How AI Can, and Can't, Cure Cancer

Every year cancer kills over 600,000 Americans: our friends, colleagues, and family members. It can strike suddenly and fatally, even in those who have done everything right for their health. We know woefully little about how to prevent it. We have few effective weapons once it spreads. The statistics are worsening, as cancer is increasingly killing young people. We must move beyond hollow promises, light on specifics, which simply promise a cure for cancer. Fundamentally, a promise without a plan is a lie. We need a plan as urgent and unrelenting as the disease itself: a plan with the scale, coordination and resolve to end it.

Major tech companies are racing to create artificial superintelligence (ASI).

Current artificial intelligence already operates beyond human capabilities in specific domains with well-defined boundaries, such as in chess or image classification. By contrast,  ASI would be AI that substantially exceeds human cognitive capability across the vast majority of domains.

Tech companies have pitched ASI as the answer, making cancer cures their flagship promise. The pitch is seductive: summoning superintelligent AI genies to grant unlimited wishes like unimaginable economic growth, breakthrough treatments for devastating diseases, and reversing climate change. "Think of the children!" they implore, pulling heartstrings by invoking illness, suffering, and hope. Curing cancer is a big promise, and one that is universally considered one of the most noble and good things we can fund. But rarely is the substance of their promises examined.

The logic appears airtight: If we can create systems of superior intelligence to humans across all domains, surely they will solve what has eluded our brightest scientists for decades. In 2024, Anthropic CEO Dario Amodei suggested that "AI-enabled biology and medicine will allow us to compress the progress that human biologists would have achieved over the next 50-100 years into 5-10 years," calling this the "compressed 21st century." The potential for infinite benefit, we're told, justifies near-infinite risks and infinite investment. Any regulatory or resource constraint that might slow this race becomes unconscionable when cancer cures hang in the balance.

This approach makes a dangerous assumption: that insufficient intelligence is the primary barrier to new cancer therapies. Somehow raw computational power alone is the thing that can overcome the complex landscape of data gaps, biological complexity, regulatory constraints, and misaligned incentives that have caused billions in previous healthcare investments to fail.

In fact, exponential growth in biomedical knowledge is already here. The doubling rate of medical knowledge was 50 years in the 1950s and by some estimates was down to every 73 days by 2020. Yet this intelligence explosion has not significantly moved the needle on cancer mortality or greatly increased annual new drug approvals. An abundance of knowledge and an oversupply of brilliant scientists have not moved the needle on more cures. The ASI narrative shapes capital allocation, policy priorities, and public expectations. The National Cancer Institute’s 2025 budget, which funds most fundamental cancer research in the United States, was $7.2bn, a mere 1.3% of the $540bn projected total spend by private markets to build out ASI in 2026. The opportunity cost for medical progress is significant. The share of biotech funding is at a 20 year low, while unprecedented sums of VC dollars are directed to ASI development - the markets at least believe the hype.

The obsession with the pursuit of multibillion dollar superintelligence obscures what AI can already do, nearly for free. Today’s AI capabilities already deliver real medical value, not through Big Tech's pursuit of god-like machines, but through targeted solutions for specific problems. Even Google DeepMind's AlphaFold succeeded by focusing on one well-defined challenge: protein folding. Across pharma companies, biotech startups, and academic labs, AI is already cracking concrete bottlenecks in cancer treatment: drug discovery, toxicity prediction, and clinical trial efficiency. This is where investment belongs.

But developing practical AI tools to solve problems and remove friction is fundamentally different from chasing ASI genies that assume raw computing power will magically cure all ills. Every complex problem demands we ask: is this bottlenecked by insufficient intelligence? If not, what's actually blocking progress? If so, can current AI solve it? For cancer, today's AI already addresses real intelligence and efficiency gaps in drug development.

The deeper question remains: is intelligence truly the fundamental barrier to curing cancer or are we misdiagnosing the problem entirely? In this essay, I leverage my background in both medicine and AI to examine the battle of AI vs Cancer and understand who will win.

Who Am I and Why Did I Write This? 

I’m Emilia Javorsky, MD, MPH and I am the Director of the Futures Program at the Future of Life Institute. Throughout my career I’ve had the opportunity to work “bench-to-bedside” from basic science, co-founding startups, conducting clinical trials, medicine, regulatory compliance, to public health. In 2017 I became motivated to work on how to ensure AI advances human progress and to ensure that we’re not taking on risk without benefits. At this point many in the biosecurity community, an area that overlapped with my work in public health, were thinking about the potential risks emerging from artificial intelligence.

Since the launch of consumer-facing LLMs and multi-billion dollar AI fundraising rounds, these two previously disconnected worlds—medicine and AI—have collided with the “promise” of Artificial Superintelligence. This is the magic genie which many companies suggest will grant us cancer cures. The corporate promise is unsurprising, but what is strange is that the claim has gone largely unexamined. I have seen how a new therapy is developed, and intelligence, super or otherwise, was definitely not the bottleneck. I believe we need to break down the promise and examine both the real and exciting ways current AI tools can advance medicine and also flag what the holdups actually are to medical progress.

Ultimately, my choice to write this was not my professional background but an examination of my own intense, visceral response to the cancer-curing promise. I lost my father to esophageal cancer shortly after starting medical school in 2011. He went to the doctor for a pesky cough he’d developed over the past year. There were no other symptoms. When the usual suspects were eliminated, he had an endoscopy. He went in with a cough and came out with a terminal diagnosis. I scoured my medical literature, and still seared in my memory is the table of five-year survival statistics that showed “20%” staring back at me.

We were lucky to live in a hub of leading academic medical centers, visiting each one to understand and compare what treatment options were available. Medicine did not have any great answers: a savage surgery that rendered you unable to eat, toxic chemotherapy to carpet bomb your body, and radiation therapy to shrink the unresectable tumor. Even the combination of all three was unlikely to even get him into that fortunate 20%. My father ultimately chose quality of life with the time he had left and to have radiation therapy. He lived a wonderful two years, feeling tired, but overall quite well. One day in November he started slurring his speech. We went to the ER at a major academic medical center. The doctors said the cancer had metastasized to his brain, but they had state-of-the-art proton therapy and he’d be home by Christmas. He passed away in the hospital a week later.

I share this story because it is almost everyone’s story. Many of us have loved someone who has died of cancer. We know survivors whose lives have been forever changed by the disease, and how they often live with the perpetual fear that it could return. We all have felt the promise and betrayal of hope. Receiving a diagnosis, assuming medicine must have good tools to help, only to discover that for many cancers, there is little that can be done.

One of the most intense memories I have around my father’s passing was the, ultimately false, promise that I’d have one more Christmas with him.

Flippant promises by tech CEOs that their technology will cure cancer must not go unexamined. At best, the promise reflects a bias towards over-optimism and naivety about the state of medicine. At worst it’s leveraging our collective human hope as a tool to raise funds and to shield their technology from criticism. Market reality dictates that given the unprecedented dollars flowing into ASI, money is flowing away from biotech and medical innovation, which now struggle to raise funds for promising, breakthrough ideas.

The unprecedented mega-rounds being raised by AI companies raise a societal conversation: if we are spending all of this capital to cure cancer, is superintelligence the right bet? In writing this I revisited the survival rates for esophageal cancer 14 years later, the number staring back now is 21.9%, largely unchanged. Yes, let's make curing cancer an urgent national priority, but let's couple it with an honest analysis of why progress is stalled, what are the most promising solutions, how AI can really help us and how to best allocate finite funds and start saving lives.

AI Excels At Problems That Are Rules-Based or Data-Rich, Biology Has Neither

AI excels in domains with complete rules, objective win conditions, instant feedback, and no physical constraints. Medicine has none of these. It offers incomplete information, stochastic outcomes, physical constraints, and delayed consequences. Win conditions are subjective and can take years. AI can help, but history shows us that intelligence alone only suffices in domains specifically structured to reward it.

In the human body, rapid cellular changes are more likely to be cancer than progress.

Silicon Valley's mental model for progress is fundamentally shaped by Moore's Law. This creates an entire culture that expects, plans and budgets for exponential improvement. When tech leaders look at medicine, they instinctively assume similar dynamics should apply. But biological systems are not semiconductors. Evolution optimized tradeoffs in human biology over billions of years for robustness and redundancy. Safety mechanisms that prevent harmful mutations can also slow beneficial changes. Unlike software where updates change systems instantly, biological interventions operate on systems that evolved to resist rapid change. In the human body, rapid cellular changes are more likely to be cancer than progress.

Further, biology imposes fundamental limits on the compressibility of time. You cannot speed up a pregnancy with more engineers, and you cannot compress clinical research beyond the rate at which disease progresses in human bodies. Human biology is bounded by the timescales of cells, organisms, and populations.

Even as computational power and medical knowledge increased exponentially, life expectancy gains have been linear at best, and FDA drug approvals have remained flat for decades. AI companies hang the promise of medical progress on ASI amounting to a “country of geniuses in a data center,” yet there is already an oversupply of human genius scientists.

Fundamentally, superintelligence does not equal super-solutions. Grand societal challenges are rarely intelligence-limited problems but systems-limited ones. The uncomfortable reality is that the primary bottlenecks to curing cancer are systemic problems and misalignments within our current power to change. Even if superintelligent AI genies existed today, their wish-granting ability in medicine would be severely constrained by factors no amount of compute can overcome.