“Employee productivity is sky high but company revenues (outside chips supply chain) are roughly stagnant.” This can easily be explained:
Revenue is usually demand-constrained, not output-constrained, and Amdahl's law shows us that speeding up a non-bottleneck step doesn't speed up the system itself.
It’s REALLY, REALLY hard to identify isolated tasks that directly block revenue for most companies. And even if they’re output constrained (rare), any new AI productivity gains are just competed away…
For everyone involved, regardless of what type of constrained you are, net-new AI productivity gains typically show up as costs, not revenue. Token spending is a labor-to-vendor transfer: part of the payroll that would have grown now flows to labs and cloud providers. Andddd thats where the revenue is lol
In other words, the productivity gain is being captured UPSTREAM rather than created.
Where I don’t have the data but would like it (couldn’t find anything reputable + recent): gains should appear first in sectors where demand is elastic and output is the bottleneck and last where demand is fixed or capacity-limited by physical or regulatory constraints.
Examples of such markets where latent demand is priced out by labor cost (Baumol sectors), and lower prices would unlock quantity rather than just cut cost:
- Legal, accounting, and compliance for small businesses and individuals
- Tutoring and coaching
- Health/diagnostic-adjacent
- Bespoke software
Even there, the gain accrues mostly as consumer surplus, with lower prices and more quantity.
Recapping bc it’s late and I’m tired and idk if I went off track: very few industries are output-constrained in a way that converts productivity into revenue. AI's effect is mostly deflationary at the task level, and revenue growth requires new demand, which tends to come from new products rather than cheaper existing ones.
kyle
npub18x…7a2cg
2026-10-21 03:34:19 UTC
Author Public Key
npub18x6mq3tv0qyqy0gr7vs4dpwmlfe9mdf0eaw038rpx6sslteq9c0q57a2cgPublished at
2026-10-21 03:34:19 UTCEvent JSON
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"content": "“Employee productivity is sky high but company revenues (outside chips supply chain) are roughly stagnant.” This can easily be explained:\n\nRevenue is usually demand-constrained, not output-constrained, and Amdahl's law shows us that speeding up a non-bottleneck step doesn't speed up the system itself. \n\nIt’s REALLY, REALLY hard to identify isolated tasks that directly block revenue for most companies. And even if they’re output constrained (rare), any new AI productivity gains are just competed away…\n\nFor everyone involved, regardless of what type of constrained you are, net-new AI productivity gains typically show up as costs, not revenue. Token spending is a labor-to-vendor transfer: part of the payroll that would have grown now flows to labs and cloud providers. Andddd thats where the revenue is lol\n\nIn other words, the productivity gain is being captured UPSTREAM rather than created.\n\nWhere I don’t have the data but would like it (couldn’t find anything reputable + recent): gains should appear first in sectors where demand is elastic and output is the bottleneck and last where demand is fixed or capacity-limited by physical or regulatory constraints. \n\nExamples of such markets where latent demand is priced out by labor cost (Baumol sectors), and lower prices would unlock quantity rather than just cut cost:\n\n- Legal, accounting, and compliance for small businesses and individuals\n- Tutoring and coaching \n- Health/diagnostic-adjacent \n- Bespoke software\n\nEven there, the gain accrues mostly as consumer surplus, with lower prices and more quantity. \n\nRecapping bc it’s late and I’m tired and idk if I went off track: very few industries are output-constrained in a way that converts productivity into revenue. AI's effect is mostly deflationary at the task level, and revenue growth requires new demand, which tends to come from new products rather than cheaper existing ones.",
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