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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →MIT Technology Review’s October 2, 2026, newsletter pairs a contest that tracks estimated biological age with an opinion arguing that today’s large language models do not reason as AlphaGo did. The two stories raise different questions: what an age-related score can show, and what counts as reasoning in AI.
What is the Younger biological-age contest?
In a report by Jessica Hamzelou in MIT Technology Review en español, Younger is described as a six-month competition for people trying to lower their estimated biological age. Each participant’s six-month period starts with baseline measurements. The contest uses multiple measures and names two kinds of winners: the person with the largest gap between chronological and estimated biological age, and the person whose estimate falls the most during the contest. Read Hamzelou’s report.
Those categories are not interchangeable. The largest age gap can describe a difference already present at baseline; it does not necessarily mean that person’s estimate fell during the contest. The other category specifically rewards the largest change in an estimate.
What the early figures show
| Contest detail | What the report said |
|---|---|
| Organizer’s target | Christin Glorioso hoped to reach around 500 participants. |
| Sign-ups | Glorioso reported around 120 sign-ups at the time of the article. |
| Leaderboard | Seven baseline entries were listed at that point. |
| Duration | Six months for each participant, starting from that person’s baseline measurements. |
| Start timing | The report said the event was to start officially in January. |
The enrollment and leaderboard figures are a snapshot from the report, not a final count or a description of the contest’s eventual results.
#1 Best Overall
Can a biological-age test show that someone is getting younger?
It can produce a lower estimate on a later measurement, but that is not the same as reversing chronological age or proving that a person’s health has improved. Younger’s leaderboard tracks changes in estimates derived from a collection of measures; the report does not establish that its combined score is a validated clinical outcome.
The report illustrates how sharply individual outputs can differ. One 47-year-old participant received an estimated biological age of 68.1, while her chair-rise measure mapped to an age score of 100. Another participant said TruDiagnostic had put her aging rate at 0.75, which the report described as equivalent to nine months of aging in a year. These are individual test outputs, not diagnoses or evidence that either person had biologically reversed aging.
Rank #2
The chair-rise result also shows why a single component needs context: a striking sub-score should not be read as a clinician’s overall assessment. A biological-age result is an estimate, and its meaning depends on what was measured and how the measures were combined.
Why the contest’s measures and timing matter
Physician Hillary Lin said she would have preferred more blood tests for a fuller picture of health and questioned whether six months would be long enough to show changes in biological-aging measurements. Those concerns matter because a leaderboard can only be as informative as its measures, their repeatability, and the interval between tests. The report does not establish how well the contest’s composite predicts health outcomes or how much variation to expect between repeated measurements.
Rank #3
If you are evaluating a biological-age test or program, look for what domains it measures—such as blood markers, physical function, or cognition—whether it gives one score or separate components, how repeatable the results are, and whether the outcome has been validated against health outcomes. The Younger report does not provide enough detail to rank specific tests or identify an intervention that reliably lowers biological age.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does the newsletter say about LLMs and reasoning?
The edition’s second item is an opinion by Thore Graepel, identified in the newsletter as University College London’s chair of machine learning and a core member of the AlphaGo team at DeepMind. The newsletter says Graepel contrasts AlphaGo’s surprising move against Lee Sedol with the abilities of today’s AI, and argues for a new approach to machine reasoning drawing on AlphaGo’s architecture. It quotes him: “It was AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks.” Read the newsletter edition.
That is Graepel’s position, not a settled finding about every current AI system. The newsletter offers a short synopsis rather than the full opinion, so it does not establish the detailed case behind his claim or define exactly what he means by reasoning.
AlphaGo is a useful point of comparison because Go has explicit rules and a clear outcome: a system can propose and assess moves in a bounded game. That context can help explain why performance in a game is not automatically evidence of general reasoning across open-ended tasks. A 2026 discussion in The Atlantic considers game-based planning and the challenge of defining general intelligence, but it does not settle whether general-purpose LLMs reason. Read the Atlantic analysis.
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So the headline question “do LLMs reason?” remains a question of definition and evidence, not something this newsletter synopsis resolves. Graepel’s argument is the subject of the item; the newsletter alone is not enough to assess its complete reasoning.
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