Yesterday's signals, distilled.

Yesterday's signals, distilled, A look back at March 17.

Tokens as compensation. Frontier models as legal territory. Quantum as an extension of CUDA. Edge inference as the default, not the exception.

The connective tissue: compute is being financialized, contractualized, and embedded into existing stacks, not treated as a separate “AI initiative.”

This isn’t a model race story anymore.

It’s a control-plane story, who owns the meter, the contract, and the workflow where intelligence runs.

If your plan still assumes “we’ll pick a model and a cloud and build on top,” you’re already behind. The real game is: how do you keep optionality when the meter, the legal rights, and the hardware roadmap are all converging around someone else’s incentives.

BLUF

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COMPUTE / CONTROL PLANE

Tokens, contracts, and who owns the meter

Nvidia CEO Jensen Huang can’t stop talking about “AI tokens”, per Business Insider, he’s framing tokens as the unit CFOs should use to think about AI budgets and consumption.

In parallel, tokens are being positioned as the abstraction layer between raw FLOPs and business value, a way to normalize spend across models, workloads, and time.

The Bet: Nvidia is betting that whoever defines the unit of account for AI spend will control how enterprises perceive cost, value, and lock-in.

So What? This is an attempt to move the control plane for AI from “GPU hours”, an infra metric, to “tokens”, a business metric. Once your board and CFO think in tokens, they’ll benchmark vendors, teams, and products on that basis. That shifts power to whoever issues, meters, and reports those tokens. If you ignore this and keep talking in “instances” and “FLOPs,” you’ll sound like a cost center while others sound like a product line.

The Risk: If token definitions diverge across vendors, you get the equivalent of multiple incompatible “kilowatts”, confusing, non-comparable units that obscure true cost. That confusion can stall internal adoption or trigger blunt cost-cutting when finance loses trust in the numbers.

Action: • Translate your AI cost reporting into a token-like metric this week, even if it’s internal only. • Ask your infra providers how they define and meter tokens; map that to your own usage so you’re not surprised in QBRs. • In new product specs, require PMs to express unit economics in “cost per 1,000 tokens” or equivalent, not just “per user” or “per request.”

Jensen H