State of AI 2025 with Nathan Benaich: Power Deals, Reasoning Breakthroughs, Real Revenue
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What are the latest breakthroughs in AI reasoning and chain‑of‑thought models?
I think you can't ignore the fact that the sums of money going into this industry are truly a gargantuan. Circularity of these deals is interesting. Things can flip quite quickly. One gigawatt of a data center for AI basically costs $50 billion in Capex. On an annual running basis, it costs between like another eight to nine, maybe even eleven billion dollars to run. Companies are trying to do deals with anybody who has any capacity. In the short term, what many GPU data centers are getting. powered on is just uh gas turbines. Well that we've come to the point where we just want like an AI that works on our computer, but like to get that, you need to have so many more powerful systems collaborate with you.
That was the first time you had a system that could show its reasoning. Since then to now, the progress is pretty astounding.
Hi, I'm Matt Turk from Firstmark. Welcome to the Mad Podcast. Today I'm excited to welcome back Nathan Banish, founder of Airstreet Capital, to discuss the 2025 edition of his State of AI report, a must-read on where the field really is. We cover a lot, including why power is a new bottleneck, reasoning and chain of action robotics, and the business reality, revenue, margins, and what it means for builders and investors. Please enjoy this great conversation with. Nathan. Nathan, great to have you back. Thanks for having me. The state of AI 2025 is out. And um as always, it's essential reading for anyone who's serious about understanding AI. This year it's uh three hundred and twelve slides of goodness.
A bit of a big year in
AI. Every year I try to cut it down a little bit, um, but this year it just felt like we were Sharing it with various subcommunities of the AI of the AI community. Uh and each time we did that, the robotics folks would be like, Hey, it's a little bit light on robotics, can you add some more? And then we send it to the bio folks. They'll be like, Why don't you cite this paper or that paper? And hence the inflation
Amazing. All right. So we're certainly not going to cover everything in this conversation, obviously. As always, the report is available in its entirety for free at uh stateof.ai. So um we're going to riff on some of the most important topics and IDs in the report, but obviously people can go uh and check out the report directly for more. All right. So uh starting from the top, in the world Of research, you mentioned that 2025 was a your reasoning got real. Uh so how far have we come in the last twelve months?
I'd see pretty far. Um about twelve months ago or so we had I think the very early inklings of it with O one preview, uh potentially around like this time last year. And uh that was the first time you had a system that could kind of show its reasoning, show its stepwise process to get to a more complicated answer. And this has generally been the dream in in AI for a long time. And uh and since then to now, I'd say like the progress is pretty astounding. One of the areas that that progress has kind of unveiled itself is in mathematics and other verifiable domains where you can like explicitly say, yes, the system works or doesn't work. And you know, we saw gold medals on the International Math Olympiad by a couple of labs, including OpenAI and DeepMind.
that area probably with uh if you'd asked the experts again how long it would have taken, would have probably been a decade. Then in areas a bit closer to my heart in biology and science, we've seen reasoning models uh kind of be used as a as an AI coscientist. So just as a human would be reading lots of papers, planning experiments, running the experiments, and then doing data analysis and then reformulating their hypothesis as a result. There's examples of uh models doing that in lieu of uh human, which is exciting because there's way, way too many papers uh to read. You know, A AI people kind of complain that it's like 50,000 papers a year and say in biology and chemical Chemistry and physics is probably an order of magnitude more than that.
Um, and so uh DeepMind has shown that you can integrate this kind of reasoning model to uh sort of decipher new targets for disease, new mechanisms that were actually also proven in a wet lab scenario um post facto.
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Chapters
8 chapters
1
What are the latest breakthroughs in AI reasoning and chain‑of‑thought models?
0:00–8:06
2
How are reasoning models being used as AI co‑scientists in biology and math?
8:06–16:03
3
What is the “chain‑of‑action” shift in robotics and why does it matter now?
16:03–24:27
4
Which AI companies are finally generating real revenue and how fast are they growing?
24:27–32:41
5
Why is power becoming the new bottleneck for AI data centers?
32:41–41:30
6
Is NVIDIA’s dominance ending or will a multi‑silicon ecosystem emerge?
41:30–50:05
7
What is “sovereign AI” and how are nations trying to secure AI access?
50:05–57:55
8
What are Nathan Benaich’s top three predictions for AI in the next 12 months?
57:55–1:03:08
Speakers
1 identifiedMore from The MAD Podcast with Matt Turck
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