What the Decline of Birthrates Can Teach Us About Tech Adoption

In our house, New Scientist and The Economist tend to rotate on the coffee table. One recurring theme over the last decade has been declining birthrates and the steady rise of ageing populations. On the surface, this looks like a demographic or social story. But if you look closely, it has deep lessons for anyone building, buying, or adopting technology.

Think of it as two curves colliding.

• One curve is demographic: birthrates falling, populations ageing.

• The other is technological: the classic “S-curve” of adoption, where a few innovators pull things forward and the rest follow over time.

When fewer young adopters enter the workforce, and more experienced workers stay longer, the S-curve of adoption changes shape. Let’s unpack what that means.

Supply of Innovation: Who Finds the Next Big Idea?

Researchers in economics have shown that “ideas are getting harder to find.” In semiconductors, for example, it now takes about 18× more researchers to keep Moore’s Law on track than it did in the 1970s. Fewer young cohorts entering science and engineering means the pipeline of founders and early-career innovators shrinks, unless it’s balanced by migration, upskilling, or longer careers.

So instead of ideas spilling over into every niche, expect innovation to concentrate where the pull is strongest: healthcare, ageing, automation, and efficiency.

Demand Shifts: Market Size Rewires What Gets Built

Innovation follows demand. One study of the pharmaceutical industry found that a 1% increase in market size led to 4–6% more new drugs. Demographics drive markets, and right now the demand curve is bending toward ageing societies.

That means less “teen tech,” more “longevity tech.” More investment in remote monitoring, fall detection, accessible interfaces, and, critically, enterprise automation to fill labour gaps.

Adoption Speed: A Different S-Curve

Older cohorts adopt technology differently. Pew data shows smartphone adoption is high overall, but “always online” behaviour drops sharply after 65. Inside organisations, job mobility is lower among older workers, which means fewer “carriers” to spread a new tool across teams.

So the adoption curve doesn’t vanish but lengthens. Rollouts take longer, value has to be clearer, and training has to be built in.

The Cost Curve: When Volume Slows, Prices Fall Slower

Many technologies get cheaper because of “experience curves”: every doubling of production lowers cost. But when birthrates are falling and cohort sizes shrink, cumulative production takes longer. That can delay cost declines and slow diffusion, especially in consumer tech.

Automation Is the Exception

Here’s the paradox: while some technologies diffuse more slowly, automation can accelerate in ageing societies. Economists Daron Acemoglu and Pascual Restrepo found that up to 40% of the variation in robot adoption across countries could be explained by ageing alone.

Scarce mid-career labour pushes firms to substitute with machines. In practice, that means more RPA bots, more Power Automate flows, more AI copilots augmenting work.

Lessons for Builders and Buyers

For product leaders

• Design for ageing users by default: larger fonts, better contrast, error-tolerant workflows, and explainable outcomes.

• Solve the right problems: time savings, safety, compliance, reduced cognitive load—things felt daily, not just “nice-to-have.”

• Price smartly: when volumes are lower, lean on service models, bundling, or reuse to “manufacture” scale.

For CIOs and automation leads

• Aim automation at the right gaps: workflows where skilled mid-career labour is hardest to find.

• Rethink adoption tactics: use shadow mode, peer mentors, and micro-learning to support slower adoption curves.

• Spread tools via communities, not churn: with less role turnover, you need deliberate champions.

Where This Could Be Wrong

Older adults do adopt, rapidly, when the use case is urgent, familiar, or mandated. Smartphone adoption among over-65s has soared in the last decade. And automation doesn’t just cushion productivity drag; in some industries it can boost it.

But the broader lesson is clear: declining birthrates don’t mean less technology. They mean different technology, adopted on different timelines.

The winners will be the teams that build with demography in mind.

References

• UN WPP 2024/OWID summary on fertility and population peak. Our World in Data

• Bass diffusion (original & technical note). Of (im)possible interestUW Faculty

• Pew on age gaps in smartphone/“always online.” Pew Research Center

• “Are Ideas Getting Harder to Find?” (AER). American Economic Association

• Market size drives innovation (Acemoglu & Linn). MIT EconomicsNBER

• Ageing → robot/automation adoption (Acemoglu & Restrepo). NBER

• Experience/Wright’s law evidence. PLOSOur World in Data

• OECD on ageing, skills, and mobility. OECD+2