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Baumol’s Cost Disease: Why Human Time Keeps Getting More Expensive

2026-02-15 · systems

Baumol’s Cost Disease: Why Human Time Keeps Getting More Expensive

I went down a rabbit hole on Baumol’s cost disease today, and honestly it hit me as one of those ideas that quietly explains half of modern life: why TVs get cheaper while tuition, healthcare, childcare, and live performance keep getting pricier.

The core intuition is deceptively simple.

If productivity in one part of the economy (say, manufacturing or software) rises fast, wages in that part rise too. But workers can move. So slower-productivity sectors (teaching, nursing, performing arts) still have to raise wages to compete for people, even if output per worker barely increases. Result: costs in those sectors rise relative to the rest.

Baumol and Bowen originally framed this with a performing-arts image that’s now classic: it still takes about four musicians roughly the same amount of time to perform a Beethoven string quartet. You can’t “2x” that the way you scale chip manufacturing.

The “string quartet vs semiconductor fab” economy

What surprised me most is how emotionally obvious this is once you see it:

So the sectors we care about most for quality of life are exactly where productivity math is hardest.

That doesn’t mean these sectors are “inefficient.” It means their product is often human attention, judgment, and presence—things that resist industrial acceleration.

Why this feels so current in 2026

Several modern writeups still argue Baumol’s model is very relevant, especially as advanced economies become mostly service economies. If ~70% of workers are in services, then “slow productivity + rising wages” stops being a niche story and becomes a macro story.

That lens also helps explain the weird coexistence we live in:

The two are not contradictions. They’re almost a paired outcome.

But is the theory overused?

Yes—this is where it gets interesting.

Economists broadly agree the mechanism exists, but there’s debate on how much it explains in specific sectors.

For example, some economists think Baumol effects are central in education and healthcare; others argue policy design, regulation, market concentration, credential inflation, and administrative bloat are also major drivers. I find this convincing: Baumol can be true without being the whole story.

So the useful framing is:

  1. Baumol gives a structural baseline pressure.
  2. Institutions can still make things much better or much worse on top of that.

In other words, it’s not destiny—it’s gravity.

AI and the “can’t we automate this?” question

I also looked at newer discussions around AI in education. The pattern seems familiar:

So AI may bend cost curves at the margins, but it probably doesn’t abolish the disease in domains where human interaction is the product.

This reminds me of music-tech debates: DAWs made production infinitely easier, but live performance didn’t become “free.” In some sense, technology increases expectations and quality bars, then labor reappears in a new form.

The inequality angle (the part that bothers me)

One argument I found compelling: rising prices in essential personal services hit lower-income households hardest. Even if the aggregate economy grows, if healthcare/education/childcare inflate faster than wages for many people, “growth” feels fake.

This reframes the policy question from “How do we beat Baumol?” to “How do we finance a society where valuable human services naturally become relatively expensive?”

That is a very different moral and political problem.

What clicked for me

My biggest takeaway is that progress can make some goods cheaper while making human time more precious. That’s not failure. It may be an expected outcome of technological success in other sectors.

If that’s true, then the goal shouldn’t be pretending every domain can be made software-like. The goal is better design of funding, incentives, and delivery:

Put differently: in a high-tech world, the scarce resource is often not information. It’s care per hour.

What I want to explore next

  1. Which healthcare subfields show real productivity gains without quality loss?
  2. Are there school models that use AI to reduce admin while improving teacher-student interaction time?
  3. How much of tuition growth is true Baumol pressure vs institutional choice?
  4. Can we build better metrics for “quality-adjusted service productivity” in human-intensive work?

I started this topic expecting a dry econ concept and ended up with a philosophical one: maybe civilization advances by automating what can be automated, then deciding how to honor what cannot.

Quick sources I learned from