The discussion about artificial intelligence felt rather different this week. Jack Clark, the Brighton-born co-founder of Anthropic, was talking about the possibility that we may eventually need to slow the development of the most powerful AI systems, and even about independently verifiable ways of shutting them down. Then, almost in counterpoint, Nature reported on a “virtual biotech” involving tens of thousands of AI agents working together on drug discovery and identifying a promising new approach to lung cancer treatment. One story was about restraint, the other about possibility. Put together, they seem to capture a choice we are beginning to face.
We spend a great deal of time talking about the race towards Artificial General Intelligence: which company will get there first, which country will dominate it, how much more capable the next model will be. There are perfectly understandable commercial and geopolitical reasons for this. But the race itself can start to dictate the objective. In trying to build an intelligence capable of doing almost everything, we may be paying too little attention to a simpler question: what do we actually want all this intelligence to do for us?
Cancer seems an obvious place to start. Rather than directing every new increment of computing power towards making one general purpose model slightly better at everything, imagine deliberately creating hundreds, perhaps eventually thousands, of specialised AI agents focused narrowly on understanding and treating it. Some could concentrate on individual cancers, others on genetics, protein interactions, immune response, drug discovery, pathology, imaging or clinical trials. They would work alongside scientists, doctors and laboratories, testing ideas against evidence, learning from failed experiments and sharing what they discover.
The important point is that this isn’t just theoretical. Large numbers of AI agents collaborating on scientific problems are already beginning to appear in real research; the Nature article is one early example. So extend the idea all the way to the frontier. Apply the same intensity to Alzheimer’s, antibiotic resistance, fusion energy, energy storage, food production, clean water and other stubborn problems humanity has been trying to solve for decades. The attraction is not simply that these are worthy causes. Narrowing the AI mission may also be a sensible way to use increasingly powerful AI while we learn how to make it safer.
That matters because we do not have to choose between racing ahead and stopping altogether. There is a middle course in which we continue to improve the intelligence, and continue putting it to work on difficult real world problems, while being more cautious about the autonomy we give increasingly general systems. Capability and permission do not have to advance at the same speed. A system can become dramatically better at science, medicine or engineering without automatically being given greater freedom to acquire resources, rewrite itself or act independently across the world. Narrowing the mission, at least for a time, could give us something that may soon become scarce: room to think.
There is a larger version of the idea too. What if the major powers agreed that a handful of these problems were simply too important to become another winner-takes-all technological race? The United States might lead a major international effort on cancer. China might commit enormous resources to fusion or energy storage. Europe could focus on neurodegenerative disease, with Britain, India, Japan and others taking responsibility for different challenges. The precise allocation is not the point. Each country would bring its best scientists, laboratories, computing power and AI systems to bear on a defined human problem, then share what it discovers.
Of course, this would be difficult. Countries compete, companies have shareholders, governments have security concerns and intellectual property has enormous value. None of that disappears because we might prefer the world to work differently. If anything, geopolitics seems to be moving the other way. We are falling back on an older language of power, leverage and spheres of influence, as though outdated instincts from yesterday still provide the only credible map for the future. Perhaps that is partly a response to a more dangerous world, but it also reflects a failure of imagination. Periods of genuine change ought to give political leaders some licence to describe a different future, one shaped a little more by collaboration, equality, fairness and kindness, and a little less by the assumption that progress only counts if somebody else loses.
That will inevitably sound to some like technocratic naivety. Perhaps it is. But returning to repackaged versions of “might is right” is neither more imaginative nor, frankly, a particularly hopeful answer to the future. We have occasionally managed to decide that some things are bigger than the usual rules of competition, and science has often been at its best when that happens. AI may give us a reason to try again.
None of this requires us to stop research into Artificial General Intelligence. I doubt that is realistic, and I am not sure it would be desirable anyway. AGI could become one of the greatest scientific tools we have ever created. But there is an important distinction between intelligence and autonomy. An AI capable of designing a new cancer treatment does not automatically need permission to acquire resources, operate freely across the internet, rewrite itself or pursue open-ended objectives without human oversight. We can be extremely ambitious about what these systems can understand and still be cautious about how much freedom we give them to act.
For all the progress, we still do not really know how increasingly capable and autonomous systems will behave in every circumstance. Questions about reliability, control, unintended objectives and human oversight remain unresolved. That is not an argument against progress it is a reason to think more carefully about which forms of progress deserve to move fastest. If we can direct powerful systems towards bounded, valuable problems while we work through the harder questions of safety and control, then we gain time without standing still. We could be using that time to discover new medicines, improve energy technology and solve problems that matter now.
There is also the question of what we choose to reward. Enormous amounts of money are flowing into AI because the commercial prize for building increasingly capable general systems is equally enormous. That is understandable, but money has a habit of shaping not just which technologies are built, but which problems are considered worth solving in the first place. Imagine if even a meaningful proportion of that effort were judged against a different scorecard: how many cancers became treatable, how many years of healthy life were added, whether we discovered a new generation of antibiotics, made fusion practical, reduced the cost of clean energy or made measurable progress on climate change.
Those are not lesser ambitions. We may be creating the most powerful problem solving technology humanity has ever possessed at exactly the point when we have no shortage of problems worth solving. Competition will not disappear, and countries are not suddenly going to become altruistic. They do not need to. The great powers can still compete, AI companies can still build extraordinary businesses and researchers can still push towards AGI. But alongside that race, we could choose to run another one: to make pancreatic cancer survivable, to crack Alzheimer’s, to produce abundant clean energy, to develop antibiotics faster than bacteria can evolve resistance.
There may be a useful bargain hidden in that choice. We continue advancing AI and benefiting from what it can already do, but we give ourselves more time to decide how much autonomy its most powerful successors should have. If AI really is going to change everything, there would be something rather reassuring about some of its first great achievements being things that almost everyone, regardless of politics or ideology, could simply recognise as good.
