As labs continue scaling in a compute constrained world, the cost of serving frontier models will increase, which will compound the financial incentives of model providers to augment and replace human knowledge workers with the highest gap between their total cost of employment (TCE) and the cost of automating their jobs.

On March 24th, Anthropic published an update to their Anthropic Economic Index. One major finding was that users are querying Claude for tasks with diversifying economic value, including "personal queries around sports, product comparisons, and home maintenance". They observe that this broadening is consistent with standard technology adoption curves.

On the other hand, enterprise API usage displayed no evidence of economic diversification. Across 1 million sample conversations, average task value increased from $50.4/hr to $50.7/hr, and task usage share for the Computer and Mathematical occupation group increased from ~59% to ~62%. Enterprises are continuing to leverage Claude for tasks with high economic value.

Model providers are financially incentivized to serve applications with the highest realized economic value per unit of compute for at least two related reasons: increasing revenue efficiency of compute, which allows for allocating more compute for research while satisfying investors; and increasing profitability.

To illustrate with a crude example, model providers could scale more efficiently by automating a software engineer with TCE $300k at a compute cost of $10k, compared to an executive assistant with TCE $100k at a compute cost of $5k, compared to a school teacher with TCE $150k at a compute cost of $10k (all figures annualized).

One might contend that all three of the above applications have negligible compute costs relative to economic value. Given these figures, no job would be safe from automation. Furthermore, if advancing capabilities is the primary driver of rising costs per FLOP, then the true cost of automating human labor may be even lower.

The key observation is that cloud service providers will sell their compute to the highest bidder. A model provider which generates $30 of value per unit of compute via software automation can afford to outbid any competitors which generate $20 via automated executive assistants or $15 via automated teaching. Following the economics cliché of "supply equals demand", the market price of compute in a supply-constrained market should increase until the market is able to clear.

Recent events suggest that the compute market is supply-constrained. Although model providers lock in compute via private long term contracts, on-demand compute pricing presents a glimpse into current market conditions. On SF Compute, the cost of an H100 has increased from $1.4/hr at the start of 2026 to $1.7/hr presently, compared to under $1/hr and as low as $0.5/hr during mid 2025.

In his most recent Dwarkesh Podcast interview, Dylan Patel claimed that labs are locking in H100s for more than $2/hr and further predicted that model providers will charge higher API costs this year to "destroy demand" because of capacity constraints. Demand destruction would disproportionately affect enterprises which can no longer generate enough value to justify spending on API calls, protecting occupations which are low-paying, too expensive to automate, or both.

On March 24th, the day when Anthropic released its updated Anthropic Economic Index, OpenAI announced that it would shut down its Sora app. According to mainstream media, the crux of the decision was that Sora could not and would not deliver enough revenue on compute. 

In a compute constrained world, automation will be limited to tasks which realize the highest economic value over the human baseline. Like most economic predictions, this one is likely to be wrong, but it could be a useful starting point for modeling the short and medium term future.