Startup Ideas You Can Now Build With AI

There's all this, like, tooling and infrastructure still to build. There's clearly still a bunch of startups yet to be built in just the infrastructure space around deploying AI and using agents.

But if you're living at the edge of the future and you're exploring the latest technology, like, there's so many great startup ideas, you're very likely to just bump into one.

You apply the right prompts and the right dataset and a little bit of ingenuity, the right evals, a little bit of taste, and you can get, like, just magical output. Welcome back to another episode of The Light Cone. Every other week, we're certainly realizing there's a new capability, a million-token context window in Gemini 2.5 Pro. It's just really insane right now, and the thing to take away from that, though, is that we have an incredible number of new startup ideas, some of which are actually very old, and they can only happen right now. Harj, what are some of the things you're seeing?

Well, one thing I've been thinking a lot about recently is, what are types of startup ideas that couldn't work before AI or didn't work particularly well that are now able to work really, really well? Uh, and one idea that is very personal to me, um, would be recruiting startups, since I ran a recruiting startup, um, Triplebyte for almost five years. And I think, um, something that I've clearly seen is there was a period of time when we started Triplebyte, so around 2015, where recruiting startups were kind of like a really popular type of startup. Um, and I think a lot of the excitement around those ideas back then was this idea of applying marketplace models to recruiting, 'cause there were marketplaces for everything except how to hire great people, and specifically great engineers. And we started Triplebyte with the thesis of, you don't just want to let anyone on your marketplace. You want to build a really curated marketplace that evaluates engineers and gives people lots of data about who the best engineers are. And this was all pre-LLM, so we had to spend years essentially building our own software to do thousands of technical interviews, to squeeze out every little data point we could from a technical interview so that we'd effectively built up this labeled dataset that we could run machine learning models on. But we didn't even get to do that until, like, year three or four, um...

Yeah. And initially, it was, uh, actually a three-sided marketplace in that you needed to hire an interviewer in between to get that human signal.

Yeah, we had- we had companies hiring engineers, we had the engineers looking for jobs, and then we had engineers we contracted to interview the engineers. (laughs) Um, so it was like, lots of things going on right now, um, and all of the evaluation piece of it at least, now with AI, is very, very possible. I mean, s- we can, specifically with the AI code gen models, you can do code evaluation, um, and I think probably one of the hot AI startups at the moment is this company called Merkle, which is essentially similar to the Triplebyte idea. I mean, it's a marketplace for hiring software engineers. Um, but I think what AI has unlocked for them is the evaluation piece of it, they could just do on day one using LLMs. They didn't need to build up this big labeled dataset. And they've been able to expand into other types of knowledge work, um, quite easily. For us to have gone from, like, engineers to analysts to all these other things would have taken years 'cause again, we had to rebuild the labeled dataset. Um, but with LLMs, you can just do that on, you know, day one effectively. And so I think this whole- this whole class of recruiting startups that are trying to evaluate humans at being good at specific tasks or not, um, is a really interesting space that's much more exciting to find good startup ideas in now than it was five years ago.