Hello and welcome to Eye on AI. It’s Jeremy here, filling in for Sharon who is on vacation. In this edition…CNN sues Perplexity…IBM and RedHat form $5 billion bug patching project…Snowflake signs a $6 billion deal with AWS…and the White House gives U.S. intelligence agencies $9 billion to build their own AI chip cluster. Just a few weeks ago, it seemed that ‘tokenmaxxing’ was all the rage inside many companies. The idea was: if you wanted to find out which employees were being most innovative in deploying AI agents, you should track their token usage. (Tokens are the units of data that AI models process; a token is equivalent to about a word-and-a-half of English language text.) The more tokens expended, the more productive that employee’s AI agents were, or at least, the more AI-forward and innovative that employee was trying to be. That was the idea anyway. Meta, Amazon, OpenAI, and many other companies even established formal or informal leaderboards of token usage and encouraged engineers and developers to compete to see who could use the most tokens in a given period of time. Of course, Goodhart’s Law still holds (it posits that any measure that becomes a target, ceases to be a good measure) and tokenmaxxing had some predictably perverse results. At Amazon, the Financial Times reported, some employees spun up AI agents to complete wholly meaningless or unnecessary tasks just to keep up their token usage stats, which were now being used by managers to assess employee performance. Also, all those tokens are hardly free, and some companies have gotten sticker shock from their Anthropic and OpenAI bills. So, now many companies seem to be pulling back from the tokenmaxxing ethos and even limiting which employees can use third party AI agents, at least those that use the most advanced AI models as the “brains” inside the agentic harnesses. Meta took down the informal tokemaxxing leaderboard its employees had created. Microsoft has cancelled Claude Code subscriptions for employees in several key product divisions, according to reporting from The Verge. Uber said it had burned through its entire 2026 “token budget” in just the first four months of the year, in part due to high usage of Claude Code. Meanwhile, Salesforce CEO Marc Benioff has said his company’s Anthropic bill will be about $300 million this year and that he wished there were a “smart router” that could determine which queries actually required the most capable, and most expensive, models and which could be handled by smaller, less-capable-but-capable enough, cheaper alternatives. Many executives are also saying token spending isn’t translating into firm-wide return on investment. Uber Chief Operating Officer Andrew Macdonald told a podcast last week that the ride-hailing firm has been struggling to connect the boost in the productivity of some workers with any company-wide impact. “If you‘re not actually able to draw a direct line to how much useful features and functionality you’re shipping to your users,” he said. “[The token costs are] harder to justify.” The net result is that the days of tokenmaxxing are over.
Why AI spend is still not producing ROI
But that still leaves the broader question of why this disconnect exists between AI spend and ROI? Certainly explicitly rewarding tokenmaxxing doesn’t help, since it fails to align employee incentives with company goals (see that Amazon example). Azeem Azahar, the author of the Exponential View newsletter, who is as good a thinker on the economic and business impact of AI as anyone, argues that the current AI productivity paradox may simply be the expected “productivity J-curve” one would expect with any new, general purpose technology.
Unlike with a technology designed to make a particular process better, which can often have immediate positive productivity impacts, it often takes considerable time for people to figure out how best to deploy a general purpose technology. During this “figuring it out” period, productivity can actually fall rather than increase. This is because companies need to spend time and money experimenting with how to use the new technology, often without seeing a positive bottom line impact. Only later, once people figure out the optimal ways to redesign business processes around the new tech, does productivity experience a sudden acceleration. The classic example of this that Azhar goes into some depth on is the invention of electricity and its impact on manufacturing. The first thing factories did with electricity was to replace gas lighting with electric lighting. That was a cost savings, but didn’t really change much in terms of the firm’s output. (And there was some cost in installing the lights and wiring the factory, which even muted those savings.) The physics of steam meant that pre-electric factories were built with a central engine that powered many, or even all, of the factory’s equipment off a single drive shaft. So, the second thing factories did was replace the large central steam engine with large electric motors, which they still used to run clusters of machines off central drive shafts. This was cheaper than trying to reconfigure the whole factory. But it turned out to not be very efficient or operationally cost-effective. Productivity gains in one part of the production floor often simply caused bottlenecks elsewhere on the assembly line, and overall the factory saw little gain. It was only when companies began electrifying individual machines and reorganizing the entire layout of factories, that firms saw big productivity boosts.
Very few firms are getting to Stage 3
Azhar predicts that the same thing will happen with AI, but that most firms are sort of stuck in stage one or stage two of this evolution. I think he’s probably right. Tokenmaxxing is easy. Redesigning workflows is hard. Harder still—and something which Azhar doesn’t talk about—is rethinking entire business lines, i.e. what products or services the firm sells, and even business models. This gets at the fundamental purpose of the company. This is where the really big value from AI is. It’s about reinvention, not redesign. But most companies are still not thinking big enough. Because most existing businesses are being too small minded about how they use AI, AI-native firms have a great opportunity right now. They will be able to move faster and to steal significant market share from incumbents before the legacy companies can effectively respond. It’s much easier to invent a new business from the ground up than it is to try to gut-renovate an existing one. (This is also why it may be more difficult than many private equity firms hope to simply add a dash of AI to their portfolio investments and hope to flip the businesses at higher valuations.)
Ok, with that, here’s more AI news.
Jeremy Kahn [email protected]@jeremyakahn
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