Every entrepreneur faces decisions about scope. Should you start small and test the concept? Build infrastructure for gradual expansion? The conventional wisdom says yes—move incrementally, validate as you go, scale carefully over time.
Demis Hassabis, CEO of Google DeepMind and 2024 Nobel Prize winner, took a different approach when his team solved one of biology’s biggest challenges. “So proteins are what everything in your body relies on. They make biology possible and living possible. And what’s important about them is their 3D structure,” Hassabis explains in the interview below. “So in the body, they fold up into kind of 3D structures, and those structures determine what function they have or partially determine what function they have. And so the protein folding problem is really about, can you predict this 3D structure just from the one-dimensional amino acid sequence?”
For 50 years, determining a protein’s 3D structure required hundreds of thousands of dollars and years of human effort. His team built AlphaFold, a system that predicts these structures in seconds.
The obvious next step would have been setting up a website where scientists could request specific proteins. That’s how similar services had always worked—you submit a request, wait a few days, and get your result back. But during a team meeting in 2021, Hassabis did some quick math on his phone: 200 million known proteins, their available computing power, ten seconds per protein. He realized they could fold everything in about a year.
His reaction was immediate: “Why don’t we just do that?” Instead of building a request infrastructure, they folded all 200 million proteins and released the entire database for free. Over 3 million scientists now use it, and pharmaceutical researchers say almost every drug developed from now on will likely use AlphaFold in its process.
For entrepreneurs, this moment reveals something about recognizing when incremental thinking becomes a limitation. Sometimes the infrastructure you’re planning to build for gradual service delivery costs more—in time, complexity, and opportunity—than just solving the entire problem at once.
The question isn’t always “How do we test this carefully?” Sometimes it’s “What if we actually have the capacity to solve this completely right now?”
Watch Hassabis’s full conversation below to hear more about AlphaFold’s impact on drug discovery, his approach to ambitious problem-solving, and his vision for applying AI to humanity’s biggest challenges.
