Meta engineers reportedly had an internal leaderboard called Claudeonomics. It is available to all 85,000 employees, and 60 trillion "AI tokens" have been burned in 30 days. The company even created digital badges like "Token Legend" and "Cache Wizard." Some employees are reportedly leaving agents running overnight to climb the rankings.

On a recent podcast, Jensen Huang said something that sounds wild but landed like a warning: if a $500,000 engineer isn't burning at least $250,000 in tokens a year, he'd be "deeply alarmed."

At the same time, economists are calling a paradox.

A recent NBER study of 6,000 executives across the US, UK, Germany and Australia found that nearly 90% of firms said AI has had no impact on employment or productivity over the past three years. Average usage was 1.5 hours a week. Apollo's chief economist Torsten Slok invoked the ghost of Robert Solow: "You can see AI everywhere except in the macroeconomic data."

So which is it? Engineers burning $4m of tokens a month to ship features no company has ever shipped before? Or thousands of CEOs reporting no productivity gain at all?

The answer is both.

And the gap between those two things is where your career, company, and prospects get made or broken for the next 10 years.

Here's the equation every company now lives inside:

Model this with whatever outcome you want — more leads, faster deploys, new products shipped, more uptime. Tokens are the fuel. The operating model is the organization you've built to burn them.

The Meta engineers burning $4m in tokens and the CEOs reporting no productivity gain have one primary distinction: They’re running different operating models. 

One is built to convert tokens into features. 

The other bought ChatGPT seats and called it transformation. AI adoption is alone is not effective. You don’t get productivity from buying seats like you did with SaaS. You need an effective operating model that produces a faster speed to outcome. Most companies don’t have this.

That explains the AI productivity gap.

AI adoption theatre is over.

The rest of this is an unpacking of how you get towards the right operating model.

Why change your operating model, and pivot the whole company if AI is hype? 

In the NBER study. Torsten Slok and Acemoglu's model shows 0.5% growth over a decade from AI. They're all saying the same thing: the average firm isn't getting anything out of AI.

And they're right. About the average firm. And that’s a dangerous thing. The cash flow statement itself is not wrong. It is just looking at the fuel bill before the AI factory has been designed.

Demis Hassabis, co-founder of DeepMind and Nobel laureate for AlphaFold, said it cleanest:

The criticism of AI token usage will always sound smart, especially when grounded in backward-looking data. But if you look inside the companies getting the benefit from AI, it becomes clear that the long term belongs to a different population.

Meta looks at a distance like it is speed-running its own unprofitability. They burnt roughly $80bn on VR and mostly gave up. They've committed to nearly 10GW of data center capacity and are tracking towards $100B in compute buildout. And then they hand all of that to 85,000 employees and have them compete on a leaderboard.

The top user burned 281 billion tokens in 30 days. That's roughly $4.2 million at Opus list prices, for one employee.

Clearly ridiculous, bubble-like behavior that is unsustainable (Meta has reportedly closed the leaderboard since its details leaked). But if you just revel in the ridiculous, you miss the signal.

Input metrics are always ridiculous until the output metric catches up.

Token spend will go the same way. Not as an end in itself. As the input that creates shipping velocity and revenue per employee. Tokens are the proxy — the visible, measurable thing you can put on a leaderboard that correlates, imperfectly, with engineers doing the thing.

If you can ship more code, and critically, more features to production, you can win users and new revenue. The canonical example is Anthropic. Anthropic shipped 120+ features in the first 90 days of 2026 — more than one a working day across Claude Code, Cowork, API, and models.

Anthropic is the cleanest example of the loop: more shipping creates more usage, more feedback, more revenue, and more permission to keep shipping. This is a feature velocity we've never seen before, and it is giving them a competitive advantage in the most competitive battle technology has ever seen.

Anthropic’s Cat Wu told the Lenny Podcast that Anthropic itself has gone from 6 months, to 1 month to between 1 week and 1 day for new product releases.

Feature velocity is a power law. The companies that ship products faster grow their revenue faster, grow market cap faster, and attract investor dollars faster. Everyone else compounds in the opposite direction. 

The CEO of HubSpot has a name for the upside of this equation: OutcomeMaxxing. What is the maximum outcome you can generate for the tokens you put in?

But that forces us to ask a harder question: How do you know you're heading in the right direction? What's the equation inside your company that measures the inputs that create the outputs from AI token usage? Elon Musk famously measures dollars per kilogram to orbit.

Outcomes are lagging indicators. So the first management problem is observability: can you see which tokens became shipped work, and which tokens became slop?

New data from Ramp shows costs are spiking across the industry:

Most companies haven’t figured out how to turn token use into effective outcomes. It’s just burn more and hope for the best.

And the best tell that this is a real problem, not marketing fiction, is in the Ramp engineering team's own account of discovering it:

It is 4:23 p.m. on a Tuesday in early spring and a latency alert fires at Ramp HQ. The system is slowing but every downstream dependency is within SLA, and the error rates are stable. So the team audits their own token volume and finds the culprit: a software package upgrade caused a Gemini model to generate phantom reasoning tokens by default.

Nobody asked for chain-of-thought. The model produced it anyway. And billed for it.

Ramp says AI token overspend is much worse than SaaS sprawl — at least there you had a monthly bill to check. With AI tokens, what do you check? What’s the dashboard? So Ramp built what they needed: an ability to measure token usage directly, the same way you can measure and manage SaaS spend.

When you can see the token cost, you can cut it by 20% in an afternoon.

Two things are now true:

So the NBER study is right that most firms aren't getting productivity gains from AI. They're using it 1.5 hours a week. They bought ChatGPT seats and called it transformation. The BCG "AI brain fry" study found that workers using four or more AI tools actually report lower productivity than those using three or fewer. 

Unmeasured AI tool use proliferation is a drag.

AI adoption is not effective AI adoption. In most cases, it is the opposite.

The thing observability gives you isn't just "cut waste." It's the ability to tell which teams are burning tokens productively and which are running agents in idle loops, and who’s just using ChatGPT to produce slop instead of doing the work. 

Tokenmaxxing for status is waste. 

Tokenmaxxing for shipping is the moat. 

Freda Duan from Altimeter has a useful equation for why AI spend explodes:

The first half is adoption. As more users perform more tasks and the company builds more agentic workflows. And as labs burn more tokens per task, multiplied by the cost of each token, AI spend increases. It’s the fastest-growing line item for most high-growth companies.

Are those tokens being burned worth it? What is the fundamental value they create? Here it would be lovely if we could just multiply the number of tasks completed by value per task:

But we all know quantifying the value of a task can be more art than science. Still, companies will begin to hunt for ROI from token spend, and it will change how vendors price too.

In fact, it has already started. Salesforce now sells Agentforce Flex Credits, with pricing that looks less like SaaS seats and more like paid units of work: credits × cases × users. IBM Bob has Bobcoins. Every vendor is inventing a currency for machine labor. Which looks to me like the market trying to price something SaaS never had to price directly: work done. Labor.

Tokens are only expensive if the task they complete is cheap.

Without observability, it’s impossible to know how many tokens were spent on what task. With observability, at least you’re grounded in data. Once you can see where the tokens go, you can ask the question Jack Dorsey asked.

Look at Block.

In February, I wrote about Block cutting 40% of its staff and the stock ripping 24%. I asked whether it was AI-washing, genuine restructuring, or both. The answer has since filled in.

Dorsey went on Sequoia's Long Strange Trip podcast in April and described the new ideal architecture of the company in a post-AI world:

Discourse and reactions are over-indexed on the cuts, and under-indexed on the implications of removing middle management as a coordination layer.

The human cost is real. The management lesson is also real.

Block is asking a question most large companies are avoiding: If AI can observe work, route information, summarize decisions, expose bottlenecks, and give every IC leverage, how many coordination layers does the company actually need?

Middle management exists to create visibility, route information, translate priorities, and escalate decisions. AI-native tooling starts to automate parts of that coordination layer.

Once every engineer has tokens, every team has dashboards, and every decision has an audit trail, you need fewer people whose job is merely to know what is happening and pass it upwards.

That does not make layoffs good. It does make the question unavoidable.

Tokens are calories for the AI firm. Some companies are eating more and getting slower. More tools. More subscriptions. More agents are looping in the background. More slop. Some companies are metabolizing those calories into muscle. Faster product cycles. Tighter feedback loops. Higher revenue per employee. Less coordination drag.

You should be asking yourself how your company and career change in a world where:

The ability to go from idea to in-production to iterated-in-production faster than your competitor. AI compresses the time toward zero. 

The NBER study found 90% of firms report no productivity impact from AI over the past three years.

Ramp found the top quartile of AI spenders on its platform more than doubled revenue since 2023.

Both are true.

One is the average firm.

The other is the firm rebuilding itself around token-to-outcome conversion.

The difference is whether anyone in the building can answer one question:

What outcome did the tokens buy?

If someone can, you have an operating model.

If nobody can, you have a subscription.

ST.