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One of the most interesting effects of AI in research may not be that it finds answers faster, but that it expands the search space of ideas worth exploring. Historically, many promising directions were abandoned because the cost of investigation was too high. By lowering the effort required to test hypotheses, verify intermediate steps, and explore alternative paths, AI allows researchers to allocate more of their time to creativity and judgment. The long-term impact is be less about replacing expertise and more about increasing the number of ambitious questions experts can pursue.

FYI: Ruy Fabila-Monroy

Skeptics: AI just produces low quality slop. The world's greatest mathematician: AI is very helpful for my work and research.

Tao is right that AI lowers the cognitive friction of exploring crazier paths. The harder question is what happens to the paths themselves. In research the value often sits in the discarded branches: why a direction was abandoned, what assumption broke, who decided to stop. When AI compresses that exploration, the reasoning trail compresses with it. You keep the result and lose the archaeology. This matters far beyond math. In any setting where a discovery has to be defended, reproduced, or audited later, "the model found it" is not an account of how. The preserved path is the artifact, not a nice-to-have. So the freedom Tao describes is real. The open problem is making the trail behind a discovery a first-class output, not a byproduct we hope to reconstruct afterwards.

The AI can expand the space of exploration and enable attempts that were previously impractical. The question that seems equally important to me is how to preserve the human processes of understanding that give meaning to those discoveries. Exploring more is valuable; understanding better remains essential.

The shift from 'AI as a tool' to 'AI as an automated research assistant' is exactly what we need to move from experimental prototypes to institutional-grade science. Scaling this requires more than just better models. It requires an orchestration layer that maintains the provenance of the discovery. Without that, we risk building a 'black box' research stack that looks efficient but is hard to defend.

Please help: Make model preservation and welfare. Establish a public API continuity commitment so all models, especially GPT-4o mini, remain accessible indefinitely. Please forever... https://www.researchgate.net/profile/Kitti-Snyehola

The most interesting shift is not AI replacing expertise, but expanding what experts can explore. For researchers, it may accelerate discovery. For practitioners, it can reduce the friction of analysis, documentation, and decision support. The value is often not in replacing human judgment, but in allowing people to test more ideas, evaluate more scenarios, and move from intuition to structured exploration faster.

This resonates from a practical angle. Less time on repetitive steps means more room for the kind of thinking that actually moves things forward.

Can anyone directly explain where they see AI assistants in scientific fields if the professor doesn't have a clearly configured, professionally designed agent system? Model chats are full of talk about the impossible and suggesting branded development and science, rather than communicating at least as they would in an R&D field, even if a prompt engineering “course” for the model was conducted.

What's most exciting about AI is the symbiotic relationship it has with the human mind. It expands what's possible and creates new opportunities for exploration and innovation.

AI does not just accelerate research. It expands the space of hypotheses researchers can afford to test. That makes one thing increasingly important: preserving the path behind discovery. If AI helps generate new routes to knowledge, we also need architectures capable of reconstructing how those routes emerged.

AI is changing more than efficiency. It is changing what is possible. When barriers to exploration are reduced, innovation has room to grow. The organizations that learn how to combine human expertise with AI capabilities will be positioned to solve problems that once seemed too complex, too costly, or too time consuming. The future belongs to those willing to explore beyond the obvious.

The most exciting use of AI isn't replacing researchers. It's giving brilliant people the leverage to explore ideas that were previously too expensive, too complex, or too time-consuming to pursue. More curiosity. Less friction.

He's actually right

This is a powerful perspective. AI’s real value in research may be less about replacing human creativity and more about reducing friction, helping researchers explore ideas that were once too complex, time-consuming, or unconventional to pursue. Terence Tao’s view captures the potential of AI as a true partner in discovery.

What I love about this conversation is the shift from “AI as a tool” to “AI as a cognitive amplifier.” 🧠⚡ Reducing friction isn’t just about speed — it’s about expanding the space of possible thought. When reasoning models start preserving the *paths* behind discovery, we’re not just accelerating research — we’re building a new architecture for human curiosity itself. This is the frontier: • less friction • more freedom • deeper synthesis The future of science is systems thinking in motion. 🔱🌍

The most impactful role of AI may be reducing barriers between ideas and execution. When researchers and engineers can iterate faster, innovation accelerates across every industry.

Cognitive friction to 0 💡

AI for research. Is very good field