At the start of the year, I made seven predictions about how 2025 would unfold. Six months in, it's time to mark my own work. From AI capability breakthroughs to autonomous vehicles, climate extremes to workforce transformation, I examine what I got right, what I missed, and why the 2027-2028 period will be when vertical AI hits the real economy in force.In this episode you’ll hear:The AI wall that never came: Ten-million-token models exist, O3 scores 25% on Frontier Math vs GPT-4's 2%, but some models are inconsistent and overthink problemsWhen bots officially out-talk humans: My modeling shows LLMs crossed the threshold of producing more text than humans sometime this summerThe Waymo vs Uber SF battle: They've beaten Lyft and expanded to New York, but Tesla's Austin robo-taxi fleet changes the competitive landscapeClimate and energy predictions that were "too easy": Record climate extremes, 30% solar growth, and Indonesia's stunning EV jump from 20% to 80% in two yearsWhat I completely missed: The AI capex boom, humanoid robots at Figure/BMW/Amazon, and workforce impact with CEOs reporting 20-50% AI assistanceWhy getting too many predictions right is a problem: I reflect on whether scoring too well means I didn't push boundaries enough in my forecastingThe 2027-2028 turbulence ahead: Why four-year-old AI startups challenging incumbents while early adopters reap deep organizational benefits will create economic turbulenceOur new showThis was originally recorded for “Friday with Azeem Azhar”, a new show that takes place every Friday at 9am PT and 12pm ET. You can tune in through my Substack linked below.The format is experimental and we’d love your feedback, so feel free to comment or email your thoughts to our team at [email protected]’s links:Substack: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar?originalSubdomain=ukTwitter/X: https://x.com/azeemTimestamps:(00:00) Grading my predictions from January 2025(01:23) #1: No AI Wall(03:59) #2: Warp-speed deployment(05:16) #3: Bots out-talk humans(06:24) #4: Waymo overtakes Uber in SF(08:31) #5: Climate extremes intensify(09:09) #6: Solar keeps breaking records(10:06) #7: EVs shift up a gear(11:12) The problem with predicting too accurately(12:01) What I missed(12:14) The CapEx boom around AI(13:56) The rise of humanoid robots(14:36) AI's impact on the workforce(18:40) Looking ahead(18:48) Infrastructure first, apps next(19:52) 2027/2028 will be a "period of fireworks"(21:39) When we'll find out if AI is a bubble(23:02) A question for the futureProduction:Production by supermix.io and EPIIPLUS1 Ltd Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Full Episode
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 deployments.
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 out-talk 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 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 markets.
So there were seven predictions, and I had some watch list themes around geopolitical volatility, demographic decline, and climate and capital, which I didn't have 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 whether a model is capable not just of producing text, but also reasoning through problems. Now, in both cases, I asked ChatGPT03 to mark my work And it said, you are accurate.
There's been no slowdown in AI progress, that ultra-long context models have emerged, and AI models are shattering reasoning benchmarks. And that's true. I mean, there is this 10 million token context window with Meta's Lama Force Scout model. But I want to say that even though that model was released, Meta had had all sorts of issues with its Lama models and there were problems at the time.
So Wildchat GPT has very gracefully given me that as the evidence point that I was right. I don't think that's a best evidence point. There are a couple of other models out there, Megadev, and I believe Gemini has one in testing, which are up at that 10 million level. And at the same time, O3 itself has really, really pushed its benchmark scores.
So on the frontier math test, which GPT-4 used to score 2% on, O3 got 25%. And I think that this is clearly what we are seeing. And if you're using the tools, you have noticed that they've got better. Although, as people also know, they're really, really quite unstable. Something that happened with our team was that we had standardized on Gemini Flash 2.0 for some of our internal workflows.
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