Too Complex for Humans, Solvable for Machines: Arm's Rene Haas Says AI Will Cure Cancer

The chief executive of the UK’s most valuable tech company has made one of the boldest medical claims yet from the semiconductor industry: artificial intelligence will find a cure for cancer within our lifetimes — a solution that humans alone could never reach.

Rene Haas, who leads Cambridge-based chip designer Arm Holdings, told the BBC’s Big Boss Interview podcast that while modelling how a DNA marker is impacted by cancer is currently “too complex” a problem — not only for humans but for the computers that run today’s AI — the trajectory of the technology makes a solution inevitable. “AI is going to… find a cure for cancer that today you and I, other humans [could] not in our lifetimes. I believe in our lifetime, AI will help cure cancer,” he said.

Why a chip executive is talking about cancer

Arm designs the CPU blueprints inside hundreds of billions of phones, cars, smartwatches, and gadgets worldwide. Earlier this summer, its peak share price amid the AI boom made it — in cash terms — the most valuable UK-based company in history. Its majority owner, Japan’s SoftBank, holds a range of tech investments including a stake in OpenAI. Haas himself stepped down from the board of pharmaceutical giant AstraZeneca in April, giving him a rare vantage point spanning both silicon and biomedicine.

His argument rests on computational complexity. “Modelling a cell, modelling a human, modelling how a DNA marker is impacted by cancer — it’s too complex a problem, not only for humans today, but the computers that run AI,” Haas explained. “However, going forward, as we feed more and more of the models into these computers, and the computers get more sophisticated to run the models, they’re going to solve it.”

The researcher’s counterpoint: data beats data centres

The BBC also spoke to Prof Chris Bakal of the Institute of Cancer Research, London, and CEO of Sentinal4D, who offered a more grounded — and arguably more interesting — perspective. In his view, the real question is no longer whether we use AI in cancer research, but what we feed it. His lab trains AI on data generated from patient samples rather than internet-scraped corpora.

“It is not scraped from the internet. It does not need a giant data centre to run. The future of medical AI will not belong to whoever builds the biggest computer. It will belong to whoever has the right measurements,” Bakal said. Properly targeted prediction “could cut years from the time it takes to develop new treatments. This is where AI delivers real benefit to patients.”

The tension between the two views — raw compute scaling versus curated biological data — is fast becoming the central strategic question in computational biology.

Humanoid robots within five years

Haas predicted AI would drive widespread humanoid robots within the next five years, with Arm-designed chips powering self-learning machines “in a very large way” across manufacturing, cleaning, security, bridge building, and repairs within a decade.

“With artificial intelligence, these robots can see, learn, and essentially be reprogrammed for new tasks,” he said. “The robot that was programmed to make a bed can also learn how to arrange the towels in a room, or clean the dustbins, or whatever you want to go off and do.”

On employment fears, he pushed back: while there would be “some change” for workers, “the estimates of jobs going away and being completely replaced by machines is a bit overstated” and outweighed by new opportunities. He similarly dismissed concerns about an AI stock-market correction, citing long-term demand.

The bottleneck: chips, not ideas

The most consequential part of the interview concerns supply. Haas said Arm’s power-efficient technology now runs in half of AI data centres worldwide — a shift that has pushed Arm into selling its own microchips. Meta asked Arm to develop the Arm AGI chip, and “demand has been off the charts” — more than $2bn worth since its launch in March.

Yet the industry’s expansion is being throttled by chip supply. Haas pointed to planned multi-gigawatt data centres in France and the US, and even proposals to put data centres in space. “We are absolutely in a supply-constrained environment. Can you get enough chips? We need more fabs before we can put a data centre in space,” he said.

On whether chip manufacturing could return to the UK despite government talks, he was blunt: “I don’t think it’s necessary for the UK to [build] fabs. Fabs are very expensive. They take a lot of specialised workers… and there’s a pretty broad ecosystem for those.” He noted that half of Arm’s employees remain in the UK, making it “by far and away the largest employer” in British tech.

Analysis: when the platform seller sells hope

Haas’s cancer claim should be read in context: Arm is not a biotech company, and its CEO is simultaneously warning that not enough chips exist to satisfy AI demand. The two messages reinforce each other — the grander the promise, the stronger the argument for building more fabs and data centres that happen to run on Arm IP.

That doesn’t make the claim baseless. AlphaFold’s protein-structure predictions and AI-designed drug candidates already in clinical trials have shown that machine learning can compress biological discovery timelines. What Haas adds is a timeline attached to compute: today’s models cannot simulate a cancer-affected cell at sufficient fidelity, but scaled models on scaled hardware, he argues, will cross that threshold.

The sobering corollary is his supply warning. If AI-driven medical breakthroughs genuinely depend on exponential compute, then chip shortages and data-center bottlenecks become public-health constraints — not merely commercial ones. When a shortage offabs slows the modelling that could cut years off drug development, industrial policy and oncology research become awkwardly intertwined.

For now, the claim stands as a marker of how far AI rhetoric has travelled: from chatbots to cancer cures, delivered by the man whose instruction sets may well run the machines that get there first.