How to build an AI startup: Go big, be weird, and embrace potential doom
It is said that the earth Home to over 10,000 AI startups. They are more abundant than cheetahs. Their number is more than the red magic wood. Of course, this number is just a guess – startups come, startups go. But last year, more than 2,000 of them received the first round of funding. As investors spend billions of dollars on artificial intelligence, it’s worth asking: What are all these thriving creatures doing?
I decided to get as close to as many new AI founders as possible. The goal wasn’t to try to pick winners, but to learn what it means to build AI products in real life, and how AI tools have changed the nature of their work. How terrifying it is to compete in such a crowded field. It was like trying to dance on the revolving surface of the sun. OpenAI provides an update, and a slew of posts on Brutally!
Is this a revolution that will end with so many engineers? Of course, not all of them can survive. A startup is an experiment and most experiments fail. But include thousands of them in the economic landscape and you might know what the near future holds.
It’s Navi Anand Founder of a company called Bindwell. When we had a video call, he half-smiled and spoke vaguely as he told me how he uses custom AI models to produce pesticides. The Bindwell website once described the models as “insanely fast” and claimed they could predict the results of tests that could take days in “just a few seconds.” Hearing Anand explain how he applies AI drug discovery principles to agricultural crops, it’s easy to forget that he’s 19 years old.
Anand grew up in India reading Hacker News with his father, and was building large language models of his own by the middle of high school. Before graduating, 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 an aspect of protein behavior. This has caused a stir among scholars about X. This article has been cited in a prestigious journal. They decided to try to create a startup, brainstormed ideas and worked on protein-based insecticides. Then, as legend has it, a member of Wood Sprite (sorry, venture capitalist) contacted LinkedIn and offered them $750,000 to drop out of high school and college and work full-time at the company. They accepted and started. The teens knew almost 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 lab, where they personally squeezed droplets of promising molecules into small vials. (Theoretically, a protein-based compound could target grasshoppers or aphids more precisely, rather than killing humans, earthworms and bees.) I asked him how he acquired the skills needed to work in a wet laboratory. “I hired a friend,” he said happily. The friend coached him in the summer before returning to college in the fall. “Now I can do some biochemical tests,” says Anand. “Not like a wide range of tests, but initial validation of our models in the wet lab.”
Huh, I thought about how many teenagers had built up their LLM in a few months, learned the biochemistry of pest control, used their models to identify potential molecules, and were now pulling samples in their own labs, and it didn’t seem like a gimmick. In fact, when I counted all the things they did, it seemed completely ridiculous. I expected to hear that AI tools would accelerate parts of company building, but I had only a vague understanding of the scale of their impact. So, in my next interview, with the founders of a 14-month-old startup called Roundabout Technologies, I got straight to the point: what has changed and to what extent.
