Ash Jogalekar

[Ash Jogalekar](https://x.com/curiouswavefn)[@curiouswavefn](https://x.com/curiouswavefn) 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.

Ash Jogalekar ✔ @curiouswavefn · 6h Most of scientific research is spent in the valleys. You're climbing one ridge at a time, surrounded by trees. But every once in a while, you reach a small rise where the fog clears. Suddenly you see not just the mountain you're climbing, but half a dozen neighboring peaks, and you realize they're connected. AI gives me that feeling surprisingly often. It doesn't climb the mountain for me. But it lets me glimpse the landscape.

##### Conversation[Ash Jogalekar](https://x.com/curiouswavefn)[@curiouswavefn](https://x.com/curiouswavefn) As a scientist, AI has made me feel the most intellectually alive and excited I have felt since I was a graduate student and postdoc more than 20 years ago. Every day I can start with an idea in the morning, and by lunchtime, I see a testable, rational, well-thought-out hypothesis forming in front of my eyes. And every day, the possibilities seem endless, like mountains beyond mountains. What a time to be alive. Here's a case in point. I'm collaborating with a professor, an experimentalist, who is trying to solve a thorny problem in his field. There's one particular molecule that he is using in his experiments that seems to result in radically different crystal structures compared to similar molecules. What's happening here? He has come up with a few different hypotheses that could explain the differences but is not a theoretician and needs to tease them apart. On Thursday, I started an investigation using AI at his bequest. The AI immediately confirmed the hypotheses that he had in mind and added a few of its own. Then it started its exploration. The investigation was carried out in three different phases, each of increasing difficulty; the first one using classical physics, and the second and third using quantum mechanical techniques of increasing rigor. This tiered strategy is the right one. By Thursday evening, I had the glimpse of an answer. Most of the hypotheses had been examined and rejected. Two stood out, although the AI identified one as more a mechanism through which the other one operated rather than a root cause. It immediately pivoted to the higher-level, more rigorous calculation. Every time I interacted with the AI, it was more like a dialogue between a professor and a bright student or scientific collaborator than a mandate issued to a tool. The feeling was very much of a process where the AI and I were solving a problem together. I steered the conversation several times, pushed back, suggested course-corrections, acknowledged my own wrong ideas as well as the AI's and went back and forth. The AI was successful in keeping multiple requests in its memory, stacking them by priority while never losing the conversation thread. By late Friday morning, there had collected enough data from the more rigorous calculation to corroborate the suspicion that it was really just one hypothesis that was the root cause. It then moved on to the next step, which was to come up with a distinct set of novel molecules that would confirm the hypothesis beyond any reasonable doubt. In addition, it launched an even more rigorous calculation at a higher level of theory. By the end of Friday, roughly 48 hours later, using this multi-layered approach of increasing rigor, backed up by references, and made useful and actionable by testable experiments, the AI had arrived at a solid, rigorous conclusion. Now imagine doing this every day, about any topic under the scientific sun, in any scientific field, so that your intellectual labor is multiplied a million-fold. Mountains beyond mountains. What a time to be alive. [View quotes](https://x.com/curiouswavefn/status/2070947867452440855/quotes) This was Opus 4.8, although I get similar results with GPT 5.5. One thing I have found is that you need at least GPT 5.4 or Opus 4.7 for doing any kind of serious, multistep science.[8.4K](https://x.com/curiouswavefn/status/2071079384564523044/analytics)[Akshat](https://x.com/star_stufff)[@star\_stufff](https://x.com/star_stufff) [Jun 28](https://x.com/star_stufff/status/2071259426678530122) I love the phrase “mountains beyond mountains”:)[2.2K](https://x.com/star_stufff/status/2071259426678530122/analytics) I borrow it from Tracy Kidder's wonderful biography of the late doctor and humanitarian Paul Farmer.[1.9K](https://x.com/curiouswavefn/status/2071261869663526933/analytics) Now imagine being able to submit all of those experiments to a cloud lab and then getting all the results emailed to you within hours, fully analyzed for you to update your hypothesis. That future is here This is a beautiful description of what AI can be when used well. What stands out most is how you stayed in the driver’s seat the entire time — steering, pushing back, correcting course, and treating the AI as a genuine collaborator rather than an oracle. That dialogue dynamic This approach is what I have found to work as well... let it do its own work, just ends with errors but really engaging with the process in all its messiness really yields good results. But don't forget to fact check, we gotta do the leg work too.