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The October 2, 2026, edition of The Download puts two very different claims side by side: a contest called Younger that tracks changes in biological-age estimates, and an opinion by AI researcher Thore Graepel arguing that today’s AI lacks the kind of reasoning behind AlphaGo’s celebrated move. The contest can show changes in measurements; it cannot, by itself, prove that someone has become biologically younger. Graepel’s claim is an argument, not a settled verdict on whether LLMs reason.
What is the Younger biological-age contest?
Younger is a competition in which participants try to lower estimates of their biological age. Each participant’s six-month clock starts after baseline measurements. The contest recognizes both the largest gap between chronological age and estimated biological age, and the greatest reversal in a participant’s estimate, according to MIT Technology Review en español’s October 2, 2026, report.
The newsletter’s reporter, Jessica Hamzelou, said she had signed up for the competition. The framing question is worth keeping in view: “But is it even possible to measure whether someone is getting younger?” The contest attempts to answer that with measurements, but its leaderboard is not the same thing as a clinical finding that a person’s health or aging process has reversed.
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Enrollment and timing at the time of the report
| Contest detail | What the report said |
|---|---|
| Sign-ups | Around 120 people had signed up, according to organizer Christin Glorioso, as reported by MIT Technology Review on October 2, 2026. |
| Organizer’s target | Glorioso hoped to reach around 500 participants, the report said; this was a target, not a confirmed final total. |
| Leaderboard | Seven baseline entries were listed in the report, an early and limited snapshot rather than a broad population sample. |
| Participation period | Six months for each participant, beginning with that participant’s baseline measurements. |
| Official start | The event was scheduled to start in January, as reported on October 2, 2026. |
What do the early biological-age results show?
The report gives examples of striking outputs, but they illustrate what the scoring can produce—not proof of rejuvenation or a diagnosis.
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- A 47-year-old participant received an estimated biological age of 68.1. Her chair-rise measure was mapped to an age score of 100, according to the report.
- Another participant said TruDiagnostic had estimated her aging rate at 0.75. The report interpreted that individual result as equivalent to nine months of aging in a year.
Those values belong to individual test outputs reported in the contest story. In particular, a physical-function sub-score mapped to an age is not the same as a clinician’s assessment of a person’s overall health. The story does not establish that these figures are validated measures of whether a person has reversed aging.
Why a leaderboard change is not proof of getting younger
“Biological age” is an estimate, not a second birthday that can literally run backward. A contest can rank shifts in its chosen measures, but the result depends on what is measured, how the measures are combined, how much they vary when repeated, and whether they predict meaningful health outcomes. The MIT Technology Review report does not establish that Younger’s composite or leaderboard is a validated clinical outcome.
The report also captures concerns about whether the available measurements and timing can answer the larger question. A participant questioned the breadth of the testing, and physician Hillary Lin said she would have preferred more blood tests for a fuller picture of health. Lin also questioned whether six months would be long enough for changes in biological-aging measurements to emerge.
For anyone assessing a biological-age test or program, the useful questions are concrete:
- Which domains does it measure—blood markers, physical function, cognition, or something else?
- Does it report one combined score, several components, or both?
- How repeatable are the results, and what variation is expected between tests?
- How much time passes between measurements, and is that interval long enough for the claimed change?
- Has the measure been validated against health outcomes, rather than only producing an age-like number?
The contest report does not provide enough information to rank particular tests against those criteria. Its account also does not show that a specific supplement, intervention, or commercial test produces better health or reverses aging.
What does the newsletter say about LLMs and reasoning?
The second item is an opinion by Thore Graepel, whom the newsletter identifies as University College London’s chair of machine learning and a core member of DeepMind’s AlphaGo team. The newsletter recalls AlphaGo’s surprising move against Lee Sedol and summarizes Graepel’s view that the move showed reasoning powers today’s AI lacks. It quotes him: “It was AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks.”
That is Graepel’s stated position, not an established consensus about all AI systems. The newsletter says he left Google DeepMind and advocates a new approach to machine reasoning drawing on AlphaGo’s architecture. It offers a synopsis rather than the complete argument, so it does not provide enough detail to assess his full case or its evidence.
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Why AlphaGo is a useful—but limited—comparison
Go offers a tightly defined environment: the rules are fixed, moves have formal consequences, and a system can evaluate outcomes through play. Research on reinforcement-learning game systems such as those described in Silver and colleagues’ work on self-play helps explain how systems can achieve strong performance in such settings. A discussion of proposals, evaluation, and planning in game-playing AI also appears in The Atlantic’s March 30, 2026, analysis.
That context does not settle whether a general-purpose LLM reasons. Success in a game with explicit rules and feedback is not, on its own, evidence that a language model reasons in the same way—or proof that it cannot. The newsletter’s item presents Graepel’s challenge and proposed direction; it does not supply enough of his full essay to extend the claim to particular LLM capabilities, reasoning techniques, or benchmarks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to read the two stories together
The shared thread is measurement and interpretation, not a claim that biological aging and machine reasoning are equivalent. Younger turns selected measurements into a ranking; AlphaGo’s performance prompts a debate about what its moves demonstrate. In both cases, the headline result needs a careful account of what was measured, in what setting, and what the evidence can support.
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