Mala Gaonkar argues the most profound applications of AI are improving non-tech industries. For example, AI has improved the accuracy and speed of medical scans by 70% and is transforming the 300 million surgeries performed globally each year through robotics, reducing errors.

An oncology leader views AI's most powerful near-term application as handling tedious logistical and bureaucratic tasks, not discovering novel molecules. By automating paperwork and trial planning, AI can liberate scientists to spend more time on deep, creative thinking that drives breakthroughs.

Beyond productivity gains, AI's most transformative impact may be automating R&D to accelerate scientific discovery. This could lead to breakthroughs in health and wellness, solving problems that might otherwise take decades and fundamentally improving quality of life, not just GDP.

The current, tangible role of AI in medicine is its ability to detect subtle patterns in large datasets, radically accelerating drug discovery. Breakthroughs like AlphaFold, which predicts protein shapes, are the true near-term game-changers for aging research, while molecular manufacturing remains distant.

The "AI vs. Dog Cancer" story shows that current AI's power is not autonomous discovery, but its ability to act as a research assistant, enabling motivated non-experts to orchestrate complex scientific projects by finding and coordinating with human experts.

As AI enables early disease prediction (like Grail's cancer test), the number of sick patients will decrease. This erodes the traditional drug sales model, forcing pharma companies to create new revenue streams by monetizing predictive data and insights.

The future of AI in drug discovery is shifting from merely speeding up existing processes to inventing novel therapeutics from scratch. The paradigm will move toward AI-designed drugs validated with minimal wet lab reliance, changing the key question from "How fast can AI help?" to "What can AI create?"

A Chinese hospital's AI program is achieving early success not just by detecting cancer, but by screening asymptomatic patients' routine CT scans taken for unrelated issues. This unlocks a powerful and safe method for widespread early screening of dangerous cancers like pancreatic, which was previously unfeasible.

While AI for novel drug discovery has lofty goals, its most practical value lies in accelerating development. This includes applying AI to de-risked assets for new indications, improving delivery methods, and designing faster, more effective clinical trials, which is where the real bottleneck lies.