This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology.

What's at stake in AI's trillion-dollar gamble: When Jessica Wachter, a finance professor at the University of Pennsylvania, wanted to assess AI's impact on the economy over the next few years, she faced a long list of uncertainties. So she started with a 'remarkable fact' that is not in question: a handful of so-called hyperscalers are investing huge amounts of money to build AI data centers. Instead of trying to predict how widely deployed AI models will be, Wachter asked how fast the hyperscalers' earnings will need to grow to justify their spending through 2027, when expenditures are expected to reach nearly $1.1 trillion. The results are eye-opening. AI companies will need to achieve an extraordinary increase in productivity just to break even by 2030.

AI models need more data about biology, and Open AI is paying to create it: AI needs much more information to make important breakthroughs in curing disease. So last year Ruxandra Teslo, a policy analyst, posted an idea for supercharging medical AI systems: use data from failed biotech companies. By bidding at bankruptcy proceedings, she argued, it might be possible to obtain detailed regulatory filings, manufacturing strategies and safety data, creating what she called 'biotech's lost archive.' The Open AI Foundation, the nonprofit parent of Open AI, announced this week that it will fund her idea, paying to create 'high-quality scientific datasets.'