Alan Jacobson

The AI honeymoon is almost over

For the past two years, AI has felt like magic. * Costs looked manageable. * Capabilities kept improving. * And the narrative was simple: Build with AI, and the economics will figure themselves out. That narrative is starting to break. Not because the models stopped improving. But because the economics underneath them never

Tokens don’t measure what matters, so change the measurement

The AI industry keeps trying to solve a measurement problem by changing the model. That’s the wrong layer. The problem is not the LLM. And there is no need to change the model to solve the problem. The problem is the unit used to measure cost. Today, AI is

Tokens can’t track AI cost at scale. FBM can — without changing the model

What is “FBM?” FLOP-Based Metering – which does not require changing the LLM. It is a layer between the user and the model. Jeff Bezos didn’t change TCP/IP to build ecommerce. Amazon was built with SSL as a commerce layer on top of the internet. That’s the

Cost per outcome misses the target

By Alan Jacobson, AI Economics Strategist Digital pricing has always followed a simple pattern: move closer to the result, and pricing gets smarter. * CPM (cost per thousand impressions): pay for exposure * CPC (cost per click): pay for engagement * CPA (cost per action): pay for a result Each step reduced waste

Tokens can’t track AI cost at scale

Tokens don’t measure AI cost. They measure words. But AI doesn’t charge for words. It charges for compute. And in agentic systems, a growing share of that compute: * never becomes tokens * happens off-screen * and can’t be measured by word count So if you’re using tokens

Agentic AI broke the token-based economic model. But it’s always been broken.

By Alan Jacobson, AI Economics Strategist Everyone is building dashboards. Beautiful ones. Clean charts. Real-time graphs. Observability everywhere. But what’s the point if what you are seeing is missing the point? The entire system is built on a unit that cannot see what matters. If you are counting

We don’t need more powerful models. We need models that make dollars and sense.

By Alan Jacobson, AI Economics Strategist Another week, another “step change in capability,” according to Fortune’s Jeremy Kahn. This time, it’s Anthropic’s reportedly more powerful model, Mythos—a system described as a meaningful leap beyond its current generation. The details surfaced in an unsecured draft blog post,

Agentic AI renders tokens obsolete

By Alan Jacobson, AI Economics Strategist This week, stories in The New York Times and The Wall Street Journal highlighted something that’s been quietly building inside companies: employees are deploying AI agents that generate massive volumes of tokens—and massive, unexpected costs. The assumption behind most AI systems is

Agentic AI may render tokens obsolete as a unit of measure

By Alan Jacobson, AI Economics Strategist This week, stories in The New York Times and The Wall Street Journal highlighted something that’s been quietly building inside companies: employees are deploying AI agents that generate massive volumes of tokens—and massive, unexpected costs. The assumption behind most AI systems is

You’ve finally figured out AI at work—Now comes the bill

Agentic AI has destroyed the SaaS seat-based subscription model By Alan Jacobson, AI Economics Strategist This week, stories in The New York Times and Wall Street Journal highlighted something that’s been quietly building inside companies: employees are deploying AI agents that generate massive volumes of tokens—and massive,

9 public cloud companies where AI costs are outpacing revenue and compressing margins

Across 9 public cloud companies, AI-driven cost of revenue is growing faster than revenue itself — with divergence of +300 to +3,700 bps and early margin compression. In several cases, cost growth is 2–5x revenue growth. The AI Cost-Revenue Divergence (ACRD) detection framework compares revenue growth, cost-

“Whoever builds it will own the entire AI pricing infrastructure.”

What is “it?” Jeff Bezos built Amazon on SSL, making it safe to buy online. AI is built on tokens. But tokens fail in agentic workflows, where much of the work happens off-screen and generates no tokens for billing. So the future of AI isn’t in tokens – it’