Bending the Curve: How AI is Transforming Scientific Discovery
Google DeepMind Institute (AI lab research) · 8 Oct 2026
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Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding supports the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
A DeepMind Institute essay on AI in scientific discovery. It points to evidence that research productivity has been falling, with ideas getting harder to find, and argues that AI could help restore the science-driven innovation that underpins long-run economic growth.
Source excerpt
Scientific and technological progress has long driven economic growth and led to dramatic improvements in living standards around the world. In his 2025 Nobel lecture , economic historian Joel Mokyr attributed this to a “positive feedback loop”: new technology drives waves of scientific advances that, in turn, fuel further innovation and progress. But he also warns that sustaining this positive loop is never guaranteed. Recent history illustrates Mokyr’s point. The U.S. and E.U. entered the 1990s with relatively similar economic growth rates, but the two have since diverged substantially. The U.S. maintained roughly 2% annual increases in output per capita , largely powered by successive science and technology booms and the broad diffusion of innovation across the economy, while the E.U. has mostly stagnated . Today, the type of scientific progress that drives growth faces significant headwinds. Across semiconductor physics, agriculture, and pharmaceutical R&D, researchers have documented a decrease in productivity that suggests ideas may be “getting harder to find.” Sustaining historical rates of innovation now requires exponentially larger research teams and capital expenditure. Decades of accumulated knowledge have led scientists into narrower specializations, longer paths to PhDs, and prohibitive coordination costs for achieving breakthrough innovations across teams. Overcoming these hurdles is critical because there are still so many open questions, along with many questions not yet asked, whose answers are key for unlocking new possibilities for human progress. There is widespread optimism that artificial intelligence could help counter these headwinds, fulfilling a promise of computational science that researchers have pursued for more than 70 years . The journey began in the 1950s when Alan Turing and John von Neumann first applied foundational concepts of computation to the natural sciences. In the decades that followed, the field progressed from early symb
About this publication
Lab or institute research, not peer reviewed. A report or blog post published by an AI lab or research institute on its own site. It has not been through independent peer review, and the publisher may have a stake in the findings.
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