AI, Quantum Computing and the Future of Scientific Discovery
Can AI solve humanity’s hardest mathematical problems without quantum computers — and what happens to science if it can?
In my previous reflections on quantum computing and artificial intelligence, I argued against a simple “AI versus quantum” narrative.
I still believe that.
Artificial intelligence and quantum computing are fundamentally different technologies with different strengths. AI is already extraordinarily capable at pattern recognition, language, reasoning, search, prediction and increasingly scientific discovery. Quantum computing offers something different: the ability to manipulate quantum states in ways that could eventually make certain simulations and mathematical operations tractable when they are prohibitively difficult for classical machines.
My expectation was therefore a heterogeneous future:
CPU + GPU + AI + QPU, with each type of processor handling the part of a problem for which it is best suited.
IBM is already explicitly pursuing this idea through what it calls quantum-centric supercomputing, in which CPUs, GPUs and quantum processors operate together rather than one replacing the others.
But the extraordinary developments in mathematics during September 2026 force me to add another layer to this architecture.
Human science.
And perhaps that is the most important layer of all.
Because the question is no longer only whether AI and quantum computing will work together.
We now have to ask:
Could AI solve some of humanity’s hardest intellectual problems before useful fault-tolerant quantum computers even arrive?
And, if the answer is yes, what does that mean for mathematicians, scientists, universities, research funding and scientific culture itself?
What actually happened with Navier–Stokes?
On 8 September 2026, OpenAI published what it describes as a solution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems established by the Clay Mathematics Institute.
The scale of the experiment is remarkable.
OpenAI says it began on 1 September after hearing rumours that two Millennium Prize Problems might have been resolved. It then launched groups of AI agents against all of the still-open Millennium problems and several related research questions.
For Navier–Stokes, the successful group eventually involved around 10,000 concurrent AI agents. The agents could read from a cached version of the internet, run code, communicate within groups and explore different approaches. OpenAI says the Navier–Stokes effort consumed approximately 130 billion output tokens across 2.7 million agent messages. Across all the mathematical problems attempted, the experiment consumed around 300 billion output tokens and 4.9 million messages. The agents reached the proposed resolution after roughly 88 hours, followed by another 17 hours of Lean formalization and verification.
That is worth thinking about carefully.
This was not simply a chatbot producing a clever answer.
It was closer to a new form of computational research organization: thousands of reasoning processes exploring different routes simultaneously, exchanging intermediate discoveries, eliminating unsuccessful directions, consolidating promising ones and eventually producing a formally checkable mathematical argument.
That could represent something far more consequential than another improvement in chatbot intelligence.
It may represent the emergence of industrial-scale reasoning.
But has Navier–Stokes officially been solved?
This distinction is extremely important.
OpenAI says its proof establishes finite-time singularity for the relevant formulation and has released both the analytical argument and a Lean formalization. Nature reported the announcement as potentially the first truly major open mathematical problem solved by a computer.
But in the formal sense of the Millennium Prize, we should still call this a proposed solution, not a completed and officially recognized one.
The Clay Mathematics Institute has very specific rules. It will not directly accept a proposed proof. The work must first appear in a qualifying publication. At least two years must then pass, during which the mathematical community examines it, and the solution must achieve general acceptance among mathematicians before Clay will consider awarding the prize.
OpenAI itself says it does not intend to claim the $1 million prize.
So enthusiasm is understandable, but academic caution is appropriate.
Lean verification is enormously valuable because it confirms that a formalized chain of reasoning follows the specified logical rules. But mathematicians still need to establish that the formal statement corresponds precisely to the Millennium formulation, examine assumptions, understand the conceptual construction and determine how it fits into the existing mathematical literature.
Mathematics is not simply about reaching the last line of a proof.
It is also about understanding why the proof works.
And now the Hodge Conjecture rumours
Almost immediately after the Navier–Stokes announcement, rumours began circulating that OpenAI might also have solved, or be close to verifying, the Hodge Conjecture, another Millennium Prize Problem.
As of 13 September 2026, there is no public OpenAI paper demonstrating a solution to Hodge and no announcement from the Clay Mathematics Institute confirming one.
So I would not write that OpenAI has solved the Hodge Conjecture.
But there is now something more substantial behind the speculation than there was when the rumour first appeared.
On 10 September, OpenAI told The New York Times that, since completing Navier–Stokes, it had made “substantial progress on another Millennium Prize problem” and was considering how to share the results. It did not identify the problem.
The Hodge Conjecture is therefore still a rumour.
But the claim that OpenAI has made progress on another Millennium problem is no longer merely social-media speculation.
Even mathematicians are taking the possibility seriously enough to discuss what an intensive AI attack on Hodge might mean. A guest article by mathematician Burt Totaro on Terence Tao’s blog began by observing that these are “strange times for mathematicians” and discussing the possibility of AI companies directing massive resources at the Hodge problem.
Whether the next announcement turns out to be Hodge, Birch–Swinnerton-Dyer, something else, or ultimately nothing decisive, the larger question remains.
Could OpenAI solve the remaining Millennium Problems without quantum computing?
I think the answer is:
Yes, in principle.
That does not mean OpenAI will solve them.
And it certainly does not mean simply adding more agents guarantees success.
But there is no known principle saying that the Riemann Hypothesis, Hodge Conjecture, Birch and Swinnerton-Dyer Conjecture, Yang–Mills mass gap or P versus NP require a quantum computer to be solved.
They are mathematical questions.
Ultimately, they require a valid argument.
A proof is classical information. Definitions, lemmas, logical relationships and formal verification can all be represented and manipulated on classical machines.
OpenAI has already shown that enormous parallel AI search running on classical computing infrastructure can explore a very large mathematical landscape. Its August announcement described ten results in mathematics and theoretical computer science that either resolved or substantially advanced long-standing questions across areas including geometry, coding theory, complexity theory, group theory and cryptography. Those arguments were subsequently formalized in Lean.
Google DeepMind has followed a related trajectory. AlphaProof combined reinforcement learning with formal mathematics and Lean, reaching strong results on International Mathematical Olympiad problems.
None of this required quantum computing.
That is important.
We should not confuse computational difficulty with quantum necessity.
Even P versus NP would not automatically fall to a quantum computer
Quantum computing is frequently portrayed as a machine for making every difficult problem easy.
It is not.
Quantum computers provide dramatic advantages for particular mathematical structures. Shor’s algorithm is the famous example for integer factorization; Grover’s algorithm provides a quadratic improvement for certain search problems.
But quantum computers have their own complexity class, BQP. IBM’s own quantum-computing material explicitly notes that BQP should not simply be assumed to contain the NP-complete problems.
This means that even possession of a powerful fault-tolerant quantum computer would not automatically answer P versus NP.
Settling P versus NP still requires mathematics.
So it is entirely conceivable that an AI system running predominantly on conventional processors could prove P ≠ NP before quantum computing becomes commercially mature.
There would be nothing contradictory about that.
Could AI eventually solve all unsolved mathematics?
Here my answer becomes much more cautious.
If “all mathematics” is meant literally, then no finite algorithmic system can simply complete mathematics once and for all.
Gödel’s incompleteness theorems established that any sufficiently expressive and consistent effectively specified formal system contains statements that cannot be proved or disproved within that system. Importantly, Gödel does not say that such statements can never be proved under any possible stronger set of assumptions. But when we move to another sufficiently powerful effective formal system, incompleteness reappears.
Turing and Church established related boundaries on mechanical computation. There are decision problems for which no universal algorithm exists. Quantum computers do not magically remove that limitation; undecidability also appears in quantum computational settings.
So we should separate two very different propositions.
An AI could conceivably solve many or even most famous open problems currently occupying human mathematicians.
That is an extraordinary possibility.
But an AI cannot display one day:
“Mathematics completed: 100%.”
Mathematics does not have that kind of endpoint.
Why doesn't quantum AI simply solve the problem?
This brings us back to my earlier article.
It is tempting to imagine that connecting frontier AI models to quantum computers will automatically create exponentially more intelligent AI.
There are serious reasons to doubt such a simple story.
Classical deep learning works exceptionally well because enormous amounts of human information already exist in classical digital form: language, images, scientific papers, databases, measurements, transactions and software.
Quantum processors operate on quantum states.
Putting a large conventional dataset into a quantum computer can itself become expensive. Proposed quantum machine-learning architectures sometimes assume efficient quantum random-access memory, or QRAM, but data loading and state preparation can consume enough resources to reduce or eliminate the theoretical speed advantage of a quantum algorithm.
Training quantum models creates another problem. Parameterized quantum circuits can suffer from barren plateaus, regions where the gradients required for training shrink exponentially as a circuit grows. A 2025 Nature Reviews Physics review calls this one of the central obstacles facing variational quantum computing.
An even more intriguing result is that some approaches designed to avoid barren plateaus may produce circuits that are also easier to simulate classically, potentially weakening the very quantum advantage they were intended to preserve.
So “Quantum Machine Learning” remains scientifically exciting.
But the statement:
Quantum computing will make AI exponentially smarter
is not presently justified.
In fact, AI may be helping quantum computing first
The relationship may initially operate in the opposite direction.
AI is already becoming useful in the engineering of quantum computers themselves.
One of the hardest requirements for useful quantum machines is quantum error correction. Qubits are fragile, and their physical errors must be detected and corrected continually.
Google DeepMind and Google Quantum AI developed AlphaQubit, a transformer-based neural-network decoder that learned to interpret quantum error-correction information and outperformed established decoding approaches in the experiments studied.
Google has now gone further. In July 2026 it reported a reinforcement-learning system that uses error-detection information to continuously adjust quantum-control parameters while the quantum computer remains operational. Instead of stopping a computation to recalibrate the machine, AI helps keep it stable while it runs.
This reinforces one of the conclusions from my previous article:
AI may help make practical quantum computers possible before quantum computers significantly improve mainstream AI.
That is an important inversion of the popular narrative.
Where quantum computing could become indispensable
None of this diminishes the long-term importance of quantum computing.
It clarifies where its advantage may matter most.
Richard Feynman’s original motivation for quantum computing was fundamentally about simulating nature. Quantum systems become extraordinarily difficult to represent using classical computers because the state space can grow exponentially.
That is why chemistry, materials science and strongly correlated systems remain among the most compelling long-term quantum applications.
Current research already demonstrates hybrid quantum–classical frameworks in which a QPU simulates the hardest part of a physical system while classical processors perform the surrounding calculations and interpret the measurements.
The value chain described in the Plug and Play quantum report I originally referenced also points toward this broader ecosystem: hardware, cloud access, systems software, applications and services rather than a single universal “quantum computer.”
The most realistic future is therefore not one machine replacing another.
It is specialization.
Room-temperature superconductivity is a perfect test case
After the Navier–Stokes announcement, Sam Altman responded to a social-media challenge suggesting OpenAI should solve more Millennium problems with the short reply, “ok fine.”
A few hours later, another user jokingly suggested that Anthropic had discovered a room-temperature superconductor and wondered whether a competitor might unleash another 10,000-agent swarm. Altman responded:
“you know what, let's try.”
The exchange appears much closer to playful public banter than a formal OpenAI research roadmap, so it would be wrong to claim that OpenAI has officially launched a 10,000-agent superconductor program.
But the thought experiment is fascinating because it exposes the difference between mathematics and physical science.
To solve a mathematical conjecture, AI ultimately needs to produce a valid proof.
To discover a room-temperature superconductor, AI must contend with nature.
It can generate candidate compounds, analyze existing literature, predict properties, run simulations and propose experiments.
But eventually someone must make the material.
And measure it.
AI is already accelerating that process
This is not theoretical.
A 2026 study in npj Computational Materials developed an AI-assisted superconducting-material discovery pipeline. Researchers screened more than 1.3 million candidate structures, reduced them to 741 stable candidates predicted to superconduct above 5 K, and experimentally synthesized and confirmed superconductivity in two previously unreported compounds.
That is an excellent example of the emerging scientific loop:
AI → simulation → candidate selection → laboratory experiment → new data → better AI.
But room-temperature superconductivity at ambient pressure remains enormously challenging.
A large 2025 study of conventional superconductors concluded that conventional room-temperature superconductivity at ambient pressure appears extremely unlikely given known stability and electron-phonon constraints, while leaving open high-pressure materials, unconventional pairing mechanisms and new physics outside existing models.
And this is precisely where quantum computing may eventually enter the loop.
For highly correlated electrons and quantum many-body systems, QPUs may be able to simulate regimes that classical computers struggle to represent accurately.
The future workflow might therefore become:
human hypothesis → AI exploration → classical HPC → quantum simulation → laboratory experiment → AI interpretation → human insight.
That is much richer than either “AI replaces scientists” or “quantum replaces AI.”
The Navier–Stokes controversy adds another dimension: who owns the scientific idea?
The OpenAI result did not emerge in an intellectual vacuum.
Tristan Buckmaster was already one of the leading mathematicians working on the Navier–Stokes equations. His 2019 paper with Vlad Vicol established non-uniqueness for a class of finite-energy weak Navier–Stokes solutions, a significant result in modern analysis.
That work was consequential enough to trigger a later interdisciplinary discussion among mathematicians, physicists and philosophers about what such “wild solutions” mean physically. The European Mathematical Society discussion explicitly invoked Thomas Kuhn’s concept of scientific crisis while stopping short of declaring fluid mechanics itself to be in one.
More recently, Diego Córdoba, Luis Martínez-Zoroa and collaborators developed novel finite-time singularity mechanisms in related fluid equations. Their 2023 work on Euler introduced an unusual construction based on multiple separated regions of vorticity rather than the more familiar self-similar approach.
Buckmaster and Levent Alpöge were pursuing related directions using AI tools.
Then the race accelerated dramatically.
The uncomfortable Buckmaster–OpenAI episode
According to Buckmaster, OpenAI discussed various publication arrangements after developing its result, including one proposal under which Buckmaster could write up OpenAI's proof while acknowledging the model, but without Alpöge as co-author because Alpöge works for rival Anthropic.
Buckmaster rejected that arrangement, saying it would be unethical to exclude his collaborator. OpenAI researcher Sébastien Bubeck has said the proposals were intended as efforts to collaborate and to recognize Buckmaster’s position, not to deprive anyone of credit.
An even more serious question arose because Buckmaster and Alpöge had used OpenAI's Codex while developing their own research.
Could OpenAI's system somehow have benefited from their unpublished prompts?
The latest information matters here.
OpenAI initially faced questions over whether user interactions could have influenced its system. After an investigation, the company updated its Navier–Stokes announcement on 10 September to state that Buckmaster's Codex prompts during the preceding two months could not have influenced the model in any way, including through training. OpenAI also says neither its researchers nor its agents saw Buckmaster and Alpöge's work before it was made public.
That does not eliminate the broader governance issue.
But it means I would not describe the current evidence as proving that OpenAI stole Buckmaster's work.
That claim goes beyond what has been established.
The broader confidentiality problem remains
The controversy nevertheless exposes something that every research institution should start thinking about.
Researchers increasingly use AI systems almost as intellectual collaborators.
They submit unpublished mathematical ideas, experimental designs, source code, preliminary data, patentable concepts and draft papers into these systems.
What protections apply?
OpenAI's current policy says that for individual services such as ChatGPT and Codex, content may be used to improve models unless the user opts out, while business products and API data are not used for training by default unless the organization explicitly opts in.
This creates a major governance challenge that extends far beyond this single incident.
A scientist should not have to choose between gaining access to the world's most capable research assistant and preserving confidence in the confidentiality of unpublished research.
Scientific AI will therefore need something approaching a new trust framework: clear provenance, auditable data boundaries, research confidentiality, predictable intellectual-property rules and transparent attribution.
Without trust, researchers may simply stop sharing their most valuable ideas with AI systems.
That would hurt researchers.
And ultimately, it would hurt AI too.
Was AI's Navier–Stokes achievement simply brute force?
I would be careful with that characterization.
The enormous agent count and compute budget clearly mattered. OpenAI deliberately explored many alternative mathematical routes and cross-pollinated promising results between agent groups.
That has a brute-force component.
But it is not equivalent to checking every possible integer until one works.
The proposed proof required constructing a very particular fluid configuration in which nonlinear acceleration, pressure, momentum transfer and viscosity interact in a precise way while the relevant forcing remains sufficiently regular.
The machine still needed mathematical reasoning.
A better description might be massively parallel mathematical search guided by increasingly capable reasoning models.
That itself could be revolutionary.
Human mathematics traditionally scales by adding mathematicians slowly: students are trained for years, specialists work for decades, ideas circulate through seminars and papers, and different researchers explore competing approaches.
An AI system can instantiate another thousand reasoning agents in minutes.
That changes the economics of mathematical exploration dramatically.
But the machine did not begin from nowhere
And this is where I think the most important philosophical argument begins.
Even an extraordinary AI result exists within a landscape built by generations of mathematicians.
Navier and Stokes formulated the equations.
Leray developed foundational weak-solution theory.
Decades of mathematicians developed functional analysis, harmonic analysis, PDE theory and fluid mechanics.
Córdoba, Martínez-Zoroa, Buckmaster, Vicol and many others opened specific modern directions.
The AI entered an already extraordinarily rich intellectual environment.
This does not make its contribution unimportant.
It makes the ecosystem that enabled that contribution important too.
There is a third layer in the AI–quantum stack: human science
My earlier computing model was incomplete.
I described:
CPU → GPU → AI → QPU.
I would now put something before all of them:
Human curiosity and foundational science.
Because computation needs questions.
AI needs concepts.
Formal proof systems need definitions.
Quantum simulators need models.
And experiments need hypotheses.
The future scientific stack may therefore be closer to:
Human science → AI reasoning → classical HPC/GPU compute → quantum simulation → experiment → human interpretation → new theory.
It is a circular system, not a hierarchy.
Every layer creates information that makes the others more productive.
The danger is not AI doing science
The real danger would be drawing the wrong policy conclusion from AI's success.
Imagine a government looking at Navier–Stokes and concluding:
“Why fund thousands of mathematicians for decades if 10,000 AI agents can solve a problem in four days?”
That would be an extraordinarily short-sighted interpretation.
Ten thousand AI agents can be created quickly.
Ten thousand Tristan Buckmasters, Diego Córdobas or Luis Martínez-Zoroas cannot.
A world-class mathematician is the product of years of education, mentors, failed ideas, seminars, arguments, reading, creativity and intellectual freedom.
The AI system can scale exploration very quickly.
It cannot retroactively create the scientific culture that generated the mathematical concepts it is exploring.
If AI becomes exceptionally good at taking promising human research directions and completing them rapidly, that may actually make original human science more valuable, not less.
A genuinely new idea becomes more powerful when 10,000 machine researchers can immediately explore its implications.
But the funding incentives could move in the opposite direction
This is where the concern expressed in the Arabic commentary you shared deserves serious consideration.
A result like Navier–Stokes is an extraordinarily powerful marketing story:
“AI solved a problem that the world's greatest mathematicians could not solve for decades.”
Governments, corporations and investors may understandably respond by directing even more resources toward frontier AI.
But if that financing comes primarily by removing resources from mathematics, physics, chemistry, materials science and fundamental research, we could eventually weaken the intellectual system on which scientific AI depends.
The paradox would be remarkable.
We might build machines capable of exploring ideas at unprecedented speed while gradually reducing the number of humans capable of creating genuinely new ideas for them to explore.
This concern is no longer confined to a single critical post. Recent reporting describes genuine unease among mathematicians about credit, priority, research openness and whether academics will become reluctant to share unfinished ideas in a world where AI systems and well-funded companies can move from hint to publication extraordinarily quickly.
I would not call 8 September 2026 a “dark day in the history of science.”
It may eventually be remembered as one of the most exciting.
But it could also become a turning point in how scientific incentives are structured.
Which interpretation proves correct will depend on what we do next.
Scientific freedom matters too
There is another subtle issue here.
Science progresses because researchers can challenge fashionable ideas, criticize powerful institutions and pursue questions whose commercial relevance may not be obvious for decades.
If academics begin to feel that questioning AI's role in science is professionally dangerous, then we will have created precisely the wrong environment.
The appropriate response to AI is neither technological worship nor technological rejection.
Scientists should be free to say:
“This result is extraordinary.”
And simultaneously:
“We need to examine how it was produced, how credit is allocated, how research data are protected, what happens to funding, and whether the system strengthens or weakens science overall.”
Those positions are not contradictory.
They are what serious scientific inquiry should look like.
Quantum computing still has a role that AI cannot simply absorb
None of this changes my view that quantum computing remains strategically important.
AI may become extraordinarily good at mathematics without quantum processors.
But mathematics and simulating physical reality are not the same problem.
A neural network can reason about a quantum material.
A QPU may eventually be able to represent the quantum system itself in ways that cannot be efficiently reproduced classically.
This is why I continue to expect quantum's strongest long-term impact in areas such as chemistry, materials, strongly correlated physics and selected optimization problems rather than as a universal replacement for AI.
The computing stack becomes richer rather than simpler.
And quantum does not get a free pass on energy
There is also a tendency to portray quantum computing as the future answer to AI's enormous energy requirements.
Perhaps for particular computations it eventually will be.
But that case must be demonstrated, not assumed.
Superconducting quantum systems require cryogenic refrigerators, precision control electronics, error-correction infrastructure and classical processors surrounding the QPU. Researchers have specifically warned that quantum-computing performance should be evaluated not only in qubits and speed but also in total energy consumption.
The meaningful comparison is therefore not:
one quantum operation versus one GPU operation.
It is:
How much total energy does the complete system require to solve the same useful problem?
For some workloads, quantum may eventually win dramatically.
For others, classical computing will remain superior.
Cybersecurity may be where quantum matters first
The most immediate business implication of quantum computing may actually have little to do with AI acceleration.
It is cryptography.
A sufficiently capable fault-tolerant quantum computer could eventually threaten widely deployed public-key cryptography.
NIST has already finalized three principal post-quantum cryptography standards and explicitly says organizations should begin migrating now, rather than waiting for such a machine to exist.
And the AI–security relationship is becoming interesting too.
In July 2026, NIST reported that Anthropic used an AI model to discover a vulnerability in HAWK, a lattice-based signature candidate then under evaluation. HAWK was subsequently withdrawn. NIST emphasized that the result does not compromise its finalized PQC standards such as ML-KEM and ML-DSA.
So once again the technologies are interacting in unexpected directions.
AI helps test cryptography.
Quantum motivates new cryptography.
And future AI systems themselves will need quantum-resistant protection for model weights, intellectual property, data and communications.
The future is a stack, not a replacement
This entire discussion has strengthened rather than weakened my original conclusion.
AI will not make quantum computing irrelevant.
Quantum computing will not make AI irrelevant.
AI should not make human scientists irrelevant either.
The future scientific system is likely to be heterogeneous at every level.
Humans will formulate questions, challenge assumptions and create conceptual frameworks.
AI will search, reason, synthesize literature, generate hypotheses and coordinate enormous numbers of computational investigations.
Classical HPC and GPUs will perform the vast majority of numerical and AI workloads.
Quantum processors may attack highly specialized calculations involving physical systems or mathematical structures where genuine quantum advantage exists.
Laboratories will determine whether predictions correspond to reality.
And humans will still have to decide what those discoveries mean.
So can OpenAI solve the remaining Millennium Problems?
After what has happened this month, I would no longer dismiss the possibility.
OpenAI has already demonstrated that its internal systems can deploy enormous parallel reasoning effort against frontier mathematics. The company has publicly said that it has made substantial progress on another Millennium Prize Problem.
Could Hodge be next?
Possibly.
Could the Riemann Hypothesis eventually fall to an AI system?
Possibly.
Could an AI establish P ≠ NP without ever touching a quantum processor?
Absolutely possible in principle.
But “possible” is not “inevitable.”
Some mathematical problems may require conceptual transformations that enormous search alone does not produce easily. Others may turn out to be independent of commonly used axioms. And as AI-generated mathematics accelerates, verification, interpretation and incorporation into human mathematical understanding may become the limiting factors.
The most interesting question may therefore no longer be whether AI can produce proofs.
It may be whether humans can continue to understand science at the speed AI can produce it.
One final thought
The history of computing is largely a history of addition rather than replacement.
Mainframes did not eliminate all previous forms of computing.
PCs did not eliminate mainframes.
Cloud did not eliminate local computing.
GPUs did not eliminate CPUs.
AI accelerators did not eliminate GPUs.
Quantum processors will probably not eliminate classical machines.
And AI should not eliminate scientists.
What changes is the architecture.
Each new layer makes some previously impossible task practical, while creating new dependencies on the other layers around it.
That is why I believe the central question is not:
“Will AI solve everything before quantum computing is needed?”
Nor:
“Will quantum computers eventually outperform AI?”
The more useful question is:
Which boundaries of human knowledge are limited by reasoning, which by computation, which by our ability to simulate nature, and which by our willingness to preserve the human scientific ecosystem that creates the questions in the first place?
The OpenAI Navier–Stokes result may eventually survive every mathematical test and become a landmark in the history of science.
The Hodge rumour may prove true — or disappear like many rumours before it.
But something important has already happened.
We have seen what may be the beginning of a world in which thousands of artificial researchers can be deployed against a frontier scientific problem almost instantly.
The challenge now is not merely to make those systems more powerful.
It is to build the scientific, technical and institutional architecture that makes their power productive.
Because the future of science should not be human versus AI versus quantum.
It should be:
human science + AI + classical computing + quantum computing + experiment.
Different strengths.
Working together.
And, hopefully, a much more capable scientific future.
#ArtificialIntelligence #QuantumComputing #ScientificDiscovery #Mathematics #QuantumAI #FutureOfScience #AIResearch #DeepTech #ResponsibleAI #ComputationalScience #ResearchInnovation #PostQuantumCryptography
Originally published in the VerbionPulse newsletter on LinkedIn: AI, Quantum Computing and the Future of Scientific Discovery.
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