How to Build an AI Startup: Go Big, Be Strange, Embrace Probable Doom
Earth, it is said, is home to more than 10,000 AI startups. They are more abundant than cheetahs. They are more like dawn redwoods. The figure is of course a guess – startups come, startups go. But last year more than 2,000 of them received their first round of funding. As investors pour their billions into AI, it's worth asking: What are all these creatures of the boom to do?
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 scary it is to compete in a crowded field. It all sounded a bit like trying to tap-dance on the rolling surface of the sun. OpenAI rolls out an update, and a flurry of posts on X predict the demise of a hundred startups. Brutal!
Is this a revolution that ends with so many engineers' singed feet? For sure – they cannot all survive. A startup is an experiment, and most experiments fail. But thousands of them roam the economic landscape and you just might 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 develops pesticides using custom AI models. Bindwell's website once described these models as “doing fast” and claimed that, in “mere seconds”, they could predict the results of experiments that would have taken days. When hearing Anand explain how he is bringing the principles of AI drug discovery to crops, it was easy to forget that he is 19.
Anand grew up in India reading Hacker News with his father and built his own large language models by mid-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. It got scientists buzzing on X. The paper was cited in a well-respected journal. They decided they should try to build a startup, brainstormed, and settled on protein-based pesticides. Then, the fairy tale continues, a wood sprite (sorry, venture capitalist) got in touch on LinkedIn and offered her $750,000 to drop out of high school and college and work full-time on the company. They accepted and started. The young people knew almost nothing about agribusiness. That was December last year.
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 pressed drops of promising molecules into tiny vials. (A protein-based compound can more precisely target a grasshopper or aphid, so the theory goes, and not also get the humans, earthworms, bees out of it.) I asked him how he acquired the skills to work in a wet lab. “I hired a friend,” he said cheerfully. The friend coached him in the summer before he went back to college in the fall. “Now I can do some biochemical assays,” says Anand. “Not as a whole range of assays, but basic, wet-lab validation of our models.”
Huh, I thought. That a few teenagers built their own LLMs in a handful of months, learned the biochemistry of pest control, used their models to identify potential molecules, and now pipetted them into their own lab, didn't seem shabby. In truth, once I tallied up everything they had done, it struck me as completely absurd. I had expected to hear that AI tools were accelerating parts of building a business, but I only had a vague sense of the scale of their impact. So in my next interview, with the co-founders of a 14-month-old startup mentioned Roundabout TechnologiesI immediately came up with it: Break down what has changed and by how much.
