The Big Ways AI Just Changed

**Nathaniel Whittemore** (0:00)
Today on the AI Daily Brief, Why June was the most significant month in AI in years. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
Well, friends, it is July 4th weekend. Most of my American listeners at least are washing summertime lakes, fireworks, and patriotic feelings on the 250th anniversary of this country. And yet over here at AI, we are shifting from one month to another. It is a little poetic that at the very, very end of the month, Fable 5 got back just in time for us to get back to building in July. And yet before we do so, it is worth spending a moment, I believe. Looking back at the last month, which I would argue is one of the most significant in the post-ChatGPT history of AI.
By the way, for those of you wondering, this website companion experience was actually created not with Fable, but with Codex and GPT-55.
Before we get into June, let's actually go back to May. The historian in me thinks that these two months kind of make a matched pair, telling the same story but from different angles. So the story of May was all about the shift from the AI subsidy era to the token scarcity era.
Even before May, we had started to see providers shift away from their seat-based subscription models and move towards more usage-based models. This was of course the inevitable consequence of shifting from pre-agentic to agentic workloads, which consumed just an absolutely massive amount more of intelligence than the type of queries that we were running back in 24 and 25
May was also when we started to see the chickens coming home to roost when it came to enterprises that had run out to start token maximizing.
Uber had been in the news for a couple months as it burned through its AI budget in the first four months of the year. We got more and more reports of companies turning off their token leaderboards. And all in all, May felt like the beginning of a shift to a new paradigm. Now at the beginning of June, that started to become real. At the very beginning of the month, we had Walmart moving from unlimited usage of their internal tools to token budgets. Uber made headlines when it set a $1,500 per month cap on AI spend. And these stories and the others, like them, reinforced the idea that token efficiency and token discipline were going to become important new aspects of the AI landscape, particularly in the enterprise. And starting then and throughout the month, new approaches, new architectures, efficiency became the name of the game. Now it's a little reductive to assume that before this, every company was just applying the most advanced frontier model to every workload. But that's honestly not that far off from what I think the average situation was with most companies. Frankly, at most companies, AI adoption hasn't proceeded to the point yet where they would really even need to be thinking about efficiency because most companies are just consuming such a vanishingly small portion of the total intelligence that they will ultimately consume. And yet for those companies on the vanguard, there was very clearly a new emphasis on new efficiencies, new model architectures, shifting to lower cost models, including Chinese open weight models, which would become a little more fraud as we would see later in the month. We also saw some indications of the infrastructure around AI adapting as well. Independent benchmarking company, Artificial Analysis, shifted around some of the metrics in its core intelligence index to better reflect agentic usage. And very quietly in a story that I still think is wildly under discussed is Microsoft pushing not only a new set of proprietary models that they had trained from the ground up, but a new product where they would post train models to the specific criteria and requirements of a particular enterprise customer.
I think this missed notice A, because it was surrounded by a million other Microsoft announcements, but B, it was just before we really started talking about token efficiency as the important idea du jour.
But the month really kicked into high gear when on June 10th, Anthropic released Fable 5
And while historically it has often been the case that labs have underwhelmed when they've shifted to entire new numerical categories, such as when OpenAI went from the GPT-4 class to the first GPT-5 model, Fable 5 was not that. It was immediately and clearly much more powerful, particularly around technical and coding use cases. But honestly, as I've said a couple of times, as much as the initial narrative in those first couple of days was that it made more difference for those coding tasks and the InverOpus and GPT wouldn't be as apparent in other areas, I have found that not to be the case. I think the improvement over the other models in every area is extraordinarily clear. Still, for the first 48 hours or so after Fable 5 was released, the name of the game was finding your most complex and challenging problems and letting Fable 5 just absolutely rip on them. The best way that I can describe what was different about it, given that I am non-technical and can't compare the elegance or proficiency of the code of 48, for example, to Fable 5, one of the ways that I can describe how it felt different was that if I look back over all the things that I have done throughout the course of 2026 with any of the various coding models, I very frequently get to 80 or 90 percent of a project and then just don't finish it. Now, in some cases, that's because the initial test or results weren't exactly what I wanted or just my priorities shifted elsewhere. But in a number of cases, it was because while the coding models had made the activation energy low enough to just get started, they hadn't obviated the completion energy to actually get the thing done. Fable 5 was the first model that made it feel fairly insignificant not only to start those big coding projects but to just finish them as well. And any of you who have enjoyed the new AI Daily Brief website that chunks every episode down into individual shareable components is living in the benefit of that. This is a project that I had had kicking around for weeks at that point. And because Fable 5 came around, I just decided to get it done and done it got in one fell swoop. And thank goodness because as we know now, Fable 5 wouldn't be around all that long.