Earth, it is said, is home to over 10,000 AI startups. They are more abundant than cheetahs. They are more than the redwoods. The figure is, of course, a guess—startups come, startups go. But last year, more than 2,000 of them got their first round of funding. As investors sink their billions into AI, it’s worth asking: What are all these creatures doing with the boom?

I decided to approach as many recent AI founders as I could. The goal was not to try to pick winners, but to see what it’s like on the ground to build AI products—how AI tools have changed the nature of their work; how terrifying it is to compete in a crowded field. It all sounded a bit like trying to tap dance on the undulating surface of the sun. OpenAI rolls out an update, and a flurry of posts on X predicts the slaughter of a hundred startups. Brutal!

Is this a revolution that ends with so many engineers’ singing feet? To be sure—they cannot all survive. A startup is an experiment, and most experiments fail. But run thousands of them across the economic landscape and you might just learn what the near future holds.

Navvye Anand is the co-founder of a company called Bindwell. When we got on a video call, he spoke with a half-smile and vaguely posh manner as he told me how he was developing pesticides using custom AI models. Bindwell’s website once described these models as “insanely fast” and claimed that, in “mere seconds,” they could predict the results of experiments that would take days. Hearing Anand explain how he is bringing the principles of AI drug discovery to tumors, it was easy to forget that he is 19.

Growing up in India reading Hacker News with his father, Anand was building his own large language models halfway through high school. Before he graduated, he, his co-founder (now 18) and two other friends from summer camp published a paper on bioRxiv, about an LLM they built to predict a facet of protein behavior. This has scientists buzzing about X. The paper has been cited in a respected journal. They decided they should try to build a startup, brainstormed and decided on protein-based pesticides. Then, the fairy tale continues, a wood sprite (sorry, venture capitalist) reached out on LinkedIn and offered them $750,000 to drop out of high school and college and work full-time on the company. They accepted and started. The teenagers knew next to nothing about agribusiness. That was last December.

Five months later, Anand and his co-founder opened their first biological testing lab in the San Francisco Bay Area, then moved to another, where they personally press drops of promising molecules into tiny vials. (A protein-based compound could more precisely target a grasshopper or aphid, the theory goes, and not also take out the humans, earthworms, bees.) I asked him how he picked up the skills to work in a wet lab. “I hired a friend,” he said cheerfully. The friend coached him during the summer before he returned to college in the fall. “Now I can do some biochemical tests,” says Anand. “Not like a whole series of tests, but basic, wet-lab validation of our models.”

Huh, I thought. That a few teenagers had built their own LLMs in a handful of months, learned the biochemistry of pest control, used their models to identify potential molecules, and were now pipetting away in their own lab didn’t seem shabby. In fact, once I picked up on everything they did, it struck me as completely absurd. I expected to hear that AI tools speed up parts of building a company, but I only had a vague sense of the scope of its impact. So in my next interview, with the co-founders of a 14-month-old startup called Roundabout Technologies, I got straight to it: Break down what’s changed and by how much.