Companies will spend more on tokens than they do salaries very soon.
Application layer companies have no defensibility, the model is the product.
Hiring researchers will cost you tens of millions of dollars today.
Everything you think you know about defensibility, token spend, labour displacement, will be changed following this discussion.
I condensed the core ideas which changed my thinking from my conversation with @BrendanFoody at @mercor_ai below.
1. Why Frontier AI Labs Could Become $10TN Companies
Critics once questioned whether foundation model labs could keep pricing power in a competitive market. Their revenue velocity now suggests the opposite. The opportunity around leading frontier models is so large that it could absorb a major share of macro demand. At least one AI lab may become a $10TN company within five years.
2. The Capacity Bottleneck: Demand Doubling Overnight
For top infrastructure and data providers, growth is no longer limited by customer acquisition. It is limited by execution. Demand is scaling so fast that leading companies could double revenue overnight if they had enough capacity. The challenge now is how quickly they can mobilize specialized human networks and build high-fidelity environments for enterprise demand.
3. Why Forward Deployment Will Determine True Value Creation
Defensibility in the software layer is getting harder because the model itself is becoming the product. True value creation will come from post-sales forward deployment, not pre-sales GTM. The durable edge is training agents on tacit customer knowledge and layering automated services on top of software.
4. Is the Stated Revenue Really Revenue or GMV?
The stated revenue is not GMV because the talent network is only one part of a vertically integrated value chain. Customers buy complete tasks for model improvement, not simple marketplace listings. With 30% to 40% gross margins, the business owns the full lifecycle, from sourcing experts to deploying AI project managers and running quality checks.
5. The Inversion of Corporate Opex: Token Spend vs. Salaries
In high-growth AI companies, token spend for internal agents has already surpassed employee headcount costs. As operations, interviewing, accounting, and fraud detection move to agents, capital allocation shifts from salaries to inference compute.
6. Why Token Spend Inside Companies Will Keep Increasing
Driven by Jevons Paradox, enterprise token consumption will keep rising as models improve and costs fall. Companies will use more compute to unlock higher-order reasoning, not less. F500s are responding by building evaluation systems that let them hot-swap models and optimize inference budgets.
7. The Tens of Millions Talent War for AI Researchers
The market for top AI researchers is severely supply constrained, with demand far above available talent. Companies are offering compensation packages worth tens of millions in stock per year to secure elite researchers. This wage spike shows that world-class research talent remains the core bottleneck in AI.
(links below)
