Longevity, Robotics and the Race Between Two Powers

Calls to slow AI are positioning. A developer asking for a pause is telling rivals its own model is far ahead, and telling regulators it welcomes oversight. A handful of American labs, with Chinese rivals close behind, are racing towards artificial general intelligence (AGI) in a geopolitical battle over who captures the productivity gains and controls the supply chains robotics will rebuild.

The job losses will be real, but predictions of AI wiping out humanity distract from the tension already here: longer lives and shorter careers. The technology taking our work is the same one promising us more years to live. Medicine is where AI lands next, and hardest.

Harnessed properly, AI lifts productivity enough for the economy to grow fast without stoking inflation. Living standards follow, but only if the gains are shared. Yet the early money goes to capital, not labour. AI cannot be stopped; what can change is the capital-labour split, and it must: no society tolerates a permanently lopsided one.

Nobody slows down

Nobody who is winning wants a pause, and nobody who is losing can afford one: every player faces the same payoff matrix, and the dominant strategy is to run. Roughly 90% of the world’s AI computing capacity sits in America and China; everyone else rents it on their terms.

White collar first, then the robots

The first jobs to go are junior white-collar roles, and white-collar work is the well-paid end of the labour market: Morgan Stanley puts median pay in the most exposed occupations at about US$97,000, against US$46,000 in the least exposed.

Firms rarely start with redundancies. They stop hiring graduates, stop replacing leavers and push the work onto existing staff, so headcount falls quietly and margins improve. The cost comes later: junior work is how a firm trains its future senior staff, and without it the next generation of partners is never made.

Next comes the humanoid supply chain. Unitree’s Shanghai listing in August, oversubscribed more than 8,000x by retail investors, was speculative, but the scale of the bid says the market has decided humanoids are the next trillion-dollar industry. In its September 2026 Physical AI report, Goldman Sachs lifted its base case for global shipments to 75,000 this year and 6.5 million in 2035.

Robotics is also where the supply chain battle is fought: whoever automates the factory, the warehouse and the port sets the cost of making and moving goods. Nor is automation waiting for humanoids: China installed 295,000 conventional industrial robots in 2024, 54% of the world’s total. Controlled environments go first; the tradesperson who walks into a different house every morning goes last.

Transport is next, and physical work stops being safe ground: Uber’s chief executive can imagine most of its trips being fulfilled by robots in 15 to 20 years. We expect it sooner, in as little as 10 years, with robot uptake as parabolic as ChatGPT’s was after 2022.

AI, drugs and the robot surgeon

AI designs the drug; robots run the experiment, then the operation. AlphaFold is the proof point: 60 years of laboratory work had mapped the structure of 0.1% of the 200 million known proteins, and AI predicted nearly all the rest in about a year. A predicted structure is not a drug, but it removes years of early work.

Drug development takes more than a decade, and each drug that reaches market costs about US$2.6 billion once failures are counted; fewer than one in ten entering trials is approved. Time and failure are the bottlenecks, and AI attacks both: better candidates in Phase I, better-matched patients and smaller trials in Phase II, fewer failures in Phase III.

Last year a Johns Hopkins robot completed a lengthy phase of a gallbladder removal on pig tissue unaided. Surgeons are not immune: first the robot assists, then it operates while the surgeon supervises.

Prevention is moving in parallel. Gallup’s National Health and Well-Being Index, published in July 2026 and revised on 8 September, reported that 11% of American adults now take a GLP-1 drug for weight loss, up from 3% two years ago, while the diabetes rate has eased to 12.8%, its first reading under 13% since 2022. Medical technology has usually raised health spending by keeping people alive longer in poor health; GLP-1s are the clearest mass-market exception since vaccination, because they cut the risk of the disease itself.

Longevity, and who pays for it

Put AI’s channels together and the destination is longevity. We will still age; the aim is to spend our last 15 to 20 years in better health. The lifespan-healthspan gap (years lived in poor health) is 9.6 years globally and widening, and 12.1 in Australia.

The bigger problem sits outside the hospital. Superannuation and insurance rest on actuarial assumptions built around retiring at 65, and every one of them moves. We tell people longer lives will be paid for by working longer while investing in technology designed to need less of their labour. Each proposition is sensible alone; together they demand an answer to who employs the healthier 65-year-old, and nobody has one.

Five extra healthy years are five more years of earning and saving for someone still employed, and five more years of drawing down for someone whose occupation has shrunk. The biology is identical. The finances are decided by what you own.

Care costs could absorb much of the gain. Productivity in Australia’s non-market sector, health and care above all, sits below its 2007 level, and the NDIS alone is budgeted at A$56 billion this financial year. That is the cost disease of the service economy: care is labour, its productivity does not grow, so its relative price rises as everything else gets cheaper. No amount of drug discovery cures that.

That is where robotics comes back in: hospitals and homes were built for humans, the strongest argument for machines shaped like them. If AI drugs extend healthspan and care robots make the remaining treatment affordable, the cost disease finally has a treatment.

The politics arrive in November

The US midterms on 3 November are the first test. A lost job in a town is a political event; a productivity statistic is not. The national accounts already show the transfer: labour’s share of American output is the lowest since records began in 1947, profits a record. Can American capitalism carry a middle class with little capital?

The worst case: the top 1% takes more, the middle hollows and the anger turns on the technology, not the distribution. Rome is the old warning: wealth concentrated, farmers lost their land, the republic fell.

No company can cut its payroll and assume its customers’ incomes are untouched; one firm’s wage bill is another’s revenue. Household wealth increasingly sits in AI stocks: the wealth effect holds up the consumer, AI holds up the wealth effect, and AI is coming for the incomes of the households that own it. For now the first leg of the transfer lifts earnings, and spending has not given way.

China is a close second at worst: its best models trail the American leaders by four to eight months at a fraction of the price, and downstream of the model it is already ahead. It built its grid before it needed it, while American data centres queue for years to connect.

It is easy to see the Chinese authorities directing AI at cheaper, higher-quality social services while the American version, so far, enriches the people who build it. China’s capitalism-lite model lets the state steer the gains, and its legitimacy rests on rising middle-class living standards. If the test is whether the gains reach citizens, China is better placed.

What it means for investors

The transfer to capital favours shareholders in aggregate, but AI exposure is not an investment case by itself: the entry price matters, and the business must keep some of the gains.

AI makers must earn a return on heavy capital spending. Users win where costs shrink and revenue does not: a firm paid for an outcome keeps the saving, while one paid by the hour or per user hands it to the customer. Claims processing, bank and insurer back offices and fixed-fee professional services fit best.

The evidence is in the accounts, not the announcements: rising output per employee, and better cash margins after the cost of models, integration and supervision.

Australia has the most to gain and the greatest urgency: labour productivity is barely 1% above its 2015 to 2019 average, and AI is the one productivity lever that needs no legislative reform. As we argued in July, the advantage sits with small and mid-sized companies that turn a decision into a working system quickly. The ASX 20 is legacy capital, much of it stranded.

Healthcare is where the change runs deepest. For big pharma, AI cuts both ways, answering the patent cliff while making today’s drugs obsolete, and prevention will also make some healthcare businesses redundant.

In robotics, the earlier opportunity rests with industrial adopters, not robot manufacturers. Could that save the German car industry? Robots make BMW’s cars cheaper to build, but they do not make more people want to buy them.

For rates, the build-out is inflationary while power, chips and builders are scarce, but the outcome is deflationary as the economy does more with less. September sat squarely in the first phase: the US 10-year yield hit 5.3%, its highest since 2002, on those same scarcities. In the second phase, policy rates stay higher but not restrictive, and 10-year yields fall as the inflation premium fades, flattening the curve. That is increasingly the market’s longer term central case.

Living with AI

We would be wrong if an international agreement slowed the race, if the American labs regained a decisive lead and kept prices high, if implementation costs and competition ate the savings, if AI drugs were still failing in the clinic in 2028, or if windfall taxes and public stakes after November cut the return to capital.

Greater longevity is arriving, and careers too short to fund it are the default unless wider capital ownership and policy spread the gains. We will live longer with AI in both senses: more healthy years because of it, more years alongside it. The technology does not decide who benefits; owning a share of the capital does.