Early in 2020, as cities around the world began locking down in response to covid, a few researchers were still able to continue to run their experiments. Even though they, like everyone else, had been prohibited from entering their labs, they were able to log into ‘cloud laboratories’ and submit their trials remotely, leaving it to robotic arms and automated instruments to execute their instructions from a distance.
What was a quaint convenience in the midst of a crisis is now a widespread reality, as software, robotics and artificial intelligence (AI) have come together to bring the concept of ‘work-from-home’ to scientific experimentation. Around the world, commercial cloud labs have already begun to invert traditional scientific workflows to the point where, instead of researchers moving between their instruments, samples travel through robotic pathways.
Self-driven laboratories take this one step further. By embedding AI directly into these autonomous laboratories, they can move beyond just executing instructions to actively generating them. These intelligent automated systems are not only able to identify new experiments and carry them out using robotic infrastructure, but also analyse their results and, based on the feedback, decide what needs to be done next. In the process, the long cycle of experimentation can be collapsed into a continuous feedback loop.
The immediate consequence of all of this will be a dramatic acceleration of the timelines of scientific progress. When a year of human research can be compressed into weeks or even days, thousands of experimental variants can be explored in parallel. In such a world, failure is cheap and discovery through relentless iteration is not just possible but inevitable.
In fields such as drug formulation, protein engineering and materials science, these capabilities can radically transform the economics of scientific work.
However, as we have learnt repeatedly, any attempt to reduce friction often spells unintended consequences. In accelerating the pace at which scientific research can be conducted, are we inadvertently exposing ourselves to harms that we have so far had no reason to worry about?
Any AI system that helps identify the cure for a disease can just as easily be used to identify chemical and biological agents that can make us ill. In a previous article, I told the story of MegaSyn, a machine-learning algorithm developed to identify never-before-seen compounds with a high probability of curing diseases.
In its process of eliminating from a long-list of suitable molecules those that had toxic side effects, the system ended up generating a list of unimaginably lethal substances that were not only more potent than the most toxic chemical agents known to us, but also effectively untraceable, since many of them had not yet been discovered.
As terrifying as this sounds, all that Megasyn does is identify potentially toxic substances. To make use of this knowledge to actually build harmful biological substances, someone would have to take those theoretical formulae and synthesize them into actual products. This not only requires access to a fully equipped laboratory, but also personnel who not only have the expertise required to use it but also the moral ambivalence to do so regardless of the consequences. As autonomous laboratories become a reality, this barrier will soon come down.
This is not a hypothetical risk. Most biological AI systems are lightly regulated. Many are open-source. Few incorporate meaningful safeguards. The cloud labs that exist today operate in a regulatory grey zone—even though they can run highly powerful experiments. Legal frameworks such as the Biological Weapons Convention that were designed for a world in which physical facilities and human-controlled research were the only means to create biological substances will struggle to adapt to this new AI reality.
That said, self-driven cloud laboratories offer us unprecedented pathways for clinical experimentation. In the right hands, this could improve our ability to develop life-saving treatments and enable personalized treatment at scale. As serious as the potential harms might be, there are reasons aplenty to try and find a way to make this work safely.
If we want to achieve this uneasy balance, we urgently need to update our treaties and amend our laws. But we cannot stop there. As we build automated laboratory systems, accountability must be engineered into them from the start. Experiments devised, implemented and refined by AI agents must be identifiable, auditable and traceable to human decision-makers.
Cloud laboratories have enabled remote science by making research resilient to physical disruption. In doing so, they have also removed many of the frictions that were, unbeknownst to us, keeping us safe. Rapid advances in AI have not only accelerated this process, but also enabled a massive expansion of scientific capabilities.
There is usually a small window between the birth of a new technology and society’s recognition of the harms it can cause. This is the period in which it can go unregulated and be used without permission from authorities. With AI evolving rapidly, that window matters a lot more than many of us realize. And in the case of self-driven laboratories, particularly, we must ensure that it is kept as tight as possible. Given the harms that could result, we need to not just close it quickly, but see that it is never wide enough for catastrophic outcomes.
The author is a partner at Trilegal and the author of ‘The Third Way: India’s Revolutionary Approach to Data Governance’. His X handle is @matthan.
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