the latest memes to dolphins.

One day, soon, we will all have a universal translator in our pocket. Communication barriers will shrink. We will be more connected. And, this technology will be increasingly accessible to all. Efficiency gains with language models will change how we travel, work, and interact across cultures, making everyday life even better.

Thanks for reading.

- Chris