Google's research head distinguishes between innovation—the continuous, iterative process of improvement applied across product and research—and true breakthroughs. Breakthroughs are fundamental shifts that solve problems not previously solvable in principle, such as the Transformer architecture that underpins modern AI.
Instead of a linear handoff, Google fosters a continuous loop where real-world problems inspire research, which is then applied to products. This application, in turn, generates the next set of research questions, creating a self-reinforcing cycle that accelerates breakthroughs.
The primary impact of quantum computing won't just be faster calculations. It will be its ability to generate entirely new insights into complex systems like molecules—knowledge that is currently out of reach. This new data can then be fed into AI models, creating a powerful synergistic loop of discovery.
Google is moving beyond AI as a mere analysis tool. The concept of an 'AI co-scientist' envisions AI as an active partner that helps sift through information, generate novel hypotheses, and outline ways to test them. This reframes the human-AI collaboration to fundamentally accelerate the scientific method itself.
Contrary to fears of displacement, AI tools like 'AI co-scientists' amplify human ingenuity. By solving foundational problems (like protein folding) and automating tedious tasks, AI enables more researchers, even junior ones, to tackle more complex, high-level scientific challenges, accelerating discovery.
While a tight product-research link is beneficial, it creates a management challenge where teams get so excited about implementation they neglect the next big research question. The research leader's role includes making the difficult judgment call to shift focus back toward long-term discovery, even amid product success.