On 8 September 2026, MIT Technology Review published its Innovators Under 35 list, spotlighting 35 people from nine countries, selected from more than 550 nominations with the involvement of 44 expert judges. Among those featured are geneticist Yuan-Cheng (Ryan) Lu with a technology for reversing ageing to restore vision, founder Hannah Erli, who teaches chips to recycle energy, Lorin Meru with a method for producing cheaper and cleaner steel, and entrepreneur Danijar Hafner, who is developing AI agents capable of planning ahead.
This list is not merely a roll call of names. It captures a moment when young researchers and entrepreneurs, under the pressure of global challenges — disease, climate change and the energy crisis — are choosing not abstract breakthroughs but concrete, scalable solutions. Their work shares one thing: the integration of AI into traditional fields where purely biological or engineering approaches once dominated. Lu uses genetics with elements of computational modelling, Erli uses AI to optimise chip energy consumption, and Hafner uses agents for long-term planning in the real world.
The selection story reveals hard constraints: the judges assessed not only scientific novelty but also results already achieved, as well as the potential to affect real problems. Out of 550 nominations, only those whose projects had already demonstrated progress made it to the final — whether a device to stop postpartum haemorrhage in Tanzania or the processing of invasive algae in Kenya. This is not a random choice but a reflection of the fact that institutions like MIT Technology Review now value not only brilliance but also the ability to work under conditions of limited resources and urgent societal needs.
The public image of the list — “young geniuses changing the world” — diverges from reality: many of them already carry reputational and institutional debts. They depend on grants, investors and regulators who demand rapid applied results. Their pattern of behaviour under pressure is not radical revolutions but targeted improvements that can be implemented here and now. Hafner, for example, does not promise general AI but focuses on agents capable of taking context and consequences into account, because that is precisely what the market and regulators need today.
Compare this with the situation in sport: a coach does not change the whole team at once but strengthens weak positions in order to win the next match. So it is here — innovators strengthen bottlenecks in existing systems rather than rebuilding them from scratch. This explains why the list contains so many projects at the intersection of AI and “dirty” industries: climate and biotech require not only ideas but also proven effectiveness under real constraints.
In the end, the 2026 list shows not simply new names but a shift in how society now seeks solutions: through young specialists ready to work at the intersection of disciplines and to bear responsibility for practical results. Their choice is not an accident but a response to the pressure that forces a focus on what can be measured and implemented tomorrow.
