Anj Midha

speaker

721 appearances 1 recordings 1 series first heard Apr 2026 last heard 14 Apr

Anj Midha’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 1 in all, peaking in Apr 2026 with 1.

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: Anj Midha on Investing $300M into Anthropic | The Early Days of Anthropic & How 21 of 22 VCs Turned it Down | The Four Bottlenecks to Compute | What the China Has Smashed and Why We Should Be Worried · 14 Apr 2026 podcast

Are there some limits to the transformer architecture versus diffusion models?

And what I've come to realize is if you solve the culture problem, you can solve the research and the algorithmic problem.

Then the bottlenecks of context feedback, which is what is the data you need to keep doing frontier research over and over again, is step number one.

Because actually, I think that is also where you have the most business and commercial advantage.

There's lots of alpha and value to be gained in pre-training, mid-training, and so on.

But that last mile where you deploy a model or an agent in some new domain, and then you collect feedback on how it's performing in real time.

And then, like I was saying, here we do physical verification of material science at Periodic.

Wherever there are some unique context feedback loops that are missing today, that's where you probably have the biggest bottlenecks on capabilities.

And so what you should be doing if you're trying to advance the frontiers is going, okay, you know, these models suck, for example.

About a year ago, as an example, I realized there was a lot of talk about models being good at physics and chemistry.

AI for science.

And I was a visiting scientist at the applied physics department at Stanford.

And we started benchmarking these models, Claude, Gemini, and so on.

And surprise, they sucked.

They were so bad.

There's this disconnect between the marketing hype of AI for science and the reality where these models are terrible.

At the time, at least, they were starting to get good at code, but they were terrible at scientific analysis.

You know, the conclusion was pretty simple.

They were just missing a lot of the physics and chemistry data you need to reason about the physical world.

But to do that, we don't have enough of that data on the internet because the internet is mostly pre-trained data about things like blogs and blah, blah, blah, and coding.

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