Avi Wigderson

And I was wondering if you could give the intuitive example of why randomness is a function of the observer's computational power.

OpenAI, Anthropic, Cursor, and Vercel all use this product to make their lives better.

And the problem it solves is when you're building SaaS or an AI product and you want to sell to other companies, there's all these requirements you need to meet.

There's SSO, there's SCIM, there's RBAC, there's audit logs.

These are all things that take time to integrate but aren't the main focus of your app.

WorkOS is an API layer that lets you meet all of these requirements in just a few lines of code.

So let's say you have a new SaaS product and you want to sell to other companies.

WorkOS will solve all of these critical feature gaps for you.

You can check them out at WorkOS.com to learn more and get started.

And I appreciate them for supporting my work and sponsoring this podcast.

Is there an intuitive explanation for the relationship between problem hardness and randomness?

So when you say that the quality of the randomness is a function of the computational power of the observer, one thought that comes to mind is, imagine I have maybe infinite compute, then nothing is truly random.

Is that accurate to say?

Like, you know, if I had infinite compute, the weather is not random, nothing is random.

I saw when I was doing my research that there are ways to create higher quality randomness by aggregating weaker sources.

How does that work?

I vaguely remember skimming a bunch of papers, and one of the papers had this figure where it was a two-dimensional array of numbers, and I think they were drawing rectangles or something like that.

Is that one of the ideas?

I saw that you had done some work in zero-knowledge proofs.

I was wondering if you could explain what is a zero-knowledge proof and maybe we talk about its significance.