2026-05-28 04:24:34
I had tea with a senior exec at a well-known public tech company last month. She has about a thousand engineers working for her, and nearly every one of them works with Claude Code. They are producing more lines of code, submitting more pull requests, getting more done. Productivity is up for individuals, but she doesn’t seeing proportional gains at the organization level. As she put it to me: “one plus one plus one plus one equals one-and-a-half.”
She is not alone. Uber’s COO Andrew Macdonald went on record this week saying that the relationship between AI investment and results is not there yet:
I think maybe implicitly there is more that is getting shipped, but it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25% more useful consumer features.’
AI has delivered something. I have felt it; my team has felt it; most users have felt it, which is why we keep returning and using more of it. Two years ago, only a dozen Anthropic customers were spending over $1 million a year on Claude1; today, more than 1,000 do. More impressively still, Anthropic’s average corporate customer increased their spend by a factor of five in the past year.
But in more than three years since ChatGPT’s release, only 27% of executives say AI has met their ROI expectations. What do we make of the other 73%? Could their expectations be too high? Or too low? Do they even have the right class of expectations?
In a way, we can’t tell, but we can feel the vibes. And the vibes are that individual workers are getting faster and more productive. But for now, those individual gains from AI do not compound into firm-level ROI.
That is the puzzle we are going to solve in today’s essay.
Let’s go!
Back in 1987, Robert Solow famously pointed out that you could see the computer age everywhere but in the productivity statistics. He was right for the next few years, then wrong. Paul David realized this was a common problem with general-purpose technologies. They systematically depressed measured productivity in their early stages because companies needed to invest in all sorts of hard and soft know-how before the gains appeared. calls this the productivity J-curve: general-purpose technologies are a drag in their early years because firms have to make complementary intangible investments before the gains materialize.
Paul David’s 1990 paper on electrification is the canonical account of why a general-purpose technology can sit inside firms for ages before we see the results. The story he tells – building on Warren Devine’s earlier survey of the shift from shafts to wires – runs through three phases. And those phases map onto where AI is now.
Stage 1: The lightbulb
One of electricity’s first factory roles was the simplest – lighting. A brighter floor was safer than one lit by gas, and cleaner than one lit by oil. But work still flowed through the same sequence of people, machines, shafts and belts. Electricity had improved the workers’ immediate environment, but it did not change the factory’s operating logic. When ChatGPT was first released, it did something similar. It increased how quickly we could write emails; individuals sped up on some tasks, but the firm did not. This is Stage 1 of AI transformation: the lightbulb.
Most of the AI products we see today are all about individual productivity. Yes, there are enterprise plans for ChatGPT and Claude and whatever else. But the unit of work is still the task that the individual has to hand. The enterprise plan just lets them quickly access the corporate skills repository.
Stage 2: The group drive
The next stage of electricity adoption in factories focused on cost savings rather than productivity. Louis Bell wrote in 1891 that large central steam plants could be five to seven times more coal-efficient than small engines. Factories bought power from central stations and installed electric motors to drive their existing shafts and belts.
Then, a professor of electrical engineering, F. B Crocker, and his colleagues found another application. Electric power frees the shop floor from the shafting. Mechanical power belts, tools and oil, the mess of the factory floor, could all be moved; machines no longer had to be arranged in parallel lines beneath shafts.
The open question was how many motors a factory needed: one per tool, or one per group of tools? The latter became known as group drive. It was a single motor that powered a cluster of machines via a shared shaft.
Group drive preserved existing layouts, reused sunk capital, needed fewer motors, and gave many of electricity’s benefits without the cost of rebuilding the plant. It was cheaper and easier than the alternatives, so it won and dominated factories through the 1890s, 1900s and into the First World War.
AI agents are better than chatbots. They can handle whole workflows rather than single tasks. But, like group drive, they are attached to the existing organizational geometry. In the case of electricity, this was the shop floor layout. For AI, it’s the web of processes designed by the companies well before anyone knew what an LLM was.
An AI recruiting agent speeds up a process that was previously done by humans and an applicant tracking system. Their recruiting pipeline may shrink from weeks to hours. A customer service agent takes on more tickets than the support team could before. The logic behind these examples is essentially cost-saving – fewer support tickets need humans, recruiters screen more candidates with the same headcount and marketers create more variants without additional staff. The firm isn’t making consequential decisions any faster. This is Stage 2, the group drive, machines turning faster on the same shafts.
Stage 3: The unit drive
It was only later, once the organizing logic of the factory moved to throughput from cost-saving, that the deeper value of unit drive, one motor per machine, became clear. In 1913, Ford’s Highland Park plant decided to orient machines and workers around the workflow rather than the geometry of shafts and belts. Over the next decade (1919–1929), as more factories adopted unit drive, US manufacturing labor productivity grew by 5.4% a year.
The pattern for AI will mirror the pattern David spotted for electrification. Stage 1 speeds the individual, Stage 2 a workflow and Stage 3 the firm.
You’ll be familiar with other maturity models, like Carnegie Mellon’s Capability Maturity Model. These are useful, but they treat each stage as a monolithic capability tier. A capability tier tells you how well a firm does a “fixed thing.”
Our ladder is different. The stages are organizing logics. The logic is the goal the firm is pursuing with the technology. A workshop that installed electric lighting was no worse than Ford’s factory, but it was pursuing a different goal: a safe way to illuminate the workshop floor. You didn’t get to an assembly line by adding more lightbulbs. Factories were pursuing cost savings and everything was organized to fit that goal, from layout to staffing to supplier relationships.
A maturity model will often tell you to do more of what you are doing to move forward. We reckon that what matters is what you are trying to do. And our stages hint at why companies deploying AI might get stuck. A firm does not graduate stage by stage on every axis. An individual developer who is 50% more productive with AI tools but must submit to traditional review cycles will find themselves in a queue. The product team that can prototype faster than ever but needs to wait for sign-offs, will build up a backlog of features. A sales team, supercharged by AI-assisted proposals, may close deals faster than legal can review them.
As firms move towards Stage 2 on execution, those managerial layers, how decisions get made, stay where they were. We call that mismatch congestion. It is the buildup of individual and then team outputs waiting for somewhere to go. To become a Stage 3 firm, you need to rebuild around decision speed between workflows, not the speed of individual workflows. If you are already suffering from congestion, adding more workflows and more output to a blocked decision pipeline will only make things worse.
We discovered this at Exponential View. We could prototype, even build, new features faster than our usual processes for releasing them. We’re figuring out how to fix that – and we’re only a team of eight, so we are sympathetic to the challenges of larger orgs.
To speed up those decisions, AI needs to be able to take them. The role that managerial oversight has may be the thing preventing the cycle time from shortening. AI will need a new cognitive layer that allows the firm to interpret signals without a worker as an intermediary. If a customer sends a feature request to customer support, it typically passes through a support agent and then a product manager, who decides whether it belongs on the roadmap, before a developer codes it weeks or months later. With AI, the signal is observed by an agent. It orients against the roadmap and the codebase. It decides whether the feature is worth drafting. It builds it. Hours, not weeks. This is the Stage 3 firm, the unit drive.
2026-05-25 15:00:52
Hi all,
Last week, we explored what tokenmaxxing means for CFOs and how firms can buffer unexpected AI costs.
We go further today to show you why AI bills are hard to forecast today and what will happen as we crack that problem.
We estimate that the number of tokens processed per quarter has grown by around 17,000x over four years.1
Token prices have collapsed during this time. Demand for machine intelligence is highly elastic, meaning that as prices fall, consumption increases by more than the decline in price.
One reason is that cheaper tokens have made agents economically viable. At the same time, agents use tokens at rates that are orders of magnitude higher than those of chatbots for single-turn queries. That shows up as the total tokens processed per output token—advanced models do a lot of processing below the surface that a user doesn’t see.
A lot of this growth is driven by China’s domestic demand and its model providers, especially ByteDance and Alibaba.
When you use an AI agent, the final result you get is really just a summary of all the work the agent has undertaken. There may be dozens of tool calls to browse the web or load up a file to check and validate the work it has done. All of these are steps consume tokens: they become hidden multipliers.
The first of these is token amplification.2 A coding agent that operates over 10 turns might need to re-read its full context every turn. That repetitive reading of context could use as many as 55x more tokens than a single-turn query for the same task.
Actual active inference is probably only 15-20% of the total token consumption. The rest is invisible work that you, as a user, and possibly the company paying for it all, haven’t modelled.
The long tail of tool calls
Agents make anywhere between five and twenty-five tool calls per task. And each call adds more context, tokens and API costs. It also increases the likelihood that the model will need to retry the task to get it right.
2026-05-24 10:44:54
Consistently insightful, with no agenda beyond understanding how technology shapes our world. — Vincent N., a paying member
American college graduates are angry. In my Saturday column, I write:
The narrative around AI has been about promises for tomorrow, but sacrifices today. […] Engaging with this resistance won’t be effective if AI leaders throw distant fantasies at people dealing with muddy water coming from their kitchen tap today.
An OpenAI reasoning model solved an 80-year-old open problem in discrete geometry. The interesting point here is that the reasoning model found an unexpected bridge between two different fields of math. Discrete geometry and algebraic number theory are meaningfully separate cultures. An expert in one field might know something about the other, but not at the depth required for breakthrough research.
The human mathematicians who validated the proof reckon that the connection the AI found was both unexpected and non-obvious. In a sense, it’s reminiscent of AlphaGo’s Move 37.
Because AI systems can span different domains of knowledge, my hunch is that one of the biggest dividends we will see from them in these fields will be connecting domains that live in isolation because of the structure and specialization of modern science.
But of course, another way AI will impact research is by speeding up the scientific method: the full hypothesis-experiment-analysis loop.
Robin, a multi-agent system, completed the full cycle of hypothesis generation, experimental selection, data analysis and refinement. Human scientists ran specified experiments in a wet lab. The outcome was the identification of an existing drug that could be repurposed to treat macular degeneration.
2026-05-23 18:56:28
Anthropic will turn a profit this quarter, two years ahead of schedule. This is off the back of extraordinary revenue growth. In the second quarter this year, the company is likely to gross $10.9bn in revenue, more than its entire lifetime revenue to date. Operating profits should weigh in at $559 million. It’s an extraordinary achievement and one set to feature in the annals of business history.
But the backlash against AI is growing ever stronger.
Earlier this week, Google’s former boss, Eric Schmidt, was soldily booed at a college commencement address
At another college commencement, boos shocked the commencement speaker who proselytized AI. Joanna Stern interviewed one of the college students, Houda Eletr:
Joanna: Do you use AI?
Houda: Throw it away….if you create this big thing that is supposedly going to save a human’s life, then you should give it to me in a human way.
A reader from New York sent me this photo:
There will be more booing.
Consider data centers and their local impacts.
At a hearing, American politician Alexandria Ocasio-Cortez showed a jar of mud-brown water collected from a tap in Georgia, shortly after Meta started building a data center.
The narrative around AI has been about promises for tomorrow, but sacrifices today. AI leaders have warned that the world could be destroyed by this technology.
They warned that large numbers of people, particularly graduates, would lose their jobs. But everyone knows the founders are making more money than Croesus. And now, there is construction, a physical manifestation, a real inconvenience, something you can point at.
And for what? So we can explore the stars using intelligent spaceships and ‘colonize the galaxy’ according to Demis Hassabis. (Demis is by far the most sympathetic of all the AI leaders, but even his messages feel out of touch.)
Engaging with this resistance won’t be effective if AI leaders throw distant fantasies at people dealing with muddy water coming from their kitchen tap today.
Neither will facts. The US energy system is getting a much-needed boost in investment to upgrade energy generation and the grid to more modern technologies. I am certain that in a decade we’ll look back on this as the time when the US grid prepared for the 21st century, becoming more resilient, efficient and greener. But that well-informed argument is completely irrelevant in the face of today’s resistance.
Fantasies or facts, these are all fodder for the technorat. Globalization or NAFTA made a graph on some economists’ slide deck go up. Grid renewal and ‘productivity’ excited the same neurons, but don’t speak to the heart.
2026-05-18 18:53:34
Hi all, happy Monday.
This week’s Monday edition looks at one of the fastest-growing, least predictable costs in the AI stack: tokens.
Let’s go!
Vibe-coded for this edition: Play our token budget word quiz and win a prize.
Uber CTO Praveen Neppalli Naga shared last month that his 5,000 engineers had depleted their entire 2026 token budget in just four months. So has ServiceNow.
Agentic adoption was bound to drive this kind of demand and the finance has to respond. CTOs are increasing their tech budgets this year – nearly 50% say their budgets are up by 10%. (As a side note, we believe that 10% is marginal given the token explosion we are experiencing.)
A token explosion is great news for engineering teams, but a real headache for CFOs who signed off on modest pilots months ago. 71% of companies exceeded their AI budgets in 2025 and over half of the surveyed finance bosses say cost management is their greatest concern.
Partially because AI costs can be highly variable and tricky to manage. The cost is not fixed as it was with good, old fixed-seat SaaS.
Exponential token use is diffusion in action. In the US, the average monthly spend on AI by large enterprises grew 36% to $85,000 between 2024 and 2025.1
Labs in China told us that coding is where most labs are throwing their resources right now, their P0 in engineering terms.
2026-05-17 11:32:49
This is the most relevant newsletter I receive in my inbox every week. – Martin T., a paying member
Hi all,
Suffocated by export controls, Chinese AI labs have developed an efficiency moat that may define the AI market’s development over the coming years. My report from a trip to China.
In this Sunday’s edition:
Anthropic powers into Main Street as Microsoft and OpenAI consciously uncouple
Who should really control your AI?
Voltaire, the Enlightenment entrepreneur
Anthropic’s CFO Krishna Rao gave a fascinating interview on Patrick O’Shaughnessy’s podcast. It’s a cornucopia of insights: for one, the firm’s enterprise customers increased their spending by a factor of five over the past year. That 90% of Anthropic’s code is written by Claude Code is well known, but Krishna says that 90% of finance reporting is now AI-driven as well. Cowork is growing faster than Claude Code (which grew from zero to $1 billion in just six months). Krishna’s been on quite the ride himself – he joined Anthropic two years ago when revenues hit $250 million and now presides over annualized revenues approaching $50 billion – a fivefold increase in as many months. Recommend a listen.
Elsewhere: Anthropic leads OpenAI in business adoption, according to Ramp.
OpenAI and Microsoft are no longer exclusive. OpenAI now gets to play with other resellers and Microsoft gets better access to the startup’s AI, as well as drops complicated revenue-sharing arrangements.
It’s been a profitable marriage, if not an entirely happy one.
Microsoft’s $13 billion OpenAI investment has yielded over $30 billion in revenue in the two years since ChatGPT’s launch. OpenAI was the biggest customer of Microsoft Azure’s AI business, ploughing $23 billion into the hyperscaler. (Our estimates suggest up to 60% of Azure’s AI revenue came from OpenAI). Microsoft’s operating margin expanded by 3.9% over the last three years.