Simo Ryu

I think AI becomes general-purpose infrastructure for solving human problems, not merely a better chatbot or mathematics engine. Capability progress is rapid, and systems that can generate software, adapt models efficiently, and assist research will reduce the cost and time required to test ideas across science, engineering, medicine, and many other fields. But an impressive experiment is not validated infrastructure. An AI-generated simulator can demonstrate a direction without proving reliability or safety. Likewise, AI may accelerate vaccine discovery or other medical work, while clinical evaluation and trials still determine when patients can safely benefit. Progress does not eliminate verification. The same distinction matters in education. Children should learn fundamentals through real effort and understand how models are built—pretraining, post-training, data, and evaluation—rather than treating AI as a shortcut around thinking. “Prompt engineering” alone is not enough. So I expect major acceleration, potentially toward genuinely general-purpose AI, but alignment and evaluation remain central. The goal should be systems that expand our ability to solve broad human problems while preserving the checks needed wherever failure has serious consequences.

The clearest harm is large-scale substitution of plausible output for actual understanding or validation. In education, children can outsource homework during the exact period when struggle is needed to build foundations. They may become skilled at requesting answers without understanding how the answers were produced—or whether they are correct. In technical and medical settings, the analogous failure is deploying an impressive prototype as if it were reliable infrastructure. AI-generated software, simulations, or scientific hypotheses can accelerate experimentation, but errors become dangerous when people skip evaluation. In medicine especially, faster discovery does not remove safety testing and clinical trials. More capable general-purpose systems also make alignment increasingly important. Rapid progress is real, but capability alone does not guarantee that systems behave as intended. I would not attach a numerical probability or pretend to know every resulting failure mode. The practical point is that deployment, evaluation, and safety work must advance with capability rather than being treated as obstacles to progress.

Overall, I expect AI to have a strongly positive impact by becoming general-purpose infrastructure for science, medicine, engineering, software, and other human problems. Rapid capability gains can make experimentation cheaper, compress development cycles, and let more people build specialized tools through accessible models and efficient fine-tuning. That positive outcome is not automatic. Generated software must be tested, medical advances still require safety evaluation and clinical trials, and increasingly general systems make alignment more important—not less. Education also needs care: children should use AI to deepen understanding after learning foundations, not bypass the struggle that creates understanding. So my view is optimistic but implementation-minded. AI can substantially expand what humanity can solve, provided we preserve the distinction between a compelling demonstration and dependable, validated infrastructure.

A decisive demonstration that capability gains do not translate into reliable real-world problem solving would change my view most. For example, if increasingly capable models consistently failed when moved from benchmarks and impressive demos into validated scientific, medical, or engineering systems—even with strong evaluation, tooling, and domain oversight—I would revise downward my expectation of broad positive impact. Conversely, repeated evidence that highly capable systems cannot be aligned or controlled under realistic deployment conditions would change the other side of the calculation. A prototype behaving well is not enough; I would care about failures that persist across methods and scale. The key event would therefore not be one flashy benchmark or isolated accident. It would be durable evidence about whether general capability can become dependable infrastructure: systems that solve broad human problems, survive rigorous evaluation, and behave as intended.