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  Title	Aubrey de Grey's $100K hypothesis challenge: Your AI co-pilot for longevity research
  ID	26835770
  External ID	39ec5f9adfc08cc5934f2d8a87acf9b5
  Author	Sarah Dunsby
  Published Date	Nov. 10, 2025, 6:43 a.m.
  Summary	Explore Aubrey de Grey's $100K hypothesis challenge and see how AI can transform longevity research across the globe.
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  URL	https://londonlovesbusiness.com/aubrey-de-greys-100k-hypothesis-challenge-your-ai-co-pilot-for-longevity-research
  Sentiment	Negative: 0.01046    Neutral: 0.86018    Positive: 0.12936
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• Content Details

  Content ID	34566288
  Key	1a0d6bcd-200a-4645-95e8-9e74cd783f89
  Content	What if breakthrough aging research didn't require decades of institutional gatekeeping? What if the next revolutionary longevity hypothesis could come from anywhere—a graduate student in Singapore, an independent researcher in Brazil, or a medical professional with a bold idea but no lab access? That's the premise behind Aubrey de Grey's $100K Hypothesis Challenge, an unprecedented initiative that places the pioneering gerontologist's decades of unpublished longevity research directly into the hands of researchers worldwide through an AI agent trained on cutting-edge science that most investigators will never see. An AI agent built on Aubrey de Grey's unpublished knowledge Aubrai isn't just another chatbot regurgitating published literature. The AI agent has been trained on Aubrey de Grey's unpublished lab data from the LEV Foundation, including insights from the Robust Mouse Rejuvenation (RMR) project—one of the most ambitious attempts to double the remaining lifespan of middle-aged mice through multi-target rejuvenation therapies. This gives Aubrai a first-mover advantage in generating novel, commercially relevant hypotheses. While Google and OpenAI are building scientific agents that can read papers, Aubrai goes further by integrating de Grey's proprietary experimental data, real-time research updates, and decentralized funding mechanisms. Think of it as having a scientific collaborator who knows not just what's been published, but what's currently being discovered in one of the world's leading longevity labs. Simply tag @Aubrai_ on X (formerly Twitter) with your longevity question, and the agent responds with everything from complex scientific analysis to practical health guidance. But its real power emerges when researchers use it as a co-pilot to develop and refine novel hypotheses about aging mechanisms, therapeutic interventions, or experimental designs. How the challenge works The $100K Hypothesis Challenge operates at the intersection of artificial intelligence and decentralized science, creating a pathway from idea to funding that bypasses traditional institutional barriers. Here's the process: Researchers worldwide use Aubrai to explore longevity questions, generate hypotheses, and refine their thinking through iterative dialogue with an AI that understands both the published literature and cutting-edge unpublished work. Once you've developed a compelling hypothesis—perhaps about a new clinical treatment, biological mechanism, or experimental approach—you submit a research proposal through the challenge portal. Dr. Aubrey de Grey personally reviews submissions and selects the most promising hypotheses. Winners become eligible for up to $100,000 in milestone-based research funding. This isn't grant money that disappears into overhead costs. Funding flows from IP-Token 'Ignition Sales' on the Bio Launchpad, part of VitaDAO's VitaLabs incubation program that transforms high-risk, high-reward longevity ideas into tokenized intellectual property and biotech spinouts. The system also includes a unique referral incentive: if you know someone who might submit a winning hypothesis—a graduate student, medical professional, or independent researcher—you can earn a 20,000 $BIO bounty by connecting them with the challenge. From hypothesis to blockchain-verified discovery What happens when you generate a valid hypothesis using Aubrai? It gets hashed onto the Base blockchain through Molecule's Proof-of-Invention protocol, creating an immutable record of your contribution. This blockchain-verified trail ensures that scientific progress is tracked, and value flows upstream to the researchers whose early insights—including negative results—paved the way for later breakthroughs. This addresses one of science's most persistent problems: the invisible contributions that never make it into publications. How many graduate students have generated insights that informed later discoveries but received no credit? How many 'failed' experiments actually eliminated dead ends that helped other researchers succeed? Proof-of-Invention creates a permanent record of these contributions. Valid hypotheses eventually feed into Aubrai's knowledge graph, enriching the agent's capabilities. As validated data emerges from experiments, it can be tokenized as IP-Tokens—onchain knowledge assets that can be licensed to pharmaceutical or biotech firms, with revenues cycling back to researchers and contributors. The RMR2 advantage: Aubrey de Grey's moonshot experiment Aubrai's training on Aubrey de Grey's Robust Mouse Rejuvenation study provides researchers with an unprecedented edge. RMR2 represents one of the largest mouse lifespan experiments ever attempted, testing combinations of rejuvenation therapies with the goal of achieving what de Grey calls aging's 'AlphaFold moment'—proof that multi-target rejuvenation actually works and is worth scaling. The agent has already demonstrated its value in de Grey's lab. In planning RMR2, which involves nearly a dozen overlapping studies, Aubrai suggested methodological refinements and flagged dosing considerations that researchers only discovered after weeks of manual literature review. The AI identified experimental limitations before they became problems and proactively suggested workarounds. 'Having the agent at our disposal has been transformative for our planning pipeline,' Aubrey de Grey noted, explaining that Aubrai 'identified points of consideration we had not yet encountered through literature, and it was proactive in suggesting ways to circumvent foreseen limitations.' As RMR2 progresses, findings will feed directly into Aubrai's knowledge graph, continuously updating the agent with the latest experimental insights from de Grey's laboratory. This creates a feedback loop where cutting-edge research informs the AI, which then helps generate the next wave of hypotheses, which feed into new experiments, and so on. Beyond Web2's walled gardens AI-driven biology is experiencing explosive growth, but most tools remain trapped in Web2—disconnected from capital, coordination, and shared knowledge infrastructure. Google and OpenAI can build agents that read papers and generate hypotheses, but those insights die in private conversations, inaccessible to the broader research community and divorced from mechanisms for funding or validation. Aubrai breaks these barriers by integrating scientific AI with onchain infrastructure for data sharing, continuous funding, and discovery monetization. The agent actively engages the global longevity community, not as isolated users of a chatbot, but as contributors to a growing knowledge commons where validated insights become valuable, tradeable assets. 'Aubrai represents a radical new way to scale collective scientific intelligence,' de Grey explains. 'By fusing my team's expertise with decentralized AI and the wisdom of the global longevity community, we're creating something unprecedented—a scientific AI agent that doesn't just process information, but actively helps researchers turn discoveries into real-world therapeutics.' The future of scientific discovery For Paul Kohlhaas, founder of Bio Protocol, Aubrai demonstrates blockchain's potential beyond speculative assets. 'Just as Substack gave writers the ability to monetize outside legacy media, Bio's infrastructure can turn scientists into the next great creator economy,' he says. The $100K Hypothesis Challenge represents a fundamental shift in how scientific discovery can be organized and financed. In longevity research particularly, where breakthroughs often die in the 'valley of death' between promising early results and human trials due to disinterested venture capital, decentralized funding mechanisms offer an alternative path. If Aubrai succeeds in accelerating even a handful of breakthroughs—if its AI-assisted hypotheses lead to validated therapies that extend human healthspan—it will prove that decentralized science can function as a viable alternative to entrenched funding structures that have long constrained biomedical innovation. The challenge is simple: Use the most advanced AI agent in longevity research to develop your hypothesis. If de Grey selects it, you get funding to test it. If it validates, it becomes an onchain asset that can fund the next wave of research. And somewhere in this cycle, we might just find the interventions that push humanity closer to longevity escape velocity. Ready to explore what Aubrai knows about your longevity hypothesis? The agent is waiting at @Aubrai_, and the $100K challenge is open to researchers worldwide. Your breakthrough might be one conversation away. Learn more and submit your research proposal at aubr.ai/hypothesis-challenge to view the comments powered by Disqus.
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  Publisher Name	London Loves Business (#5662)
  Publisher URL	https://londonlovesbusiness.com/
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  Trust Flow Score	21
  Citation Flow Score	37

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  Data Source Name	NewsCatcher (#303)
  Data Source Type	news
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