So let's get started though with marking my work for this year. So the first prediction was that

there would be no AI wall. There was this battle between whether scaling AI models was still a

strategy that would work, that would deliver results, whether they could be actually even

built to that size or not. My second prediction was about the

speed of deployment, making certain predictions about how fast things would spread and what would

happen to the price of tokens. I made a prediction that bots would outtalk humans this year

in the production of natural language. I also predicted

that Waymo would overtake Uber in San Francisco. I noted that I expected climate extremes to

intensify significantly and that alongside this and despite the change in the political

environment, renewable deployment, particularly of solar, would despite the change in the political environment,

renewable deployment, particularly of solar, would continue to surprise to the upside.

And alongside that, that, again, despite changes to the political environment,

electric vehicles would significantly shift up a gear in their market.

So there were seven predictions, and I had some watchlist themes around geopolitical volatility, demographic decline, and climate and capital, which I didn't have, you know,

strong tests against. But let's start with the first one, which was that there would be no AI

wall. And I said, look, research is accelerating,

not plateauing. And we would likely see a 10 million token context model and reasoning breakthroughs

across some of these reasoning benchmarks. Now, both of those matter because the context window

of a model is a little bit like its working memory. It's a bit shonky as a

working memory, but it's the bit that you put into your LLM and it can manipulate back and forth.

And when you get to the end of the context window, it tends to hallucinate much, much more.

And the reasoning model, the reasoning tests like Frontier and RKGI are very useful tests for

...