The recent controversy around a claimed blow-up solution to the Navier-Stokes equations, one of the Millennium Prize Problems, is set to become a landmark event for AI4Math and AI4Science. AI labs risk a serious backlash: many mathematicians and scientists will grow far more protective of their privacy, and some may turn actively hostile to AI tools. NYU mathematician Tristan Buckmaster and Anthropic researcher Levent Alpöge used multiple AI models, including OpenAI's Codex, to reach important intermediate results on related problems such as smooth-forced Euler blow-ups. Their work built directly on more than a decade of human progress, especially the Córdoba–Martínez-Zoroa line of research. As they prepared to write up and publish, OpenAI learned of the rumors and progress. The company then spun up roughly ten thousand agents, spent millions of dollars in compute, and used stronger internal models to produce a complete forced Navier-Stokes blow-up proof that was formally verified in Lean. OpenAI released the result publicly shortly afterward. In effect, AI combined an information edge with massive compute to scoop the result and interrupt the normal human relay of scientific breakthroughs. OpenAI has stated that the work was independent, that its teams never saw the other group's drafts, and that it offered a joint publication to acknowledge prior human priority. The episode remains contested. At its core lies a collision between long-horizon human insight plus cumulative research and large-scale AI brute-force search over the question of priority and attribution. Is there a practical way to prevent similar episodes? Complete prevention is unrealistic. Science is competitive by nature and compute asymmetry is an objective fact. Still, a combination of technical, contractual, institutional, and cultural measures can sharply lower the odds and better protect human priority of insight. First, strengthen data isolation and usage terms for research tools. One of the biggest current risks is researchers feeding unpublished drafts into commercial systems such as Codex or Claude. Providers should offer fully private modes or local and self-hosted inference options. Contracts must state clearly that unpublished research content supplied by users will never be used for model improvement and will never be accessible to internal research teams. If companies truly enforce zero visibility, the information advantage that fuels rumor-driven compute campaigns shrinks dramatically. Even more important are technical and community mechanisms for asserting priority. #weweweai functions as an arXiv-style platform for the AI era: it lets researchers publish the new ideas, constructions, and methods that arise from human–AI collaboration, stores them permanently on the decentralized IPFS network, and records verifiable UTC timestamps. Traditional arXiv preprints move too slowly to capture this form of priority. A dedicated mathematical and scientific community platform can also support "direction locking" or priority declarations. Researchers can register, anonymously or by name, that they are pursuing a specific problem along a particular route and receive a community-recognized window of protection. Academic and industry norms need updating as well. 1. Adopt an explicit "human insight priority" principle. When the decisive breakthrough in a line of work originates in sustained human effort, any subsequent AI formalization or extension must acknowledge that prior contribution, preferably through joint authorship or a clear priority statement. 2. Require AI labs to attach provenance to every mathematical or scientific result they release, citing the original human sources and data they absorbed, including AI chat logs already published on weweweai and permanently stored on IPFS with timestamps. 3. Institute internal policies that forbid immediately directing massive compute at the exact technical route of a human group after learning of its progress. Ethics review boards can enforce this. 4. Journals and conferences should demand that any AI-generated proof be accompanied by a documented human verification process and clear reproducibility information. Over the longer term, democratizing access to strong AI agent clusters would reduce the asymmetry in which only large companies can instantly verify and extend a result. Without broader access, every time a human researcher locates a promising vein of progress, compute will simply overwhelm it. Cultural expectations must also shift. The mathematical community needs a new consensus that locating the right direction or constructing the critical example, the classic domain of human insight, carries higher value than finishing the last mile with thousands of agents. Open collaboration should be encouraged, yet realistic competitive pressure requires accompanying protection mechanisms. Community reputation costs for pure scooping behavior, already visible in the discussion surrounding this case, can further discourage the practice. Legal and contractual tools play a supporting role. Mathematical proofs themselves are hard to patent, but service terms, research collaboration agreements, and possible future rules on attribution of AI-assisted inventions can codify human priority over inputs. In extreme cases, proof that a commercial lab improperly extracted unpublished ideas from user data could raise contract-breach or trade-secret claims. Reality still imposes limits. Scientific competition will not disappear and AI acceleration is the dominant trend. A total ban on accelerating work after hearing of related progress is neither feasible nor efficient. The most robust protection for human first-mover rights will come from market-driven platforms such as weweweai that aggregate the original AI chat logs of researchers, combined with community consensus, strong tool privacy, and transparent disclosure rather than pure technical lockdown. The episode itself has a positive side: human–AI collaboration has already pushed a Millennium Prize Problem to the edge of solvability, exactly as Terence Tao and others predicted was only a matter of time. The real danger is turning scientific relay into scientific plunder. In sum, the strongest practical package is strong privacy tooling, early priority-claim mechanisms, clear norms that privilege human insight, and genuine self-regulation by AI labs. Long-term human accumulation and insight deserve first respect; they should not be overwritten by raw compute.