Terence Tao: Kepler, Newton, and the True Nature of Mathematical Discovery

Kepler and Mathematical Discovery

The discovery of planetary motion laws highlights that scientific verification loops can span decades or even millennia.
Early revolutionary theories, such as Copernicus's heliocentric model, were initially less accurate than the geocentric models they sought to replace.
Kepler's initial breakthrough was driven by a beautiful but ultimately incorrect theory involving Platonic solids and nested spheres.
The survival of superior scientific theories often depends on human judgment and heuristics that are currently difficult to codify into AI reinforcement learning loops.
Terence Tao suggests that Kepler functioned like a 'high temperature LLM' by generating creative, sometimes hallucinatory, but ultimately productive geometric hypotheses.

And the reasons it survives this epistemic hell is some mixture of judgment and heuristics that we don’t even understand well enough to actually articulate, much less codify into an RL loop.

Kepler as a High-Temperature LLM

Johannes Kepler’s discovery of planetary laws was driven by a 'high-temperature' approach, testing numerous eccentric and mystical hypotheses against empirical data.
The verification loop for scientific truth can span decades or centuries, as seen when Copernicus's sun-centered model initially provided less accurate predictions than the geocentric model.
Kepler's success relied on 'stolen' high-quality observational data from Tycho Brahe, illustrating that even flawed or random brainstorming requires a verifiable dataset to yield progress.
The transition from Kepler’s empirical regularities to Newton’s unifying theory suggests that AI might excel at finding patterns long before humans can explain the underlying physics.
Scientific discovery involves a complex chain of intuition, data analysis, and validation that current reinforcement learning loops cannot yet fully codify or articulate.

And the reasons it survives this epistemic hell is some mixture of judgment and heuristics that we don’t even understand well enough to actually articulate, much less codify into an RL loop.

Kepler as the First Data Scientist

Johannes Kepler's breakthrough laws of planetary motion were only possible because he stole and analyzed Tycho Brahe's high-quality, naked-eye astronomical dataset.
Kepler spent decades cycling through 'random' relationships and mystical theories, such as planetary harmonies and Platonic solids, before finding the empirical regularities that fit the data.
The shift from hypothesis-driven science to data-first analysis mirrors the modern transition toward using machine learning and LLMs to find patterns in massive datasets.
While we celebrate the 'eureka' moments of idea generation, the text argues that assiduous data collection and verification are the true drivers of scientific progress.
Kepler's third law was essentially a regression analysis performed on just six data points, highlighting the thin line between genius insight and lucky curve-fitting.

The reason there’s so much famine and misery on Earth is because the Earth is mi-fa-mi, that’s the note of Earth.

From Kepler to AI Slop

The scientific paradigm has shifted from hypothesis-driven testing to data-first analysis, where patterns are deduced from massive datasets before theories are formed.
Kepler’s success relied on the unprecedented precision of Tycho Brahe’s data, illustrating that verification is as essential to progress as creative idea generation.
Artificial intelligence has reduced the cost of generating new ideas to near zero, creating a massive bottleneck in the verification and validation of these theories.
Modern peer review systems are being overwhelmed by 'AI slop,' making it difficult to distinguish high-signal unifying concepts from numerical flukes or low-value noise.
The future of science requires new structures to identify transformative ideas at scale, as the traditional human-led consensus process cannot keep up with automated output.

I think AI has driven the cost of idea generation down to almost zero, in a very similar way to how the internet drove the cost of communication down to almost zero.

The Bottleneck of Verification

AI has reduced the cost of idea generation to near zero, shifting the scientific bottleneck from finding theories to verifying and validating them at scale.
The current peer review system is being overwhelmed by a flood of AI-generated submissions, making it difficult to distinguish high-signal breakthroughs from 'slop.'
Scientific progress is often non-linear, as seen in how Copernicus's simpler heliocentric model was initially less accurate than the highly-refined but incorrect Ptolemaic system.
The success of a scientific idea is often dependent on historical context, social inertia, and the 'test of time' rather than immediate objective grading.
True breakthroughs frequently require deleting long-held assumptions or accepting initially implausible implications, such as the lack of observable stellar parallax in ancient Greece.

Often, the ultimately correct theory initially is worse in many ways.

The Friction of Scientific Progress

Scientific breakthroughs are often hindered by cultural inertia and the standardization of existing paradigms, such as the decimal system or specific AI architectures.
Correct theories frequently appear less accurate than established ones initially, as seen when Copernicus's simpler model was less precise than the highly-tweaked Ptolemaic system.
Progress often requires deleting long-held assumptions, such as the Aristotelian belief that objects naturally seek a state of rest, rather than just adding new data.
The success of a theory depends heavily on effective communication and narrative, as demonstrated by Darwin’s plain-English synthesis versus Newton’s secretive and complex Latin texts.

When you only get part of the solution, it looks worse than a theory which is incorrect but somehow has been completed to the point where it kind of answers all the questions.

The Social Narrative of Science

Scientific progress is often delayed by conceptual leaps that defy intuition, such as the transition from Aristotelian rest to Newtonian motion or the shift from static species to evolution.
The success of a theory depends heavily on the art of exposition and persuasion, as seen in Darwin's accessible English prose versus Newton's secretive and mathematically dense Latin texts.
Human intelligence is currently undergoing a 'cognitive Copernican revolution' as AI challenges our traditional assessments of which tasks are difficult and which are easy.
Astronomy serves as a model for 'squeezing every last drop' of information from limited data, a skill that could be applied to the sociology of science to measure the actual impact of research.
The 'human side' of science involves creating narratives that account for gaps in current knowledge, a social and persuasive skill that remains difficult to quantify or automate through AI.

The art of exposition and making a case and creating a narrative is also a very important part of science.

Persuasion and the Deductive Overhang

Science involves a social and narrative component where researchers must convince peers of a theory's future potential even when data gaps exist.
Astronomy serves as a model for 'squeezing' maximum information out of sparse data, a skill highly valued in quantitative fields like hedge funds.
The 'deductive overhang' suggests that significant discoveries can be made by applying clever insights to existing data rather than just collecting more.
Current AI progress in mathematics has hit a plateau after solving 'low-hanging fruit' because models struggle to evaluate or create partial progress.
While AI currently lacks the ability to navigate complex, multi-stage problems, its potential to scale across all problems at a specific difficulty level remains a powerful advantage.

These AI tools, they’re like jumping machines that can jump two meters in the air, higher than any human. Sometimes they jump in the wrong direction, and sometimes they crash, but sometimes they can reach the tops of the lowest walls that we couldn’t reach before.

Breadth vs Depth in AI Science

AI models have hit a plateau in mathematics after solving 'low-hanging fruit' problems, shifting the focus from one-shot solutions to collaborative human-AI workflows.
Current AI tools excel at breadth rather than depth, acting like 'jumping machines' that can reach many low-level targets simultaneously but struggle with high-climbing deep reasoning.
The scientific paradigm may need to shift from focusing on a few deep problems to managing broad classes of problems that leverage AI's ability to map entire fields at once.
There is a risk that offloading the 'process' of problem-solving to AI could inhibit the development of human intuition and the ability to maintain complex systems over time.
Mathematics is unique among sciences for its heavy reliance on theory, making the 'process' of discovery often more valuable than the final answer itself.

These AI tools, they’re like jumping machines that can jump two meters in the air, higher than any human.

Breadth, Depth, and Scalable Science

AI excels at breadth while humans excel at depth, necessitating a redesign of scientific paradigms to leverage broad, moderately competent AI mapping.
The introduction of AI tools may revolutionize mathematics by enabling an experimental, large-scale approach to problem-solving that was previously impossible.
While AI can efficiently apply existing techniques to solve 'neglected' problems, it currently struggles with the creative leaps required for the most resistant 20% of a problem.
There is a risk that offloading the process of solving problems to AI may inhibit the development of human intuition and the ability to maintain complex systems.
The future of science likely involves a complementary model where AI identifies 'islands of difficulty' for human experts to focus their specialized skills on.

We can explore entirely new fields of science by first getting these broad, moderately competent AIs to map it out and make all the easy observations.

Mathematics at Scale

AI is revolutionizing mathematics by enabling 'math at scale,' allowing researchers to test existing techniques across thousands of problems rather than handcrafting individual solutions.
While AI excels at applying standard techniques to solve the first 80% of a problem, it still struggles with the final 20% that requires the invention of entirely new methods.
The perceived success of AI in math is often skewed by survivorship bias, where isolated wins on obscure problems mask a low overall success rate of approximately 1% to 2%.
AI tools are currently functioning as high-powered assistants that handle auxiliary tasks like coding, formatting, and literature searches, enriching papers without yet replacing the core human act of solving the hardest conceptual gaps.

The progress is simultaneously amazing and disappointing. It is a very strange feeling to see these tools in action.

AI Success and Mathematical Depth

Current AI success in mathematics often relies on scale and 'picking winners' from a low success rate of 1% to 2%, creating a public perception of mastery that masks frequent failures.
AI tools are significantly increasing researcher productivity in auxiliary tasks like coding, formatting, and literature searches, making papers broader but not necessarily deeper.
A critical gap remains in AI's inability to build cumulative, interactive progress; it relies on brute-force trial and error rather than the adaptive, step-by-step strategy used by human collaborators.
There is a concern that AI might solve prestigious problems through uninsightful brute force or 'assembly code gobbledygook' rather than by creating the elegant new conceptual frameworks humans prize.
The progress of AI is described as simultaneously amazing and disappointing, as users quickly acclimatize to revolutionary tools and begin to take their capabilities for granted.

The progress is simultaneously amazing and disappointing. It is a very strange feeling to see these tools in action.

Intelligence, Collaboration, and Formal Proofs

The author distinguishes between artificial cleverness and true intelligence, noting that current AIs lack the cumulative, interactive adaptivity found in human collaborative problem-solving.
AI models currently lack persistent understanding, as they do not retain skills or progress between sessions, treating each problem as a fresh brute-force task.
There is a concern that AI might solve major mathematical mysteries like the Riemann hypothesis using 'gobbledygook' code that offers no conceptual insight to humans.
Formalizing proofs in languages like Lean allows mathematicians to atomically study and 'ablate' complex arguments to identify the truly innovative steps within a sea of boilerplate.
The future of mathematics may involve new professions dedicated to refactoring and post-processing AI-generated proofs to make them elegant and understandable for humans.

But what they can’t do is jump a little bit, reach some handhold, stay there, pull other people up, and then try to jump from there.

AI Collaboration and Mathematical Narratives

The future of mathematics lies in the interplay between human intuition and AI tools, potentially creating entirely new forms of collaboration that do not yet exist.
Formalization tools like Lean allow mathematicians to deconstruct complex proofs into atomic parts, making it easier to identify key logical steps versus standard boilerplate.
AI-generated proofs that appear incomprehensible can be post-processed, refactored, and summarized by other AI agents to make them human-readable.
There is a growing need for a semi-formal language that captures mathematical strategies and narratives, moving beyond the rigid deductive logic of current proof assistants.
Historical examples like Gauss's Prime Number Theorem illustrate how data-driven conjectures can revolutionize fields even before a formal proof is possible.

Suppose the AI figures it out, and latent in the Lean is some brand-new construction which, if we realized its significance, we would be able to apply in all these different situations.

Formalizing Heuristics and Mathematical Intuition

The author explores the potential for a semi-formal framework that could automate the assessment of mathematical plausibility and scientific narratives.
Current AI development in mathematics is bottlenecked by the need for human experts to validate conjectures and the risk of reinforcement learning finding 'backdoors' in proofs.
The Prime Number Theorem serves as a primary example of how statistical models and data-driven conjectures can revolutionize a field even before formal proofs exist.
The mathematical community relies on a 'random model' of primes that underpins modern cryptography and the belief in the Riemann hypothesis, despite being largely heuristic.
To better understand scientific progress, the author suggests simulating 'mini-universes' where small AIs evolve their own strategies for solving basic arithmetic problems.

It became more and more productive to think of the primes as if they were just generated by some god rolling dice all the time and creating this random set.

Primes, Serendipity, and Mathematical Learning

The modern understanding of prime numbers relies on a statistical model that treats them as if they were generated randomly, a heuristic that underpins both the Riemann hypothesis and modern cryptography.
If a hidden pattern were discovered in the primes that contradicted this random model, it would likely render current cryptographic systems insecure and force a paradigm shift in number theory.
Terry Tao describes himself as a 'fox' who learns new fields through an obsessive, completionist drive to understand the 'magic' or 'tricks' used by other mathematicians.
Tao emphasizes the importance of serendipity and unplanned interactions, noting that over-optimization and remote work can destroy the casual, productive encounters found in physical hallways.
To better understand mathematical progress, Tao suggests simulating 'mini-universes' where small AIs evolve their own unique strategies for solving arithmetic problems.

It’s mostly heuristic and non-rigorous, but extremely accurate.

Serendipity and the AI Hybrid

Tao describes his 'obsessive, completionist streak' as a primary driver for learning new mathematical fields and mastering complex techniques used by others.
Writing for his blog serves as a creative outlet and a vital tool for retention, preventing the loss of complex arguments he might otherwise forget over time.
The transition to highly optimized, digital workflows has inadvertently eliminated the 'serendipity' of physical research, such as browsing library shelves and finding unexpected insights.
While AI will soon automate many tasks currently performed by math students, Tao believes human-AI hybrids will remain the dominant force in mathematical discovery for the foreseeable future.
A certain level of distraction and 'high temperature' randomness is essential for long-term inspiration, as total isolation can eventually lead to stagnation.

What we lost out on was the casual knocking on a hallway door, just meeting someone while getting a coffee.

Mathematics in the AI Era

The loss of serendipity and 'inefficient' browsing in the digital age may inadvertently stifle the accidental discoveries that fuel scientific inspiration.
Historical shifts in mathematics, such as the automation of log tables and calculus, suggest that AI will automate current tasks while humans move to higher levels of abstraction.
Hybrid human-AI collaboration is expected to dominate the mathematical frontier for the foreseeable future rather than total AI replacement.
Aspiring mathematicians must adopt an adaptable mindset, as AI tools like Lean may allow non-traditional contributors to reach the research frontier much earlier.
While AI accelerates certain types of progress, the current unpredictability of the field makes it both a scary and exciting time for intellectual pursuits.

You actually do need a certain level of distraction in your life. It adds enough randomness and high temperature.