What the hell happened with AGI timelines in 2026? – Rob Wiblin
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What caused the sudden 2026 'vibe shift' in AGI expectations?
Earlier this year, I put out what might just be the worst timed thing I've ever published. I set out to explain why everyone was again feeling bearish about AI and people's timelines to AGI were getting longer and longer. And just as I was wrapping up the research for it, everything changed. Vibes did a hard 180-degree turn. Take famed programmer Andrej Karpathy, for example. Last October, he said AI agents were slop, just not there, and that it would take another decade to actually make them work. Then, just two months later, he tried some new AI coding agents and said this instead. I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse.
Clearly some powerful alien tool was handed around. The resulting magnitude 9 earthquake is rocking the profession. So what the hell could we have learned about AI that quickly that we didn't know already on some level? And are we interpreting that evidence correctly? Or is 2026 just yet another cycle of self-perpetuating AI hype like we've seen before? Today, I'm going to go through the big new pieces of evidence we've gotten about AI this year and carefully analyze what they show and what they don't. Then I'll explain the debates that still split the AGI bulls from the AGI bears. Debates which remain live because we haven't gotten clear evidence to settle them either way. Let's go. It's almost hard to remember how bearish the world was about AI back in October 2025.
Reasoning models didn't seem to be generalizing as much as first hoped. AI agents were around, but everyone found them too unreliable to actually use for anything. Technically, T55 was bang on the existing capabilities trend, but in the public imagination, it was a massive flop. And meanwhile, inside industry, the narrative was dominated by a series of dorkish Patel interviews about how AI wouldn't be able to do significant jobs until it's a hundredfold more reliable or develops continual learning or grows up in the way animals do or receives some other really major architectural improvement. Well, what a difference two months can make. The key breakthrough moment was Cloud Opus 4.5. It wasn't just Andrej Karpathy whose mind was blown by this.
Over December, tens of thousands of other programmers using it through Cloud Code started shouting to anyone willing to listen to them that as far as they could see, AI agents were really good now. Finally, they could leave Claude to do significant projects on its own without fully expecting it to trip over its own feet in the first five minutes. And AI Twitter filled up with entire computer games and apps built by Claude over 10 to 20 hours of independent work from a single short prompt. The arrival of these highly capable AI agents was a big surprise to most people, including me. After all, Literally no progress, as far as I can tell, has been made on any of the purported blockers, like continual learning or world models.
And to date, as far as I know, we still haven't gotten a better explanation for what happened than, yet again, scaling and reinforcement learning worked. We threw more data and more compute at the best models. And as they got smarter, their chance of screwing up any individual step in a long chain of them fell low enough that it was finally faster to delegate a well-specified task to them than it would be to attempt that task yourself. Maybe the only surprise, though, was that we were so taken aback by this. The EPO Capabilities Index is an effort to zoom out and blend together AI performance on 40 benchmarks across a wide range of different domains. If I had to look at just one number, this is the indicator of frontier progress that I personally care the most about.
The EPO Capabilities Index finds that overall, AI progress was roughly constant from 2022 to 2024. And then with the arrival of reasoning models in September 2024, it started advancing two to three times faster than before, a trend that continues through the present day.
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