Will we have Superintelligence by 2028? With Anthropic’s Ben Mann
episode
No Priors: Artificial Intelligence | Technology | Startups
41 min
2 speakers
8 chapters
transcribed 1 month ago
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What criteria does Anthropic use to decide when a model like Claude 4 is ready for release?
Hi listeners and welcome back to No Priors. Today we have Ben Mann, previously an early engineer at OpenAI, where he was one of the first authors on the GPT-3 paper. Ben was then one of the original eight that abandoned SHIP in 2021 to co-found Anthropic with a commitment to long-term safety. He has since led multiple parts of the Anthropic organization, including product engineering and now labs, home to such popular efforts such as Model Context Protocol and Claude Code. Welcome, Ben. Thank you so much for doing it. Doing this.
Of course. Thanks for having me.
So congratulations on the Claude Four release. Maybe we can even start with like, how do you decide what qualifies as a release these days?
It's definitely more of an art than a science. We have a lot of spirited internal debate of what the number should be. And before we even have a potential model, we we have a roadmap where we try to say, based on the amount of chips that we get in, uh, when will we theoretically be able to train a model out to the Pareto efficient compute frontier? So it's all based on skill. Scaling laws. And then once we get the chips, then we try to train it. And inevitably things are less than the best that we could possibly imagine, because that's just the nature of the business. It's it's pretty hard to train these big models. So dates might change a little bit. And then at some point it's like mostly baked, and we're sort of like slicing off little pieces close to the end to try to say, like, how is this cake gonna taste?
comes out of the oven. But uh as Dario has said, until it it's really done, you you don't really know. You can get sort of a directional indication. And then if it feels like a major change, then we give it a major version bump, but we're definitely still learning and iterating on this process. So yeah. Well
the good thing is that you guys are uh, you know, no less tortured than anybody else in your naming scheme here.
Yes. The naming schemes in AI are something else.
How does Claude 4 improve on previous versions in coding and reward‑hacking behavior?
So you you folks have a a simplified version in some sense. Do you wanna um mention any of the highlights from four that you think are especially interesting or you know, those things around coding and other areas we'd just love to hear Your perspective on that?
By the benchmarks, four is just dramatically better than any other models that we've had. Even four Sonnet is dramatically better than three seven sonnet, which was our prior best model. Some of the things that are dramatically better are, for example, encoding. It is able to uh not Do it's uh sort of off target mutations or over eagerness or reward hacking. Those are two things that people were really unhappy with in in the last model where they were like, Wow, it's so good at coding, but it also makes all these changes that I definitely didn't ask for. It's like, Do you want fries in a milkshake with that change? And you're like, No, just do the thing I asked for. And then you have to spend a bunch of time cleaning up after it.
The new models, they just do the thing. And And uh and and so that's really useful for professional software engineering where You need it to be maintainable and reliable.
My favorite uh reward hacking behavior that has happened in more than one of our portfolio companies is if you write a bunch of tests or generate a bunch of tests to, you know, see if what you are generating works, more than once, like we've had the model just delete all the code because the tests pass in that case, which is, you know, not progressing us really.
Yep. Or it'll have like, here's the test and then it'll comment like Exercise left for the reader, return true. And then you're like, Okay, good job model. But we need more than that.
Maybe Ben, you can talk about how users should think about when to use the Claude Four models and also what is newly possible with them.
So more agentic, longer horizon tasks are newly unlocked, I would say. And so in coding in particular, we've seen uh some customers Using it for many, many hours unattended and doing giant refactors on its own.
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Chapters
8 chapters
1
What criteria does Anthropic use to decide when a model like Claude 4 is ready for release?
0:05–1:58
2
How does Claude 4 improve on previous versions in coding and reward‑hacking behavior?
1:58–7:36
3
What new long‑horizon, agentic tasks does Claude 4 enable for users?
7:36–14:11
4
Why is model specialization important and how might future AI models be organized?
14:11–21:00
5
How does Anthropic use Reinforcement Learning from AI Feedback (RLAIF) and constitutional AI to improve safety?
21:00–26:40
6
What is the Model Context Protocol (MCP) and why is it becoming an industry standard?
26:40–33:13
7
Is a 2028 timeline for superintelligence realistic according to the economic Turing test?
33:13–39:20
8
How does Anthropic plan responsible scaling and future product strategy after Claude 4?
39:20–41:23
Speakers
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