Sonnet 4.5 & the AI Plateau Myth — Sholto Douglas (Anthropic)
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What is the main topic discussed in this episode?
So finally, this year is where the compute like super cycle is like beginning properly. People have said that we've hitting a plateau every month for the last three years. I look at how these models are produced, and every part of it could be improved so much. It is a primitive pipeline held together by duct tape and the best efforts and elbow grease and late nights. And there's just so much room to grow on every part of it. I think worth crying from the rooftops. Anything that we can measure seems to be improving really rapidly. Bet on the exponential.
Hi, I'm Matt Turk from Firstmark. Welcome to a special episode of the Matt Podcast for the release of Claude Sonnet 4.5 this week with the incredible Shelter Douglas, a leading AI researcher at Anthropic. In this conversation, we go behind the scenes of how Sonnet 4.5 became the best coding model in the world and what happens when you enable AI agents to work for 30 hours straight. Beyond the launch, we talked a bunch about Frontier AI, how big AI labs operate and how we are well on our way to AGI. At my request, Sholto made this conversation very approachable by breaking down a lot of key concepts such as reinforcement learning, computer use, and AI benchmarks in plain English without the jargon.
Please enjoy this great chat with Sholto.
What is the rapid release cadence at Anthropic and why does it matter?
Schulter, welcome. How you doing? It's great to be here. Congratulations on uh the release of Sonnet uh 4.5, which is the big news of of this week. Um I was just looking back as I was prepping for this, and um I was struck by the pace of releases uh at uh Anthropic, uh in particular Sonnet 3.7, which was like this huge deal. At the time, like I in my in my mind, if you had asked me, I would have said, Oh no, that was last year. But in fact it was just in in February of this year. Yeah. Wha what's the right way to think about that pace of of releases? Is that a proxy for for progress accelerating?
Yeah. I I think it's approximately a couple things. One is that there's now this uh two paradigm regime where previously you did pre-training scaling and reinforcement learning scaling, and now we're in a mix when in a mix of the two, basically. Um and so I think that gives you more opportunities to update models, um, because it means that you can make advancements along multiple frontiers. Uh and then that means that yeah, you sort of say end up shipping more frequently. Uh Uh, I think it's also a reflection of the fact that this is now two-ish years after Chat GPT, uh, two and a half years after Chat GPT.
How are the Opus, Sonnet, and Haiku model tiers defined and differentiated?
And so the post-Chat GPT investment cycle is finally hitting, where like compute availability is is increasing and and all of this. And so it means that you should expect actually the pace of progress to be and because there's lead times in uh the in commissioning chips, basically. Uh so Even if you as much as you wanted chips last year, it would have been impossible to get them because T SMC was, you know, booked out and so forth. Uh so finally this year is where is where the compute like super cycle is like beginning properly. Um In effect. Yeah.
Okay, great. Um maybe for situational awareness for people listening to this, uh the sonnet, this opus. Yes. Is it still haiku somewhere? Maybe walk us through the differences between those models.
Yeah. So we uh release models along three categories, uh three tiers. So Opus, which is the smartest model, Sonnet, which is uh the sort of mid-tier model, and haiku, which is the the fastest, cheapest model. Um one of the interesting things about uh this most recent release is actually Sonnet is smarter than Opus. And this has happened before. In fact, this happens last year. It's a reflection of fast progress because uh it It is cheaper to train uh you know mid-tier models than large models. And so what happens is that you end up doing a lot of progress on smaller models. Eventually, you need to choose when to scale up and and sort of get the benefits of scale in a model. Often you make progress fast enough that.
Your mid-tier model is is is super is like great anyway.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–1:08
2
What is the rapid release cadence at Anthropic and why does it matter?
1:08–2:16
3
How are the Opus, Sonnet, and Haiku model tiers defined and differentiated?
2:16–5:01
4
What experiences shaped Sholto Douglas’s path from fencing champion to AI researcher?
5:01–12:26
5
How is “taste” defined in AI research and why is it crucial for coding agents?
12:26–21:43
6
Why does Anthropic focus intensely on coding as its primary research bet?
21:43–30:59
7
How do 30‑hour autonomous coding sessions work and what breakthroughs enable them?
30:59–42:05
8
What role does reinforcement learning play in the recent performance gains of LLMs?
42:05–1:10:03
Speakers
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