DFJ, Khosla, Data Collective, Monsanto Ventures (Syngenta Ventures until 2017)
The Engine, Boston University, the Sloan Foundation, investors from the US and Germany, including angel investors .
True Ventures, Casualidad Capital, Bloomberg Beta, Three Leaf Ventures, Jazz Venture Partners
Focus: Drug discovery
What it does: Searches for medicines using software that simulates how millions of different compounds will interact with a target protein in the body
Founded: 2012
Based in: San Francisco
Capital raised: $22.5 million
Focus: Material design
What it does: Uses software that simulates how crystals will grow during manufacture to come up with new materials and processes
Founded: 2017
Based in: Cambridge, Massachusetts
Capital raised: $12.5 million
Focus: Drug discovery
What it does: Identifies genetic mutations linked to disease and uses machine learning to match those with the most promising existing treatments and drugs in development
Founded: 2015
Based in: Toronto
Investment last 2 Years: $15.6 million
But is there a place for such companies in the market?
Maybe not right now. Investors, skittish from seeing a number of similar companies flame out, haven’t yet flocked to AI-based materials and drug discovery startups. A few still get funding—California-based biotech Insitro this year has raised $100 million, while Recursion Pharmaceuticals in Salt Lake City has raised $121 million across several rounds. But it has also become clear that using AI to develop new drugs and materials is a long-term game that demands a ton of money and patience, perhaps more than most venture investors are willing to offer up.
“Computer-aided drug discovery has been a goal since I was a grad student in the 1990s,” says Bessen, the economics lecturer. “But it turns out drug discovery is a really tough problem to solve.”
It’s still early days, of course, and breakthroughs could happen. Lab work driven by AI and accelerated by robots could very well tease out winning compounds. But Buonassisi says he believes that successful AI-drug discovery and materials companies will be built with a new thinking around investments, one that looks more like the way we fund America’s roads, military, and National Institutes of Health.
“I’ve worked in startup communities and venture capital communities, and it’s still often about how you can pitch something quickly and sizably sell it,” he says. “Here, there are so many solid deliverables between concept and commercial product that I’m not sure it’s suitable for conventional venture funding.” But these industries are too important not to try.
It’s tempting to see the surge in AI and the hype around it as history repeating itself. After all, many a past AI breakthrough failed to match expectations after misunderstandings crept into the narrative, a phenomenon known as the AI effect. Machine learning as it’s practiced today has been enjoying a new “success,” aided by progress in availability of high compute power and the big data revolution. It’s plausible to think that, just as with past AI summers, the public now expects too much.
Take self-driving cars. Do I sound bearish? I am not. I am hopeful that future generations will look back and laugh at all the fearmongering of today, when we fret about human drivers and machines sharing the road. I believe that 20, 30, or 50 years from now, the roads will be far safer, with fewer accidents, involving fewer human drivers who are mostly, but not entirely, replaced by AI. However, I accept that without owning a machine that can think, that isn’t sentient, we won’t get a significant amount of autonomous vehicles on the road.
Additionally, many of the debates among those who invest in AI today are not grounded in the present AIs current capabilities, nor are the worst (scientifically unsound) fears of AI going to destroy or take over humanity. To be clear, these fantasies are not very useful. Worse, they might hamper progress. Furthermore, the real risks that these unattainable fantasies raise—North Korean and Chinese AI conquering the United States; rogue narco-AIs running cartels; oligopolistic price-fixing—are concentrated in the imaginations and projections of the opined. Along with AI ethics concerns, fake news, and Prius-loving millennials running amok, I feel there is the potential to stymie the true progress around AI—in the hopes of avoiding these nonthreats.
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