The Download: AI's Trillion-Dollar Gamble and OpenAI's Biology Data Bid
By Thomas Macaulay | September 16, 2026
Plus: Nvidia and Meta CEOs have rejected calls for a coordinated AI slowdown.
What's at stake in AI's trillion-dollar gamble
When Jessica Wachter, a finance professor at the University of Pennsylvania, set out to assess AI's impact on the economy over the next few years, she faced a long list of uncertainties. So she began with a "remarkable fact" that is not in question: a handful of so-called hyperscalers are investing enormous sums to build AI data centers.
Rather than 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 — a target that, in 2026, looks increasingly ambitious as capital continues to pour into compute infrastructure at a pace that outstrips near-term revenue projections across the sector.
Take a closer look at what it will take for the AI buildout to pay off.
—David Rotman
AI models need more data about biology, and OpenAI is paying to create it
AI needs data — and in biology, that data is often scarce, expensive, or locked behind institutional walls. OpenAI is now betting that paying to generate it will unlock the next wave of scientific discovery.
As the race to build more capable biological AI models intensifies, access to high-quality, proprietary datasets has become a key differentiator. OpenAI's move signals a broader shift: rather than relying on publicly available research, leading labs are increasingly commissioning or licensing specialized data to train models on tasks like protein design, drug discovery, and cellular simulation.
Nvidia and Meta CEOs reject calls for coordinated AI slowdown
In a notable rebuke of growing calls for a coordinated pause or slowdown in frontier AI development, the CEOs of Nvidia and Meta have publicly rejected the idea — arguing that slowing down would cede strategic advantage and stall progress on safety research itself.
The pushback comes amid heightened debate in 2026 over the pace of AI deployment, with regulators, researchers, and industry leaders divided on whether voluntary slowdowns are feasible or even desirable. For Nvidia and Meta, the message is clear: the race is on, and they intend to keep running.
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