Artificial intelligence is being asked to write computer code, produce advertising campaigns, generate movies, analyze financial markets, design weapons, operate factories, and reorganize the modern workplace. Billions of dollars are pouring into a technological revolution that promises to alter virtually every aspect of human existence.
Yet an obvious question deserves considerably more attention: When will the enormous computing power being assembled by the world’s leading AI companies be systematically directed toward curing diseases that medicine has spent decades failing to defeat?
Few diseases make the question more urgent than amyotrophic lateral sclerosis.
ALS remains one of medicine’s most unforgiving diagnoses. Scientists understand considerably more about the disease than they did a generation ago. Researchers have identified genetic mutations, abnormal proteins, inflammatory processes, cellular dysfunction, and numerous biological pathways associated with motor-neuron degeneration. Several treatments can modestly alter aspects of the disease for some patients. Experimental approaches involving gene therapy, antisense technology, stem cells, peptides, neuroregeneration, immune modulation, and other mechanisms continue to be investigated.
But the fundamental reality has not changed enough.
A person diagnosed with ALS is still entering a race against progressive neurological destruction while researchers attempt to understand a disease whose biological complexity has repeatedly defeated conventional drug development.
This is precisely the sort of problem AI should be attacking.
The traditional medical-research model depends heavily upon human beings developing hypotheses, designing experiments, analyzing results, publishing findings, and then beginning another cycle. It has produced extraordinary achievements, but it is inherently constrained by the amount of information any researcher or research team can absorb.
Modern biomedical science has generated something entirely different: an ocean of information.
Genomic databases, protein structures, clinical records, pathology reports, imaging studies, laboratory experiments, clinical trials, failed drug programs, molecular databases, published papers, and decades of accumulated research now contain relationships that no individual scientist could possibly comprehend simultaneously.
Artificial intelligence potentially can.
Imagine an AI system specifically constructed around ALS research and given access to essentially the entire body of legitimate scientific knowledge concerning the disease. Instead of asking researchers to read several hundred studies, the system could analyze hundreds of thousands of papers, datasets, molecular interactions, clinical outcomes, genetic variations, and failed experiments.
More importantly, it could search for relationships humans have missed.
Perhaps a drug abandoned for one neurological disorder affects a pathway relevant to ALS. Perhaps patients with unusually slow progression share biological characteristics buried across separate datasets. Perhaps combinations of existing compounds produce effects that individual drugs do not. Perhaps apparently unrelated research involving mitochondrial function, protein aggregation, neuroinflammation, axonal repair, or cellular metabolism contains pieces of the same puzzle.
AI does not guarantee that such connections exist. But searching for them is exactly what machines are becoming exceptionally good at doing.
The obstacle is increasingly less about computational capability than priorities, organization, access to data, and incentives.
America’s largest technology companies are spending extraordinary sums constructing data centers and developing increasingly powerful models. Pharmaceutical companies possess enormous proprietary libraries of compounds, trial results, and biological information. Universities and government agencies possess additional mountains of research.
Unfortunately, much of this knowledge exists in separate institutional silos.
That is where leadership is required.
The federal government does not need to nationalize medical research or dictate scientific conclusions. It could instead remove barriers to cooperation, establish carefully governed biomedical data standards, encourage companies to make failed clinical-trial information available for computational analysis, expand privacy-protected research datasets, and create incentives for AI companies, pharmaceutical firms, universities, hospitals, and disease organizations to collaborate.
Private philanthropy could play an equally important role.
Imagine a billion-dollar competition devoted to developing an AI system capable of identifying genuinely promising ALS therapeutic targets. Imagine similar programs for pancreatic cancer, glioblastoma, Huntington’s disease, Alzheimer’s disease, and other devastating illnesses. The objective would not be producing another chatbot capable of discussing medicine. It would be creating specialized scientific systems designed to generate hypotheses that laboratories could test.
There must, of course, remain a human firewall between computational prediction and patient treatment. AI can generate enormous numbers of plausible hypotheses, including wrong ones. Laboratory validation, animal studies where appropriate, carefully designed human trials, independent replication, and rigorous clinical judgment remain indispensable.
But caution should not become an excuse for institutional inertia.
Terminally ill patients experience time differently from bureaucracies. A regulatory discussion lasting three years may appear reasonable from an institutional perspective. For someone with an aggressively progressive neurological disease, three years can represent an eternity they do not possess.
That reality should create urgency without abandoning scientific standards.
The great promise of artificial intelligence is not that machines will replace physicians or scientists. It is that machines may allow them to investigate biological complexity at a scale previously impossible.
We are approaching an extraordinary historical choice. Humanity can devote unprecedented computing resources primarily toward making advertisements more efficient, entertainment more personalized, financial trading faster, and office work cheaper. Or some meaningful portion of that technological power can be deliberately aimed at humanity’s oldest enemy: disease.
ALS would be an appropriate place to begin.
There are thousands of families for whom artificial intelligence is not primarily an interesting technological development. They are watching a clock.
They deserve to know when Silicon Valley, the pharmaceutical industry, universities, medical institutions, philanthropists, and government research agencies intend to treat curing terminal disease with the same urgency now being devoted to building the next generation of artificial intelligence.
The machines are becoming powerful enough to ask questions science could never realistically ask before.
The question now is whether we have the will to point them at the problems that matter most.