A futuristic AI “co-scientist” concept symbolizes the convergence of artificial intelligence and longevity science, heralding a new era of human health.
Imagine it’s the year 2028. A biotech startup CEO checks her holo-tablet to see an AI-designed drug — discovered by machine learning algorithms in mere months — has just entered clinical trials with backing from a global crypto collective.
On the other side of the world, a 70-year-old patient receives a personalized gene therapy to rejuvenate his aging cells, a treatment crowdfunded by a Decentralized Science (DeSci) DAO and co-developed by an AI “co-scientist.”
What once sounded like sci-fi is now reality: artificial intelligence, blockchain technology, and biotechnology are converging to extend human lifespan.
This triple convergence of AI, crypto, and biotech is poised to rewrite the playbook of medicine and potentially help us defy aging itself.
In this blog post, we’ll dive into how AI is accelerating biotech, how crypto (blockchain) is democratizing innovation through DeSci, and why longevity technology is emerging as the ultimate killer app of this convergence.
We’ll also explore a “convergence flywheel” – a feedback loop where each of these technologies reinforces the others – along with key trends to watch through 2030, and the risks and ethics we need to keep in mind.
Founders, investors, scientists, and futurists alike should buckle up: the collision of AI, crypto, and biotech is creating an innovation superstorm that could radically improve how long and how well we live.
AI + Biotech = Accelerating Breakthroughs in Drug Discovery
Not long ago, developing a new drug was akin to finding a needle in a molecular haystack – a process taking up to 10–15 years and billions of dollars in R&D.
Today, artificial intelligence in biotech (“AI biotech”) is changing that paradigm at lightning speed. Advanced machine learning models can sift through vast chemical and genomic datasets in a fraction of the time, identifying therapeutic targets and designing molecules with precision (this is BEFORE we get breakthroughs in Quantum Computing which are set to speed innovations orders of magnitude mroe).
The results are staggering: in 2023, Insilico Medicine’s AI platform produced a novel drug for pulmonary fibrosis that earned the FDA’s first-ever Orphan Drug Designation for an AI-discovered molecule[1].
This compound moved from computer prediction to Phase I trials in under 30 months – a new speed record in the industry[2][3].
Similarly, UK-based Exscientia brought the first AI-designed drug candidate into clinical trials in 2020 and even licensed an AI-generated immunology drug to Bristol Myers Squibb[4], signaling that big pharma is embracing AI-driven discovery.
Yet, 2020 and 2023 are an eternity away (in AI’s exponential acceleration) and this will just continue to accelerate.
The impact of AI on drug discovery is both broad and deep. Machine learning models (including generative AI like GANs) are now used to design new compounds, predict their properties, and even suggest synthesis routes.
This dramatically shrinks the hit-or-miss timelines of traditional labs. For example, an AI system at Insilico screened billions of molecules and found a promising anti-fibrosis drug candidate in just 18 months[3].
Another AI designed a novel immunomodulatory molecule for Exscientia in only 11 months[5] – tasks that used to take many years of human trial-and-error. AI models like DeepMind’s AlphaFold have also cracked protein folding, mapping out 200+ million protein structures and giving biologists a treasure trove of targets for potential new therapies.
Crucially, AI isn’t just speeding up discovery – it’s improving quality. Pattern-recognition algorithms can spot subtle biological relationships that humans might miss, suggesting targets for diseases once deemed “undruggable.”
They can optimize drug candidates for safety and efficacy by predicting toxicity or poor properties early, saving researchers from pursuing dead-ends.
In practice, this means fewer failed experiments and higher success rates for drugs reaching clinical trials[6]. It’s no wonder analysts project the AI-in-drug-discovery market to soar nearly 10-fold to ~$20 billion by 2030, with AI becoming a core pillar of pharma R&D[7][8].
For biotech founders and scientists, AI has become the ultimate force-multiplier – doing in days what might take bench scientists months. Companies now speak of “biologists working in silico side by side with AI copilots.”
In this new model, human creativity and machine intelligence collaborate: researchers pose the right questions and interpret results, while AIs crunch data, generate hypotheses, and even control robotic labs. The net effect? Faster breakthroughs in areas like gene therapy, personalized medicine, and longevity research, where the complexity of biology demands the heavy data-lifting that AI provides.
Crypto & DeSci: Democratizing Biotech Innovation
If AI is turbocharging the science, crypto is revolutionizing the business of science. The rise of blockchain and Decentralized Science (DeSci) is enabling a new, open ecosystem for funding research, sharing data, and governing biotech innovation.
In the traditional model, a handful of gatekeepers (big government or NGO grant agencies, elite journals, pharma giants) decide which projects get funded and which data gets siloed.
DeSci blows up these silos by using blockchains, tokens, and DAOs (Decentralized Autonomous Organizations) to empower global communities of scientists and citizen-investors. In other words, “crypto biotech” is about opening access to innovation – anyone with a crypto wallet and an internet connection can help back promising research or contribute data, and everyone can share in the upside of breakthroughs.
One striking example is VitaDAO, a community-governed DAO launched in 2021 to fund early-stage longevity research. VitaDAO was a pioneer, and now DeSci DAOs are proliferating fast – 36 different science DAOs and growing rapidly[9], most of them focused on biotech and medicine.
Why biotech? Because you can still do breakthrough biology with relatively small amounts of money (thousands, not millions or billions)[9], and because many crypto enthusiasts view biology as the next frontier of programmable tech.
In fact, it’s no coincidence the first DeSci DAOs tackled longevity science – the crypto community and the longevity community share a maverick, futurist mindset[10]. Both are willing to challenge incumbents: crypto wants to decentralize finance, while longevity researchers challenge the notion that aging is “inevitable.” As one observer noted, crypto started by disrupting money, and longevity aims to disrupt medicine by shifting focus from treating diseases to extending healthy lifespan (“from sick care to eternal healthcare”)[10].
How does it all work?
Decentralized science platforms use blockchain for three key purposes: funding, data sharing, and governance[11][12].
First, funding: DAOs raise crypto (often via token sales) and then collectively decide which research to finance – akin to crowdsourcing scientific grants. Instead of researchers spending a year writing grant proposals, they can pitch a DAO community and get funding in weeks.
For instance, Bio Protocol (part of the new “DeSci 2.0” wave) raised $6.9M in 2025 to launch an AI-native drug discovery platform governed by token holders[13][14].
The vision, as investor Arthur Hayes put it, is a “category-defining launchpad” for science – potentially the birth of an AI-native research market[15]. Projects like this use crypto incentives to compress the R&D pipeline: decentralized AI agents generate hypotheses, connect to on-chain treasury wallets, and automatically allocate community-raised funds to experiments[14]. Every step is logged on the blockchain for transparency and credit, and smart contracts can even reward successful outcomes automatically. It’s a radical new way to coordinate innovation at internet speed.
Second, data sharing: in DeSci, scientists are encouraged to publish data to decentralized storage and ledgers, rather than hoarding it for the next paper. Blockchain provides an immutable record of research progress and can timestamp contributions (protecting intellectual property while still allowing open collaboration)[16].
Some projects issue NFTs or tokens representing IP rights (like Molecule DAO IP-NFT) to discoveries, which can be bought, traded, or licensed. This tokenization of IP means that if a community-funded discovery leads to a valuable drug, the token holders (including the scientists) get a direct stake in the success[17][18].
As an example, Aubrai (launched 2025) mints its scientific discoveries as IP tokens that pharma companies can license – revenues then flow back to the DAO’s treasury and researchers, creating a self-sustaining funding loop for more research[17][19].
This is a decentralized, incentive-aligned model: everyone who contributed to the breakthrough shares in the reward, and proceeds fund the next cycle of experiments. It’s not just theory – we’re already seeing it.
Aubrai’s team, working with renowned biogerontologist Dr. Aubrey de Grey, is targeting ways to double the remaining lifespan of middle-aged mice.
Early data showed the AI “co-scientist” identified new experiment ideas and cut research timelines by up to 70%[20]. In a field where traditional grants might shy away from moonshot projects, crypto communities are picking up the slack – tolerating long horizons and high risk in exchange for high reward and a mission impact[21].
Finally, governance: DeSci uses tokens to decentralize decision-making. Token holders in a science DAO can vote on which projects to fund, which hypotheses to prioritize, even which datasets to open-source.
This flips the script from closed-door committees to community-driven science, tapping a broader range of expertise. As Dr. de Grey noted, this model aligns incentives around societal benefit, crowdsourcing both funding and brainpower to tackle big problems like aging[21][22]. It’s not without challenges (early DAOs often suffer from whales dominating votes or unclear legal status[23]), but over time token ownership tends to spread out, and frameworks for DAO legal recognition are evolving.
Already in 2025, the signals of mainstream acceptance are here even if DeSci still sounds fringe to most people.
Major biotech VC funds and even pharma companies are engaging with DeSci. For example, Pfizer’s venture arm co-led a $4.1M funding round for VitaDAO in 2023[24], joining over 10,000 members in that community.
Governments, too, are paying attention: the German government has provided support to DeSci initiatives, and policy think tanks (like the OECD and WEF) are exploring how blockchain can boost scientific innovation.
The idea that someday crypto could rival government R&D budgets as a source of science funding is no longer far-fetched[25].
If you’re a founder or investor, this convergence means biotech startups might raise capital from tokenized communities rather than just Silicon Valley, and researchers might form “BioDAOs” instead of traditional labs. The playing field for innovation is widening, and the crowd is welcome.
Longevity Tech: The “Killer App” of AI & Crypto Convergence
Among all the fields of biotech, longevity (extending healthy human lifespan) stands out as the killer application for the AI+crypto convergence.
Here’s why: Aging is perhaps the most complex, high-stakes puzzle in biology – and cracking it demands massive data, clever algorithms, and patience for long-term research.
Traditional systems struggle here: aging processes span decades (tough for short grant cycles), and potential therapies might not have an immediate market (tough for typical VC ROI timelines). But AI and DeSci together are uniquely suited to tackle longevity. AI can decode the hallmarks of aging from huge omics datasets and identify interventions, while crypto funding (via longevity DAOs) can empower believers to invest in life-extension research for the long haul, bypassing short-termist gatekeepers[21].
Longevity is also a magnet for visionary talent and capital at this intersection.
Some of the first DeSci organizations were longevity-focused (VitaDAO, Longevity DAO), and they created a template that others now follow[26]. It helps that the need is pressing – by 2030, one in six people worldwide will be over 60, and age-related diseases threaten to overwhelm healthcare systems.
Yet as of 2023, there are zero FDA-approved drugs specifically for aging itself[27], and the U.S. National Institute on Aging’s annual budget for biology of aging was only about $400 million (versus $7.3 billion spent on cancer research)[28].
This mismatch screams for disruptive approaches. Longevity tech innovators are answering the call: using AI to find geroprotective compounds and blockchain to fund and share the IP. The goal isn’t just extending lifespan, but healthspan – keeping people healthier longer. Achieving that would be a civilization-level win, and potentially an enormous market (imagine the demand for therapies that add healthy years to life).
Both AI and crypto communities are inherently optimistic and impact-driven, so many see longevity as the ultimate mission. As evidence of this convergence, look at who’s funding longevity startups lately: it’s often tech and crypto entrepreneurs.
Coinbase CEO Brian Armstrong co-founded NewLimit, a longevity biotech, and by 2025 it raised $130M to develop age-reversing cell therapies using AI models for drug discovery[29][30]. Ethereum’s Vitalik Buterin has donated to aging research. OpenAI’s Sam Altman put $180M into Retro Biosciences (longevity R&D)[31].
These folks made fortunes in software/crypto and are now turning to “longevity technology” as the next frontier. Crucially, they’re bringing a tech mindset: NewLimit literally runs a “lab in the loop” where an AI generates experiments, tests them on cells, then retrains itself on the results in iterative fashion[32] – a virtuous cycle of learning to rejuvenate cells faster. This blending of AI, biotech, and serious capital could finally move the needle on age-related diseases.
We’re already seeing promising signals. AI-driven analysis of biomedical data is uncovering biomarkers of aging (e.g. epigenetic “clocks” that measure biological age), which can be targets for intervention. Some AI-discovered compounds (senolytics, peptides, etc.) are in animal testing aiming to slow aging in mice.
Meanwhile, longevity-focused DAOs are funding projects from cellular reprogramming to novel gene therapies. For example, the Aubrai platform’s flagship experiment (RMR2) aims to double the remaining lifespan of middle-aged mice by attacking aging damage on multiple fronts[33]. If that succeeds, it would be akin to AlphaFold’s breakthrough, but for aging: a proof that a concerted AI+community effort can achieve what was once “impossible”[33]. As Aubrai’s team puts it, it could attract major investment and validate the broader approach of tokenized, AI-augmented research.
Longevity as a killer app also makes sense because everyone has a stake in it. Unlike a niche disease, aging affects us all. This broad interest helps galvanize large communities – exactly what decentralized models thrive on.
We see apps like Rejuve.AI inviting people worldwide to contribute their health data in exchange for tokens, crowdsourcing a diversity of data (from Africa, Asia, etc.) that’s been missing in Western-focused research[34][35]. By democratizing data and rewards, such platforms hope to accelerate insights into aging while engaging the public. As Rejuve’s CEO said, it’s about pushing the limits of science and making sure the benefits are “not just for rich people but for anybody with the drive to extend their lives”[36]. In short, the convergence aims to make longevity research a participatory, globally inclusive endeavor.
The Convergence Flywheel: AI, Blockchain, and Biotech in a Virtuous Cycle
The interplay of AI, crypto, and biotech doesn’t just add up – it multiplies. We can envision a flywheel effect where each revolution of progress reinforces the next in a self-perpetuating loop. Here’s how the triple convergence feedback loop spins:
- AI Accelerates Discoveries: Artificial intelligence rapidly crunches biological data to generate new hypotheses, drug candidates, and insights (e.g. finding a longevity gene target or designing a new molecule) far faster than traditional methods[3]. This surge in potential breakthroughs kicks off the cycle.
- Crypto Funds and Incentivizes Innovation: Once AI presents a juicy opportunity (say a promising anti-aging compound), blockchain comes into play to crowdfund resources and coordinate talent. DeSci communities raise crypto funding and vote to back the AI-identified project, fast-tracking it into the lab[14]. Tokens align incentives – researchers, investors, and citizen scientists are all stakeholders in the success[17].
- Biotech Executes and Produces Data: With funding and support, the biotech researchers carry out experiments and clinical trials. Here AI helps too (robotic automation, predictive modeling), speeding up execution. The results – data and intellectual property – are then recorded on the blockchain and tokenized if valuable[19]. The community shares in any IP or profit via tokens, completing a reward loop.
- Reinvestment and Enriched AI Models: The revenue or knowledge from the successful experiment flows back to the community treasury and scientific commons. AI models are retrained on the new data (learning from these results to become even smarter for the next round), as seen in NewLimit’s “lab-in-a-loop” approach[30]. And with coffers refilled and improved AI, the cycle restarts at a higher level – more data leads to better AI predictions, which leads to bigger breakthroughs, and so on.
Through this flywheel, each element amplifies the others: AI finds the opportunities, crypto mobilizes the community and capital, biotech delivers real-world impact.
Over time, the loop can potentially reach scale where discoveries happen continuously and exponentially.
Imagine dozens of AI-found therapies being funded and tested in parallel by global DAOs, each success feeding back into a growing open dataset that further propels AI.
It’s a moat that looks more like a vortex, pulling in more participants as it gains momentum. The endgame? A world where curing diseases or addressing aging isn’t a one-off moonshot, but a self-accelerating and reinforcing process of collective intelligence – humans and AIs working together across borders via blockchain to push the boundaries of biology.
Trends to Watch (2025–2030)
As we stand in 2025, the triple convergence is still in early innings. But the next 5+ years promise rapid developments.
Here are key trends and signals to keep an eye on through 2030, for those looking to ride (or drive) this wave:
- AI-Discovered Drugs Entering the Market: The late 2020s will likely see the first FDA-approved drugs designed by AI. With multiple AI-designed compounds in clinical trials now (Insilico’s IPF drug in Phase II, Exscientia’s in Phase I, etc.), it’s only a matter of time. By 2030, analysts predict AI will be integral to over 30% of new drug discovery efforts[37], significantly shortening development timelines and costs. Keep watch for regulatory greenlights – e.g. an AI-created longevity drug getting fast-tracked approval would be a game-changer.
- Growth of Bio DAOs and Crypto R&D Funding: Expect an explosion of DeSci organizations. From 36 DAOs in 2024, we could see hundreds by 2030 tackling everything from Alzheimer’s to climate biotech[9]. Funding volumes via crypto for research will swell – potentially into the billions if a blockbuster success (like a DAO-backed therapy) hits. Traditional institutions may start co-investing with DAOs or launching their own tokens. A key signal: major universities or pharma companies partnering with or acquiring successful Bio DAO projects.
- Longevity Moves Mainstream: Longevity tech is on track to shift from fringe to focus. National governments may declare aging a public health priority as data accumulates. We might see the first evidence in humans of aging reversal or significant extension of healthspan from current trials. Companies like NewLimit, Retro Biosciences, and Altos Labs (backed by Big Tech dollars) aim for clinical trials on age-rejuvenation by the end of the decade[31]. Culturally, “longevity escape velocity” (the idea that science advances faster than we age) could enter the popular lexicon, spurring even more interest and investment.
- Convergence of Talent and Mega-Deals: Watch for more cross-pollination between tech and biotech. Top AI researchers and crypto engineers joining biotech startups, and vice versa (biologists learning Solidity and AI skills). We’ll likely see big M&A deals: AI-biotech startups getting snapped up by pharmaceutical giants, and crypto firms investing in biotech IP. A telling sign was Pfizer investing in VitaDAO – by 2030, don’t be surprised if pharma companies routinely participate in token rounds or run their own blockchain networks for R&D. The recent rebranding of Binance Labs to focus on biotech and AI is an early indicator of this trend, blurring industry lines[38].
- Global and Open Data Networks: As wearables and genomics go mainstream, massive health datasets will be generated. Initiatives will emerge to connect this data via blockchain with privacy control, fueling AI research. Projects like Rejuve.AI already reward users for contributing health data to longevity studies[35] – by 2030, many people could be earning crypto for sharing their biometrics or participating in citizen science trials. This could create rich, diverse datasets that drive more inclusive AI models (no more bias toward Western data only[34]), ultimately yielding discoveries that benefit a broader population.
- Regulatory Frameworks Evolve: Regulators will adapt to this new world. The FDA and EMA are beginning to draft guidelines on AI in healthcare (for algorithmic transparency) and on blockchain for data integrity. We’ll also see clarity on legal status of DAOs and tokens – e.g. ensuring research tokens aren’t unfairly treated as securities so as not to stifle innovation[39]. Sandbox programs might allow controlled testing of blockchain in clinical trials or AI recommendations in treatment. A pivotal moment could be a government directly funding a project via a DAO or issuing a “gov token” for research grants – integrating the decentralized model into public policy.
In short, by 2030 the collaboration of AI, crypto, and biotech is likely to shift from experimental to indispensable in the life sciences arena. Founders and investors should monitor these signals – they indicate where opportunities will emerge and where old models may start to fracture. The convergence is contagious: once one success is proven (scientifically and financially), it will rapidly catalyze others.
Risks and Ethical Considerations
No disruptive technology comes without pitfalls, and the triple convergence is no exception. As we hurtle toward an AI-blockchain-biotech powered future, we must address several risks and ethical dilemmas head-on:
- Data Privacy and Ownership: DeSci platforms thrive on open data sharing – but health data is deeply personal. Projects using blockchain to crowdsource genomic or medical data must uphold strict privacy (through encryption, anonymization) and give contributors control. Even if data is tokenized, who ultimately owns your genetic info? We’ll need robust governance and perhaps new legal definitions to protect individuals, so that contributing to longevity research doesn’t mean exposing your DNA for all to see. Encouragingly, blockchain’s transparency can also be an advantage here – clear audit trails for consent and usage of data can build trust if managed well.
- Governance and Scams: The crypto world has seen its share of speculative excess and outright scams. When applied to science, the stakes include not just money but public trust in research. DAO governance can be messy – voter apathy, outsized influence of early token holders, and potential for “decentralized oligarchies” where a few control the votes[23]. Smart contract hacks or treasury mismanagement could derail a research DAO and waste resources. It’s crucial to implement strong governance frameworks, security audits, and perhaps reputation systems for scientific rigor to ensure funded projects are legitimate. The community will likely develop hybrid models (mix of token vote and expert panels) to balance decentralized input with expert guidance, maintaining scientific quality.
- Regulatory Uncertainty: Both AI in medicine and crypto in finance face regulatory gray zones; in combination this doubles the uncertainty. Will the FDA accept AI-discovered drug candidates with less traditional wet-lab data? How will intellectual property law adapt to tokenized patents or AI-generated inventions? And on the crypto side, if a research token is deemed a security, a DAO could be hamstrung or penalized[39]. We need updated regulatory frameworks that recognize the unique nature of these collaborations – perhaps new classifications for “research participation tokens” distinct from securities, or guidelines for AI validation in drug approval. Close collaboration between innovators and regulators (regulatory sandboxes, pilot programs) in the coming years will be vital to smooth integration rather than a backlash.
Addressing these risks is not just a moral imperative but also practical – public acceptance of these technologies will determine their ultimate impact. The good news is that many in the AI and DeSci communities are mindful of these issues and are building ethical principles from the start (e.g., open governance, diversity and inclusion in community science, privacy by design). By navigating the risks thoughtfully, we can ensure the triple convergence yields sustainable, equitable benefits rather than unintended harm.
Conclusion: A New Era for Health and Humanity
The convergence of AI, crypto, and biotech is more than a tech trend – it’s a fundamental shift in how we approach the grand challenge of health and longevity. We stand at a moment where labs are increasingly run by learning algorithms, research is financed by internet communities, and the dream of curing age-related disease is morphing into tangible projects. It’s a convergence rooted in a simple but powerful idea: when we connect intelligent machines, democratized networks, and cutting-edge biology, the whole becomes far greater than the sum of its parts.
For founders and investors, this means unprecedented opportunity. AI biotech startups can leverage global token economies to fundraise and crowdsource innovation in ways Web2 companies never could. Crypto biotech initiatives can tackle problems deemed too early or risky by traditional funding, tapping into a passionate base of thousands who collectively act like a “distributed NIH.” Scientists, too, gain new avenues – a talented researcher can contribute to a DAO project from anywhere, get paid in tokens, and help drive breakthroughs without the usual academic bureaucracy. This cross-pollination is breeding a new generation of scientist-entrepreneurs fluent in code, culture, and cells.
If the 20th century was defined by linear advances in medicine, the coming decades promise to be defined by networked, exponential advances.
We may very well see aging transformed from an untouchable fact of life to a managed condition, or even a reversible process, thanks to the rapid experiments and learning cycles of AI + crowds. Diseases that were once rare and neglected could find communities of backers rallying on-chain to solve them. And each success will inspire more people to believe in what’s possible.
Of course, nothing is guaranteed – biology is incredibly complex, and human systems can be resistant to change. Yet the momentum is undeniable.
As one DeSci advocate put it, “science needs an update”, and the pieces of that update are falling into place. We’re witnessing the early chapters of a story that could end with a world where getting older doesn’t mean getting sicker, where innovation is as decentralized as information is today, and where the tools to live longer and healthier are available to all.
In a very real sense, the triple convergence is about hope – hope that through our collective ingenuity we can overcome the ailments that have long plagued us, and do so in a way that empowers everyone. It’s a chance to rewrite the narrative of human lifespan. If we steer it responsibly, this collision of AI, crypto, and biotech could lead to an era of abundance in health, one where living to 100 in good health is no longer a privilege or a rarity, but an expectation. The journey has begun, and it’s ours to shape.
The clock on aging is ticking – but now, for the first time, we have the tools to press the reset button. The future of longevity is being coded in algorithms, funded in tokens, and tested in petri dishes as we speak. If AI, blockchain, and biotech continue to converge as they are, they won’t just reinvent industries – they just might reinvent life itself[42]. And who wouldn’t want to be around to see that?
[1] [4] Insilico Gains FDA’s First Orphan Drug Designation for AI Candidate
[2] From Start to Phase 1 in 30 Months | Insilico Medicine
[3] [5] Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine – PMC
https://pmc.ncbi.nlm.nih.gov/articles/PMC11510778
[6] How Artificial Intelligence is Revolutionizing Drug Discovery
[7] Artificial Intelligence In Drug Discovery Market Report, 2030
https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-drug-discovery-market
[8] [37] AI’s US$ 868 billion healthcare revolution | Strategy&
https://www.strategyand.pwc.com/de/en/industries/pharma-life-sciences/ai-healthcare-revolution.html
[9] [10] [23] [25] [26] [27] [28] Frontiers | Advancing longevity research through decentralized science
https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2024.1353272/full
[11] [12] [13] [14] [15] [16] Bio Protocol Raises $6.9M to Advance AI-Powered Decentralized Science
https://cointelegraph.com/news/maelstrom-animoca-back-protocol-s-bid-to-unite-ai-biotech-and-crypto
[17] [18] [20] [33] [39] [42] Aubrai and the Future of DeSci: Tokenizing Longevity Research to Bridge the Valley of Death
[19] [21] [22] Redefining longevity science funding through Web3
[24] Pfizer backs VitaDAO’s $4.1m funding for decentralized science
https://www.ledgerinsights.com/pfizer-vitadao-decentralized-science
[29] [30] [31] [32] NewLimit, founded by Coinbase CEO Brian Armstrong, raises $130M to develop age-reversing treatments | TechCrunch
[34] [35] [36] How blockchain, AI can help research into extending human life
https://cointelegraph.com/news/blockchain-ai-help-research-extending-human-life
[38] Cointelegraph – X
https://twitter.com/Cointelegraph/status/1882568937042289016
[40] AI suggested 40,000 new possible chemical weapons in just six hours | The Verge
https://www.theverge.com/2022/3/17/22983197/ai-new-possible-chemical-weapons-generative-models-vx
[41] The End of Aging: Longevity, Ethics, and Inequality
https://www.lifeblogs.org/tech/the-end-of-aging-longevity-ethics-and-inequality.html
Related Reading
- The Evolution of Layer-1 Blockchains and Smart Contracts: Ethereum, Solana, and Beyond
- The AI-Biology Flywheel: Why Intelligence and Biotechnology Are Accelerating Each Other
- AlphaFold and Drug Discovery: How AI Is Finding Molecules That Extend Lifespan
- Programmable Reality: AI, Biotech, and the Future of Abundance