Aging and death have long been topics that only philosophers, hypochondriacs, and charlatans were happy to dwell on. But in recent years, this eternal yet unwelcomed subject has drawn growing interest from scientists and business figures alike — driven, above all, by the rise of AI, which has called into question the assumption that aging, as we know it, is simply the natural order of things. Aging is now understood as a set of measurable processes: the accumulation of cellular damage, regulatory failures, and the gradual erosion of the body’s systemic resilience. And if that’s the case, it can be significantly slowed down.
Where researchers once faced an overwhelming tangle of disparate biomarkers, patient histories, and molecular interactions, AI works to detect patterns invisible to the human eye. For proponents of this approach, machine learning is the tool that could help pinpoint the root causes of aging. Gero has built its entire operation around AI-driven identification of therapeutic targets in age-related diseases.
The scientific foundation is solidifying fast — but any conversation about average lifespans exceeding 120 years risks sliding quickly into either techno-utopianism or quackery. That’s why we’re especially drawn to voices that approach the subject simultaneously as entrepreneurs, investors, and technological pragmatists: not as a fairy tale about eternal life, but as a long engineering challenge with concrete milestones, real risks, and measurable interim results.
Yury Melnichek is exactly that kind of voice. An entrepreneur and investor connected to the Gero and Doctorina projects, he works at the intersection of AI, medicine, and longevity research. In this conversation, we discussed whether slowing aging is a serious scientific proposition, how large-scale medical datasets and machine learning contribute to that goal — and why the prospect of living to 120–150 years no longer looks like science fiction to some researchers, but rather a matter of time, resources, and the quality of scientific models.
2Digital: What is Gero to you, what do you see as its core goals?
Yury: Gero is a company that uses machine learning and AI with the aim of halting human aging — if we’re speaking in terms of a stretch goal. If we’re talking about more realistic, near-term objectives, then our goal is to radically slow human aging and, in doing so, extend human lifespan.
2Digital: What real-world examples do you give people who don’t believe that an average human lifespan of 120–140 years is possible — or that slowing, even reversing, aging isn’t nonsense?
Yury: Why try to convince anyone? When the evidence base exists and the drug is on pharmacy shelves, that’ll be the time to talk. That’s one side of it.
But on the other side — if we’re not talking about winning an argument but about understanding what’s actually happening in the world — there’s a rather radical example I’m fond of. For millennia, people debated why humans can’t fly, and they came up with perfectly reasonable, convincing explanations for it. Yet the dream of flight persisted for roughly as long as the dream of endless life. And then, quite suddenly, flying became mundane. Why couldn’t the same thing happen with longevity?
What’s more, Gero isn’t claiming that human life will be infinite. We’re more interested in thinking through what would happen if aging were halted entirely. Today we can say that once the body reaches maturity, it begins to fall apart at an accelerating pace. The probability of dying rises exponentially — doubling roughly every eight years, as Gompertz first demonstrated in 1825.
So what happens if you stop that destructive process? There is a statistical study suggesting that in such a scenario, average life expectancy would reach 700–800 years. That’s not immortality. Disease, accidents, and so on aren’t going anywhere.
Beyond that, quite a few species simply don’t age, and perhaps most remarkably, some of them are mammals. And since we are mammals too, that’s the most encouraging news of all. The animal in question is called the naked mole-rat — a small subterranean rodent from East Africa.
You may know that there’s a proportional relationship between a mammal’s lifespan and its body mass. Mammals the size of a mouse typically live around two years — yet naked mole-rats, as far as we can currently tell, live an average of 30–40 years, and possibly longer. Time will tell exactly how long. What researchers have not found, however, is any of the classic hallmarks of aging in naked mole-rats.
2Digital: You recently said: “If God exists, I wouldn’t hire him as a programmer of the genetic code — the code quality is poor.” Please elaborate.
Yury: Biologists know that evolution tends to find rather peculiar routes when optimizing living species. The giraffe is a classic example: one of its nerves — the recurrent laryngeal nerve — runs all the way down the full length of the neck and back up again, even though the point-to-point distance is only about ten centimeters.
There’s a human example too: the optic nerve connects to the front of the retina rather than the back, which is why we have a blind spot in each eye.
In essence, evolution is an algorithm of incremental improvements — and those improvements are far from always optimal. Humans have already extended the lifespan of the nematode worm by up to eleven times by removing errors from its genome. Of course, primates are vastly more complex organisms, which is why unpicking their genetic code and correcting its flaws only became feasible with the advent of AI.
From an evolutionary biology standpoint, an organism has no incentive to repair defects that emerge after reproductive age. Yet humans already live far longer than their reproductive window — and that window can be extended significantly.
2Digital: What are the first concrete, practical steps toward slowing aging that we can talk about right now?
Yury: Getting people to accept that their lifespan could be multiplied several times over is no small feat — it’s akin to convincing someone two hundred years ago that flying was perfectly normal. So in the near future, we plan to bring an anti-aging drug for dogs to market. There’s real demand for it. Dogs live 10–13 years and age through the same two aging pathways as humans. Extending their lives by 50% would be the first milestone.
We believe that drugs proven to work in dogs will prove effective in humans too. If it works in dogs, the next step, predictably, is people.
2Digital: Gero was founded in 2015, and you joined in 2018 — eight years have passed. How far has the company come in that time? Can you briefly walk us through the key milestones?
Yury: The first major milestone is the deal with Chugai Pharmaceutical — Japan’s leading pharma company and a member of the Roche Group. In business terms, this is the most significant result the company has achieved: it involves serious money and a genuine prospect that a drug targeting age-related frailty and life extension will eventually reach pharmacy shelves.
In essence, we developed a vaccine that produced remarkable results in mice, significantly extending their lifespan. Naturally, one wants to publish the evidence — but that evidence is precisely where the commercial value lies, the know-how we transferred to Chugai Pharmaceutical. So we have a conflict of interests there. Chugai specializes in antibodies and is now engineering antibodies that target the same protein as our vaccine. This approach allows for more precise delivery, since once a vaccine is administered, antibody production cannot be controlled.
Of course, we want to do something meaningful — to tackle not just commercial goals, but transhumanist ones. Halting the aging process sounds grand, even daunting. But for me, not making the attempt would be far worse. You have to try. And already today we see that an average lifespan of 120–150 years is not the stuff of science fiction. Reaching those thresholds is more a matter of solving fairly grounded, practical problems.
What’s more, our research suggests that at such an average age, it would be possible to maintain quality of life and the body’s functional capacity at the level of a 40-year-old — essentially freezing biological age for nearly 100 years, and in some cases even rolling back decline from a 60-year-old state to a 40-year-old one.
Returning to milestones: there is also the collaboration with Pfizer in 2023, focused on identifying therapeutic targets for fibrotic diseases using Gero’s ML platform. It was more of a research endeavor than a direct translational one, but it was important nonetheless.
Pfizer was able to provide us with data from tens of millions of American electronic health records. We were particularly gratified that the model we trained jointly with Pfizer independently rediscovered the same target — the protein implicated in aging — that we had previously identified by procuring and analyzing physical blood samples from human donors. That very target was ultimately licensed in the Chugai Pharmaceutical deal.
2Digital: Can we sketch out a rough timeline of how the science of aging might progress — roughly when we might reach certain average life expectancy milestones, and why?
Yury: As I’ve said, within ten years — and more likely much sooner, within two or three — anti-aging drugs for dogs will reach the US market. The process is accelerating because regulatory requirements for veterinary drugs in the US have been significantly relaxed. If you can demonstrate safety, the rest of the path is fairly straightforward. That means getting a drug to market far faster and cheaper than before.
Once those drugs are out there, it will soon be proven in practice that the concept of life extension actually works. The drugs will be used in animals that have already lived half their lives, and they will add three to five years — roughly a third of the animal’s lifespan.
I think it will take about ten years from now before a comparable drug for humans becomes available. Initially, it would be aimed at people over 65 who lead a moderately healthy lifestyle. Their biological age, as I mentioned, could be rolled back to that of a forty-year-old and maintained there for quite some time. Average life expectancy would rise to 120–150 years — the overwhelming majority of which would be high-quality, fully functioning life.
As for lifespans of 600–700 years, I believe that solving that problem is no harder than landing on the Moon. What’s currently missing is focused attention, investment, and the sheer number of scientists engaged in the research. So the main problem here isn’t time — it’s the scale of resources brought to bear.
2Digital: The question of life extension has preoccupied humanity throughout its entire history. Why, then, is there so much skepticism among investors toward this field?
Yury: I was asking myself exactly that question back in 2018, when I first invested in Gero. One possible answer lies in psychology: the human brain processes the fear of death in a particular way — we push it deep into the subconscious. The prospect of death, arriving sooner rather than later, is simply something people don’t want to think about.
There’s another psychological dimension too: the promise of life extension is probably the oldest and most pervasive fraud scheme in human history. We carry accumulated assumptions that anyone promising a long and healthy life is, most likely, a charlatan.
Which, incidentally, makes this space very attractive for venture funding. Because venture capital is precisely about betting on something the whole world disbelieves — and then being proven right.
That said, belief in this kind of medicine is growing with every passing year. I think that within the next ten years, the evidence will become compelling enough — the dog case, the first results in humans — that the world as we know it will change profoundly.
2Digital: Let’s talk about the medical dataset problem — data heterogeneity. Does Gero face it, and if so, how is it being addressed?
Yury: It was a challenge before, and it remains an open one. Here I’d like to bring in our other project — Doctorina (we’ve covered it before; see the article — Ed.). Because the collaboration between a medical AI that helps people and a project like Gero is a genuine win-win.
Doctorina is currently in pre-launch preparation. We can already see that it delivers sound recommendations to users around the world, and we’re gearing up to scale. But someday, when Doctorina commands a meaningful share of global users, it will become possible, first, to match people — based on their individual conditions — with pharmaceutical companies developing drugs that are a particularly good fit for exactly those patients.
The benefit here is twofold: patients get early access to treatment for their conditions, while pharma gains access to the patients most likely to respond to those drugs. This substantially improves the odds of a drug succeeding in clinical trials. And naturally, there’s a vision of using data on how people fall ill and how diseases progress over time to advance new algorithms for identifying therapies for age-related diseases.
In essence, through Doctorina, the Gero project could gain access to a dataset that is unique, granular, fully interoperable, and rigorously structured across every relevant domain. And that, of course, will have a direct impact on research quality.
Even the dataset we worked with through the Pfizer collaboration — which encompassed hundreds of thousands of records, if not millions — turned out to be too small for the problems Gero is trying to solve.
The idea is to do the following: train a model on a large dataset without genomic data, one that predicts future diseases from a person’s medical history — much like a LLM predicts the next token in a sequence; loosely speaking, the next word or the next character.
In effect, such a model would gradually develop an understanding of how people break down. And with that model in hand, we can begin to view disease as a failure of multiple systems — in line with the failure mode framework developed by one of the founding fathers of reliability theory, Waloddi Weibull.
Such a model cannot be trained on hundreds of thousands of users — not even on a million. But on a hundred million, it can. And with a billion users, it would be better still.
2Digital: Following your logic, Doctorina could itself be a longevity tool — predicting, in collaboration with Gero, the diseases and complications a person might develop in the future?
Yury: Doctorina already handles this remarkably well. It identifies diseases in their earliest stages, helps stop them in their tracks, and in doing so directly extends people’s lives. I hope that Doctorina will become one of the platforms where Gero can apply its model directly — the one that predicts future diagnoses from a patient’s medical history.
2Digital: What would that look like from the user’s perspective?
Yury: Doctorina would tell you: given your medical history, we recommend that you proactively schedule — or have regularly, at a certain frequency — this or that test or examination. In practice, it would all look quite unremarkable. Most people would never realize the scale of what happened under the hood of that single recommendation — the tectonic shift in predictive medicine it represents. That, to me, is where the real beauty lies.