Are we approaching a new AI winter?

Jun 07, 2026

I don’t know about you, but I feel like the mood around AI is shifting slightly. What once was the promise of the imminent disappearance of every knowledge worker in society so we could all enjoy our free time and hobbies, is cooling down and moving towards the realisation that this new technology in its current form still has limits. While it most certainly will eventually make some humans obsolete, it won’t be this early the promised Philosopher’s Stone that everyone was preaching.

I feel like we may be approaching the end of the LLMs and agents honeymoon. And please don’t get me wrong, I am not saying that this technology is not going to be useful, but that we are going to start seeing less optimism for a bit until we get to the next summer season.

This winter is not happening as a result of the technology plateauing or reaching a ceiling (which I don’t feel in a position to make an informed decision about) but due to a lack of adoption and technological diffusion. But coming from crypto I can tell you with some authority, winters are great to build without the distraction of the constant noise, and now is the best time to work on AI adoption.

> Special thanks to Pablo Grueso for helping shape and strengthen my opinion on the topic after our brief conversation about the matter in a car ride. Cheers!

What tokenmaxxing was

For about eighteen months, the dominant corporate theory of AI adoption was simple: the more your employees used AI, the better. Everyone needed to start adopting this new technology, become AI-native, and explore how their capabilities and output could be augmented. AI usage became the target metric. Companies built internal leaderboards, set token-consumption targets, and measured AI success the way they once measured digital transformation, by adoption rate, not by outcomes.

I am indeed referring to the infamous tokenmaxxing, i.e. feed the models as many tokens as possible, maximise throughput as this will maximise your production of results. The assumption baked into it was that more input would produce proportionally more output and consequently value. I still ask myself how this could be thought of as a good idea in the first place.

Amazon, for instance, ran a leaderboard called KiroRank, an internal ranking system that scored engineers by their activity on Kiro, the company’s AI developer platform. It seemed like a reasonable way to measure who was actually using the tools. What happened instead was predictable in retrospect: engineers assigned autonomous agents to run unnecessary tasks just to climb the rankings. Token consumption went up, but useful work didn’t follow (oh, surprise!). Amazon’s senior vice-president Dave Treadwell eventually told staff: “Please don’t use AI just for the sake of using AI. Use AI to help you solve customer problems, to help you solve business problems, to innovate”. However, the leaderboard wasn’t incentivising that behaviour, but the use of more and more tokens (fuck compacting your context). Obviously, the leaderboard was shut down.

Amazon replaced it with something more sensible: tracking whether engineers were regularly producing useful code with AI, not how many tokens they were burning through (a more subjective metric, harder to measure, but more aligned with the real output they were looking for).

This is not an AI problem, per se, but another example of the design of policies that do not have the goals and incentives in mind. But when AI was going to solve everything, more tokens could translate into more solutions. Turns out that AI may need to be conveniently steered in order to solve things, and strategy and domain knowledge doesn’t burn as many tokens and require humans to actually work.

First warning sign that we may have not figured out how to adopt this technology just yet.

Why it stopped making sense

The clearest data point on the ROI of the tokenmaxxing failure came from Uber. The company’s CTO revealed that Uber had burned through its entire 2026 Claude Code budget by April, four months into the year. The COO, Andrew Macdonald, then said publicly what a lot of people were already thinking: “That link is not there yet”, meaning the link between AI token consumption and features that users actually wanted. Uber pumped the brakes (again, oh, surprise!).

Michael Burry, who made his name betting against the 2008 housing market, described AI tokenmaxxing as a “crazy, rushed, temporary phase” driven by “quota-driven, leaderboard-driven, management-mandated overconsumption.” He drew explicit parallels to the late-1990s dot-com bubble and backed his view by purchasing put options on 1 million Nvidia shares. We’ll come to this comparison to the dot-com bubble in a few paragraphs.

Fortune’s analysis put it more formally: most companies are stuck at Stage 1 or 2 of AI adoption, i.e. basic implementation, workflow redesign. Real value requires business reinvention, the kind that most incumbents aren’t actually attempting. The companies eating their lunch are AI-native from the start.

This is not the same as saying AI doesn’t work. It’s saying that we still don’t know how to efficiently use and apply AI. This is why tokenmaxxing failed as a metric for AI adoption and proficiency in the company. Optimising for the measurement rather than the outcome, produces exactly what you’d expect: lots of activity, not much value.

If they gave me a penny every time that I’ve heard from friends and colleagues in the last few months the sentence: “since AI, I am working more than ever, it’s like there is no time to catch up with new developments”. And my question to them is always the same, “do you think that you’ve produced more value than before AI?” Spoiler alert: the responses differ greatly :) (related to this, this may be a good moment to read this post that I wrote a few months ago about how “we are not scared of AI, we are scared of irrelevance” if you haven’t already.

Yet another point in favour of my thesis about now being the best time to start adopting AI, but one where the public narrative will start feeling colder.

The convenient excuse

But AI adoption is not only becoming an obsession for some, but an excuse for others. Across 2025 and into 2026, a pattern emerged: companies announcing large-scale layoffs with AI as the stated rationale. Amazon (~30,000), UPS (~48,000), Oracle (~30,000), Microsoft (~23,000), Salesforce (~5,000). Roughly 80,000 jobs in 2026 alone, with 45+ CEOs citing AI as a driver (see this source I came across in my research).

Jack Dorsey cut Block from over 10,000 to just under 6,000 people and was explicit about it: “We’re not making this decision because we’re in trouble. Our business is strong... But something has changed.” Brian Armstrong at Coinbase put it similarly: “I’ve watched engineers use AI to ship in days what used to take a team weeks” (and then they have a massive outage). He announced the end of “pure managers” and described the goal as “rebuilding Coinbase as an intelligence, with humans around the edge aligning it.”

I don’t think these CEOs are lying. AI genuinely does change what’s possible with a smaller team. But AI is also functioning, in many of these cases, as a socially acceptable frame for a contraction that was coming regardless. Companies overhired during the low-interest-rate boom, and the correction was inevitable. AI provides a clean narrative, shifting the cause from “we made bad hiring decisions” to “the technology changed.” Both things can be true at once, but I am not buying the AI narrative just yet.

What I don’t expect (at least not yet) is a massive wave of net job destruction. The more likely near-term pattern is a contraction while people and organisations figure out what AI actually is, followed by an expansion once a new generation of AI-native professionals emerges who know how to use these tools with the right judgment. That’s historically how transformative technologies diffuse. It’s rarely a smooth upward curve, and it’s almost never the story that gets told during the first wave.

I am an AI-pilled myself, and I think AI is a different technology and will create a completely different revolution to the ones we are used to, but I feel it’s still early. Narrative-wise is great, but I can’t stress it enough, we still haven’t figured out the best way to use this technology.

The codebase regret

And here comes the reason that convinced me to write why I thought “winter is coming” as a result of us still figuring out how to use this technology. Some developers have started posting publicly, in increasing numbers, that they regret how heavily they relied on AI to build their codebases.

Dragos Nedelcu, a senior developer, wrote about generating roughly 150,000 lines of AI code across a production project. After a few months, he faced a mess: duplicated logic with almost no reusability, dead code everywhere, unit tests that didn’t assert anything meaningful, and cascading bugs that spread across seven or more files simultaneously. His conclusion was blunt: “It is faster to just start over instead of correcting hundreds of lines of messy AI-generated code.” Another engineer mass-deleted 14,000 lines of AI-generated code after seven months, the codebase shrank from 41,000 to 27,000 lines while keeping all the same features, and the bug rate fell 73%.

Let’s be honest, we’ve all faced this at some point in our relationship with coding agents (at least did).

Victor Taelin, who builds the HVM programming language and someone I’ve been following for years, documented his pain in real time on X. He used Opus to implement a new approach in a single day: 3,000 lines of C, 5x performance improvement. Then spent the next fifteen hours auditing it, finding what he called “retarded shit”: a case where the model had silently assumed HVM5 was supposed to handle under- and over-applied functions, implemented a massive system for that assumption, and never asked. None of it should have existed.

His conclusion shows another reason why I think that we may have not learnt how to use this technology effectively just yet: “I went from 0 to 95% in the first 5 hours. Yet, 15 hours later, it is still not 100%... if I have to read it all, review it all to ensure there is no retarded shit... what did I achieve by using AI, other than that dopamine anticipation?”

That last phrase, dopamine anticipation, is the most honest description of vibe-coding I’ve read. Oh, that beautiful AI slop slot machine that we’ve all become so addicted to.

Luis Ángel Alda, a Spanish developer, put the structural problem well: AI produces systems that are “locally correct, globally incoherent.” The models are good at optimising the next step. Architecture is precisely the opposite, a long-horizon, intuition-driven discipline that requires what he calls “feeling the software.” You accumulate that from years of building things, watching them fail, and rebuilding them. AI doesn’t have it. What it has is extremely good local pattern completion, which is useful for a large number of things and actively harmful when you need global coherence.

Again, none of this means AI is useless for coding. I use it constantly and it has genuinely changed how much I can ship. But there is a difference between using AI as a tool with judgment and using it as a substitute for judgment. The people posting these regret stories mostly did the latter, they handed over the architecture, not just the boilerplate.

The skill that actually matters here is knowing when to use it and when not to and this is what this winter will be about. That takes time and accumulated failures to develop. We’re all still learning it. I include myself. That “human/engineering taste” is still very much needed.

, @sngular, @xoelipedes y @ChristianPalou no sabe lo que ha hecho y me acogen para contar como pastorear agentes.\nNuevo contenido desde la de VLC (4 meses es un m

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