@adlrocha - Are we approaching a new AI winter?

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@adlrocha - Are we approaching a new AI winter?<br>An adoption crisis and the end of an AI cycle are aligning<br>adlrocha<br>Jun 07, 2026

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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.<br>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.<br>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.<br>> 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.<br>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.<br>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.<br>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).<br>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.<br>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!).<br>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.<br>Fortune’s analysis put...