Everything starting from mid-2026 is incredibly wrong, which is pretty poor predictive performance considering they published it mid-2025. List of misses:<p>- In mid-2026, China will be six months behind the released models of the American labs. (In mid-2026, China released Kimi K3, which was equivalent to the best released U.S. model at the time, Claude Fable.)<p>- In mid-2026, China will nationalize all domestic AI labs and centralize them into a new special economic zone, the "CDZ," created around a nuclear reactor. 80% of all chips in China will be redirected to the CDZ. This uh. Obviously didn't happen.<p>- In late 2026 OpenAI/Anthropic will release a tiny model that outperforms their previously-best 2026 model (Astra/Fable), that is priced 10x cheaper, and they will market fine-tuning for it. (Lol.)<p>- AI will increase the stock market by late 2026 by 30% (in fact the S&P 500 is up 13.9% YTD, which is pretty good, but not at all out of the ordinary).<p>Their predictions for 2027 also seem pretty wrong: e.g. they expect continual learning to be solved in January, and RSI <i>including from a research perspective</i>, not just "optimize CUDA kernels," to also be solved in January, which I suspect no serious lab employee believes anymore. Most lab employees believe RSI will happen (as do I!), of course, but not in two and a half months.<p>By early 2027 they expect cyberwar with China because China has stolen the weights of a model and smuggled it into the CDZ.<p>By mid-2027 they expect true superintelligence, where essentially all engineers and researchers are useless, unable to comprehend the advances the AI is able to make and largely unable to even usefully manage or steer AI, who are better at self-managing than a human would be.<p>Considering their 2025-mid-2026 predictions were basically "progress continues as normal and nothing crazy happens," it's not super surprising that they were more or less right about <i>that</i>. Their 2026/2027 predictions were the spicy ones, and none of them have happened, or are even on track to happening!
Agree with most of this, but:<p>> In mid-2026, China released Kimi K3, which was equivalent to the best released U.S. model at the time, Claude Fable.<p>I don't think this was, or is, true: <a href="https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index?models=claude-fable-5%2Ckimi-k3%2Ckimi-k3-low" rel="nofollow">https://artificialanalysis.ai/evaluations/artificial-analysi...</a> - even from a vibe check alone, it was pretty clear that K3 was missing Fable's special sauce, especially for more hands-on problems.<p>The Chinese model lag depends on who you ask, but the current estimate seems to be about four months: <a href="https://stateofopensource.ai/" rel="nofollow">https://stateofopensource.ai/</a><p>> In late 2026 OpenAI/Anthropic will release a tiny model that outperforms their previously-best 2026 model (Astra/Fable), that is priced 10x cheaper<p>We don't know the sizes of any of these, but GPT Luna and Claude Haiku 5.5 are tremendously competitive for their cost, and in some ways beat their larger-sized predecessors. I'd say there's enough doubt that I'd be willing to provide partial points to this claim. (No finetuning, yes.)
The prediction was a 10x <i>price</i> reduction, so equivocating about "we don't know the size" doesn't save their prediction — a 10x price reduction would imply the model was likely more than 10x smaller in parameter count, and the price factored heavily in their prediction (they predicted its low cost would destroy other competition, since no one else could have such a capable model at that low price). And Haiku/Luna are nowhere near competitive with Astra or Fable, which doubly damns their prediction. I'm surprised you would pooh pooh the equivalence of K3 and original-Fable (which has been widely shown across many different benchmarks and even human preference data), while claiming an equivalence between <i>Haiku</i> and Fable!
Wow shocker. Crazy unfounded prediction website that lots of people said was bupkis at the time turned out to be wrong