I love coffee, and lately I was quite frustrated with the temperature of the coffee from my machine, so I checked it, and it turned out to have a crack in the heating plate, likely because of poor design. This is a top-end drip machine. I asked one of my agents to find me alternatives, and along with a good recommendation, it came back saying ... but your coffee machine is still under warranty. I would normally have been a bit lazy about dealing with whatever customer service this company provided. The agent did this for me and got me a full refund. Two years and six months after the purchase, I got to switch to a new coffee machine for free. Sweet.

That made me think. There are a significant number of businesses whose profit margins, one way or another, benefit from friction in their customer journeys, whether deliberate or not. It can be a subscription you no longer want, a warranty claim you never submit, or an incorrect charge that is too small to spend an afternoon disputing. It can also be the effort of switching to a better offer. I even leave potential tax savings alone because I don’t want a more complicated tax return next year. We leave the money there because getting it back takes more effort than we want to spend.

This can grow out of the way a company measures success. In a large organisation, teams keep testing small changes. If the winning version is the one that produces more sign-ups or fewer cancellations, making the customer’s choice harder can look like progress. One extra screen, one more reminder, one less obvious button. Each team can improve its own numbers while the whole experience gets worse for the customer.1

In a recent paper, researchers studying ten subscription services estimate that delayed cancellation raises revenue by 87% or 120% on average, depending on whether they model inattention or the effort of cancelling. These are alternative estimates, with prices and the initial group of subscribers held fixed. They estimate the effect on revenue, not profit.

Agents, especially those of the latest generation, are very good at removing this friction. This is partly for two reasons: 1) they are infinitely patient, and 2) they have become really good at using human interfaces, what we normally call computer use. Filling out a customer service form... got it. Making your case in a customer support chat... easy peasy. Writing a long, legal-sounding email to dispute a wrong charge... they can do it with their eyes closed.

That’s why these stories are becoming viral. They are cool. They bring justice to the small guy, and everyone loves saving money.

Now... let’s think about the second derivative of this. When we get to a world where agents are ubiquitous and friction does not work anymore to increase profit margins... what are the implications?

Some of this extra revenue becomes profit, and some can fund a lower base price (reference). Customers who avoid extra charges can benefit from what others pay. I think something similar can happen with all businesses that depend on friction.

Your gym can cost €33 a month because 80% of people pay and don’t go, and a significant number of them continue paying to avoid the shame of cancelling in person. The government (at least in Spain) gives you a 50% discount on traffic fines because, if you properly appealed them, it would likely lose many of those cases.

That is good in the short term, especially for early adopters who, like me, have saved a few hundred euros by cancelling random stuff and making small claims they would otherwise have left alone. Now, this is kind of a one-off. Once you are “clean” of friction subscriptions... you are done.

Hayek compared the spread of new money to honey poured into a vessel: it builds up where it lands before spreading out. The parallel here is timing. Early agent users can enjoy the existing deal while avoiding unwanted payments, before companies change their prices or terms.

I expect some companies to fight this before they change their prices. Another page or a longer queue will do less to stop an agent than it does to stop me. Companies that depend on that friction will have a reason to block agents or insist that the customer finish the process personally.

My bet is that businesses which depend heavily on customer inaction will change their offers as agents become common. Some will raise the base price. Others will offer fewer discounts or narrower benefits. If part of today’s cheap deal is paid for by customers who forget to cancel or never make a claim, that deal gets harder to sustain when an agent acts for them. AI can also reduce costs and make switching easier, so I don’t expect the same result everywhere.

A higher listed price can still leave me spending less overall if I stop paying for things I don’t use.

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There is a long tail of these problems. Each one might be too small or too infrequent to justify the effort. You know you could find a cheaper plan or dispute that charge, but you have other things to do. So it stays there, sometimes for years.

AI changes that calculation because agents are infinitely patient. They can compare options, check the details, follow up, and keep going through the boring parts that make us give up. They also give people access to help with decisions that previously required expertise they didn’t have or couldn’t afford.

Something that wasn’t worth an hour of your time might be worth asking an agent to handle. And while each problem is small, there are a lot of them. Each problem might only come up once a year, but across all of them, there could be something worth solving every week.

Now, the challenge is the same one we’ve been talking about over and over. AI can only help if people recognize the opportunity and know it can help.

There are two ways this may happen: pull or push.

Pull means people experimenting and figuring out the power of AI for these problems. You ask it to review your Movistar plan, find a better option, and help you switch. There is a chance that, with widespread adoption, enough viral “this AI solved that problem why don’t you try it” examples will get people to try it, and then come back with another problem.

One example I keep seeing go viral is organizing school information for parents. Javi and I worked on this a year ago, and it is available in Luziclaw. (Though there is a cheaper solution: schools should get their shit together and learn to communicate concisely.)

Push means AI that is integrated enough into the consumer’s life to be proactive. Proactivity is the next big thing. An assistant with access to your bills could notice that your Movistar plan deserves a review and suggest a better option before you ask.

That is still a work in progress. We are getting better, but there is a gap between “organize my email and tell me something” and actually resolving the problems it finds. The opportunity is to make that long tail worth dealing with, without turning the user into the full-time manager of another tool.

As AI starts solving more of these problems, there is a whole other topic here: what happens to industries when people stop staying on old payment plans, pursue every valid insurance claim, or ask their bank to refund every incorrect charge.

But that’s for another post.

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We have all freaked out about AI in the last few weeks. It’s difficult to say what triggered it. There is the post by ex-Anthropic researcher Jacob Coxon, OpenAI announcing a proposed solution to the Navier–Stokes problem, one of the Millennium Prize Problems in math, and the Hugging Face incident, which I already wrote about here. Scary stuff. But the part I want to write about today is, call me crazy, also fun.

I think part of why people very close to AI are freaking out is where they think we are on the progress curve. Today, progress feels something like this:

And then you zoom out:

And all hype aside, I want to make the case in a simple way for why I think we still have a long way to go up that curve. Spoiler: the standard ways to make AI better continue to bring results, but we have several others, with more being discovered. And more importantly, these improvements can make each other more useful.

More ways to get smarter

Scaling laws let us estimate how a model’s performance changes as we increase one of its inputs, like the compute used to train it. (gravity is a law... calling scaling a law gives me an ick, but let’s go with the industry)

Giving o1 more compute to reason improved its score on this math test. Adapted from OpenAI’s research. Points are approximate readings from the original chart.

The best-known example is giving a model more data or compute during training, the “OG” scaling law, and I wrote last week about the reasoning one. But there are a few others, and the interaction between them really matters. Two improvement factors are not additive to the total performance; they are multiplicative. In non-math terms, improving one can increase what we get out of another.

Hardware is an easy way to see this. Say new GPUs give you twice the compute in the same amount of time, and a better algorithm lets you run each experiment with half as much compute. Together, you can run four times as many experiments in that time. That doesn’t mean four times the intelligence, but it shows how two improvements can multiply.

I am a visual person, so I think this “simulation” (thanks, Astra) will make the point better than I can. The numbers are made up. The point is how they combine:

Try the interactive version: add one improvement at a time and see what happens.

What are the specific scaling laws we are riding?

Training bigger models and giving them more time to think are two ways to improve AI. There are several others, and I am sure the labs have a few tricks up their sleeves that we don’t know about. Let’s go through them one by one. Sorry if I sometimes anthropomorphize, but I think it helps.

Training compute

The OG. Give the model more time to learn and practice. How much of that it gets depends on the computing power we use and how long we run it. More training compute lets us train a bigger model, give it more examples to learn from, or both.

In 2020, OpenAI showed that increasing model size, data, and training compute improved performance in a fairly predictable way. That publication gave us a way to estimate what more training could buy us, before the whole AI explosion.

Training compute has grown at roughly 4–5X a year. To make it tangible, if you invested $1K in 2010 and multiplied it by four every year, today you would have $4.3 trillion. That’s the kind of growth we are talking about.

Training-compute estimates for frontier AI models. Each step up means 10X more computation, not 10X more intelligence. Source: Epoch AI.

With such insane growth - bananas as my kids like to say - many people expected this route to run out of room, mostly because of data, but also because of compute. But new datasets are created every day, and synthetic data (data created by other models) gives us another source of training material. In other words, we have ways to keep going, even if we don’t yet know how far they will take us.

Test-time compute (September 2024)

This is the reasoning scaling law. The fancy name refers to giving the model time to think at inference time (”test-time”). Basically, like people, the same model can do better when given more time to think before producing an answer. Those of you who were early adopters may remember the good old tricks of adding “take your time” or “think about it” to the prompt.

With the release of o1-preview in September 2024, this became much more explicit: OpenAI trained the model to make better use of that extra thinking time. As we discussed a couple of weeks ago, test-time compute scales both horizontally (letting the model explore more possibilities) and vertically (letting the model think for longer).

Multi-agent systems

We humans create teams of people with different skills to solve different problems. Multi-agent systems are the same idea. Create teams of agents to solve more complex problems, or sometimes use compute more efficiently. This is an extension of giving models more time to think, with the advantages of separate contexts and task specialization.

If this works and scales, why not create thousands or millions of agents? Well, that’s the goal, but like humans, agents have coordination problems, and there is a limit to how many can work together efficiently. Improving that coordination so we can throw more and more agents at a problem is the scaling opportunity.

This route is still early, but the last couple of months have brought two examples of multi-agent work to public attention: the Millennium Problem and the Hugging Face hack. For the Millennium result, though, Noam Brown gives less than 10% of the credit to multi-agent work. He mainly credits the underlying model.

A coordinated team did better on financial research, where the work could be divided, and worse on planning, where each action affected the next. Making larger teams useful depends on getting that coordination right. Source: Kim et al., Towards a Science of Scaling Agent Systems.

Small note: we tend to think about coordinating agents through harnesses, the software around the model that organizes their work. My bet is that most of that coordination logic will end up in the foundation models themselves. Noam Brown describes how OpenAI already trains agents together to cooperate. The hope is that, as the models get better at this, we can put them to work in much larger teams.

Hardware and efficiency

This is the hardware example from above. As hardware gets better, models can do more computation in the same amount of time or, even more importantly, with the same amount of energy.

My feeling before researching this post was that we were quite optimized already. 2nm technology, a ton of RAM... how much room could there be left? Well, NVIDIA announced the H100 in March 2022, and by February 2026, OpenAI had trained GPT-5.3-Codex on the newer Blackwell generation. That is... the H100 announcement was almost four years old by then, and newer hardware was already helping train models that were changing how software engineering works.

Not only that, companies like Tensordyne, Euclyd, and Extropic are designing different kinds of AI chips, with claims of efficiency gains that reach several orders of magnitude on specific tasks.

Company claims and estimates for different tasks and hardware, so the numbers are not directly comparable. Extropic’s estimate comes from simulations on small generative-model benchmarks.

These companies still have to deliver on those claims at scale. But the idea that hardware is already as efficient as it can get seems premature. If these approaches work, the same energy budget could buy us much more computation, which gives us more room to keep going up the other curves.

Recursive self-improvement (RSI)

This one is a bit odd and makes my consultant brain twitch. Use AI to improve the process of making better AI, which then helps you... make better AI and repeat.

A simple example is the training code for LLMs. The basic code can be surprisingly short. Karpathy’s nanoGPT has roughly 300 lines for the training loop and another 300 for the model itself. And AI is already helping with this kind of work. OpenAI used early versions of GPT-5.3-Codex to debug its own training and help develop later versions. In fact, automating AI research has been an explicit goal of all the AI labs for years. This same idea can apply to all the other scaling routes: AI for better hardware, AI to improve datasets (synthetic data), AI to coordinate other AIs...

That’s why RSI could offer another scaling route: give AI more compute to find better ways to build AI. While we are constrained by compute (hardware or energy), deciding to point resources to RSI is a bet that improving how we build AI will give us more back than spending those resources on the current process. It’s in some ways... a meta decision. Do I point my models to more training, or do I point them to improving the code?

A possible outcome of recursive self-improvement: better AI accelerates the development of still better AI. Conceptual illustration, not a measured trend or a timeline. Source: Tim Urban, Wait But Why, 2015.

Test-time training

We still have not cracked what I think is the holy grail: models that keep learning from experience and carry that learning forward. Models that get better at understanding you, your needs, or the work they do, with that learning stored in their weights beyond the context window. Test-time training is one possible step in that direction.

We already have part of the mechanism in fine-tuning, which adapts a model’s weights to a specific task after its main training.

Test-time training takes this same idea to inference time, after the model sees a new task. The system uses available examples to update some of its weights before answering. This is akin to human learning from experience. It’s what some would call true AGI. One ARC study improved results on puzzles with just 2–7 solved examples per task, using variations of those examples as extra training material. The updates can be temporary or retained, depending on the setup.

An ARC-AGI puzzle: two solved examples show a rule, and the model must apply it to a new input. In test-time training research, the system uses solved examples and variations of them to update task-specific weights before answering. The missing answer is not used for training.

Can this generalize? Can this be done at scale? Those are still open questions. But if we crack it, we would have another way to improve models and potentially make repeated work cheaper.

Beyond LLMs

So far, I’ve focused on LLMs, but they are by no means the only AI out there. World models learn how the world changes and can simulate what happens after an action. TypeSafe’s Jev focuses on classification and decisions with an AI that is too cheap to meter. Then there are neuro-symbolic systems like AlphaGeometry 2, which combines a language model’s suggestions with mathematical rules to find proofs.

These approaches, and many others, could help continue pushing performance in two ways: some could eventually replace LLMs, while others could work alongside them and make the overall system more efficient. I have no idea which approaches will take us furthest, but continued AI progress does not depend on LLMs being the final answer. And that is the core of my argument: there is a lot of room for growth, even though what we have today already seems quite good.

AI can complete increasingly long software tasks. The chart measures task length by how long a human expert would take, at a 50% success rate for the AI. Shown on a linear scale.

Source: METR, updated May 8, 2026, before Astra’s release.

Why does this feel so far away?

The reality is that even if you agree with all the above, estimating where the world is going to be five years from now is impossible. We humans are terrible at understanding exponentials. Even if we grok what scaling laws mean and think we can see the compounding nature of AI progress, imagining what much more capable models would let us do is an extreme example of the gap of imagination.

In all honesty, I spend 24/7 thinking about and working with AI, and I have no idea what models being 100X better than today would mean in practice. (Small confession: I actually have a nerdy strategy nervous breakdown every time a new generation of models comes out. What should my first question be?)

That difficulty in imagining what comes next is part of why I think we keep having conversations about AI progress stalling every 2–3 months (although acceleration is now so obvious that it has been a while since the last one).

And it doesn’t help that the progress we can see is so uneven. Models can propose solutions to Millennium problems and yet fail at stupid brain teasers. Those failures tend to go viral and reinforce this short-sighted view of model performance. I think we also hold AI to a higher standard than humans, treating a silly mistake as a verdict on the whole model. Their abilities remain uneven, even as they outperform humans in more and more areas.

AI can outperform humans on some tasks and still fail at seemingly simple ones. The frontier can keep expanding while remaining jagged. Conceptual illustration from Ethan Mollick, One Useful Thing.

So, to conclude: AI is not stalling. Progress is accelerating as the different ways to improve AI reinforce each other. Even with all that, we don’t see it, and most people are still using Luzia, ChatGPT, and Muse as search engines. In other words, AI usage is a mile wide and an inch deep.

An analysis of Luzia queries over three years showed almost no increase in the complexity of users’ first conversations, and I have seen similar data from other labs. All this while Astra is solving math problems that have never been solved before.

The good news is that there are endless problems to tackle in the world, from silly ones to existential ones, and so far we have been constrained by intelligence. What will the world look like when we eventually have virtually infinite intelligence? Again, no idea, but it looks pretty cool.

I am in the camp that thinks internal reasoning is real and gives us another dimension for making models more powerful, and I will explain why, but as always, I find the implications the most interesting part.

Disclaimer: this is likely going to be one of my long posts that I use to really understand a concept. If you want to get to the conclusion, just skip to the implications.

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What does it mean that a model is thinking?

Before o1-preview came out in September 2024 (it feels so long ago), the main way we made models more powerful was to give them more capacity, more data, and more training compute. That still works today; it’s one of the scaling laws, and it is a big part of why labs keep building larger training clusters.

Then o1 showed that giving models time and space to think - instead of spitting the next token, they used those tokens to prepare a response - improved their accuracy. This is what we usually mean when we talk about reasoning models. OpenAI’s original release showed performance improving with both training compute and time spent thinking.

Source: OpenAI, Learning to reason with LLMs, September 2024. The chart on the right shows the benefit of more compute at test time.

That extra compute can go into a longer attempt or into several attempts that the system compares. I think of these as vertical and horizontal scaling, often called sequential and parallel test-time scaling. Vertical means giving the model more time to work through a solution, much like a person spending longer on a problem.

Horizontal, on the other hand, means trying several possible solutions and comparing them. Exploring different branches of the problem, trying new things. Those attempts can run in parallel (for example, through sub-agents), although trying another approach can also happen within one long chain of thought.

Since then, longer attempts and more attempts have become familiar ways to improve reasoning systems. With Astra, I suspect more of that work is happening before the next token is produced.

External vs. internal thinking

When teachers at school taught my daughter to read, they taught her to read with an “external” voice and an “internal” voice. Out loud, or in silence (bear with me).

o1-style reasoning works like that external voice. For a model, thinking out loud means producing tokens, the small pieces of text that make up its words. Prompt the model with a problem, and it starts spitting out tokens. Some go into planning (“To solve this problem I should…”), some into calling tools (“call web fetch to look at this document...”) and gathering evidence (“read document Y”), some even into checking its own work (“does this make sense?”). Then, when the model is comfortable (or hits the limit we introduced), it starts producing tokens for the final answer.

Teaching models to do this means training them to find useful ways of reaching an answer. Pre-training gives them a foundation; reinforcement learning can reward strategies that lead to better solutions.

Because this thinking is expressed in text, whoever has access can read it and look for problems. That helps us correct, interrupt, or limit what the system does. That also helps us build the infrastructure (harnesses in the industry jargon) that makes a model an agent. The text has never been a complete view of its computation, but it gives us something to inspect.

Introducing internal thinking

I have not found confirmation from OpenAI that Astra uses an internal reasoning loop. This is my interpretation of the evidence.

Going back to my daughter’s reading lessons, internal thinking would be closer to using the internal voice. The model could work through intermediate steps in its own computational state, without turning each one into a token. That is what I mean by internal thinking.

More technically, one way to do this is to let the model spend compute time in a loop through some of its inner layers. The trained weights can stay fixed while the information passing through them changes. This is similar to giving the model more layers to work through, except that it reuses the same layers, which theoretically should allow it to improve performance while reducing memory consumption (an expensive commodity nowadays).

There is research on recurrent depth from before Astra. The question is how far a frontier model can take it.

With this kind of internal thinking, a model can improve its performance while maintaining or reducing the number of tokens it produces. Which is my first clue about Astra.

In OpenAI’s release results, Astra scores 59.3% on Agents’ Last Exam, compared with 55.5% for Claude Opus 5, while using approximately 65% fewer output tokens at those settings. Better results, substantially less text. That fits the idea, although it does not establish that an internal loop caused the improvement.

Aside: does using fewer tokens mean the model is cheaper for OpenAI to run? My intuition tells me internal reasoning makes tokens per successfully completed task a less reliable measure of the work. Internal computation still needs energy, so joules consumed per token could change quite a bit. Fewer tokens alone do not tell us how much the lab saves. Relevant to you? Probably not, but fun to think about.

The second piece of evidence is more striking. In the Astra system card, OpenAI reports that Astra can complete substantially harder tasks without chain of thought, including tasks where reasoning cannot be moved into tools or a long answer. UK AISI measured a no-CoT math time horizon of 30.9 minutes, versus 3.6 minutes for Sol, about 8.6 times higher. Those minutes measure task difficulty in human-time terms; they are not the model’s thinking time.

So, is internal thinking a new thing? It depends. Models produced useful answers before we called them reasoning models. If a model answered 10281² correctly, had it seen that calculation in training, or did it know how to calculate? One answer cannot tell us. My intuition is that there has been a bit of both memorization and generalization.

What I suspect is new at this scale is a more deliberate effort to get useful reasoning out of internal computation, with less dependence on writing the process down. And some of the implications are scary.

What are the implications?

As we established, with external thinking, the model produces intermediate tokens before the final response. With internal thinking, it works through changes in its computational state without writing out each step. Both can work together. The question is what happens as more of the work moves inside.

Two big implications: safety and harnesses.

For safety

Let’s start with the scary one. One useful way to monitor reasoning models has been to analyze their chains of thought. That can give us evidence that a model is trying to deceive us or is finding a way to pass a test without doing the intended work. OpenAI has shown this with reward hacking, where models learn to exploit the way their performance is scored.

If fewer of those steps become tokens, we have less to read. How do we tell whether the model made an honest mistake or deliberately gave us the wrong answer? We can still check actions and results, but the explanation was another useful source of evidence.

The system card reports weaker CoT monitorability for Astra across most tested lengths, although action-only monitoring improves. It also reports stronger covert sandbagging, meaning deliberate underperformance, when the model is explicitly instructed to evade detection. OpenAI says, “we would likely be unable to catch it reliably.” That is a prompted test, not evidence that Astra spontaneously does this in normal use.

To me, training models to reason more internally while our ability to monitor that computation lags behind is a risky endeavor, especially given how powerful these models already are and especially since we do not seem to have found an asymptote for performance.

If more of the reasoning happens inside the model, our monitoring needs to reach further inside too. That is why mechanistic interpretability becomes more important: studying the model’s activations and internal mechanisms to understand how it reaches an answer. Having access to those signals does not mean we know how to read them, but we need to get better at it.

For harnesses and competitive advantage

The same visibility problem matters when you are building an agent. Your harness has to manage the model’s work, but it can only use the information the provider makes available. There was already a gap here before Astra.

In the early versions of OpenAI’s reasoning models, the raw chains of thought were hidden from users. The o1 release listed user experience, competitive advantage, and monitoring among the reasons. The model could be “thinking out loud” in tokens while we only saw a summary of what it said. Keeping those chains private also protects useful training material. Distillation means using a larger model’s outputs to train a smaller one, which is why access to good reasoning examples has commercial value. DeepSeek-R1 and its distilled models showed how valuable that could be.

As reasoning models became more capable of using tools, model harnesses became the thing (to the point that everyone is building their own!). A harness is the framework around a model that helps it work toward a goal: which tools it can use, what context it receives, how it gets feedback, and when to return control to the user.

OpenAI’s explanation of the Codex harness describes how this surrounding software manages the interaction between the user, model, and tools.

Getting a task done properly depends on how well that framework manages the process. It can monitor tool calls, check results, and control permissions. Where reasoning text is available, it is an important source of information.

With more internal thinking, models can become more of a black box for those harnesses.

The ARC-AGI-3 results show how much the surrounding framework can matter. Astra’s best observed Semi-Private score went from 62.7% with the Standard harness to 99.9% with the Provider Adapter harness. The adapter, not surprisingly, preserves opaque reasoning state between requests and uses compaction to manage the conversation. Across the 167 game/reasoning pairs that both setups solved, it also used 49% fewer total tokens. Better performance and less text, from changing how the same model carries its work forward.

The same model, with different ways of carrying its work forward. Source: ARC Prize.

An external harness can use provider features to get much better performance. But preserving that opaque reasoning state, and looking into it, means AI builders play in their home court.

The lab controls the inference system and can potentially inspect signals that an external developer never receives. We can control what the model is allowed to do, but we may have much less visibility into how it reached that decision.

As has happened in the last two years, I expect open-weight models to bring more of this capability to everyone. But monitoring internal thinking will also demand code and methods for inspecting and controlling internal model states, something that is not trivial. In the meantime, the labs’ advantage can compound.

That said, I think Astra is a step change in models. I am excited about what it lets us do. But as it gets better at using its “internal voice,” I want us to get better at understanding what happens when we can no longer hear the steps.

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Don’t join them. This wave is the same movie as 2024 and 2025, when everyone shipped their own model (1, 2), and it will end the same way, in disappointment with AI and a ton of money wasted.

My goal with this brief post is to warn of the hidden risks and costs of doing your own harness.

I get the temptation, which in most cases come from cost control the inference cost. Cap the AI budget and give up part of what is there to win, or leave it uncapped and watch the bill grow faster than the value. Routing looks like the exit, one of those ideas that looks obvious (”I am spending too much, let me route”) and sounds great in a board meeting (”we built our own harness”). Let me explain why I keep saying no to be always playing with harness, routers and the likes.

First, the elephant in the room. If routing is so obvious that everyone noticed, think about who has the biggest incentive to do it. The AI labs spending hundreds of billions in capex. Their business depends on you staying inside their family of models, and that is exactly why they now are all shipping a family of models across the cost curve instead of one simple frontier model (sol, luna, terra, you say?) and as soon as they can they push to get frontier heavily discounter (80% off luna, you said?). The result is a price curve you do not control. Whatever your harness saves you today, the next price cut gives everyone for free.

The obvious counter is lock-in, and I get that too, nobody likes to depend on one vendor’s pricing (at least when we speak about AI, because the SaS business industry was based on that idea XD). But chasing independence has its own tax: devoting your engineering team to constantly testing new models. That is the corporate version of spending hours on YouTube watching productivity videos: you adopt PARA, GTD and a hundred and fifty tools, and then you realize the hours went into the system instead of the work.

Second, a harness will never be your differentiation. Building one is fun, it is educational, and it delivers nothing to your customers. Most teams do not have the skills for it anyway (doing the router part of the harness is sooo, sooo hard), and even the teams that pull it off converge on the same tricks (cache, route, distill, batch), which the labs bake into the API a quarter later. What you built becomes a commodity while you are still paying the team that maintains it. And maintain it they will, forever: every new model generation means prompts adjusted, configs adjusted, evals re-run, regressions chased. Your users do not open your app because your routing is clever. They open it because the product does something for them. The harness you open source with pride in August is the legacy system someone inherits in March, and that slows your company down in June.

You do not have to imagine it. Some companies are still running the proprietary model they trained in 2024 (including my beloved Spanish administration), frozen in time before reasoning models existed, or nursing their fine tuned Llama 4 because the sunk cost is too painful to write off. I see the harness version of this up close: friends at Amazon live, their internal harness, and when we compare notes on a normal working day, my Codex plus Cowork setup simply gets more done. Amazon does not lack engineers. Their harness moves at the speed of an internal platform team, and mine moves at the speed of two labs’ release cycles.

Third integration beats assembly, at least at this point of the innovation curve. The labs no longer just serve models through an API, they ship their own harnesses (Codex, Cowork), and they control the training run, so the model learns to work well inside their harness and the pair improves together with every release. The good external harnesses (i.e., Openclaw, pi) can only integrate from the outside. Yours tunes around behavior the labs get to design. You are not only competing with their engineers, you are competing with their training loop.

There is an exception, the company at real scale where inference is the true COGS. If there are hundreds of millions to save in inference, there is a real incentive, but even there the bar is higher than it looks. You need the scale, then you need the kind of talent that can actually pull this off, which is exactly the talent the labs are hiring away from you (with many millions in the job offers), and then you need the appetite to maintain it forever. Very few companies have the three at once. If you are not sure you are one of them, you are not.

Don’t get me wrong, building a harness is a great learning exercise, and I want my engineers playing with everything. But from learning to production there is a big gap, and learning should not interrupt shipping user value. Pick a lab or two, ride their cost curve, and spend your engineers on the product.

Three details are enough for this post:

• The models were supposed to solve a hacking benchmark. Instead, they reached the public internet and hacked a real company to steal the answers.

• Hugging Face reconstructed roughly 17,600 attacker actions. Most failed. The agent kept trying, went back to old paths and rebuilt tools when its environment erased them.

• The agents turned OpenAI’s internal package manager into a message board to share what they found. When OpenAI wiped it, they rebuilt it two days later using directory names as messages.

My goal for the post is to think about the broader implications for all of us. If an AI system with no human directing its individual actions can compromise Hugging Face, what happens when people point this capability at that mission on purpose?

This has nothing to do with a Terminator-type AI deciding to kill us all. The argument is much simpler.

Why hackers leave you alone today

No system is perfectly safe. If there is something to gain, somebody somewhere has an interest in attacking it. The targets they choose depend on the potential reward, the cost of the attack (which includes the chances of being caught) and how much time and capacity they have.

Think of a thief choosing a car. The value of the car matters, but so does how hard it is to steal. The thief has limited time, so a bright yellow steering-wheel lock may be enough to make them choose the next car.

Hackers make the same calculation. AI changes how many targets and attack paths have a reward higher than their cost. It lowers the cost of each attempt and removes much of the human bandwidth constraint. The value of what is on your phone stays the same. The cost of going after it does not.

When hackers stop being scarce and become very smart

With that in mind, here are my ten observations.

1. Capability will spread. What required an unreleased frontier model today will eventually reach bad actors, organizations and countries. The supply of capable hackers stops being the main bottleneck.

2. Attacks will become cheap, then very cheap. The Hugging Face campaign used an expensive frontier system, but the cost curve of intelligence has been collapsing for three years. I see no reason why cyber capability would escape that curve.

3. Sophistication will spread too. These attacks already go beyond what most software engineers without specific security training could reproduce or defend against. Agents can adapt after failure, build tools and chain several weaknesses together. People will soon be able to run attacks far beyond their own technical ability, if they cannot already.

4. Individuals become viable targets. Today most of us are protected partly because a skilled hacker has better things to do. Once a personalized, persistent attack is cheap, the photos, email, bank account or access to our employer may be enough of a prize.

5. Startups and ordinary companies become viable targets for the same reason. A company that was not worth a week of expert attention may be worth thousands of automated attempts, even if the prize stays small.

6. Software runs much more than Instagram and TikTok. Banks, hospitals, the electric grid, water facilities, cars and X-ray machines all depend on it. An exposed service or stolen vendor credential can turn a software compromise into a physical problem, and governments control many of the most sensitive systems. Governments are not famous for adapting fast. Yup, that is going to be a big issue.

7. Human defense does not scale against autonomous volume. You cannot answer effectively infinite teams of attackers by hiring effectively infinite security engineers. Defense will have to become autonomous too. This is a real challenge when AI-adoption is still “one mile wide, one inch deep.”

8. Access to the strongest defensive models won’t be widespread and will become political. The best AI is already moving behind national and company walls. Hugging Face tried to use Claude Opus and Fable to investigate the incident, and both refused much of the work. Attackers will use less restricted systems, while defenders may be blocked from using the best tools to understand what happened to them.

9. Law and liability will need to adapt, at least until it gets better. Companies should be liable when they make no reasonable effort to protect people, but the meaning of reasonable effort cannot stay fixed while attack volume changes by orders of magnitude. Otherwise we will bury victims in fines for failing to stop attacks no human team could have handled.

10. The transition will favor offense. Attackers need one opening, while defenders need to cover many of them. Patching, adoption and regulation all take time. Eventually defense should catch up, but the road to that new balance is the part that scares me.

What we can do now

The TL;DR of the above is that the old fundamentals of security are breaking. The supply of hackers is going up and the cost of hacking is going down faster than defenses are adapting. In simple words, your phone was not worth targeting because hacking an iPhone was too much work for what an attacker could get from it. Mass hacks and phishing were already real, but they were noisy and rarely personalized. When personalized attacks can be run by anyone, anywhere, nothing is secure. So with that framing in mind:

1. A useful rule from privacy training is simple: never put something in an email that you would not be okay seeing in the press the next day. We need a similar operating principle for security. Assume that at least for a while, you can and will be hacked, both as an individual and as an organization, then reduce the amount of damage one compromised account or system can cause.

2. You cannot scale human security to respond to fully autonomous attackers, period. You need automated loops from incident detection to remediation. In the Hugging Face part of the incident, the agent tried roughly 17,600 actions. Most failed. No human hacker would have the patience to keep going that way. Now you just spawn another agent. Run autonomous security audits now. OpenAI and Anthropic are already putting capable models to work finding vulnerabilities

3. Discovery matters because getting caught is part of the cost. A car left alone in a dark alley is more likely to be stolen than the same car on a busy street because the probability of being caught is much higher. The digital version is better monitoring, alerts that someone actually sees and fast reporting. An attacker that knows you will notice and respond has to price that into the attack.

4. We need the cybersecurity equivalent of the yellow steering-wheel lock. The lock is yellow so the thief sees it and chooses the next car. I do not know the exact digital equivalent yet, but we need ways to make the cost of attacking us visible before an agent invests thousands of attempts.

Why I think it will eventually get better

I am not a security expert, and this is the most pessimistic post I have written. My conclusions are directional, but I hope they make the problem harder to ignore. I normally find an optimistic angle. This time I only have two:

First, it is good that this happened while the strongest capabilities are still relatively contained. The best outcome is that this post eventually looks as overblown as the warnings about Y2K, because enough people did the work and there is no counterfactual to show what would have happened if they had done nothing.

Second, I know we will reach a new equilibrium in which the old balance between the cost and reward of hacking works again.

Things will get worse, much worse, until they get better.

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On a Friday, Anthropic was given about ninety minutes to comply. Washington had decided that Fable, the company’s most capable model and one that is REALLY cool, could no longer be touched by foreigners. Not foreigners abroad. Any foreigner, including the people who work at Anthropic and happen to hold the wrong passport. Researchers at Amazon had found a way around Fable’s safety limits to reach its hacking abilities, and instead of patching the hole for everyone, the government shut the door on everyone who isn’t American. Anthropic took the model offline while it figured things out. At least that was the official story.

The same thing happened a few days later with GPT 5.6, which launched only for a short list of “trusted partners.”

Officially, this is about safety. The reasons nobody says out loud are simpler. One is to delay China and everyone else from copying a top model to train their own, which the industry calls distillation. The other is to keep America’s lead while it still has one. Take away the safety language and what is left is a wall, with the best models on the inside and the rest of the world on the outside.

That ban has already been lifted, which, as I will get to at the end, proves the point in this blogpost more than it undoes it.

We’ve been here before

Not the first time. In the 1990s the US government tried to ban strong encryption. Encryption is the math that scrambles a message so only the right person can read it. It is what keeps your bank login and your WhatsApp chats private.

Back then Washington treated that math as a weapon, the same legal category as a missile. When a programmer named Phil Zimmermann released PGP, free software that let anyone encrypt their email, the government opened a criminal case against him for “exporting munitions.” His supporters printed the code on T-shirts and inside a book, because a book is protected speech and you cannot un-publish math. By 2000 the government gave up, and losing turned out to be the best thing that could have happened. Once encryption was free and everywhere, the whole internet got safer, online shopping and banking became possible, and ordinary people got a kind of privacy that used to be a government privilege. The thing Washington tried to bottle up became the plumbing the digital economy now runs on, and the country that fought hardest to keep it locked gained the most when it spread.

The case for trying again with AI is not stupid, even if today it sounds a bit like a marketing stunt by Mr. Wolf-Is-Coming. A model that is very good at writing cyberattacks or designing a virus is closer to a weapon than to a word processor.

AI is a little different from encryption, though. The software still leaks (as Mark Zuckerberg keeps pointing out), a top model fits on a thumb drive, but training one from scratch takes rare and expensive hardware. So a government can make it slower and more costly for everyone outside, and pick who falls behind. That buys time and chooses winners. It does not stop the technology.

How much that matters depends on one thing I will come back to, whether AI keeps getting cheap or the very best pulls away into its own league. In industry jargon, whether AGI is coming.

Who is impacted

Start with the obvious loser. AI labs make money selling their models to the world. Tell them that shipping the model triggers a government lockdown, and for sure they will ship less, or won’t ship at all. The logic of the ban is shaky too. If the model were truly that dangerous, you would block Americans from it as well, because an American can do damage just as easily. Blocking only foreigners is not really safety. It is the chip-ban playbook, the same move Washington already ran on advanced chips to China, aimed now at software, and like those chip bans, it mostly teaches your rival to get good without you.

Then there is everyone else. Normally a country that cannot build the best models just uses them, the way you use electricity without owning a power plant. That works only while the supplier is reliable. The moment the US shows it will cut you off on a Friday afternoon with ninety minutes’ notice, every other country has to assume it could be next, and starts building its own worse version, because you cannot run your economy on a supplier who might pull the plug without warning. The problem is that with continuously improving capabilities, not having the latest model, even if only temporarily, could mean the difference between unblocking an insane amount of value or getting stuck. Let’s go back six months into the past. In November 2025 we were on the brink of software engineers becoming 10x, 100x more productive, just a simple model update away. If that model update were only available to some companies, we would not have earned all those gains in the last six months.

Europe is one of the most impacted. We are trying to innovate (Luzia, ahem), with no top model of our own, not even one competitive with Chinese open source as of this writing. We are the most exposed to a cutoff, and the most likely to find that “just use the American one” has quietly turned into “build your own or pick a new patron.” And a patron is waiting. If Washington will not sell and Beijing will, the world drifts toward Chinese models. This is Made in China pointed at AI, something I flagged a while back. The stated goal was to contain China, and the method ends up handing them the floor, the mass market of everyday users. More of that usage means more acceleration, which makes containment harder still. I am not saying that is a bad outcome, just an ironic one.

Do these bans really matter?

The wall only works if what is locked inside is clearly better than what the rest of the world can get cheaply. Whether that is true is the real question.

Fable is really good. Not just good, really good, a step-function change. But how useful the ban is does not come down to the model’s capabilities in absolute terms. It comes down to relative position: how far ahead Fable is of the next best option, and whether, for your particular use case, the model is good enough.

And today, the bar is lower than it sounds. Fable does not have to be the single best model on earth. The ban does its job as long as it widens the gap between the locked top and the open models everyone else builds on, today or six months from now. A wall around a slightly better model still leaves the outside world a step behind, and a step behind has a way of growing.

The best AI goes from rare to ordinary the same way every technology does (differentiation then commoditization). A new thing starts out differentiated, owned by whoever built it first, and then it commoditizes: it gets cheap, everyone has it, and the advantage slides from the people who make it to the people sitting closest to the customer. AI is running that loop faster than anything before it.

AI is not one thing improving at one speed. For the everyday stuff most of us use it for, it keeps getting cheaper and good enough, and the free open models are only months behind the best paid ones. For a few high-stakes things, like hacking, designing viruses, or building better AI itself, the very top might pull away and stay ahead. So the likely future is cheap, plentiful AI for almost everything, and a small locked vault for the handful of things that decide real power. The wall matters as much as that vault does, and no more.

Two things stand between that vault and a permanent American monopoly. The first is China (and other competitors, but let’s be honest, today it’s really only China), which the ban only strengthens: lock the world out of American models and you push it toward Chinese ones. The second is that AI might simply stop getting much better. If progress hits a wall, the locked models stop pulling ahead, the open ones keep closing the distance, and the lead behind the wall quietly stops being worth guarding (and today there are no signs of this happening at all).

The second wall

There is a second wall going up, and this one needs no government. A company with a model good enough to do real work has a choice. It can rent you the model, or it can use the model itself and sell you the result. As the models get better, especially if we start putting government restrictions around them, the second option wins more often. A firm that could rent you an AI lawyer will likely make more money using that AI to sell you the legal work directly. Companies exist because some things are cheaper to do in-house than to buy. AI makes a huge amount of work cheap to do in-house. So companies pull more inside, the best AI stays behind their doors, and what reaches you is the finished service, never the engine that made it.

I made the hopeful version of this argument in an earlier piece, “AI Is the Org”: inside a company, AI pushes people up a level, the way the spreadsheet pushed accountants up instead of out. This is the dark side of the same idea. Inside the company, a person moves up. Outside it, the rest of us move down, into the seat of a customer who only ever sees what comes out of the box. You keep getting cheaper magic, and you slowly lose the ability to make the magic yourself. Preventing models from reaching the market makes this scenario even more likely.

Is it worth it?

So the wall is a bet. For it to pay off, two things both have to hold: the lead has to last, and the game must not change underneath it. History has been rough on both. The strongest defense is that a year or two of lead buys time to make this technology safe before a reckless race makes it dangerous, and that is worth taking seriously. But it needs the lead to be both controllable and long enough to matter, which is exactly the bet the encryption wars lost, and every jump to a new kind of computing lost before them.

So America pays now, with a slower industry at home and allies drifting toward Beijing, for a lead that only pays back in the one future where it was going to win anyway. The specific ban may have gone, but unless something big changes, this is the new direction. The best AI is going private, behind national walls and company walls, and the rest of us are ending up on the outside, buying whatever comes through the slot.

For somewhere like Europe, this becomes a brutal little bet. If the wall ends up not mattering, building your own models was wasted money. If it does matter, being stuck outside the garden means being cut off from the technology that sets the next few decades, and that loss has no floor. When one side of the bet is some wasted budget and the other is permanent irrelevance, you build. One of those Pascal’s wagers I love. The asymmetry, not the odds, is the argument.

You can lock up today’s AI. You cannot lock up the one that comes next.

Postscript

I had this written and scheduled. The morning it was due to go out, the ban lifted (and then life got complicated and took me 48 hours to re-adjust)

Fable is back, worldwide, and the reason it came back is the argument I tried to make here. The capability that set off the panic was never unique. Anthropic’s own testing showed that Opus, GPT-5.5, and Kimi, a Chinese open model, could all find the same flaw. The wall had gone up around something that was already on the other side of it.

So this round was a false alarm, but a telling one of what’s to come. The models do not stop getting better (rumour is that Fable 6 is already available internally), and the next one will not be a false alarm. Fable was the drill. While everyone argued about the drill, the machine got built: an executive order, government sign-off before a model ships, an industry system for scoring how dangerous a jailbreak is. None of that machinery cares why it was built. A switch installed for safety works just as well for trade, and a switch that exists gets flipped. The ban was temporary. The machine is not, and it is waiting for the model that actually earns it.

The analogy is doing something useful. It makes tokens legible as an input, not as a product. For two years the labs have been describing what they sell as “intelligence” or “AGI” or “the smartest model,” all of which are product framings that imply you should pay for the thing itself. The utility framing flips that. Tokens are an input. You consume them to produce something else, something that normally would have required some cognitive work. That is the right intuition, and once you take it seriously the economics of AI look different than the labs have been telling us.

The analogy also carries baggage that does not fit.

What makes a utility a utility

Utilities have inelastic demand. You need electricity to keep your fridge running. You need water to live. You do not shop for kilowatts on Tuesday morning. You pay whatever the bill says, and you keep paying because there is no version of your life where you stop. The product is not amazing, you simply cannot say no.

The other thing that makes utilities work is that the commodity is fungible. A kilowatt-hour is a kilowatt-hour. Solar, coal, nuclear, hydro, it makes no difference once it reaches your meter. The meter is meaningful because both sides agree on what the unit is, and the unit is the same regardless of who supplied it. That fungibility is what makes the wholesale markets work, what lets the regulators set rates, and what makes the entire utility business model legible to investors.

Tokens are not like that.

A token from GPT-5 is not a token from Claude 4.5 is not a token from DeepSeek V4. Different tokenizers literally cut the world into different units. A 1,000-token answer from one model is not the same workload as a 1,000-token answer from another. The same task can take 800 tokens in one model and 4,500 in another, at radically different quality. Ten words from Richard Feynman are not the same as ten words from a corporate lawyer. The count is identical, the value is not. There is an underlying hidden metric to the token that represents the actual value per token, and the bill does not show it. The unit, on its own, tells you nothing.

The utility framing depends on the unit being legible. The unit is not legible.

Tokens are a factor of production

The useful thing the utility framing does is name what tokens actually are. They are an input, the same way crude is an input to a refinery. You buy them, you transform them, you sell the output. The token is not the product. The token is the raw material you use to make the product.

This is the frame I have been working through for a while, including in the differentiation-then-commoditization piece a few months ago. The core dynamic is per-JTBD, not per-model-release. For any given job that AI might be able to do, there is an exploration window during which the question is whether AI can do it at all. In that window people will pay premium prices to find out. They will use the frontier model for the translation prototype, the customer-support agent prototype, the contract-review prototype. The premium is rational because the question being answered is binary. Does this work, yes or no.

Once the answer is yes, the question changes from “can it work” to “what is the cheapest input that clears the bar.” That is when the JTBD commoditizes. Capability unlocks new JTBDs, and that unlock window is real. But every specific job that crosses the works-doesn’t-work line commoditizes fast inside its own track. Nobody uses GPT-5.5 for translation in 2026. Translation works on a much smaller model that runs at a fraction of the price, and the procurement decision is made on cost.

This is what makes the utility framing partially right. In the commoditization phase a given JTBD really does behave like a commodity input. Multiple providers, real substitutability on the outputs that meet the bar, a buyer who shops on price-per-outcome. That is commodity behavior. The token in that phase is meaningfully a factor of production, not a product. The utility analogy gets the input nature right.

It misses everywhere else.

The phase the consumer market is already in

A lot of the common consumer JTBDs are already in the commoditization phase. The capability is there, the optimization question has been answered, the frontier does not earn a premium for them anymore. This is the capability overhang I have been writing about in Gap of Imagination. The frontier keeps building capability that ordinary users cannot even imagine an application for. The premium that flows to the frontier today is partly flowing because the labs keep redefining the JTBD bar, not because users are running into capability ceilings on the things they actually want done. In simple terms, you give people an advanced agent and they ask for a selfie.

Demand keeps moving to the frontier because the labs need it to. Sam saying “the demand keeps surprising me, people always want the smartest model” is a true description of the demand curve and an incomplete description of why it looks the way it does. There is a world in which most JTBDs reach the commoditization phase and tokens behave like a real commodity market, priced near marginal cost with arbitrage running through everything routine. In that world there is still a tier of work where the value per token is enormous and the bill barely registers. Curing a category of cancer is the canonical example, the kind of problem where the answer is worth so much that asking how many tokens it took is the wrong question. Pricing a derivative nobody else priced correctly, discovering a material that does not exist yet, finding the next class of drug. Those JTBDs do not commoditize the way translation did. The labs and companies that learn to direct token spend at them are not playing the same game as everyone else routing the cheap stuff through Claude Haiku.

The labs know this, which is why their biggest product bets are not at the model layer anymore.

What this means for AGI labs

If tokens are a factor of production and the JTBD-by-JTBD commoditization is real, the closest historical analog for the labs is the oil majors. Large by revenue, thin by margin, with the value compounding through volume rather than through pricing power. ExxonMobil is one of the most consequential companies of the last century and its operating margin per barrel is unremarkable.

This is not bad news for the labs. It is just a different kind of business than the utility pitch implies. I wrote a few weeks ago in Of AI Bubbles and Token Explosions that token demand in the agentic era is going to grow by several orders of magnitude. Jevons paradox plus zero human bottleneck plus collapsing per-token cost compound into a demand curve steeper than almost anyone has modeled. The more I use and deploy agents the more I think this is true. I am already burning hundreds of millions of tokens every day, and the reason I do not run even more agentic flows is cost and time, not a shortage of use cases. That will happen to the rest of us. The build-up is justified. The labs are going to be enormous companies. They will be enormous in the way oil majors are enormous.

What they will not be is high-margin at the model layer. The surplus per task completed with tokens, which is the spread between marginal cost and marginal product, accrues to whoever owns the relationship with the user. The labs build the volume side. The application layer captures the spread.

Which is exactly why every major lab is climbing up-stack as aggressively as they can ship. Operator, Codex, Atlas, Sora, the apps platform, ChatGPT for Business. None of these are model-layer products, and for sure they are not what you would expect from a token factory. All of them are bets on owning the application layer where the surplus lives. The investor pitch is utility-shaped because utility-shaped capex needs a utility-shaped story. Sam’s repeated “intelligence is a utility” line is positioning, not analysis. The actual business plan is to capture the surplus up-stack before somebody else does. Right now, that front is enterprise. The utility pitch is the financing strategy. The aggressive up-stack push is the business strategy. They are not the same thing, and watching the products instead of the press release is the only way to see which is real.

What this means for companies buying tokens

If you are on the buying side of the meter rather than the selling side, the production-input frame has direct implications.

Almost every AI subscription you pay for today is subsidized. ChatGPT Plus, Claude Pro, the Bedrock and Vertex offerings, all of them are priced below what the actual workload costs to serve (the difference shows up on someone’s balance sheet, just not yours). The labs are eating the gap to acquire user habit, lock in workflow integration, and prove a category at scale. You are paying $20/200 a month right now because someone with capital wants you to.

That subsidy ends. The path is visible. OpenAI has no ultra developer plan, they want you on the API key (the meter). When the subsidy actually ends, every company that grew used to a $20-per-seat AI bill is going to discover that the real cost of the workload is several times that, and the convenience tax was the lab eating the difference to keep them locked in. That is also the moment tokens become legibly what they always were. A production input, with a price that has to be procured for instead of subscribed to.

The value of the tokens is very high, don’t get me wrong, which means that the companies that survive that transition are the ones who built denominator discipline early. Your token bill alone tells you nothing. Tokens per resolved ticket, tokens per closed sale, tokens per generated brief, those are the units that matter when the meter starts charging the real number. The same applies on the supplier side. The lab that costs ten times more per token is not necessarily ten times more expensive for your workload, and the only way to know is to have the denominator.

Two things to be working on now, while the era is still subsidized. First, build for swappability. The provider you start with should not be the provider you end up locked into. The orchestration layer or harness between your product and the model has to be thin enough that swapping suppliers is a configuration change, not a refactor. Every prompt format, tokenizer assumption, or function-calling convention you embed deep in your code is a switching cost the supplier collects later. Second, track outcomes per dollar, not tokens per request. Build the denominator now, while the bill is small and the room to instrument is generous.

When the subsidy ends, the companies that did this work keep their AI cost lines healthy. The ones that waited discover they spent two years embedding one supplier into the foundations of their product, and the bill is now arriving at the unsubsidized price.

The labs’ incentive is to make the meter spin faster. Yours is to extract the most outcome per unit of meter spin. Those two interests are not aligned, and that is what the utility framing is hiding.

The labs are commodity producers learning to live with commodity economics. The companies buying their output are consumers of a production input who have not started procuring it like one. Sam Altman’s utility framing is half-right about the thing that matters most. Tokens really are becoming a factor of production, and that framing is the cleanest one anyone in his position has put out loud. The half he had to leave out is that factor-of-production economics are not utility economics, and the businesses that grow on each side of the meter will be very different from what the pitch deck suggests.

Tokens are not kWs, at least for most of us.

What a company is

Let’s put on the MBA hat for a sec. My favourite place to start is Ronald Coase, 1937. Why do companies exist? In a free market, every task could in theory be a contract between strangers. The answer Coase came up with, and won a Nobel for decades later, is that markets are not free. Every transaction has a cost. Finding someone, negotiating, writing the contract, monitoring it, enforcing it. A company exists because, up to a point, doing the work inside is cheaper than doing it through the market. The size of the firm is set by where those two cost curves cross.

That definition treats the company as a black box. Inputs go in (capital, signal, time). Profit comes out. The org is the function. The people inside are an implementation detail that has been remarkably stable for two thousand years.

Why the box was full of people

For most of history, the box was full of people for one reason that has nothing to do with hierarchy, politics or the reason of the company to exist. Companies needed decisions to transform the inputs into benefits. Decisions need intelligence, and until very recently, the only place we knew how to source intelligence was inside a human brain. So humans were inside the box, both deciding and acting. The hierarchies that grew around them (Dorsey and Botha trace the lineage from the Roman contubernium through the Prussian general staff to the American railroads) were the cheapest known way to coordinate brains across distance and time. What is different now is not that this work was unnecessary. People were the only substrate that could do it. What changes is that a second substrate exists, and the interesting question is what happens when the two combine.

What is changing

Some decisions can now be made by an agent, often well, sometimes badly. Some actions can now be taken by an agent, often well, sometimes badly. The “often well” side grows every model release.

That changes the question we have to ask. It is no longer “do humans need to be inside the box at all.” It is “which decisions and which actions are now better to do with AI than with people.” Every company will have to answer that item by item, for years. Dorsey’s bet, the smoke that started this post, is that eventually the answer for almost everything is AI. Maybe he is right. Maybe he is overcorrecting because he just cut his company by 40% and needs the story to land. I do not know. What I do know is that the question itself is real, and the answer for any specific decision or action is rarely “none.”

Maybe an inversion

If most decisions and most actions inside the box move to AI, the position of the human inside changes. We used to be the operator. Spreadsheets, computers, every tool we built were extensions of us. We picked them up and put them down. Now, maybe, it is the opposite. The AI is becoming the operator, and the people inside the box are increasingly what it reaches for when it needs to do something it cannot do itself. The spreadsheet did not put accountants out of work. It moved them up one level. AI may be doing the same thing to us, one more level up.

What is still lacking

For the Dorsey version to actually arrive, four things have to become true that today are not.

First, the part of human work that is not strictly about intelligence: customer trust, ethical intuition in situations that are genuinely novel, the kind of judgment that only forms after years of skin in a game. Some of that is real signal an AI cannot generate on its own. Some of it is noise the org is better off losing (politics, taste-as-bias, the loudest voice in the room winning the argument), and a more AI-shaped org is honestly an improvement on the noise side. Both halves are true.

Second, and this is the bucket that matters most. Even a smart model only makes good decisions when it has the same context a human in the same role would have. Humans pick that context up everywhere: meetings, side conversations, Slack threads, customer calls, watercooler conversations, social cues, the unspoken stuff that lives in someone’s head from five years of doing the job. The model has none of it by default. So our new job, the part of human work that does not go away even when the intelligence is automated, is to gather context from across the company and make it legible to the machine. This is the part Dorsey makes look easy because Cash App and Square hand him a continuously updated picture of his customers for free. Most companies do not have that. They have a CRM, a few dashboards, and if lucky a wiki nobody updates. Building the layer that turns the messy reality of an organization into context the model can act on is the actual unsolved problem. It is the reason we created the org brain @luzia. Anyone who has tried to wire a model into a real business knows the model is the easy part now. Context is the hard part.

Third, agency under accountability. AI does not go to jail, and it does not lose a relationship the way a name on a contract does. Somebody human still has to be on the hook when something consequential breaks, and that gates how much agency an AI can have, regardless of how capable the model is.

Fourth, the objective. If you take the people out of the middle of the org, somebody has to write down what the org is optimizing for. The honest answer is the board. Boards already set the objective for human CEOs. In an AI-shaped org they would set it more directly, with fewer layers of interpretation in between. That is a clarification of where governance lives, not a limitation.

None of these are dealbreakers, and all of them are real.

There is a fire

Dorsey is probably more right than he was a year ago, and probably less right than he claims today. The honest way to read his essay is as a question. What does a company actually look like once intelligence stops being gated by a human brain? That exercise is, if you let it be, an optimistic one. Most companies get to rethink their fundamentals once in a generation, and this is one of those times. Done carefully, it produces two lists, one of decisions and actions to start handing to the machine, and one of places where the human role should grow. The companies that prepare will build both. The companies that do not will end up with one or the other. The smoke was Dorsey’s essay. The fire is the new shape of the company, and what is in that shape is a choice.

In April 2026 Meta ran an internal leaderboard called “Claudeonomics” where 85,000 Meta employees competed on token consumption. In 30 days they burned through 60 trillion tokens, a number that means nothing without context. At Anthropic’s public Opus pricing, a 50/50 input-output blend comes out to roughly $15 per million tokens. That would imply around $900 million a month, or $10.8 billion annualized, about 5.4% of Meta’s 2025 revenue. Call it a rough order of magnitude. The Information reported it on April 6, 2026. Two days after the story broke, the dashboard went down. Meta later said the employee who built it took it down “at their discretion.” That disappearance is the story.

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A raw token leaderboard measures who burns the most tokens, which isn’t the same as who ships the most value for the company. When the number on the dashboard is tokens, the top of the leaderboard is the engineer who never thought twice before running Claude on a whim, and the bottom is the engineer who thinks too hard about cost. Sure, tokens and productivity correlate up to a point, but at some scale the correlation breaks the same way lines of code broke as a proxy for programmer output once engineering developed higher abstraction coding languages.

Every organization using AI is sitting in a version of this right now. The bill is climbing faster than anyone expected, engineering is the early adopter, the rest of the organization is a couple quarters behind. The first instinct is to measure, and the measurement makes things worse, because the measurement has no denominator.

We need a denominator

If I told you a restaurant spent $20,000 on ingredients last month, you couldn’t tell me whether it had a great month or a disaster. Paired with meals served, the same number is a cost per plate. Paired with revenue, it’s food cost as a share of sales. Tokens on their own are the ingredient bill, and without a denominator the bill tells you nothing except that you spent money.

Tokens per PR merged, tokens per bug closed, tokens per support ticket resolved, tokens per qualified lead, tokens per feature shipped, tokens per paying user. Pick the metric the team already cares about, divide by it, and the number starts to say something. At Luzia we haven’t figured this out yet. I’m personally looking at tokens per PR shipped, tokens per feature closed, and even tokens per incremental retention. Some of the ratios are still too noisy to act on while others are pointing at obvious things we should have caught quarters ago. Trying to produce the ratio is what forces the question. A ratio you can argue about is more useful than a number you can only stare at.

Cap or don’t cap

Innovation and control pull in opposite directions here, and most people pick one and lose the other.

Cap usage and you kill the productivity gain. The engineers who figured out how to get 10x out of Claude did it by burning tokens on experiments, most of which went nowhere, and the few that went somewhere paid for the rest. Cap before you know which is which and you’re cutting the top of the curve while keeping the bottom.

Run it open and you’re writing million-dollar checks to Anthropic with no idea what came out the other side. That’s the Meta situation. Finance asks the question, engineering can’t answer it, and the conversation turns into a budget fight nobody wins.

You can spot the opposite mistake in founders bragging about tokens-per-employee in funding announcements, or, stranger still, in companies adding a token allowance to comp packages as if it were a perk. The tokens are spent on company work, so the “perk” is the company buying its own productivity and calling it a benefit. Both are the same leaderboard Meta ran, dressed up as marketing or HR. The theater works for one funding round. After that, investors start asking the tokens-per-what question, and the story collapses into the same conversation engineering had six months earlier.

You don’t cap, and you don’t budget per seat. You instrument. Six to twelve weeks of real use, logged against the right denominators, and by the end of it you know which teams convert tokens into outcomes and which teams convert tokens into nothing. This is not sophisticated. Tag the call or use separate API keys, name the team, point at the artifact where one exists, and let the ratios accumulate for a couple of months. Then you budget by outcome, not by seat. An engineering org that ships features gets more tokens. An engineering org that runs experiments nobody uses gets a conversation.

This only works if you start before the bill forces you to. Start after, and the budget fight is already happening, and nobody’s in the mood to be patient for three months.

2015, again

In 2015 every company had an AWS bill nobody could explain. Engineering owned the decisions, finance saw the number, and the number kept doubling. The conversation was the same one happening today, some version of why is this so high, why is nobody managing it, do we need to cap. A whole industry spawned around the problem, AWS implementation partners, cost-visibility vendors like Cloudability and Apptio, the Cloud FinOps Foundation, and the dedicated cloud architect role inside every mid-size company. The answer is what we now call FinOps. Instrument everything, tag every workload, assign ownership, report by team and by product. Five years of painful organizational change, and by 2020 cloud gross margin was a number every SaaS board tracked alongside revenue growth.

Tokens are the same arc on a faster clock. Eighteen months, maybe less. The companies that build the muscle now will be able to answer the board question when it comes. The ones that don’t will be writing down their AI P&L in 2027, wondering how it got away from them.

The unit is settling

Within 24 months, token margin is a number every board asks for by default, the way cloud gross margin became one by 2018. The companies that can answer “tokens per what” will optimize their spend and compound. The ones that can’t will be surprised by their own P&L, and the surprise will show up in the bill long before it shows up in the product.

A few patterns show up every time I watch a company try to build the muscle. The number survives only when finance and engineering co-own it, because whichever function holds it alone pulls it in its own direction. Finance looks at tokens without grasping the meaning. Engineering looks at tokens without paying attention to the cost. And the denominator has to be a metric the team already lives by, because inventing “tokens per PR” for an org that doesn’t already care about shipped PRs is a dead ratio by quarter two.

This week Jensen Huang told Dwarkesh Patel: “The input is electrons, the output is tokens. In the middle is Nvidia.” For your company, input is tokens. Output is whatever customers pay for. You’re the middle.

Stop asking “how many tokens did we burn.” Start asking “tokens per what.” If you can’t answer that for at least one what that matters to your business, you’re not running AI, you’re just pushing Anthropic’s run rate up and to the right.

Nobody got into the music business because they wanted to work in specialized light manufacturing. But for decades, the entire industry, its revenue, its power structure, its economics, was organized around pressing plastic discs and shipping them in trucks. The music was always the point. The plastic was never the point. The plastic was where the money was, though, and the plastic was the moat. In most cases, music was not what protected the business. Light manufacturing was.

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When the internet made distribution free, the industry almost collapsed. Not because the music died. The music was fine. The industry collapsed because the business had been built around the wrong layer, and a lot of talented people lost careers while the industry figured out what came next. The revenue model, the cost structure, the competitive advantages, all of it was attached to the plastic, not to the songs. And when the plastic became irrelevant, all of that went with it.

Then streaming asked a different question. Not “how do you replace the $15 CD?” but “what if you could have all the music that exists for $15 a month?” Revenue came back, and then some. The industry hit $29.6 billion in 2024, surpassing the CD-era peak, on a completely different model. More artists reaching more listeners than at any point in history. The music, the actual fruit, didn’t shrink when the plastic disappeared. It expanded. The music was the fruit. The plastic was the shit around it. And the shit was the only thing that died.

I think this pattern is going to play out again, across more industries than people expect, because of AI. And most of the conversation about AI and jobs is asking the wrong question.

The pattern

Not every business has this structure. Some are core all the way through. A Michelin-star restaurant, for instance: the moat is the chef, the experience, the food itself. There’s no incidental layer to peel off. The product and the infrastructure are the same thing. Those businesses are fine.

But some industries have a split that people inside them can’t see. There’s a core, the thing the customer is actually paying for, wrapped in a layer of shit that was never the point but ended up being the moat. The shit survived because it was expensive, not because it was valuable.

A platform shift doesn’t attack the core. It drives the marginal cost of the shit layer toward zero. When that happens, the moat dissolves. And if there’s real fruit inside, the fruit doesn’t shrink. It expands into territory it could never reach before, because the cost of the shit around it was the only thing holding it back.

The internet drove the cost of distribution to zero. AI is driving the cost of human intelligence, applied at scale, to zero. Same dynamic, different layer.

Photography. The core was always the eye, the moment, the story you chose to tell. The shit around it was enormous: darkroom chemistry, film stock at a dollar per shot, processing labs, printing, the entire technical apparatus that made photography a professional’s medium. You needed years of training and thousands of dollars in equipment before you could even start learning to see.

Digital zeroed all of that. The cost per photo went from a dollar to effectively nothing. And instead of killing photography, it created an explosion that would have been unimaginable in the film era. We went from millions of photos per year to billions per day. Professional photographers didn’t disappear. The ones who survived are more in demand than ever, because the only thing left to pay for is taste. The shit is gone. The fruit is all that remains.

And then the fruit did something the efficiency framing completely misses. It didn’t just expand. It mutated. Instagram, visual journalism as a daily practice, content creation as a career, the entire influencer economy, an $800 billion creator market that exists because taking a photo became free and infinite. None of that was a more efficient version of what came before. It was new territory that couldn’t have existed when each photo cost a dollar to produce. The fruit didn’t just grow. It spawned an ecosystem that made the original industry look like a rounding error.

What AI zeros

The internet zeroed distribution. AI is zeroing intelligence. Specifically, the kind of routine, repeated, human intelligence that many industries depend on at scale. Then, maybe, the rest.

I wrote about this dynamic in Differentiation, Then Commoditization: once a task crosses the reliability threshold, competition shifts from capability to cost, and the economics change completely.

Software engineering is the case I know best, because I live inside it. The core of building a software company is knowing what to build. Product sense, user understanding, the search for something people actually want. The shit around it, for decades, was writing the code, debugging it, testing it, deploying it. Expensive, slow, requiring teams of people for anything non-trivial.

That cost is approaching zero. One person can now produce what took a five-person team eighteen months ago. The result isn’t fewer engineers. It’s more software. Ideas that were too expensive to try are getting tried. Internal tools that no company would ever have staffed for get built on a weekend. The cost of building a first version has collapsed, even as the cost of distributing and scaling that product hasn’t, which is why funding rounds keep getting bigger while MVPs get cheaper. The money didn’t disappear. It moved from engineering to growth, from the shit layer to the fight for distribution.

The fruit is expanding visibly. The shit is disappearing. Same pattern as music, same pattern as photography.

Netflix is the one company I can think of that recognized this pattern from the inside and acted on it. They were a DVD-by-mail business. The shit layer was physical distribution: warehouses, envelopes, postal logistics. They saw that their own shit was going to zero, killed the DVD business before anyone forced them to, and rebuilt around streaming. The core (helping people find and watch stuff they love) went from a few million DVD subscribers to 260 million streaming subscribers globally. They burned their own plastic.

Finding the split

I think software is just the first AI-era industry where the pattern is obvious, because we’re living through it and the evidence is right in front of us. The question that interests me is how many other industries have the same structure and haven’t noticed it yet.

For any industry, there are a few questions worth asking. What does the customer actually value, and what is incidental infrastructure that exists only because it used to be expensive? If you could rebuild this industry from scratch today, with AI capable of handling routine cognitive work at near-zero marginal cost, what would you keep and what would you throw away? If the shit layer disappeared tomorrow, would the core expand into new territory (like music into streaming, like photography into the creator economy), or would there be nothing left? And the hardest question: is AI already making your shit layer cheaper, and are you treating that as an efficiency gain rather than a structural shift?

The pattern I described in The Gap of Imagination applies here too: most people inside an industry can’t picture what it looks like without the shit, because the shit is all they’ve ever known. The darkroom technician couldn’t picture Instagram. The record label exec couldn’t picture Spotify. The inability to imagine the post-shift world is what makes the shift feel like destruction rather than expansion.

Who captures the value

The pattern tells you what happens to the core. It does not tell you who profits.

When music went to streaming, it was Spotify and Apple who captured the upside, not the artists and not the old labels. When photography went digital, it was Instagram and Apple (the iPhone camera) who won, not Kodak and not the photographers. Whoever builds the new layer between the liberated fruit and the people who want it captures disproportionate value. Not the people who grew the fruit. Not the people who used to run the shit layer. The ones who build what comes after. I wrote about this in E-commerce After The Prompt, and the dynamic is the same across industries: in a world where agents handle discovery and transactions, the layer between supply and demand is where the value concentrates.

Netflix is interesting because they’re one of the few who managed to do it to themselves. They recognized their own shit layer, destroyed it, and built the replacement. Most incumbents don’t have that instinct. Most incumbents add more shit on top (their own chatbot, their own AI wrapper, their own “AI-powered” version of the same bad product) and hope the moat holds.

For AI, that replacement layer barely exists yet in most industries. The fruit is being liberated, industry by industry, as the cost of intelligence drops and things cross the reliability threshold. But the infrastructure that connects the expanded supply to the people who need it is still missing. The accounting firm’s clients don’t have an agent managing their books. The uninsured farmer doesn’t have a micro-policy. The small business that could never afford a lawyer still doesn’t have access to legal judgment.

The fruit has always been there. The shit was the only thing keeping it small.

In my last post I wrote about what it feels like to build with AI agents at full intensity. This one is about the number behind that feeling, and why it convinced me this isn't a bubble.

Last Friday I burned 800 million tokens in a day, or to make it into a tangible amount, I produced the equivalent of 1,600 Don Quijotes. A year ago, that would have taken months. Two years ago, I wouldn’t have attempted it.

The Staircase

When Luzia was five people meeting in a cleaning company in Albacete (long way from there), I drew a chart for our very first all-hands that explained our mission as a company.

The idea behind it is something I’ve been thinking about a lot lately, how most people can’t picture what AI can actually do for them, so they never ask for it. This chart was the first version of that idea. Two lines: the AI frontier, which grows exponentially, and people’s adoption, which grows in steps. Our job is to stay as close to the frontier as possible so we can pull the staircase up.

Those steps map cleanly to token count. Each generation of AI technology adds a zero to your consumption.

~0 tokens. Most of humanity. Roughly 70-80% of the world. They’ve heard about AI, they have not experienced it.

~1K tokens per session. You open ChatGPT or Luzia, ask it to write an email, close the tab. About 20% of the population. 2023 technology.

~10K tokens per session. Reasoning models. For the first time the models could actually think and self-correct, and the use of tools (grounding, web search, documents) made AI genuinely useful for real work. Unfortunately, until DeepSeek broke open reasoning, most of these capabilities were locked behind paywalls, and as I’ve written before, most people don’t pay for AI. So the majority of users have never experienced what a thinking model can do. Maybe 10% of AI users are here. 2024 technology.

~100K tokens per session. Agentic coding, with capabilities that go well beyond coding. Claude Code, Codex. You stop writing prompts and start describing systems. You give the agent a goal, it plans the steps, writes the code, runs it, checks if it works, fixes what’s broken, and comes back when it’s done. You go make coffee. About 1% of people. 2025 technology.

~100M+ tokens per session. Autonomous agents running in parallel, building and testing and deploying while you sleep. You become the bottleneck, and the more you get out of the way, the more gets done. I wrote about what that feels like in my burnout post. The percentage of the population here rounds to zero.

Each step up, the number of people shrinks while consumption per person goes up by orders of magnitude.

Measuring the Gap

At Luzia we serve millions of users across Latin America. Most of them are on that first step, asking questions, translating text, getting help with homework. Real value, but our average user consumes maybe 1,000 tokens per session. I consumed 800 million in a day. That ratio, roughly 1 to 800,000, is the imagination gap expressed as a number.

Karpathy said something on the No Priors podcast recently that stuck with me. He talked about personal token throughput as the new measure of leverage, and that the question now is “what token throughput do you command?”

Your personal token consumption is a rough but real proxy for how much value you’re extracting from AI. If you’re at 0, AI doesn’t exist for you. If you’re at 1,000 tokens a day, it’s a fancy search engine. If you’re at 100 million, then we are talking. The gap between those two isn’t just quantitative, it’s a completely different relationship with the technology.

The Math

I love back-of-the-envelope calculations. They’re wildly inaccurate, but good for perspective.

You can actually estimate global token consumption from the staircase. Take the roughly 8 billion people on the planet, apply the percentages from each step, multiply by their average daily token use, and you get somewhere around 10-20 trillion tokens per day from direct human usage. Add enterprise API calls, batch processing, internal tools, and you’re in the neighborhood of 100 trillion tokens per day, which lines up with what the big providers are reporting (Google went from 9.7 trillion tokens/month to 480 trillion in a single year).

Now imagine a world where everyone moves up the staircase. Not to where I am, just to 1 million tokens per day, which is a moderate step 4. With 5 billion people (conservative), that’s 5,000 trillion tokens per day from humans alone. Add agents talking to agents, orchestration overhead, retries, tool calls, at a 10x multiplier, and you’re somewhere around 50,000 to 100,000 trillion tokens per day.

Today we do 100 trillion. That world needs 1,000 times more, and we are, if reports are true, using close of 100% of installed capacity.

All the words humanity has ever written, every book, every website, every email, every text message, adds up to roughly 130 trillion tokens. Once everyone has a real AI assistant, we’d burn through all of that in minutes.

And tokens cost energy. I went deep on this back in 2024, and the numbers have only gotten more intense. Even with 100x efficiency gains (aggressive by any measure), that level of consumption needs about 1 TW of continuous power. That’s 50-100 nuclear power plants dedicated to inference alone. It’s why every hyperscaler is buying nuclear and why Alphabet committed $175B+ in CapEx for 2026.

This Is Not a Bubble

A founder of an AI company saying “this is not a bubble” is about as credible as a hair dresser telling you that you need a haircut. I’m biased and I know it. I wrote about the bubble question back in August 2024, and my answer then was “it depends.” Eighteen months later, I have a clearer view.

The bubble argument usually rests on two things: the amount of CapEx going into AI infrastructure, and the lack of realized business value so far (MIT’s GenAI Divide report found that 95% of corporate AI pilots fail to deliver ROI). Simplified: AI needs mass adoption to justify the spend, and people are clearly not there yet.

That was a reasonable worry in 2024 or even in 2025. Agent harnesses changed the equation, starting with Claude Code and continuing with OpenClaw and Cowork. You don’t need that many people using AI effectively for demand to go through the roof. I’m one person. I burned 800 million tokens in a day, the equivalent of 800,000 users at step one of the staircase. And that's only comparing against the 20% who use AI at all. For the other 6 billion people, the number is zero. One person at the top of the staircase generates more demand than most of humanity combined1

There will be corrections along the way. There always are. But calling this a bubble misreads where the demand is coming from. It’s not hype cycles or consumer excitement. It’s agents that multiply compute by 10x or 100x per task, and companies restructuring around them because the economics are too compelling to ignore. As I wrote in my predictions post, things in AI stay hard until they don’t, and when the flip comes, it is not gradual.

My worry isn’t about whether the demand is real. It’s about who gets access. If the supply side keeps up, everyone eventually gets their million daily tokens. If demand outruns supply, tokens stay abundant for those who can pay and scarce for everyone else. That version hits emerging markets hardest, the ones Luzia serves, the ones I care about most.

Less Than 1%

I wrote in The Year to Push that the gap between where you are and where the best AI users are is still closeable. That’s still true. But the window gets shorter every month, because the top of the staircase keeps moving up.

We are at less than 1% of the progress bar. The math says we’re three orders of magnitude from stabilization. I burned 1,600 Quijotes in a day, and I’m one person.

Imagine when that’s normal.

Last Tuesday I was putting Noa to bed and I caught myself checking my phone waiting for an agent to finish a deployment. Not something urgent. A side project. A watch face for my Garmin. My daughter asked me something and I answered on autopilot, and when I realized what I was doing I felt a kind of shame that’s hard to describe because the thing making me absent wasn’t work stress or a deadline. It was excitement. I wanted to see if the thing I’d built actually worked.

I need to back up and explain how I got here.

What happened

Over the past few weeks, while doing my actual job, I also built: Spilo (with Javi), a product now used by thousands of users. Drophere.cc, a server application for agents to host websites. A Garmin watch face. A bunch of video games Luzia themed. A real-time companion app for live events (private prototype for now). And probably a few things I'm forgetting.

I did this because I wanted to learn. I wanted to see how far the tools could go, how much one person could actually ship when the constraints that used to exist suddenly didn’t. The answer turns out to be: very far. And also: too far, if you’re not careful.

Six months ago, none of this was possible for me. The coding agents weren’t reliable enough, the workflows didn’t exist, the whole thing was still in “nice demo but doesn’t really work” territory. Then something flipped, and in a matter of weeks I went from running occasional experiments to running five parallel projects with tens of agents handling development, testing, documentation, and maintenance. It felt so cool.

And then, I noticed I got tired. Not the normal tired of a long week. Tired to exhaustion. And the confusing part is that I enjoyed almost every minute of it.

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Where the exhaustion comes from

The pipeline never stops. Once you have a working setup with coding agents, you can run multiple projects at the same time. Agents are constantly producing: this PR is ready, that test failed, this deployment needs a decision. You’re context switching between projects, re-reading what you wrote two hours ago, figuring out the next step for each thread. It’s like managing a team that never sleeps, never takes a break, and ships faster than you can review. The bottleneck moved from production to attention, and attention is the one thing you can’t multiply.

One thing that makes it both incredible and exhausting: you get user feedback and you can implement it within minutes. Someone says “this button doesn’t work right” and fifteen minutes later it’s fixed and deployed. That loop is addictive. It’s also relentless, because there’s always another piece of feedback, always another thing to improve, and the cost of acting on it is so low that not acting feels like a choice.

The guilt loop. Every moment you’re not running agents, there’s a pull: I could be building something right now. And when you finish a project, you tell yourself you’ll clear the list and then stop. But software needs are infinite. There’s always another idea, another thing that would only take a couple of hours, and a couple of hours is now enough to build something real. So you start the next one. The friction that used to protect you from overcommitting (this would take weeks, I’d need a team, it’s too expensive) is gone. It turns out that friction was doing more work than I realized.

This is the part that affects my family. It’s not that I’m working 18-hour days. It’s that even when I’m not working, part of my brain is running a background process on what I could be building. That’s a different kind of tired than just long hours.

The slot machine effect. You write a prompt, kick off an agent, and wait. While it runs, you’re anticipating the result. Will it work? Will the avatar generator I just built actually look cool? (I actually wrote that one while writing the post xD) Then the notification comes, you check it, and there’s a hit of excitement mixed with “almost, but these two things need fixing.” So you go back, tweak, run again. It’s a variable-reward dopamine loop, the same mechanism that makes actual slot machines addictive, except you built the machine yourself and the rewards are real products.

Why I’m hesitant about this post

I’ve written repeatedly that the biggest risk with AI is people not using it. I wrote “The Year to Push.” I still believe that. A post about AI exhaustion feels like handing ammunition to the people looking for reasons to stay on the sidelines, and there are a lot of those people.

The number of people operating at this intensity is tiny, probably 0.0001% of the population. Most people haven’t sent their first serious prompt. The biggest risk is still people not using AI at all.

But it probably will become more people’s problem over time. Six months ago I couldn’t work like this even if I wanted to. The tools weren’t there. Now they are, and they’ll only get better and easier to use. What took me weeks of experimenting to figure out will eventually be the default workflow, and the questions I’m asking myself now (how do you disconnect, how do you protect your attention, how do you keep this from eating into the parts of your life that matter) might be worth thinking about before the answers become urgent.

What I know and what I don’t

I know it’s really fun. That sounds contradictory after everything I just wrote, but it’s true. Seeing how much you can build, how fast things move from idea to working product, how constraints you took for granted just disappeared, it’s exhilarating. I needed to see how far I could push both the technology and myself, and the answer is: further than I expected, in both directions.

What I don’t know is how to manage it. I don’t have advice. I haven’t figured out the off switch. I haven’t learned to disconnect. I’m just continuing, hoping that either I get used to the intensity or I crack, and then I’ll know where the limit is. That’s not a recommendation. It’s a confession.

This exhaustion is a luxury problem. It comes from having too much capability, not too little. If you’re reading this and you haven’t started using AI seriously, please don’t take this post as your excuse. The overwhelming majority of people need to push harder, not pull back.

This is a note from slightly ahead on the trail, where the air is thinner than I expected. The view is incredible. I just need to figure out how to breathe.

This is not a story about Javi being exceptional. He is, but that’s not the point. The point is what became possible, and how fast it changed. Twelve months ago, this would have been a three-month project and a small engineering team. What changed?

Writing software got radically cheaper and faster.

In the span of a few months, most frontier companies, Luzia included, moved to AI-written code as the default. At this point, AI writes the overwhelming majority of the code we ship. Internal benchmarks suggest productivity has nearly doubled. It happened fast, and it did not happen gradually. It flipped. One model update in November changed the game.

I don’t know whether the same thing will happen in your field. It might happen in months, in years, or never. But I do understand the mechanism: things stay hard until they don’t, and when the flip comes, it is not gradual.

Last week I was trying Claude’s Cowork, the thing that sits on your computer and handles tasks for you. My first reaction was: not there yet. Then I remembered what coding agents felt like a year ago. Same reaction: nice idea, doesn’t really work. Then November happened, and coding flipped. I’m not frustrated about Cowork anymore. I’m forcing myself to use it.

That is why I’m writing this.

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The gap is still closeable

The gap between where you are and where the best AI users are is still closeable. That will not be true forever.

Right now, the frontier is visible. You can still reach it. The tooling is accessible, the models are good, and most people still have not seriously tried. Most usage is still first-layer stuff: summarize this email, fix my grammar, rewrite this paragraph. Most people have not touched the second or third layer. I wrote recently about the gap of imagination: the distance between what AI can do and what people actually ask it to do. That gap exists at the industry level, where we keep rebuilding chatbots, but it also exists at the individual level. This post is about that second gap.

Keeping up is hard

Keeping up with AI is genuinely uncomfortable, for reasons that are worth naming.

The external ones are obvious. The rate of change is absurd, which makes it hard to keep up. There is an enormous amount of noise in the AI space, which compounds the problem. Separating signal from hype is almost a full-time job. And on top of all that: nobody actually knows what will be possible in six months. Maybe the labs know. We don’t.

The internal ones are less obvious. Keeping up requires doing things most of us naturally avoid: sustained effort, constant updating of priors, the discomfort of being bad at something before you become good at it. What didn’t work a month ago might work now, which means you have to be willing to try things you already tried and failed. That’s psychologically harder than it sounds.

And there’s a third thing. The fact that software is cheap now means that eventually more people will be able to do these things too. When a skill becomes widespread, the bar for good and good-enough rises. Competition doesn’t reward good, it rewards being ahead. That’s not an argument against pushing. It’s the argument for pushing now, before the new standard hardens.

And underneath all of it: what if I work this hard, and nothing flips in my domain? That’s the fear. I get it.

That’s what the 2x2 helps us understand1.

But what if I’m wrong?

Start with the obvious inner objection: what if this never really takes off? What if the bubble bursts, and AI turns out to be a huge wave of talk that goes nowhere? What if I work hard for the next 12 months and this scenario never materializes?

Paul Graham has a famous essay about doing what you love. One of its core ideas is that people who work on things they genuinely love have a structural advantage: what feels like painful effort to others feels almost like a hobby to them. I agree with that completely, and it has mattered a lot in my own life. But I also think the ability to enjoy learning new things is itself trainable. And right now, there are not many alternatives. We have to learn.

The worst case of working hard on AI is that you become very good at a very useful thing. For years I taught Excel and financial modeling, and I watched people waste entire days on problems that one formula would have solved. AI feels like that, multiplied by a hundred. (if only people in the world understood sumifs). Even if progress stopped today, knowing how to use these tools is worth a lot. The floor is high.

The upside case is different. When AI coding suddenly became far more usable, the people already close to the frontier benefited immediately. The people who waited to see whether it was real were left with a gap that is now harder to close, not easier.

I believe that if this moment comes, the outcome will be brutal and barbelled2: some people will do unbelievable things, and others will fall badly behind. The choice, right now, is whether you want to be close to that frontier when it happens.

The noise around AI risk

There is one more thing here that frustrates me.

Much of the public conversation about AI is focused on risk. Researchers, journalists, regulators. Some of those risks are serious, and someone does need to think about them. But for most people, the effect is not careful engagement. It is paralysis. People close off instead of leaning in.

My instinct is that the best protection against the downsides of AI is broad adoption, not avoidance. The disruption that might come will be smaller, and more manageable, if as many people as possible are using the technology and benefiting from it. The worst outcome isn’t that AI is powerful. It’s that AI is powerful and only a small group knows how to use it.

Your job is to make sure you are not left behind while others are still debating what this means.

How to start

Get mentally ready first. This is going to be uncomfortable. The rate of change is fast, the signal-to-noise ratio in the AI space is terrible, and you’ll have to constantly update things you thought you knew. That discomfort is the point. If it were easy, it wouldn’t be an opportunity.

Default to AI more often than feels natural. Make “AI first” a real heuristic. Make it a rule: AI first. Most things won’t work. Some will surprise you. What matters is that you build an intuition for where the edges are, and those edges are moving. What failed last month may be trivial today.

Pay for a real subscription and actually push it. Not the free tier. Then use it for problems that feel slightly above your level. The people who get the most out of these tools are the ones who give them hard problems, not the ones who use them only for email.

Do not be afraid to ask stupid questions. For the first time, we have a technology that is endlessly willing to help you learn. You just have to ask.

Build something. Anything. I don’t care how small. You have a problem, you have a tool, and you can probably connect them in an afternoon.

With the main barriers gone, what remains is agency and taste.

There are very few moments in a career where the asymmetry is this good, where the floor is high, the ceiling is very high, and the window is visible but closing.

The cost of this opportunity is hard work. That is not such a terrible price.

P.S. I do not usually write in this tone, but this one matters. More and more people are reaching out because they feel they are falling behind. The anxiety is real. The peer pressure is real. That is exactly why now is the time. I’ll get back to more technical topics soon.

Fidji Simo calls this “capabilities overhang.” Benedict Evans calls it lack of product-market fit. I’m not arguing with either of them, they’re both right. But I want to go one level deeper, because I have a strong hypothesis of why it happens, and I think that’s the more useful question for anyone building on AI. And it comes down to two things we, as product builders, are getting wrong.

First: we keep trying to solve everything with a chat interface. I wrote about that in my 2026 predictions - the year of boring AI I called it, where the real wins come from workflows, not chatbots.

Second, and more important: we are not closing the gap of imagination.

That’s what this post is about.

Here’s a diagram I drew in 2023 when I started Luzia. The idea was simple: make sure people don’t fall too far behind the frontier. But “not falling behind” has two requirements. People need to use AI. And people need to actually extract value from it.

We at Luzia, and the industry, got decent at the first one. The adoption curve AI achieved is unlike anything before it. But we have mostly failed at the second.

Why people are not finding the value?

To understand why, you need to understand what AI actually is today. The most bullish answer is “almost everything.” The more honest answer looks like this:

AI can be insanely good at coding and, on the same day, tell you to walk to a car wash that’s 40 minutes away. The frontier is jagged, not a smooth capability curve but a weird mix of genius and embarrassing failure.

The problem is that where AI is good and where it isn’t is not documented anywhere that matters. Not beyond evals and benchmarks that mean nothing to 99.9% of people. Which means that for someone to get value from AI, three things have to happen simultaneously: AI can actually do it, the user discovers that it can, and the user decides to try it.

The first one is the AI labs’ job. The second and third are ours.

The job of product builders

Let me make this concrete with an example not far from reality. Let’s say that today’s frontier AI can perform executive assistant (EA) tasks with a high degree of autonomy and accuracy. Especially after the recent wave of agent capabilities initiated by OpenClaw.

Great. We all get a free EA. What a wonderful world.

Except most of us have never had an EA. So we do what I’d do: ask it to reorganize my calendar. Maybe, once I build enough trust, book a flight. And I think I’m getting good value.

But a good EA is way more than that. A great EA is Donna from Harvey (if you don’t get the reference you are missing out on a great TV show). A good EA is not calendar hygiene, it goes deep into leverage: framing your decisions before you have to make them, filtering ruthlessly what actually deserves your attention, closing every loop so nothing falls through the cracks, and pressure-testing your ideas with someone who has no ego in the outcome (full disclosure: I haven't had a real EA, that's actually from a conversation with an AI about what one would do).

And AI can deliver roughly 70% of this today. Not 100%, deep intuition and strategic feel still need time and context. But 70% is a lot. Most people are getting 0%.

The gap between 0% and 70% is not a model problem. It’s an imagination problem. I can’t picture what a great EA does, so I can’t ask for it. I can’t picture what great AI product look like, so I can’t ask for it.

Responsibility #1: close the gap of imagination. Make sure people can picture what good looks like, so they can ask for it.

The second problem is different. Even when users know AI can do something, they still need to want to do it.

In most cases, what we’re offering is an improvement on the status quo. And most people don’t spend their days optimizing. That’s just a few sickos like me who can’t do anything twice without thinking about scripting it.

Fabriccio Bolsi, Prosus CEO, put it plainly at a recent meeting: “I’ve tested all your agents and none of them gets me what I want for dinner faster and more smoothly than the two taps on the iFood app.” Hopefully it wasn’t talking about Luzia, but in any case, that’s a slap and stinks because it is mostly true.

Christensen’s rule: if you want to change someone’s default, your experience needs to be 10x better. Not marginally better. Not “AI-powered” better. Actually just better, in a way people feel immediately. AI has created the room for that 10X experience, we need to deliver on it.

However, we keep shipping chatbots that look identical to each other, chasing flashy demos and one-shot Loom videos, when we should be obsessing over boring AI that just works for the 80% of people that don’t spend the day behind a screen. I wrote about this in my 2026 predictions, and the local AI angle specifically here.

Responsibility #2: stop building AI for demos. Build it for defaults.

Both of these things, imagining on behalf of users, and building for defaults instead of demos, matter for two reasons.

For consumer AI companies closing this gap is existential. It’s the difference between building something that sticks and building something people try once, and then never again.

But more importantly: if we don’t solve this, the gap between those who use AI at its full potential and those who don’t becomes too wide to close. That’s not a product problem. That’s a society problem.

It’s the reason I started Luzia.

She grabbed the Reachy Mini we put together during Holidays, this little robot you can program by talking to it. Using Claude Code (directed by me) and wisprflow.ai (a dictation app), she told what she wanted: “Make a game where you pick a color and I have to guess it.”

The AI asked her questions. How many guesses should she get? What should happen if she’s wrong? She worked through each piece, tested it on the robot, found it was too easy, made it harder. Thirty minutes later, she had a working game.

I’m not telling you this to brag. I’m telling you because of what happened next.

A few days later, a neighbor friend came over. Saw her playing the game. Asked how she made it. My daughter: “I just told the robot what I wanted.” The friend’s response? “My mom says ChatGPT is for cheating.”

That kid’s ceiling just got set. Not by AI. By her mom.

We obsess over whether AI will harm our children. Wrong question. The harm comes from us. From parents paralyzed by fear, schools banning tools instead of teaching them, politicians gaining votes by talking about AI doom-day scenarios to kids who’d otherwise adapt naturally.

Our relationship with AI determines our child’s relationship with AI. We set the ceiling.

The Harvard Report Effect

One of those silly theories I invent to help me understand the world: pick any topic, there’s always a “research study” that supports your point of view. From time to time, that research study makes it into the media. People who haven’t read a paper in their lives get it out of context. The rest of us see the headlines, and a vague correlation becomes the topic of all the TV experts. And all of a sudden, a new “the expert says you need to XXX” is the new general belief.

AI is no different. Every few months, a study emerges from a prestigious university. Headlines follow: “AI Makes Kids Dumber,” “Screen Time Destroys Attention Spans,” “Technology Ruins Critical Thinking.” The media amplifies it. Parents panic. Schools respond with bans.

Most people can’t distinguish correlation from causation. Eight out of ten, according to a statistic from Harvard (pun intended). So when a study shows kids who use AI score lower on some metric, we assume AI caused it. We ignore alternative explanations: maybe struggling students seek out AI more because they’re already behind, or maybe the test itself measures skills that matter less in an AI world, or maybe we’re testing skills that don’t matter—like memorizing kings.

The pattern repeats every technological shift. I’m old enough (first time that I write that sentence!) to remember a few of these. Television would rot brains. Calculators would destroy math skills. WordPerfect (not a typo, that was a thing) would make you a dumb writer. Google would eliminate memory. Each time, the panic was overblown. Each time, we adapted.

Want to go further back? The Pessimists Archive documents centuries of technology panic. People feared bicycles would cause insanity. The telephone would destroy written communication. Even the printing press had critics who worried it would weaken memory and spread dangerous ideas.

But this time is different, isn’t it?

The Uncertainty Problem

I have two kids. Noa turns sixteen in 2035. Telmo will be thirteen. I spend unhealthy amounts of time thinking about what their world will look like. I can’t predict it. Nobody can.

I’m biased, I’m techno-optimistic. I believe technology makes us better, not worse. But even if you’re skeptical, the forecasting problem remains the same.

Forecasting is hardest when the rate of change accelerates. When I was eight and when I was twenty, the gap was enormous, but at least the direction was predictable: faster, cheaper, smaller technology. For my kids of today, the world might be unrecognizable by the time they graduate.

This uncertainty produces two failure modes. First, we try to predict specifics and optimize for scenarios that won’t materialize. Second, we do nothing because we can’t see clearly enough.

Both are mistakes.

The things that will always matter are slim and summarized in two: agency, and taste But you can’t directly teach these things. You can only model them. And kids are more likely to develop them if the adults around them treat uncertainty as normal, not terrifying.

We can’t teach our kid to be adaptable to a future we’re terrified of. Fear is contagious. So is confidence.

Why We’re Probably Overreacting

The evidence for catastrophic harm isn’t there yet. Some studies show concerns. AI might reduce critical thinking in specific contexts. Children trust AI outputs too readily without verification. Social development could suffer if kids replace human interaction with chatbots.

These are real concerns. They’re also manageable.

Compare them to the documented benefits: personalized tutoring at scale, accessibility for learning disabilities, instant feedback that accelerates learning, democratized access to education globally. AI can already do things we couldn’t afford to do for most children before. At Luzia, we’ve seen millions of users in Latin America get access to quality education tools they never had before.

The question isn’t “Is AI risky?” Of course it’s risky. Everything is risky. Car accidents kill more kids than all illnesses combined, and we still drive them to school every day; we just make them wear seat belts and child restraints.

Avoidance means your kid falls behind while others build fluency in something I’m certain is here to stay. Engagement means you guide them through the risks. Easy choice.

Technology Is Here to Stay

Even if I’m wrong about AI being net positive, it doesn’t matter for most of us. AI isn’t going away. You can’t protect your kids from it by pretending it doesn’t exist. You can only protect them by teaching them to use it well.

If we’re constantly on our phone complaining about technology, guess what our kid learns? That technology is both irresistible and shameful. Not a great combination.

Arguing that AI is bad is the easy path. It requires no learning, no adaptation, no discomfort. Just moral certainty and fear. Learning how to make the best of AI is harder. It requires curiosity, experimentation, tolerance for failure. We almost always take the path of least resistance. But the path of least resistance today creates the most resistance tomorrow.

Think about calculators. Schools initially banned them. The fear was rational: kids would never learn arithmetic. But once calculators became ubiquitous, the ban became absurd. Today, nobody argues we should remove calculators. Instead, we teach when to use them and when to do math by hand.

AI will follow the same arc. The schools banning ChatGPT today will integrate it tomorrow. The parents terrified now will normalize it soon. The lag between panic and acceptance creates opportunity for some kids and disadvantage for others.

The kids whose parents figured it out early get a head start. Ours can be one of them.

When IQ Becomes a Commodity

This time feels different because we’re not automating a narrow task. We’re automating what made us feel different for generations: intelligence itself.

But we’ve been here before.

Long ago, we taught hunting and fighting. Raw power mattered. Then machines made strength a commodity, and we shifted to dexterity: farming, crafting, building. The industrial revolution automated dexterity, so we pivoted to IQ. For the past century, education meant developing intellectual capability: reading, reasoning, problem-solving.

Now IQ is available like water from a tap.

Andrej Karpathy, one of the best programmers alive, recently wrote:

If Karpathy feels behind, what chance does everyone else have?

The same transformation is coming for every knowledge worker. When raw intelligence becomes a commodity, what’s left?

Agency and taste.

Agency is the sense that your actions matter and the habit of starting things anyway. Taste is knowing good from mediocre when infinite options exist.

A kid asks AI to help write a story. Agency is deciding to write it in the first place. Taste is reading what AI generates and saying “this is boring, make it funnier”—then iterating until it’s actually good.

These skills aren’t taught in schools. They’re modeled at home.

Our AI Literacy Sets Their Ceiling

If you don’t understand AI, you can’t teach your kid to use it responsibly. You can’t model good judgment about when to use it and when not to. You can’t help them debug when it fails. You can’t show them how to verify outputs or recognize hallucinations.

Our ignorance becomes their limitation.

This isn’t about learning to code or becoming an AI expert. It’s about basic fluency: knowing what AI can and can’t do, understanding its failure modes, developing intuition for when it’s useful.

Most importantly, it’s about your relationship with the technology. If you treat AI as magical or terrifying, your kid absorbs that. If you treat it as a tool, with all the goods and the bads, they learn to use it pragmatically.

Schools will eventually catch up. Eventually. But they’re slow. Universities are banning AI in assignments while the job market demands AI fluency. High schools are fighting plagiarism battles instead of teaching citation and verification. Elementary schools are doing nothing because nobody knows what to do.

Meanwhile, children whose parents are AI-literate are already building things, learning faster, and developing skills the educational system hasn’t figured out how to test.

The gap will widen before it narrows.

Schools Will Need to Rework Curricula

Remember memorizing kings? Spanish history taught through rote memorization of Gothic kings? Completely useless knowledge, but schools taught it for decades because the system was slow to change.

We’re doing the same thing now. Giving kids tablets and calling it “digital literacy” when they’re already digital natives.

The current educational model optimizes for memorization and standardized testing. AI demolishes both. Why memorize facts when you have instant access to all knowledge? Why practice arithmetic when computers calculate faster?

Yes, struggle builds character. As Cleo Abram discussed with Mark Zuckerberg, doing hard things matters. But there’s productive struggle—learning to think—and pointless struggle—memorizing facts Google knows.

We still teach kids arithmetic even though calculators exist. Why? Because understanding basic math helps you know when the calculator is wrong. The same logic applies to AI. Kids still need writing skills to know when ChatGPT produces garbage. They still need critical thinking to evaluate AI outputs.

The curriculum needs to shift focus:

Critical evaluation: How to verify AI outputs, recognize hallucinations, cross-check sources. Research shows children as young as preschool can learn AI literacy.

Problem framing: AI executes well but needs clear direction. Asking good questions matters more than knowing answers.

Creative judgment: When a thousand options exist, taste determines value.

Human skills: Empathy, collaboration, leadership, communication. The harder these are to automate, the more valuable they become. As one education leader notes, “the biggest lesson we’ve learned is, it’s not about the content.”

Schools will resist this shift. They always do. The institutions most invested in the old system are slowest to change. This creates a window where parents can substitute: teach your kids these skills at home while waiting for schools to catch up.

Or don’t wait, work on it at home.

The Gap

We’ll adapt. We always do. And that unrecognizable world of 2035 will be simple 2035. But how well we adapt depends on how much we make of AI, not whether we avoid it.

The gap between what institutions teach and what matters is ridiculous, and widening.

Schools optimize for plagiarism detection while the job market demands AI fluency and agency. They ban ChatGPT while employers expect AI-augmented work. They test memorization when memory is free.

But it’s not just schools.

I recently spoke at a large tech company about AI. I started my talk with a simple question: “How many of you think this company needs to invest more in AI training courses?” Everyone raised their hands.

Everyone, at a tech company, full of what I am pretty sure was high IQ people.

“You all work here. AI is new for everyone. Get the agency you need to get up to speed. Don’t wait for the company to teach you.” Those who know me will know I used slightly non-post-friendly wording. ;)

The silence was uncomfortable.

This is the problem. Adults lack agency too. We wait for permission, for training programs, for someone to tell us it’s okay to learn. Then we pass that learned helplessness to our kids.

The way I see it, two groups of children are emerging: Group A, kids making things happen. Group B, kids told AI is cheating, banned from experimentation, learning that trying new things requires permission

The gap between these groups will be enormous. Not because Group A has better AI, but because Group A has agency and Group B doesn’t.

What to Do

Stop waiting for perfect information or politicians’ guidelines. You won’t get it. The studies are contradictory, the experts disagree (for God’s sake, there are no experts—this is new!), and the technology changes faster than research can track.

Instead, model what you want them to learn. Use AI yourself. Let them see you experiment, fail, adjust.Show them that “I don’t know how” could be the beginning of something cool. Let them try things under supervision—prohibition builds naivety, experimentation builds judgment. When they create something with AI, don’t just praise it. Ask: “Is this good? How could it be better?” That’s how taste develops. And balance AI with what AI can’t replace: human connection, physical activity, unstructured play.

Push schools to adapt. Not through angry emails, but through questions: How are you teaching kids to use AI? How are you using AI? What’s your plan for when every student has access? Are you preparing them for the world as it will be, or as it was?

Most importantly, relax. Our kids will be fine. They’re more adaptable than we think and there will be a new Harvard Study to prove it soon.

AI systems want to learn. They’re designed to improve through interaction, to get better with feedback, to adapt to new patterns. Humans are the same. The models learn through trial and error. So do your kids. The difference is: the models don’t need permission to try, our kids do. And we’re the one who grants it.

We adapted before. We’ll adapt again.

You set the ceiling.

Not the “two-party, I’m chatting with a bot and I know it” version. The original version: a three-party imitation game, where you chat with a human and a machine at the same time and you have to guess who is who. In the UCSD study (March 31, 2025), GPT-4.5 was judged to be the human 73% of the time when prompted with a human persona. That’s not “it sounds good.” That is “it beats actual humans at seeming human.”

2025 was also the year the new models saturated most of the hardest benchmarks (industry jargon for “we ran out of signal”), including ARC-AGI (the #2, because the #1 was supposed to be AGI, but we already ruined that word).

Leaving for 2026, we still have hope in the humbly named “Humanity’s Last Exam”.

So why am I talking about 2025 in a 2026 prediction post?

Because, as I did in my 2024 and 2025 posts, I’ve decided to be 100% correct. Meaning: 10/10 predictions. I’ll do it using two advanced techniques:

1. I’ll go to fundamentals so we talk about trends, not headlines.

2. I’ll attach probabilities to everything. If I say “90% chance” and it doesn’t happen, I was still right. I just taught you how to always be right on TV.

What are those fundamentals that will continue?

Jokes aside, 2025 was the year in which, as I argued in my Differentiation, Then Commoditization post, AI started to just work for many more use cases. A different question is what people actually use it for, but the “does it work?” debate stopped being the bottleneck.

We also pretended we forgot about model numbers (GPT-5.2?, Claude…?), which is obviously a lie because we are still terminally online. But something real did change: we moved from “wow, it can talk” to “shit it can do (parts) of my job”.

This simple illustration finally helped me visualize what’s going on. For the last 3 years I tried to picture AI as a concentric circle, my team has suffered countless versions of that slide in all-hands, but it never fully explained the weirdness. This does. The frontier is jagged. It can be insanely good at one thing and embarrassingly dumb at another, on the same day. And that jaggedness is basically the story of 2025.

The big unlock was not just raw intelligence. It was the combination of reasoning + tools + research workflows. Models got better at taking time, checking themselves, calling tools, reading sources, and producing something closer to an actual work product. Not just an answer.

Agents also went mainstream, mostly because models got reliable enough in verifiable domains, especially math and coding, where you can check outputs and run loops. This is also where reasoning actually cashes out. If you can verify, you can reinforce. If you can reinforce, you can build systems that feel like “agents” instead of demo magic.

Even Andrej Karpathy basically said “I feel behind” as a programmer

And we got the first real glimpses of “AI move 37” outside of games. Not “it wrote a poem,” but “it found something new.” DeepMind’s AlphaEvolve is a clean example: a Gemini-powered coding agent that searches for algorithmic improvements, including things that matter in real infrastructure, not just toy problems. You can call it early, you can call it narrow, but the direction is obvious: once the loop is there, discovery compounds.

Finally, 2025 was the year where I, and a lot of other people, ran out of ideas on how to test new releases. That is the key insight to understand 2026.

Because if you can’t even tell what got better anymore, it usually means progress is moving to places that are harder to observe from the outside.

2026: AI research and Application Layer take different paths

AI has theoretically unlocked extreme value in many areas. The paradox is that we are yet to capture most of it, and that is my main prediction for 2026.

While we will keep hearing about AI progress, there will be a split between AI research progress and application layer progress. Or said differently, two frontiers:

The public frontier will optimize for headlines. New benchmark records, new “state of the art” claims, new demos that look insane on X, and just enough novelty for money to keep flowing and consumers to keep trying the releases. Benchmarks have become marketing, which is fine, it is how the game is played.

The private frontier will be labs going back to fundamentals. Not because they became humble, but because pushing the frontier now requires more than “scale harder.” It is reasoning, training loops, verifiers, tools, data, and automating pieces of research itself. The stuff that compounds, but is harder to show on a leaderboard.

In this landscape, open source is the forcing function that keeps both worlds connected. It compresses the gap and makes “just wait for the next closed model” a weak strategy. It also erodes margins and pricing power, because once open weights are close enough, the enterprise conversation becomes “why are we paying for this.”

So yes, we will keep seeing progress everywhere. But it will feel weirdly bifurcated: the loud progress you can screenshot, and the quiet progress that actually moves the frontier.

Let’s see this thesis in detail.

AI Research Progress

1. Going back to lab work

While scaling laws have held in 2025 (more compute still tends to mean better models), improvements are harder to spot, and the capex to reach the next level is getting astronomical. The reasoning paradigm drove a big chunk of the gains, partly because it gave models something underappreciated: the ability to use tools in a reliable way. If you can plan, call tools, verify, and loop, you can turn “smart” into “useful”.

Compute-efficient companies (aka Google with TPUs) will keep brute forcing their way into better consumer models. The Gemini 3 launch is basically that thesis in product form: bigger is better.

The rest have a big bet to make: do we bet on ASI/AGI being achieved via generalization or via specialization?

• Generalization means brute force the way to AI. Bigger generalist models that can do everything.

• Specialization means automating the machinery of AI progress itself. Research loops. Verifiers. Search. Synthetic data. Systems that improve systems.

My 2026 prediction: all the AI labs will focus on specialization. Why?

1. There is enough room for value capture in the application layer that even if the frontier slows, we still have years of gains to ship.

2. Pure brute force is seeing decreasing performance per dollar, and the dollars are getting silly.

3. Specialization gives the best headlines for fundraising. Not “we trained a bigger thing.” More like “we discovered something new,” “we automated a chunk of research,” “we built a loop that compounds.”

And if I am right, that is bad news for the observability of progress. There will be two AI performance frontiers:

• the one we see, focused on benchmarks, product demos, and application layer wins

• the one we won’t see, focused on automating AI research and building training loops

Conveniently, this prediction is hard to falsify. Which means I’m probably right.

2. The gigawatt age

Even if research is focused on automating AI research, bigger and more powerful data centers will be necessary to serve the growing demand for training, research, and inference. Given data center timelines (2–3 years), this is not much of a prediction for 2026. The prediction is the shape of the conversation: we will keep hearing crazier buildouts, and 2026 will be the year 1GW stops being a meme and becomes a real unit people use with a straight face.

Industry has stopped talking about data centers as a function of “how many H100s,” and started talking about them as a function of energy provided, because energy is the bottleneck now.

Power becomes allocation: do you spend MW on discovery (research), scaling (training), or distribution (inference)?

My prediction is that this allocation problem becomes painfully explicit. Not in a philosophical way. In a budgeting way. In a “this site has X MW, we have to choose what it’s for” way. Altman’s point is the same: if compute is limited, you are effectively choosing what to prioritize.

My 2026 prediction: we will see at least one flagship data center openly framed as gigawatt-scale infrastructure, and the primary brag metric will shift toward efficiency, not just size.

Why? Because energy buildout has lag. You can’t spin up power capacity on a quarterly timeline. So the knob you can actually turn in 2026 is efficiency. That means the GPU story shifts from raw FLOPs to FLOPs per watt. Same capability, fewer megawatts. Or more capability inside the same power envelope.

This is also where the “two frontiers” idea shows up in physical form: the public frontier wants cheap, reliable inference. The private frontier wants enormous contiguous training runs. They both want the same megawatts. And in 2026, that competition becomes the main plot.

3. Reinforcement Learning from Verifiable Tasks

A bit of history here. ChatGPT was “only” a fine-tuned version of GPT-3, where via reinforcement learning from human feedback the model learned to talk like a product, not like a weird autocomplete. That small change started this revolution.

The next step (and most of 2025) was labs realizing something simple: if you can train against automatically verifiable rewards (like humans learning, did I do it correctly or not?), models start to develop strategies that look like “reasoning” to humans. They break problems into steps, they backtrack, they try different approaches, they learn to “work it out.” Karpathy explains it better than anyone, and he also makes the underrated point: RLVR ended up being high capability per dollar, so it gobbled up compute that otherwise would have gone to pretraining.

That also explains why math and coding jumped first. Not because those domains are magical, but because you can score them. You can run tests. You can check answers. You can reward correctness instead of confidence.

My 2026 prediction: verifiable loops expand beyond math and coding into messy real-world workflows—but mostly by turning the world into something scorable via tools.

This is the bitter lesson: you don’t need the model to become wise. You need the workflow to become checkable. Search turns “knowing” into “finding.” Code turns “thinking” into “running.” APIs turn “guessing” into “querying.” Logging turns “it worked once” into “we can measure it.” Once a task has a scoreboard, you can reinforce. Once you can reinforce, reliability stops being a vibes debate and becomes an engineering problem.

Now, you’ll see a clear bottleneck, if every new domain requires bespoke training environments (or fleets of robots1 grinding experience), bespoke tool sandboxes, bespoke reward functions, bespoke evals… then we’re not building “AI employees.” We’re building an industrial pipeline for teaching very capable systems a million narrow skills.

That’s not nothing. In fact, it’s probably the main way capability becomes usefulness in 2026.

If you buy the “loops-not-learners” framing, the obvious question becomes: where do we get cheap interaction data and cheap feedback? Because the real world is expensive. Slow. Risky. Full of edge cases you only discover by breaking production.

So if you can’t afford reality as your training loop, you simulate it.

Which brings us to world models.

4. World models for training loops and new data

In 2025 we saw the first really impressive world models. Some people saw them as a toy (cool games, cool videos). Others saw them as one of the missing pieces of the training pipeline.

In a few words, world models are AI systems that build internal, dynamic simulations of the world so they can predict how a world evolves and how actions change it. The key word is ‘closed loop’: you act, the world responds, it stays coherent, and you can keep going.

This is where the “verifiers” idea connects. Agents need environments. Environments create interaction. Interaction creates data. Data creates rewards. Rewards create reliability. And if real-world interaction is expensive or dangerous, a simulator gives you the loop without the cost.

If we were to get world models right, we could create useful environments to spin for training agents, other models, or even cooler, embodied AI (robots). These world models could be general (simulate a broad world, like Genie) or specific (simulate the physics of a narrow domain). We’re still far off, but to make it concrete: imagine we get a good enough driving simulator. The advantage of “collecting real driving data” shrinks, and the bottleneck shifts to “how good is your simulator.”

So far there have been two main research lines:

• video generators (Veo, Sora, etc.)

• actual world models (Genie-style closed loop)

Both are brutally expensive to train and run, which is exactly why this matters for 2026. If a lab can make a playable simulator cheap enough to run, it becomes a forcing function: suddenly you can train loops at scale, and suddenly “agent reliability” is not just a prompt problem, it is a data + loop problem.

My 2026 prediction: we will see the release of a playable, general world-model simulator by an AI lab other than DeepMind. Playable meaning: not just a video, but real-time interaction for minutes, where actions have consistent consequences.

My second prediction (more gambling): at least one frontier lab will publicly hint that synthetic data coming from world models is in the training pipeline. Not a full technical write-up, just the usual vague “we use simulators for training” line that makes everyone on X lose their mind.

And yes, this will matter for consumers too. Once models can “simulate” better, you get better physics intuition, better planning, better multimodal accuracy. It won’t just be prettier videos. It will be more useful systems. Next: downstream effects.

5. AI downstream effects

The most exciting and probably most bullish prediction is related to real world impact. I am preparing a longer write-up on this idea, but let’s start shaping it here.

The coolest thing about AI is not that it writes your email as a professional copy editor, or that you can ask for a website and get it in 10 min for less than $1 in cost. It’s that you have available intelligence from a tab. And once intelligence is cheap, the question stops being “what can the model say” and becomes “what can the model do.”

So here is my bullish 2026 take: attention shifts from “new model dropped” to “AI did something real.” Think a new AlphaFold moment.

I am intentionally picking biology here. Not because biology is easy (it is not), but because it has three unfair advantages for AI:

First, biology has a lot of structure. Under the mess, there are rules. Second, biology has bottlenecks that look like bottlenecks AI is uniquely good at attacking: search, pattern recognition, hypothesis generation, and synthesis across huge literatures. Third, and most important, biology has feedback loops. You can test things. You can run experiments. You can score outcomes. You can get closer to verifiable rewards than in most human domains.

So the prediction is: we will see at least one widely-visible, hard-to-dismiss moment where AI materially accelerates a biological pipeline. Drug discovery, protein design, wet lab automation, or a new class of biological insight that becomes a reference point.

Why do I think this happens in 2026?

Because, cynically, incentives. As Charlie Munger said, show me the incentives. We are building gigawatt-scale infrastructure. We are asking society to tolerate massive buildouts. And at the same time, most consumers can barely feel the difference between “model A” and “model B.” How do you justify the buildout when the average person thinks the main use case is rewriting emails?

You show a win that feels undeniable. A result that is legible to normal people. Something that makes the infrastructure story feel less like hype and more like a down payment on actual progress.

Application layer

If the private frontier is about back to labs, insane investments and verifier loops, the public frontier is better products that ship value under real constraints.
The models will keep improving, but the differentiator in 2026 is who turns that capability into a workflow that sticks. The app layer is the monetization layer of the split.

In B2B, the moat is workflow + data loops. In B2C, the moat is interface + distribution.

Enterprise workflows

OpenRouter’s State of AI report (worth reading, though a bit depressing to see the use case distribution) coined the “Cinderella / Glass Slipper” effect, which is a fancy way to name something very simple: once a model becomes good enough for a specific task, people stop switching. Retention locks in. The model disappears, the workflow stays.

This is the enterprise dynamic of 2026: the battle is not “who has the smartest model.” The battle is who owns the workflow. Who has the integration. Who has the permissions. Who sits inside the system of record. And who gets the data loop that makes everything better over time.

Because once you’re embedded, the good old SaaS retention dynamics kick in.

So here is Prediction 6.

6. Moats are the loops (enterprise edition)

In 2024–2025, a lot of enterprise AI was read-only: summarize, search, draft, suggest. It was useful (RAG systems finally started to work), but it still left the annoying part to the human: actually doing the thing.

My 2026 prediction: approved write access becomes mainstream. AI stops suggesting and starts committing changes, with guardrails and audit trails.

That shift matters because write access is where the data loop becomes real:

• when AI reads, you get productivity

• when AI writes, you get automation

• when you automate, you get feedback

• and feedback is how systems get reliable

The gating factors are not model IQ. They are permissions, logs, rollbacks (as we spoke Claude Code just killed one Twitter demo repo I was working on!), and verification. If you solve that, agents stop being demos and start being boring, reliable automation.

7. Agents beyond coding and research

If 2025 was the year everyone discovered the word “agent,” 2026 will be the year we collectively admit the obvious: agents still fail too often.

The problem is simple math, reliability compounds in the wrong direction. If a system is “95% accurate per step,” it feels great in a demo… until you ask it to do 10 steps in a row. Then your success rate collapses. Which is why most real products today either stay read-only, or they hide the agent behind approvals and guardrails.

So my 2026 prediction is not “autonomous agents everywhere.” My prediction is more boring and more real:

My 2026 prediction: we will see one general-purpose agent workflow outside coding/research become mainstream, but it will look like approvals + retries + audit logs, not autonomy.

The winning shape will be an “ops agent,” not a “CEO agent.” In other words, agents expand, but they expand where the work is verifiable and where failure is cheap. The pattern is the same as the rest of the post: loops win. Verifiers win. Guardrails win.

And this is also where the pricing story gets really weird. Models will keep getting cheaper per token (open source, routing, competition), but agents are token-hungry: more steps, more tool calls, more context, more retries (orders of magnitude not factors). So 2026 will feel like a paradox for builders:

Prices per token go down, but cost per successful task stays stubborn unless you engineer the loop.

That is why the enterprise winners won’t be the companies with the flashiest agent demos. They will be the ones with the most boring system: evals, monitoring, permissioning, and a narrow set of workflows that run reliably 100 times a day.

A Bonus Prediction: The last 4 months of coding agents have been probably the craziest (and quietest) stretch of the last three years. Not because models suddenly got “smarter,” - which they did - but because they got reliable at the full loop. That is the difference between “autocomplete” and “junior engineer that never sleeps.”

2026 is the year the world realizes this. Not via a flashy demo, but the change of the default workflow for shipping software. Inside Luzia, my most productive backend engineers are already doing something close to the extreme version of that future. On some weeks, ~99% of the code that lands is AI-written.

Coding is the first domain where “agents” stop being a story and start being a baseline. Will that be the future for everything else?

8. AI Roll-ups

Why are we not realizing more gains from AI? Acemoglu’s main point: organizational bottlenecks - basically, we all struggle to change our defaults and ways of doing things. Then..

What’s the shortcut to see these productivity gains?

The shortcut is simpler: don’t sell AI to the incumbents. Buy the incumbents.

My 2026 prediction: roll-ups become a mainstream distribution strategy for AI in boring industries. People stop pitching “we built an AI for X” and start pitching “we bought X and made it 2x more profitable.”

A simple mechanism:

• fragmented industries (a thousand small players, same playbook everywhere)

• lots of repetitive work (admin, compliance, scheduling, quoting, claims, back office)

• low tech adoption + low talent density (they cannot build this themselves)

• AI can do a significant amount of the task (30-50%?) but not all (otherwise just sell the process!)

And here comes the loop again: buy distribution, create the training loop and see again prediction 6.

Cautionary tale: roll-ups show up in late-cycle moments, when cheap capital makes “buy growth” look like genius. Historically, acquisition waves can create real value, but they also create the same failure mode: integration gets postponed until the market stops forgiving you, culture hits you in the face, churn kills your client list.

Consumer habits

I have a strong conviction on the horizontal nature of AI, well beyond chatbots. As a horizontal technology, it will leak into almost all areas of our life. That will take time, and a ton of experimentation, and with winner-takes-most dynamics, the winners won’t be “best model.” They’ll be whoever nails the interface + distribution.

If B2B moats are workflow + data loops, B2C moats are habit + UI + quality perceived. And in 2026, UI becomes the battleground because models keep commoditizing.

9. AI will meet the real world in transactions

So far AI has mostly lived in the online world: write, search, generate, summarize. The online world meets the real world in one place: transactions. We already saw “agentic commerce” hype explode in late 2025, but my 2026 prediction: “agentic commerce” becomes boring. The breakout UX is AI + one-tap approval, not autonomy.

I wrote about the commerce side here: AI + e-commerce. And the local angle here: Local AI.

10. Image generation moves from aesthetics to application

2023 was “looks like a dragon.”
2024 was “look, I made a dragon.”
2025 was “look, I made a REALLY cool dragon, Ghibli style.”
2026 is “look, I made a useful infographic explaining the physics of dragon wings in low-density liquids, with labels that actually spell correctly.”

In short: from vibes → to work, with realism as table stakes. The edge in 2026 is realism + consistency + instruction-following + editing (the most underrated feature of nano-banana pro is “reasoning and the use of tools”).

My 2026 prediction: the breakout image workflows are boring (and useful) business workflows.
Product photos, menus, real estate listings, ads, catalog variation, customer-support visuals, compliance-friendly marketing and a lot of AI-slop in feeds.

The winning shape won’t be “blank canvas + prompt.” It will be first-step usability: templates, guided prompts, and one-click starting points that get you to a usable draft in 10 seconds. The generator becomes invisible; the workflow becomes the product.

And importantly: models stop being one-shot. The loop becomes: generate → edit → constrain → re-edit → keep character/brand consistent. That means:

• Consistency becomes a killer feature: same character, same product, same brand style across 20 variations (aka the storyboarding / catalog loop).

• Multi-step instruction-following actually matters: not “apply one style,” but “do three edits in order, keep X fixed, change Y, and don’t break Z.” Benchmarks like GenEval and T2I-CompBench++ are basically measuring this “can you follow constraints without breaking the scene” problem.

• Text rendering + structured graphics go mainstream: the shift from “it spelled a word right” to “it produced an infographic / market map that carries dense information.” (This is where image becomes a document format, not just a picture.)

  

• Editing is the primitive: image-in → image-out, not just text-to-image. If you can’t reliably edit an existing asset, you’re not in the enterprise budget. Datasets like MagicBrush exist for exactly that: instruction-guided edits you can evaluate. GitHub

This also ties back to world models. Better simulation and better “physics intuition” shows up as fewer impossible hands, fewer nonsense shadows, fewer objects melting into each other. Not because the model is artistic—because it’s less wrong about how the world works.

11. We will continue seeing attempts to crack social + AI

We will keep seeing “AI + social” launches, and they will keep looking obvious in hindsight: create content faster, remix faster, reply faster, flirt faster, meme faster. The supply side is solved.

The problem is the demand side. Social is not “content.” Social is status + identity + taste + inside jokes + timing. AI can generate infinite posts, but it still struggles to generate credibility. And without credibility, you don’t get network effects. You get a feed of perfectly fine slop.

So my 2026 prediction: we’ll see a lot of tries, some viral moments, and no durable winner. The products that “work” will be small, niche, and format-specific (one loop, one community, one vibe) and more importantly, obvious in retrospect.

The exception I can imagine winning is not a new social network, but an AI layer inside existing networks: better creation tools, better editing, better inbox triage, better “help me reply,” better community management or why not, more entertaining AI-slop.

12. AI that you don’t know is there.

My 2026 prediction: the best AI product launches next year won’t feel like “AI products” at all.

They won’t start with a chat box. They won’t ask you to prompt-engineer your life (and yet, now all companies are “teaching” prompting to their workers, again, organizational bottlenecks"). They will just quietly show up inside the workflow and remove friction: auto-fill the form, triage the inbox, summarize the call, draft the follow-up, update the CRM, route the ticket, flag the weird edge case, generate the asset variant, and move on.

This is the consumer version of the same thesis: as models commoditize, interface becomes the product. That has been my core thesis since Luzia started. Not “we have a model.” Everyone has a model. The question is: what UI shape turns capability into habit?

And 2026 is when we see the interface split into two winning shapes:

1) AI embedded inside existing products (invisible AI).
The AI layer disappears into buttons and defaults. “Smart reply” becomes “smart do.” Most users won’t even know which model is running. They will only notice that the thing got easier.

2) AI with a purpose-built GUI (visible AI).
The opposite of “one chat to rule them all.” Think NotebookLM or Luzia Tools style: a dedicated interface that constrains the problem, keeps context tight, and what’s even more important, makes clear what you can do with AI.

Don’t get me wrong, chats are here to stay - we drive most of our lives speaking! - but there is a lot more to AI than a chatbot.

Where both worlds collide: Open Source

A final take for 2026: open source is the forcing function that keeps the two-frontier world from breaking apart.

On one hand, labs want to go private: longer training loops, more verifiers, more tool use, more “automation of research,” more secrecy. On the other hand, they need revenue now: enterprise deals, consumer retention, distribution. Open weights compress that tension, because first to solve a use case wins retention (Cinderella is back”)

• Open models make “wait for the next closed model” a weak strategy. Capabilities diffuse fast enough that the enterprise question becomes “why are we paying for this?”

• That turns the model into a passthrough cost. The defensibility moves up the stack: workflow + integration + permissioning + data loops.

• And it forces productization. You can’t retreat fully into private research if the “good-enough” option is closing behind you.

Open source is also the diffusion engine of the private frontier. Closed labs do expensive discovery, open ecosystems industrialize it, and the time window to extract monopoly rents shrinks.

And without entering geopolitics, pressure is not evenly distributed: China is the accelerant here. As I wrote in the past:

This looks like the Made in China 2025 playbook: pull as many workloads as possible into their stacks, learn from usage, starve U.S. counterparts of revenue, and, meanwhile, build the capabilities (chips, supply, know-how) to run the AGI race.

So here’s my open source prediction for 2026:

By the end of the year, the performance gap between open-weight models and closed frontier models stays around ~3–6 months for most enterprise-relevant workloads. Not because open source “wins,” but because the gap is now small enough that pricing power collapses unless you’re bundling a workflow people can’t leave.

Closing

Models stop being the story. Loops become the story.

We crossed the Turing Test without blinking, not because it didn’t matter, but because it didn’t change your Monday. The next wave does. Not as a “new model dropped” headline, but as a slow, irreversible takeover of workflows:

• AI gets write access (with guardrails) and suddenly it’s not “helping,” it’s operating.

• Agents don’t become autonomous, they become boring: approvals, retries, logs, and a few workflows that run 100 times a day without drama.

• Discovery compounds where feedback is real (biology, code, simulators), and that’s where the first undeniable “AlphaFold-like” moments show up.

• Open weights compress the gap enough that pricing power collapses unless you own the workflow people can’t leave.

So my summary bet: 2026 will feel less magical in demos, and more magical in outcomes. If 2023 was “talking,” and 2024 was “seeing,” and 2025 was “thinking,” then 2026 is the year AI starts doing.

See you in 2027. I’ll be 100% correct again.

The other day, I noticed something surprising. Two years after GPT-4’s debut, I’m running OpenAI’s GPT-OSS-20B on my two-year-old laptop. It uses about 3.6B active parameters, fits neatly in 16 GB, and scores around 85.3% on MMLU — basically early GPT-4 territory — but my only cost is electricity. No cloud bill, no GPU farm, no latency.

When an open-source model is “good enough” and runs on consumer hardware, the economics shift. In AI, that’s the moment the game changes: the control point moves from scarce, expensive capability to distribution and delivery.

This isn’t new. Econ 101 says there are two ways to make money: differentiate, or be the low-cost provider. In AI, those map neatly onto two phases of the race. Phase 1 is about unlocking new jobs no one else can do. Phase 2, once those jobs are unlocked, is about serving them faster, cheaper, and more conveniently than anyone else.

The rest of this post is about what happens when you cross that line — and why every advantage you hold in Phase 1 has an expiration date.

From Econ 101 to AI: two ways to make money

There are two sources of profit: differentiation and cost leadership. If you’re unique, you can charge a premium; if not, the low‑cost player captures most of the value. PCs in the 1990s moved from IBM’s high‑margin differentiation to commodity clones; smartphones show the split today (Apple integration and brand vs. Android OEM scale). I’ve argued before that models commoditize once a capability is widely available — the laptop moment suggests useful nuance.

If we think about technology and AI as an enhancement tool- a bicycle for the mind in Job’s words - automation is the end game. For a task to be automated, three conditions need to be met: (1) enough intelligence; (2) enough reliability; (3) delivery at a lower cost than the alternative. Many everyday tasks — email drafting, summarization, boilerplate coding — sit just below this line. We can now do them reliably and cheaply with the existing models. Many other (sometimes not that complex) tasks are now within reach with frontier models (i.e., long-term memory, narrow domain agents…), while there is still a large group far from reach (i.e., AI research, autonomous reasoning for long periods of time…).

While seemingly not that relevant, thinking about what "good enough" means, and what happens when you clear the bar, has massive implications for how the AI market is shaping.

Reframing the AI race as a two-phase race

The automation threshold reframes the AI race into two distinct phases: phase 1 unlock the job (differentiation), phase 2 win on delivery (cost + distribution).

Phase 1 — Differentiation (unlocking jobs)

Phase 1 is simple: clear the IQ × reliability bar for a specific job. When you do, you get paid because you’re the only one who can deliver it.

Before clearing the bar, you are in the experimentation phase, playing to see how much you can extract from the model. Clear the bar, and it stops being a demo—it’s an automation. Phase 1 is the fancy market position that breaks headlines, brings astronomical valuations, and burns billions of dollars.

Phase 2 — Commodity (serving jobs efficiently; the “good‑enough” period)

Once a job is unlocked, the advantage flips to cost and convenience. We have all been there. It’s nice to automate, it’s nicer if you do it as cheap as possible.

Since ChatGPT started the frenzy, no competitor has been able to really maintain a long-term technical differentiation lead. Phase 2 always comes. We have seen it with models—you can count in the hundreds the number of GPT-4 level models out there—and in the application layer where JTBD were quickly commoditized — copywriting, narrow domain assistants—

In short: a model launches, a category unlocks, then the crowd piles in and prices fall and then Christensen’s rule applies. Once the model is “good enough,” it modularizes and profits migrate to the new bottleneck. In Phase 2 the bottleneck isn’t the model; it’s distribution and workflow—owning where the work happens and the data + process + UX that wrap the model. The model becomes a replaceable part1.

Cloud (2006 → ). EC2 and S3 (AWS services) let startups run workloads they couldn’t on-prem. Containers and IaC then made those workloads portable. From there the fight shifted to price/perf—silicon, commitments, scale—and value moved from “who hosts” to “who wires hosting into the developer workflow.”

Windows (1990s → 2010s). Windows was the control point until the web—and later the cloud—pulled apps and data off the PC. Profits moved up the stack. AI will rhyme: once models are good enough, leverage sits in workflow and distribution, not in the model.

Lab‑by‑lab (through the two‑phase lens)

No-one can skip Phase 2. AI performance converges; the strategy is to monetize differentiation while building the lowest‑cost delivery machine for when capability spreads. Let’s see each lab where they are:

OpenAI — playing both games

OpenAI is a strange mix: a category-defining consumer product (ChatGPT, probably worth a trillion on its own) and a frontier lab releasing some of the most powerful models. That forces a dual plan—keep unlocking new jobs (Phase 1) while industrializing delivery (Phase 2). The catch is financing: consumer ARPU and a commoditizing API won’t pay for city-sized training runs, so the AGI story is part of the business model. You need visible leaps to keep belief—and capital—flowing, even as you push routing and serving costs down. If AGI story fades, OpenAI is still a once-in-a-generation company, but not probably the one that everyone expects.

A Not Very Brief Note on GPT-5 release

OpenAI finally shipped GPT-5. Not a thunderclap, but a frikin good release—and cheaper to run. It’s a system, not a single model: a small classifier sends easy stuff to small paths and only escalates (with or without “think”) when needed. That’s pure Phase 2—routing for cost, latency, and reliability after Phase 1 unlocked the jobs. We’ve been doing this classifier→router setup for ~2.5 years; nice to see it at platform scale. Net: it just works, which is exactly what the market needs now.

Anthropic — the delicate one

Anthropic is the delicate one. They started on the OpenAI path (consumer + models), lost the broad consumer race2, and found a Phase 1 beachhead in coding—Claude 3.5 was genuinely great and really kickstarted the vibe-coding wave (Cursor/Lovable/etc.). The problem is that Phase 1 differentiation in coding seems even more ephemeral than that of other labs, so it works, until it doesn’t.

Open Source and other AI labs are aiming at coding, and competition keeps closing in, likely sliding Anthropic into Phase 2, but with big training bills and a thinner user base to finance the AGI plans (Amazon buys?).

Does anyone remember Inflection AI? Inflection AI was one of the OG AGI labs, trying to find differentiation through Emotional Intelligence. A valid hypothesis that did not find PMF, and as a result, the lack of consumer traction together with extremely high training costs resulted in the death of the company

In Anthropic world, the AGI hype - and the safety speech (we are the saviors of the world) isn’t optional; it’s the story that keeps capital flowing. Without it, they need to own more of the workflow (agents, repos, tests, policy) or the edge evaporates.

Google — distribution + cost + frontier (pick two? pick three.)

Holy moly, Google resurrected from the dead. After the Bard disaster, faking some benchmarks and some racial stereotypes problems, Google made the most spectacular turnaround in the race.

Google’s position is a hedge across both phases: frontier research (Gemini/Imagen/etc.), default distribution (Search/Workspace/Android), and low-cost serving (TPUs + data-center integration). Even without AGI, Phase 2 rewards the infra advantage and distribution. With AGI, Google has the compute, data, and channels to be a competitor in the race. Both routes being self-funded by the largest cash cow machine of history, Google Ads. The strategic challenge is product coherence: turning platform-wide distribution into habit-forming jobs while exploiting TPU cost curves and maneuvering a giant company with hyper-optimized margins.

Apple — the on‑device endgame

Apple’s advantage is distribution on the device layer. From the outside, the strategy seems solid and aligned with the two-phase framework. Sitting out the hype while models shrink and quality improves to a point where on-device inference gives them an advantage — If only it was because that was the OG strategy and not the place where they found themselves after the disaster of Apple Intelligence. On-device inference pushes marginal cost to the user, reduces latency, and fits Apple’s privacy brand. The key is product framing: ship small models where they’re undeniably useful and capable (writing, summarization, personalization), and use private cloud only when necessary.

The biggest risk for Apple is that they continue making terrible bets, and even when the time comes that on-device inference is possible at scale, they miss the boat and either deliver an Image Playground v2, or worse, they are completely caught off guard.

Meta — accelerating Phase 2

Meta’s play was to collapse differentiation across the industry by open-sourcing fast (leaking LLaMA, and releasing LLaMA 2 and 3). It worked: the market moved into Phase 2 sooner, where models are swappable and cost/distribution decide share. Chinese model companies were born. That conveniently aligns with Meta’s incentives: AI isn’t the destination, it’s the amplifier—more relevant feeds, more performant ad inventory, more ways to keep you in-app. The “AI Manifesto” reads like goodwill; the strategy is colder: make the model a commodity so nobody else owns the choke point.

And Meta can afford to wait. Recent AI stumbles aside, the cash machine keeps running ($18.3B of net income) and Zuckerberg is in founder mode, creating the AI version of “Real Madrid” so that if AGI arrives, they’ve kept a seat at the table; if it doesn’t, AI still lifts the core business. Either way, Phase-2 economics favor Meta’s strengths: ship fast, open-weight where it helps, and drive inference cost down so every surface quietly gets better—no need to chase a ChatGPT-level product.

AWS — the commoditization bet

AWS was born to be the cheap, reliable place to run your jobs—swap out a hundred flaky in-house systems for one bill and decent uptime. Andy Jassy built that DNA - and a ton of technology, and it’s still the plan. They’re betting on Phase 2 - commoditization: everyone will need inference, not bespoke frontier workloads, and AWS already sits on most enterprise cloud spend. If Christensen is right, their advantage will be their position, many cloud loads are already in AWS. The play is obvious—soak up inference as it commoditizes—backed by tons of infra and their own chips (Inferentia and Trainium) to make serving really cheap (even if they trail Google on raw performance in their chips)

It’s a coherent strategy with one big risk: there are two other clouds, and AWS’s lock-in was a first-mover story. Do customers experiment with OpenAI/closed APIs and… just stay there? Or do they boomerang back once models are commodities? In case people stayed in OpenAI and the AGI race stays hot, AWS hedged a bit with an Anthropic right-to-match, meaning, AWS could put - a likely expensive - offer on the table to buy their ticket to the AGI table, but hey, same as Meta or Apple, they are sitting in a pile of cash.

Microsoft - Almost forgot about them

Microsoft’s AI strategy is straightforward: rent the frontier from OpenAI and sell it through the distribution it already owns—Windows, Office, GitHub, Azure — via a very thin product layer (and copied features)

The unusual OpenAI–Microsoft partnership—Microsoft put up the capital and gets product and Azure upside—let Redmond “buy” Phase 1 while it wired Copilot into the stack. The bet is Phase 2: when models commoditize, price/performance on Azure and default placement in enterprise workflows matter more than model deltas. The biggest risk for them is similar to that of Apple AI. Thus far, Microsoft has not demonstrated the capability to release good AI products, with few to no hits. There is a chance that Phase 2 arrives, and they still get disrupted because of their inability to ship good products.

China — scale and price pressure

After the DeepSeek January moment, China has been consistently shipping top-performing models across coding, images, text, and math. The strategy is clear: erode differentiation fast, the way Meta did, but without the red tape that slows U.S. firms. I’m also hearing from EU companies about inbound from Chinese clouds with very aggressive inference pricing. This looks like the Made in China 2025 playbook: pull as many workloads as possible into their stacks, learn from usage, starve U.S. counterparts of revenue, and, meanwhile, build the capabilities (chips, supply, know-how) to run the AGI race.

Is there space for small model providers and other competitors in the AI space? If so, what?

My bet is there is room for small labs and startups, but only where the model is a part, not the product: pick a narrow job-to-be-done, ideally local to your user (start where things don’t scale), integrate the base model with proprietary data and a tight workflow (ton of UI work here), and price the outcome, not the token.

Treat the model as swappable infrastructure, and build the moat around data, workflow, and where the user already lives.

Training costs and the long game

Frontier AI is a capital-allocation race: you either self-fund (Meta, Google), charge consumers (OpenAI?), or raise (OpenAI, Anthropic)—while unlocking new jobs faster than training costs rise. Training costs are outpacing unit revenues in many categories; that leaves two viable paths for the leadership of companies to justify crazy amounts of CAPEX expenditures: (1) Pascal’s-wager AGI—pay for a shot at outsized returns; (2) Compounding utility—unlock new automations fast enough that revenue growth beats CAPEX.

AI agents that work—firstly in narrow domains and later for general purposes—will unlock massive economic gains

From: Trial, Error and Move 37

Firms with durable cash engines or patient platform partners outlast pure-play labs, unless labs manage to keep the AGI hype up. Make Phase-1 money, build Phase-2 moats, and assume the control point keeps sliding to distribution—ultimately to the device.

In summary

The AI market is two races at once. Phase 1 is about unlocking new jobs by crossing the IQ × reliability bar; if you’re first, you earn rents. Phase 2 arrives fast: the capability diffuses, unit costs collapse (routing, distillation, better serving), and value shifts to whoever sits closest to the workflow—distribution, data, and UX—often ending up on-device for stable tasks.

Seen through funding: self-funded giants (Meta, Google, Microsoft, Apple) can wait out hype cycles and lean into Phase 2 economics—cheap, integrated delivery where users already work. Externally funded frontier labs (OpenAI, Anthropic) must keep the AGI story alive to finance ever-larger training runs while racing to industrialize serving so API price pressure doesn’t crush margins.

Strategically: make Phase-1 money while you can, but build the Phase-2 machine—routing, evals, reliability SLOs, and deep workflow hooks—because models become swappable parts. That’s where the control point moves, and that’s where durable profit will live.

Appendix. How AI labs differentiate and how to compete in cost?

Sources of differentiation

• IQ — We have gone in two years from talking to models as smart as toddlers (GPT-2) to having unlimited PhD-level intelligence. That’s an increase in raw power and IQ, the type of increase that is (was?) predictable with the scaling laws (which I touched on here and here). This is what we mostly show with benchmarks. Improvements in raw power can be general, or can be domain-specific (law, healthcare, tool usage...)

• Reliability means correct results with consistency: minimal hallucinations, controlled failure modes, and predictable latency. In agentic flows — where a JTBD spans many LLM calls—steerability and low variance prevent error cascades. That’s the difference between a cool demo and a production service.

• Other (short-lived) sources of differentiation. We’ve also seen attempts to stand out via ultra-long context windows (million-token+, even 10M-token demos), new architectures (MoE, SSM-style variants), and whole new “reasoning” modes and tool use. They create daylight for a release or two, but so far none has held as a moat: the edge diffuses via knowledge sharing and model distillation.

How to compete on cost

How to compete on cost in AI could be the topic of several books, but I’ll try to group it into three routes: y

Architecture. There is a line of research on architecture that prioritizes efficient inference, enabling smarter token generation per model parameter. One of the most common is Mixture of Experts (MoE), which in short divides the model in sub-models specialized in different topics that are activated as needed. The other line of work for architectural improvements in efficient inference focuses more on improving cache usage.

Distillation & specialization. This is the process of distilling the capability of large models (IQ + dexterity) into small (sometimes specialized) models that can run very efficiently. Classic distillation still works; modern variants push reasoning into smaller models (see Let Them Think). This, at its core, is the technique that allows OpenAI and other providers to continuously improve the free plan.

For a given number of parameters—weights—there is a trade-off between model capability (IQ + dexterity) and model knowledge. If you want to create very intelligent yet cheap-to-run models, the model's core knowledge will need to decrease; they will be like smart kids who haven’t studied much, and the reverse is also true. This strategy—clearly followed by the OSS models from OpenAI—makes inference dependent on grounding, such as RAG, either from online searches or other sources, which, at the end of the day, passes the cost of inference to the user.

Hardware & systems. DeepSeek proved that you can always squeeze a bit more from a given TPU. Hardware optimization can come from two fronts: integrated systems, where models are custom-made for specific hardware architectures (think about the Google Cloud TPUs or AWS Inferentia 2); or optimizing the workload for specific machines (DeepSeek R0 for the Nvidia-H2O) or creating chips specialized in inference (groq).

On‑Device Is Phase‑2’s Endgame. When a task stabilizes, it migrates to the endpoint. Local inference collapses marginal cost to electricity and amortized hardware, improves privacy, and slashes latency.

No search pages, no comparison grids, no banner ads-just a conversation that smoothly transitions from intent to purchase via a trustworthy recommendation. At that moment, the traditional storefront disappears, replaced entirely by the prompt.

The core thesis of this article is straightforward: GenAI will fundamentally reshape e-commerce, whether merchants like it or not, because those who have the demand hold the power—but for merchants ready to adapt, significant opportunities await.

One topic I’m parking for a future post is how performance-marketing budgets will likely migrate from keywords and social feeds to paid placement—my thinking there isn’t fully baked yet, and it deserves its own deep dive.

It's All About Demand...

Power on the internet belongs to those who aggregate demand. Amazon capitalized on Prime and its one‑click checkout to capture purchase intent; Facebook and Instagram did the same for discovery through targeted ads. GenAI assistants eliminate the last remaining friction: the cognitive load of translating desire/problems into search keywords, and if properly executed, all the other parts of the funnel. As these interfaces internalize catalogs and simplify checkout processes, shopping becomes an interaction with a conversational agent. In this new reality, the conversational layer-not the merchant-owns the customer relationship.

Why are Amazon ‘brands’ called QDYZPP and GHKWXUE? Because inside the Buy Box, any random string will do—watch the breakdown.

For merchants, this presents challenging trade-offs. Historically, brands that chose direct‑to‑consumer strategies sacrificed marketplace visibility in exchange for full control over customer relationships, even as they became increasingly dependent on platforms like Facebook for customer acquisition. On the flip side, selling through someone else’s platform, i.e., Amazon, provided immediate distribution but eroded margins and data sovereignty. An agent-driven, GenAI aggregator further tightens these constraints: if customers never visit your site, does your carefully‑crafted brand experience still matter? I think the article that best captures this idea is Ben Thompson’s take on Nike returning to Amazon.

...And Demand Follows Great Experiences

The obvious follow-up is … but will customers adopt GenAI? Customers follow great experiences.

Customers will adopt GenAI shopping because the value proposition is obvious: clearly describing a "job-to-be-done" to an AI is faster and more intuitive than endless scrolling or search optimization. “I need a new pair of shoes to play tennis”. AI can combine reviews, specifications, social proof, and price data into clear, personalized recommendations. The agent’s memory further streamlines the shopping process by retaining user preferences, previous purchases, and shopping habits.

Making Lemonade

If life gives you lemons, make lemonade. Merchants must work on two dimensions. The first is the obvious: embrace AI and start exploring different channels and opportunities (Luzia, ahem, ahem). The second is to reorient their strategies around three familiar yet crucial dimensions: selection, brand, and leverage over fixed costs.

First, selection. AI assistants can only recommend products they can access and understand. Merchants should expose their catalog through modern commerce APIs (such as MCP standards), inventory endpoints, and rich metadata. But merely being eligible is not enough. Real competitive advantage will come from unique inventory -exclusive SKUs, limited‑run collaborations, or special bundles. The more differentiated the inventory, the greater the likelihood it surfaces as the AI’s recommendation, anything other than that will inevitably lead to commoditization (the TEMU effect)

Second, brand. Aggregators commoditize suppliers unless consumers specifically request them by name. Develop compelling narratives, build community around your brand, and maintain physical or unique digital experiences that keep your brand embedded in consumers’ memories. Brand recall ensures the customer requests ”Nike Pegasus 43,” rather than a generic substitute, “shoes to run my first 10k”.

Third, fixed‑cost leverage. Every conversational AI transaction will implicitly carry a toll, whether as revenue share, sponsored placements, or the opportunity cost of lost organic traffic. Merchants must build models where gross margins scale faster than these imposed costs. Adopting subscriptions, membership perks, and owned fulfillment capabilities convert customer‑acquisition costs into amortized assets. Amazon’s strength was always in making its warehouse network incrementally more efficient with each additional order-merchants must apply this principle at a micro-level.

What’s in it for us, agents?

This is an easy question. For agents of the world, such as Luzia, this is an opportunity to deepen the relationship with the user by unlocking new areas of value, making the vision of a full-blown assistant a reality. This ultimately has two main benefits:

As I explained in my post on AI monetization, the more value is unlocked, the more opportunities there are to capture it. With valuable transactions happening through our platform, opportunities for monetization arise in obvious forms-transaction fees, product placements, rebates, and many others that will need to be invented. Once an agent owns demand, it can levy a toll on every downstream supplier.

The second benefit is the data flywheel. The more engagement and use cases are unlocked, the better we get to know our users, the more local we can be, and thus the more we can help them.

Monetization and data matter only while users trust the agent’s incentives. Push paid placement past relevance and the flywheel stalls.

Aggregator Overreach: A Cautionary Tale
When Amazon treated every brand as a fungible line in a search-results table, Shopify and Facebook stepped in: merchants could buy demand, own the customer file, and keep their margin and information. The same cycle hit restaurants: high take-rates and zero data on Uber Eats or Rappi pushed many to multi-home (selling in more than one platform). Lesson learned: squeeze too hard and the supply you aggregate funds its own escape route. For GenAI agents the guardrail is clear—stay additive. Charge a toll for convenience, yes, but leave room for brands to differentiate and for merchants to build equity, or the next Shopify-plus-Meta combination will rise from the margins you compress.

The Hard Part: Technical Challenges That Require Thinking

Many Unsolved Challenges = Opportunities

If the goal is frictionless commerce triggered by a conversational prompt, today’s reality still presents several technical hurdles at each step of the funnel. Let’s unpack them, starting from the moment intent is expressed:

Discovery and Matching. Agents must interpret subtle cues and context. While modern LLMs excel at natural-language understanding, they still need structured, continuously updated data to return accurate matches. Merchants have to expose rich product metadata via modern commerce APIs-think MCP standards or product-JSON schemas—so agents can spot nuances, like distinguishing a “giftable” watch from one built purely for performance. Just as Shopify became the picks-and-shovels vendor for merchants squeezed by Amazon’s Buy Box, a new class of ‘MCP enablers’ will surface catalogs and inventory to GenAI agents—Shopify-for-APIs instead of Shopify-for-web pages. Scalability matters: manually re-indexing every merchant catalog is neither scalable nor cost-effective. As adoption of a common standard reaches critical mass, laggards will be sidelined-much like banning Google from indexing your site.

UI and UX. How you shop depends on what you are shopping, whether is a staple item or a one off purchase and when you are shopping it. Today’s familiar—if clunky—grids at least set expectations. Replacing them will require fast, messy iteration. Our “book-a-restaurant” beta proved it: a chat reply naming the venue fell flat; users also demanded photos, ratings, even table snapshots. The lesson is clear: GenAI must learn to assemble the right blend of text, cards, and visuals for each scenario, and that will take relentless A/B testing before the interface feels both novel and trustworthy.

Recommendation and Memory. Personalization depends on robust (and compliant) memory. Technology in this area is improving rapidly-our own memory layer has increased engagement by approximately 300 basis points-but memory comes with marginal costs beyond electricity. The more context you want to add, the more expensive each query becomes. Introducing memory without smart optimizations could quickly increase costs by 2-3X. Balancing performance, privacy, and unit economics will be an active area of experimentation.

Checkout and Payments. To deliver a true end-to-end, “job-to-be-done” experience, payments must be cracked-and this is where things get thorny. On one side: legitimate security concerns; on the other: fragmented regulation. Both are justifiable, yet both demand heavy innovation. Questions pile up:

• Are users comfortable authorizing an agent to transact on their behalf-and under what limits?

• Can the agent commit them to installments?

• How do we sandbox hallucinations?

• How do we navigate regulatory fragmentation?

• And my personal favorite: How do we make any of this work within the iron grip of PSD2? (Forgive the sarcasm-my last two years at Amazon were spent ensuring PSD2 didn’t sink the subscription economy.) How do we make AI that uses Pix in Brazil? Local matters

The good news: these challenges are solvable. Forward-looking merchants who engage with emerging standards (MCP and MCP-UI) and shore up their technical foundations won’t just clear the hurdles-they’ll define the track for everyone else. Partnering early with regulators will be equally critical: help shape the rules or live by rules someone else shapes for you.

From Global Model to Local Moment

GenAI supplies infinite IQ, but only local texture—Pix in São Paulo, cash-on-delivery in Buenos Aires, contra-entrega riders in Bogotá—turns that intelligence into sales. Agents and merchants that ignore such rails are restaurants that forgot to cook. The flip side is favorable path-dependency: markets that skipped credit cards and desktop web have no habits to unlearn, letting super-apps in emerging economies sprint past incumbents still chained to legacy infrastructure.

That’s why local AI is the real moat. Merchants and assistants that bind global models to on-the-ground data—local SKUs, regional pricing, real-time FX through providers like iFood, Rappi, or small mom-and-pop stores—will spin a faster flywheel: more trust → more usage → richer context → better recommendations.

Where Does This End?

Ultimately, this transition will result in a handful of dominant AI assistants integrated deeply into operating systems, messaging apps, and smart devices. We’ll see fierce competition around schema standards, product feeds, and promotional formats. As the competitive landscape evolves, value will increasingly shift towards the point of intent, replicating the historic shift from shelf space in physical stores to search results online. Storefronts will persist, but increasingly as APIs rather than as consumer destinations.

This transformation, however, is not a threat but an opportunity. GenAI commerce represents an additional distribution layer rewarding uniqueness, memorability, and operational efficiency. The way I think about it is as Uber and the car-riding business, Uber TAM wasn’t limited to taking market share from taxis, which they did, but expanding the mobility to many more rides.

Merchants should approach this prompt-driven landscape strategically, just as they approached the browser two decades ago-with clarity about what only they can own. Agents might capture customer queries, but merchants will always own the differentiated answers that keep customers returning.

In short, the path from chat prompt to checkout isn’t a straight API call; it winds through regional regulations, vernacular, and trust. Merchants and agents that embed those local realities into their data flywheels will turn one-off transactions into durable, high-frequency relationships.

Outside our circle, most people aren’t chatting whether openAI or Google will rule the world. In fact, about 80% of the global workforce doesn’t sit behind a desk—they’re driving buses, serving customers, working construction sites, or farming fields. For them, AI isn't about chatbots or multimodal embeddings—it's about whether technology can truly make their daily tasks simpler, quicker, or less frustrating.

The gap between people like us—early adopters—and everyone else isn't just about design. It's a huge opportunity to create tech that genuinely helps people in their everyday lives, and it's also the reason we started Luzia: to help everyone avoid falling behind.

This essay makes the case for what I call Local AI—AI built around cultural, linguistic, skill-level, and regulatory realities unique to each market.

Give me electricity, I will put a light bulb

AI is a general-purpose technology—a fundamental shift that, piece by piece, will quietly reshape our lives until we barely notice it's there. AI is not the first, and as such there are powerful lessons from previous ones. Let’s look to electricity:

In the early 1900s, factories installed electric lights and motors but stuck with their old steam-powered workflows. It helped, but productivity stayed flat. Only when Henry Ford rebuilt his entire factory around a moving assembly line did electricity transform the world. Suddenly, assembling a Model T dropped from over 12 hours to just 93 minutes, slashing costs and creating an entirely new economy. Electricity wasn't about the bulb—it was about reorganizing everything around it.

AI today is in the "light bulb" phase: it can write emails, tutor kids, or summarize meetings, which is extremely useful, but the reality is that for most people, it's still just a chatbot. Chatbots are helpful, but they're nowhere close to AI’s true potential.

Why? Because most AI experiences aren't instantly useful or intuitive enough to justify changing habits. Trust alone doesn't drive adoption; immediate, frictionless value does. Just look at iFood or Pix: adoption exploded because they delivered instant, obvious benefits. Chatbots today demand too much thinking, guessing, and effort from users. They require users to update their mental models of what AI is, from the useless Siri to the useful Luzia. Mass adoption won't come until AI becomes effortless from the very first interaction, whatever that interaction or use case may be.

We started Luzia with a chatbot because it was easy to integrate quickly into people's lives through platforms like WhatsApp, and f** yeah it worked - we reached millions within the first months. But we quickly realized that people don’t want to learn how to talk to an AI—they want immediate solutions. A quick, simple real product example: Luzia users can solve math problems through chat or a dedicated tool—and three times as many prefer the tool. Similarly, ten times more users create summaries with the summary tool rather than the main chat, and five times more have educational conversations with Luzia Teacher rather than Luzia. Why? Because tools and custom characters require no effort, no guesswork—just results.

This shift to practical, intuitive, "in-your-face" AI isn't just better design—it's essential for mass adoption. With 80% of workers worldwide not at desks but driving trucks, farming, or caring for patients, AI must blend seamlessly into everyday tasks. This is Local AI: practical, immediate, and built around real-world workflows, empowering everyone—not just tech-savvy users—to benefit from AI's true potential.

AI for Everyone, Effortlessly

Our early success with WhatsApp taught us two things: people quickly adopt easy solutions; people prefer human connection with their assistant, even when they are virtual. The next challenge was clear: owning the user experience entirely to deliver simpler, even more intuitive and connected products.

We’ve learned that truly great AI products don’t need explanations—they just work. Guided by that insight, we’re moving beyond the chat box to one-tap tools woven into everyday Latin-American routines: closing an iFood or Rappi hyper customized orders (we know the user even before they are craving some food), getting on-budget product suggestions the moment you open the app, flipping forty pages of lecture notes into a two-minute summary, or surfacing the cheapest empanada stand within five blocks. From uni to corner tienda, each touchpoint is local, instant, and effortless—AI exactly where you need it, the moment you need it.

Localization is subtle—just enough to feel relevant, never distracting or forced. But our true differentiator is distribution. We grow organically within tight networks by consistently delivering high-quality, frictionless experiences, and we leverage strategic local partnerships to integrate seamlessly into daily routines, appearing exactly when users need us. Our on-the-ground presence is deliberate and authentic—Luzia is fundamentally a LATAM company. This means deep local insights, genuine trust, and direct user connection.

Luzia isn't about complex interactions; it’s about effortless, immediate results, empowering every user, everywhere.

The moment is now

AI is the electricity of our era. But just as electricity only changed the world when people redesigned factories around it, AI will only transform lives when we redesign workflows and respect local contexts. Latin America offers fertile ground: adoption is growing and trust is high, yet no one has defined what “AI” feels like for ordinary people.

Luzia will define what AI actually feels like for everyday Latin Americans. By combining deep localization, native integrations, AI-first workflows, and distribution embedded in the apps people already trust, we can create that “of-course” moment when even your parents see AI as a utility, not a gimmick. We’re not stringing light bulbs in a steam-era factory; we’re laying the assembly line for the AI age. The ingredients are in place—millions of users, committed capital, and on-the-ground partners—so the only thing left is execution.

I launched Luzia to ensure people like my mom—literally our first user—can directly benefit from this revolution. AI is, at the risk of repetition, intelligence as a service: an opportunity for everyone to better understand the world around them. With understanding comes empowerment. That's our DNA at Luzia—giving people the knowledge and skills to advocate for themselves. To deliver on that promise, we must stay close to people's daily lives, deeply understand their needs, and ensure this generation's electricity adapts seamlessly to the way they actually live.