Will we have Superintelligence by 2028? With Anthropic’s Ben Mann

**Sarah Guo** (0:05)
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 this.

**Ben Mann** (0:31)
Of course. Thanks for having me.

**Sarah Guo** (0:33)
So congratulations on the Claude for release. Maybe we can even start with how do you decide what qualifies as a release these days?

**Ben Mann** (0:43)
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 have a roadmap where we try to say, based on the amount of chips that we get in, when will we theoretically be able to train a model out to the Pareto efficient compute frontier? It's all based on scaling laws. 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 pretty hard to train these big models, so dates might change a little bit. Then at some point, it's mostly baked, and we're slicing off little pieces close to the end to try to say, how is this cake going to taste when it comes out of the oven? But as Dario has said, until it's really done, you don't really know. You can get a directional indication. 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.

**Sarah Guo** (1:49)
Well, the good thing is that you guys are no less torture than anybody else in your naming scheme here.

**Ben Mann** (1:56)
Yes.

**Elad Gil** (1:56)
The naming schemes in AI are something else. So you folks have a simplified version in some sense. Do you want to mention any of the highlights from FOR that you think are especially interesting or those things around coding and other areas? We'd just love to hear your perspective on that.

**Ben Mann** (2:10)
By the benchmarks, FOR is just dramatically better than any other models that we've had. Even FORSONNET is dramatically better than 37SONNET, which was our prior best model. Some of the things that are dramatically better are, for example, in coding, it is able to not do its sort of off-target mutations or over-eagerness or reward hacking. Those are two things that people were really unhappy with 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 and a milkshake with that change? And you're like, no, just do the thing I asked for. Then you have to spend a bunch of time cleaning up after it. But the new models, they just do the thing.
And so that's really useful for professional software engineering, where you need it to be maintainable and reliable.

**Sarah Guo** (3:02)
My favorite 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 see if what you are generating works. More than once, we've had the model just delete all the code because the tests pass in that case which is not progressing us really.

**Ben Mann** (3:23)
Yeah. Or it will have like, here's the test and then it will comment like, exercise left for the reader, return true. You're like, okay, good job model. But we need more than that.

**Sarah Guo** (3:36)
Maybe Ben, you can talk about how users should think about when to use the Claude 4 models and also what is newly possible with them.

**Ben Mann** (3:46)
More agentic, longer horizon tasks are newly unlocked, I would say. In coding in particular, we've seen some customers using it for many hours unattended and doing giant refactors on its own. That's been really exciting to see. But in non-coding use cases as well, it's really interesting. For example, we have some reports that some customers of Manus, which is a Gentic model in a box startup, people asked it to take a video and turn it into a PowerPoint. Our model can't understand audio or video, but it was able to download the video, use FFmpeg to chop it up into images and do keyframe detection, and maybe with some old school ML-based keyframe detector, and then get an API key for a speech-to-text service, run speech-to-text using this other service, take the transcript, turn that into PowerPoint slides content, and then write code to inject the content into a PowerPoint file. And the person was like, this is amazing. I love it. It actually was good in the end. So that's the kind of thing where it's operating for a long time.