Here’s the full content of a tweet by Ash Jogalekar:
I came to the present AI revolution not as a credulous enthusiast, but as someone deeply skeptical of new technologies in science. For twenty-five years I have seen too many of them arrive surrounded by extravagant claims before settling into a useful but much more modest place in the scientific toolkit.
What has astonished me is that the latest agentic systems appear to represent a qualitative change. I have now run upward of two hundred scenarios and AI for science workflows, each one navigating a complex, multistep scientific protocol across diverse fields of chemistry, biology and materials science, and I think I have enough data now to make a credible judgment. Over just a few months I have seen these models and algorithms leapfrog over increasing levels of difficulty, starting almost as a toddler and turning into an adult interlocutor. Every day, something moves my baseline for what they can do. They autonomously install, run, and debug dozens of computational tools; clean and structure data; parameterize molecules; launch calculations; and manage complicated, multistage investigations. But as it turned out, that was just the beginning.
More fascinatingly, they increasingly display recognizable scientific judgment: proposing positive and negative controls, discovering that a method does not work, generating competing hypotheses, systematically eliminating them, changing direction when evidence contradicts an initially promising idea, searching an entire target space, constructing unexpectedly sophisticated models, and finding useful analogies across distant fields. This is no longer merely workflow automation or doing the same science faster. It is beginning to feel like genuine intellectual and creative collaboration.
Interacting with the system can resemble a conversation with a smart and experienced student or colleague: ideas are proposed, challenged, refined, rejected, and unexpectedly pushed in new directions, with both the human and the AI acknowledging mistakes and adjusting course. The exhilarating possibility is the sheer multiplication of intellectual labor - the ability to explore almost any question under the scientific sun and to see those “mountains beyond mountains”, as Tracy Kidder eloquently put it. In a week a scientist or a team can come up with dozens or hundreds of ideas and hypotheses, most of them reasonable and actionable. The physical lab is now the only bottleneck, and even that is being accelerated by AI.
As a scientist, AI has made me feel more intellectually alive and excited than I have felt since graduate school and my postdoctoral years more than two decades ago. I can begin with an idea in the morning and, by lunchtime, watch a rational, testable hypothesis take shape; within days, an investigation can progress from literature and classical calculations to increasingly rigorous quantum-mechanical analysis and experimentally actionable predictions. Eating and sleeping have often taken a backseat, exercise seems like a distant goal, and every night I feel like I did when I was a kid and begging my dad or mom for just *one more story*. Except that this time it’s just *one more prompt*. One more cycle of compute. One more result that will startle or confound. Every night I have to force myself to detach from the computer, and on more than one occasion I have fallen asleep at my desk, only to wake up and see the potential for yet *one more prompt*.
Precisely because these systems are beginning to criticize our assumptions, tell us when something does not work, and think alongside us rather than merely obey us, it feels as though we may already have passed beyond the first age of AI.
Of course, these predictions and results will stand or fall based on experimental testing, that’s how science always has been, but that’s no different from the pre-AI age. More importantly, in almost every case they appear as conclusions that any good scientist will regard as reasonable, at least as starting points. And sometimes they genuinely throw up a surprise. AI-enabled science should still be judged by the novelty, rigor, reproducibility, statistical validation, and epistemic integrity of the science, not by the novelty of the technology.
But there is no doubt now that a Rubicon has been crossed, and either we cross over to the other side or get left behind. What a time to be alive.
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