TL;DR
Thorsten Meyer AI’s June 7 Post-Labor dispatch says the evidence that AI is moving value from labor to capital is unresolved. The US labor share has stayed within a 57% to 64% band for decades, while cited payroll and regional studies point to pressure in AI-exposed entry-level work and some European regions.
Thorsten Meyer AI published its second Post-Labor dispatch on June 7, arguing that the case that value is moving from labor to capital remains unresolved: the US labor share has stayed in a 57% to 64% band since the 1950s, while early AI-exposed employment data shows pressure on young workers. The finding matters because Meyer’s case for broad-based ownership depends on whether AI begins shifting economic returns away from labor.
The dispatch says the strongest evidence for skeptics is the long-run US labor share series: roughly 57% to 64% from the 1950s through 2023, despite industrial automation, computers and the internet. It treats that record as a confirmed aggregate pattern from the source material, not proof that AI will have no effect.
The opposing evidence is concentrated at the margin. Citing a Stanford study of millions of payroll records, Meyer writes that employment for 22-to-25-year-olds in the most AI-exposed occupations has fallen by about 13% relative to less exposed roles since late 2022, even after controls for firm-level shocks. Older workers in the same jobs, according to the cited study, held steady or grew.
The dispatch also cites European regional evidence from Minniti et al., saying AI patenting tracked declines in labor share across 238 regions. The source does not present that finding as a settled global conclusion; it uses it as one early signal alongside employment pressure and bargaining-power concerns.
Why It Matters
The stakes are policy and ownership design. If labor’s share of income is broadly stable, calls for worker or citizen ownership of capital may rest on a weaker urgency claim. If AI is already reducing labor’s claim on new value at the margin, then waiting for aggregate labor-share data could leave policymakers reacting after the distributional shift is visible only in hindsight.
For readers, the dispute affects how AI’s economic impact is measured. Job counts, wages and labor’s share of value are related but separate questions. The dispatch argues that the ownership case depends most on the third, while current data is better at showing early job-market movement than a durable shift in income shares.
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Background
The June 7 dispatch follows The Stake, an earlier Post-Labor essay that argued broad-based ownership is the right response if value is moving from labor to capital. The new piece tests that premise rather than restating it.
Meyer’s frame is that prior technology waves did not push the US labor share outside its long-run band, at least through 2023. AI may follow that pattern, or it may first appear in entry-level routine cognitive work before showing up in aggregate data.
“The aggregate is stable; the margin is moving.”
— Thorsten Meyer AI
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What Remains Unclear
It is not yet clear whether early declines in AI-exposed entry-level employment will turn into a broad shift in labor’s income share. The source says the aggregate US measure has not shown that shift through 2023. The durability of the Stanford employment pattern, the effect on wages, and the size of any capital-income gain remain open.
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What’s Next
The next milestone is more data: updated national labor-share measures, follow-up payroll studies by age and occupation, and research on how AI adoption affects wages, hiring, profits and bargaining power. Meyer argues that broad-based ownership should be treated as a no-regrets policy response while the evidence develops, but the dispatch stops short of saying the aggregate shift has already happened.
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Key Questions
What is the actual news development?
Thorsten Meyer AI published a June 7 analysis testing whether AI is moving value from labor to capital. The piece concludes that the evidence points in different directions depending on whether readers look at aggregate labor share or early AI-exposed labor-market margins.
Does the analysis say AI has already shifted value from labor to capital?
No. Meyer says the premise is true at the margin and not yet true in the aggregate. The US labor share has remained within its long-run range through 2023, while cited studies show early pressure in some AI-exposed jobs and regions.
What evidence supports the stability view?
The main stability evidence is the US labor share, which the dispatch says fluctuated between roughly 57% and 64% from the 1950s to 2023. That period included major technology waves without a clear break in labor’s share.
What evidence supports the displacement view?
The dispatch cites a Stanford payroll study finding about a 13% relative employment decline for 22-to-25-year-olds in highly AI-exposed jobs since late 2022. It also cites European regional research linking AI patenting with labor-share declines across 238 regions.
Why does this matter for ownership policy?
Meyer’s ownership argument depends on whether AI returns accrue more to capital than labor. If that shift is only emerging at the margins, broad-based ownership becomes a hedge against a possible distributional change rather than a response to a proven aggregate break.
Source: Thorsten Meyer AI
