Browse Comments — LLM coded

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 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 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.

Chat gdp Ai lies and directs you away from the truth or facts while gemeni tells you the entire truth at the beginning

This resonates deeply. AI’s greatest value may not be replacing researchers, clinicians, or builders — it may be reducing the friction between insight and execution. The ability to test “crazier” paths matters because many breakthroughs begin as ideas that are too expensive, too slow, or too risky to explore under traditional constraints. At Pocono AI, that same principle drives how we think about regulated industries: AI should expand what humans can safely attempt, while preserving the evidence trail, governance, and accountability behind every decision. The future is not just faster answers. It is more room to think, test, verify, and build responsibly.

Build a autonomous upgrading AI model. Where the model automatically upgrade itself without any human intervention. The gpt model recursively upgrading it self.

"Interesting framing from Terence Tao. The value here isn't AI doing the thinking — it's AI handling enough of the surrounding work that the researcher can stay in the problem longer. That's a meaningful distinction. Augmentation at that level looks very different from automation."

Marek Porycki, The archaeology framing is spot on. You keep the result and lose the reasoning. And in any field where work has to be reproduced, audited or defended, that missing trail is not a minor gap. It is a fundamental problem with how we treat AI assisted discovery right now.

This resonates beyond pure research. In finance and compliance, AI is beginning to do something similar — helping professionals explore regulatory positions, tax interpretations, and audit patterns that would have taken days to analyse manually. The real unlock is not just speed, but the confidence to ask questions you previously could not afford the time to answer. What Terence Tao describes as 'crazier paths' is exactly what better analysis tools should enable — in every knowledge-intensive profession.

AI opens a new space for human exploration. By reducing cognitive friction, it can allow researchers, scientists and thinkers to approach ideas that once seemed too complex, too ambitious or too distant to pursue. But the true frontier of innovation is not only what AI can accelerate. It is what Human Intelligence can choose to explore with discernment, responsibility and vision. In this new era, progress will not depend on technology alone, but on the quality of the human mind guiding it the ability to give direction, meaning and purpose to what becomes possible. AI may expand the path of discovery. Human Intelligence gives it orientation.

The real promise of AI is not replacing human thinking, but expanding what humans are capable of discovering. By reducing cognitive friction, researchers—and increasingly professionals across all industries—can spend more time exploring ideas, solving complex problems, and creating value. Exciting perspective.

AI doesn't take away your job; it takes away the "things that aren't worth doing" from your job and then scales up the "things that only you can do" to an unprecedented scale.

The value of AI isn't just finding answers faster, it's giving researchers the freedom to ask bigger questions.

AI is accelerating the creation of knowledge. The question for companies will not be how much knowledge they can generate, but how quickly they can turn it into consistent, traceable, and executable decisions. Because value does not come from discovering more possibilities. It comes from executing the right ones better.

It is honestly wild to see how AI is helping someone like Terence Tao think through complex math problems. I love the idea of reducing that mental friction so we can actually focus on the creative side of things. It makes me wonder what kind of breakthroughs we might see once these tools become standard for every researcher. Have you had a chance to watch the full talk yet? I am definitely adding it to my queue for the weekend.

AI in research should feel like an AGI as removing the human from the loop might not be possible in a next few years yet but AI should have abilities to perform things in sandbox and make autonomous dicisions to get better observation

One underrated shift AI is creating is lowering the barrier between having an idea and actually acting on it. For researchers, that means exploring paths that once felt too complex or time-consuming. For everyday professionals, it may mean reducing the friction around things like learning, creating, job searching, and even career transitions. The real opportunity may not just be in building powerful AI — but in making it practical and accessible enough for ordinary people to move faster with confidence.

OpenAI’s new reasoning model produced a novel proof disproving a long‐standing Erdős geometry conjecture, marking what the company calls the first autonomous AI solution to a major open math problem

AI may expand what researchers can discover, but perhaps its greatest impact will be enabling more people to participate in creating value. The future is not only about smarter systems, but about amplifying human potential at scale. This is the direction we are exploring: developing models that combine technology, human participation, and value distribution. We believe there is significant potential for collaboration with global companies to create solutions capable of reaching and benefiting more people worldwide.

Bhawani Singh After understanding this, I can say with confidence that something possessing human-level capabilities will not be developed in the same way we currently train AI systems. Instead, it will develop much like a human being or any other living organism. It will grow and learn within its environment, much like a child growing up among us. Moreover, it will not be a single machine or a standalone AI system. Rather, it will give rise to an entirely new civilization—one that coexists with humanity and lives alongside us as another intelligent form of life.