How will self-evolving AI change scientific research?

Seeing the emergence of self-evolving AI systems like AlphaEvolve, one can't help but think: will the future of scientific research be completely rewritten by AI? We may be standing at a turning point - the traditional research model of "human-conceived experiments and manual analysis of data" is being replaced by AI-driven "natural evolution of algorithms". This trend reminds me of the shock I felt when I first saw AlphaGo many years ago, only this time, AI is disrupting a much broader field of research.

How will AI reconfigure research workflows?

Imagine a mathematical researcher who no longer has to rack his brain to derive every theorem, but instead lets an AI automatically explore possibilities, come up with new conjectures, and test them. alphaEvolve's breakthrough in math has already proved that it's possible - it improved Strassen's matrix multiplication algorithm with 48 multiplications, a record mathematicians haven't been able to break in 56 years. that mathematicians hadn't been able to break in 56 years. Doesn't that sound a bit sci-fi? But it's really happening.

Interestingly, the application of AI in scientific research is not to replace scientists, but to become a "scientific Copilot" like AlphaEvolve. It can handle those repetitive, computationally intensive "dirty work", allowing researchers to focus on higher-level thinking and creativity. Take those 300 years have not been solved geometric problems, AI found a new structure of the solution, but ultimately explain the significance of these findings, or human mathematicians to complete the work.

The "Cambrian explosion" in scientific research.

If AI is compared to a catalyst, then the scientific field may be experiencing a "Cambrian explosion" type of acceleration. alphaEvolve was able to rediscover existing solutions in 751 TP3T and provide improvements in 201 TP3T on more than 50 open-ended math problems tested. Improvements. What would be the ripple effect if this closed loop of "problem finding - problem solving" were to be extended to other areas of scientific research?

Taking materials science as an example, finding new materials usually requires a large number of experiments, which is time-consuming and labor-intensive. If there is a self-evolving AI that can predict which combinations of materials might have unexpected properties through simulation and optimization, experimenters can target their efforts and dramatically improve R&D efficiency. Similar scenarios are likely to occur in drug development, particle physics, climate modeling, and other fields that traditionally require repeated trial and error.

The challenge of "democratizing" the results of scientific research

That said, this change also raises some tough questions. If AI can do a particular piece of research faster and better, will the "rich get richer" in the future? Research organizations that can afford high-performance AI tools will be able to produce more, while research teams with limited resources may face a new "digital divide". More worryingly, as AI gives more and more reliable results, will some people start to trust AI without question, abandoning the independent search for scientific truth?

But overall, the positive impact of self-evolving AI on scientific research far outweighs the challenges. Like the invention of the microscope and the telescope, it provides humans with new tools to explore the world. In the coming years, scientific papers will increasingly be signed "Human & AI" - not as a compromise of scientific rigor, but as an inevitable evolution. After all, in this age of data explosion, the human brain does need AI to help us deal with complexity that is beyond our physiological limits.