{"id":254,"date":"2026-05-29T08:42:35","date_gmt":"2026-05-29T08:42:35","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=254"},"modified":"2026-05-29T08:42:35","modified_gmt":"2026-05-29T08:42:35","slug":"tokenmaxxing-is-over-thats-because-it-never-measured-what-really-counts-to-see-roi-from-ai","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=254","title":{"rendered":"Tokenmaxxing is over. That\u2019s because it never measured what really counts to see ROI from AI"},"content":{"rendered":"
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Why AI spend is still not producing ROI<\/h2>\n

But that still leaves the broader question of why this disconnect exists between AI spend and ROI? Certainly explicitly rewarding tokenmaxxing doesn\u2019t help, since it fails to align employee incentives with company goals (see that Amazon example). Azeem Azahar, the author of the Exponential View<\/em> 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 \u201cproductivity J-curve\u201d one would expect with any new, general purpose technology.<\/p>\n

Read more What\u2019s rarer than a unicorn? Anthropic didn\u2019t just join the Series H club, it almost became the first $1 trillion private company ever<\/a><\/p>\n

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 \u201cfiguring it out\u201d 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.<\/p>\n

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\u2019t really change much in terms of the firm\u2019s 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\u2019s 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.<\/p>\n

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Very few firms are getting to Stage 3<\/h2>\n

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\u2019s probably right. Tokenmaxxing is easy. Redesigning workflows is hard. Harder still\u2014and something which Azhar doesn\u2019t talk about\u2014is 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\u2019s about reinvention, not redesign. But most companies are still not thinking big enough.<\/p>\n

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\u2019s 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.)\u00a0<\/p>\n

Ok, with that, here\u2019s more AI news.<\/p>\n

jeremy.kahn@fortune.com
@jeremyakahn<\/p>\n

FORTUNE ON AI<\/h3>\n

Exclusive: Geordie AI raises $30 million Series A to be \u2018air traffic control\u2019 for your company\u2019s AI agents<\/span>\u2014by Jeremy Kahn<\/span><\/p>\n

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Exclusive: Orbital Industries, startup using AI to discover exotic new materials, raises $50 million Series B funding round<\/span>\u2014by Jeremy Kahn<\/span><\/p>\n

Boos, AI-washing, and \u2018low-value human capital\u2019: The psychological traps CEOs are falling into when they botch their AI messaging<\/span>\u2014by Claire Zillman<\/span><\/p>\n

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America\u2019s new AI map shows something surprising: \u2018A lot of normal people are adopting AI\u2019<\/span>\u2014by Nick Lichtenberg<\/span><\/p>\n

Read more Starbucks quietly retired its AI agent just months after deployment after it miscounted coffee shop inventories and slowed down baristas<\/a><\/p>\n

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AI IN THE NEWS<\/h3>\n

CNN sues Perplexity for copyright infringement. <\/b>The news network has sued the AI company, alleging Perplexity\u2019s AI \u201canswer engine\u201d scraped more than 17,000 CNN stories, photos, videos, and other content to provide data for its AI-generated outputs. The suit contends that after negotiations over a licensing deal broke down in 2025, Perplexity continued to appropriate CNN content and falsely implied a commercial relationship with the network that does not exist. CNN is seeking unspecified monetary damages and an injunction blocking further infringement, while Perplexity has pushed back with a terse response from its spokesperson: \u201cYou can’t copyright facts.\u201d This is the first time CNN has sued an AI company. Read more from CNN <\/span>here<\/span>.<\/span>
<\/span>
<\/span>Report: Trump appoints former AG Bondi to White House AI panel. <\/b>President Trump has appointed former Attorney General Pam Bondi to the Presidential Council of Advisors on Science and Technology (PCAST), a White House advisory panel that is influential on AI policy, Axios <\/span>reports<\/span>, citing unnamed sources familiar with the decision. The panel is chaired by former AI czar David Sacks as well as current White House science adviser Michael Kratsios, and also includes tech heavyweights such as Nvidia CEO Jensen Huang, Meta CEO Mark Zuckerberg, and Oracle CEO Larry Ellison. Bondi, who was ousted as AG last month, will be tasked with facilitating coordination between the government and the tech executives on the panel, and will also take on a newly created advisory role focused on national infrastructure. The appointment comes as Bondi is recovering from thyroid cancer, which she was diagnosed with shortly after departing the Justice Department, Axios said, again citing unnamed sources.<\/span><\/p>\n

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IBM and Red Hat announce $5 billion project to patch open source code. <\/b>The initiative, which IBM is calling Project Lightwell, comes as advanced AI models, such as Anthropic\u2019s Mythos, discover more and more critical vulnerabilities in code bases. The project will see IBM and Red Hat deploy 20,000 AI-assisted engineers to create a trusted enterprise clearinghouse designed to identify, test, and patch security vulnerabilities in open-source software which is heavily-used by the majority of large corporations for many critical functions. Enterprises will access the service through commercial subscriptions, receiving validated, production-ready patches they can plug directly into their software supply chains. A cohort of major financial institutions\u2014including Bank of America, Citi, Goldman Sachs, Morgan Stanley, Visa, and Wells Fargo\u2014are already participating as early adopters. You can read more from the <\/span>Wall Street Journal<\/span><\/i> here<\/span>.<\/span>
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Snowflake inks $6 billion deal to use AWS chips<\/b>. The <\/span>Wall Street Journal<\/span><\/i> reports<\/span> that data management giant Snowflake has signed a $6 billion, five-year deal to use Amazon Web Services’ Graviton CPU chips, making Snowflake one of AWS’s largest CPU-based computing customers alongside Meta and Apple. The deal reflects a broader surge in demand for CPUs driven by the rise of AI agents, which require large numbers of the processors to orchestrate and sequence their computing tasks. CPU makers including Intel, AMD, and Arm Holdings have all seen rising sales and share prices in recent months as agentic AI has gone mainstream.<\/span><\/p>\n

Robinhood rolls out agentic AI trading features.<\/b> Robinhood has unveiled two new products\u2014Agentic Trading and an Agentic Credit Card\u2014that allow customers to connect third-party AI assistants, such as Anthropic’s Claude or the coding agent Cursor, to carry out investing strategies or spending tasks with minimal human involvement. For trading, customers can establish a dedicated agentic account entirely separate from their main portfolio, directing the AI to build a diversified portfolio from scratch or rebalance holdings as opportunities arise. For spending, agents can be given access to a virtual Robinhood Gold credit card to make automatic purchases such as snagging concert tickets or buying products when prices drop below a set threshold. Safety guardrails include isolated accounts with limited funds, spending caps, real-time activity feeds, and a one-tap kill switch\u2014though Robinhood cautions that AI agents can err or behave unexpectedly, and that users bear responsibility for monitoring their accounts. Read more <\/span>here<\/span> from CNBC.<\/span>
<\/span><\/p>\n

EYE ON AI NUMBERS<\/h3>\n

$9 billion<\/b><\/h2>\n

That\u2019s the amount of money the White House is giving U.S. intelligence agencies to help them establish their own computing clusters of sophisticated Grace Blackwell superchips from Nvidia. The chips are needed so that U.S. intelligence agencies can run their own copies of frontier AI models, such as OpenAI\u2019s GPT-5.5, and possibly Anthropic\u2019s Mythos, as well as future AI models, on their own classified networks. These state-of-the-art models require a large number of specialized AI chips to run or to fine-tune. The Pentagon has recently signed deals with OpenAI, Google, and xAI that allow their AI models to be used in classified networks. The National Security Agency is also believed to be using many of these models as well as those from Anthropic, which the Trump administration has sought to bar from being used by government agencies after the company refused to accede to the Pentagon\u2019s insistence that it allow its models to be used for \u201cany lawful purpose.\u201d The NSA is reportedly still working on some kind of arrangement that will enable it to continue to use Anthropic\u2019s model. Although the full terms of all the contracts are not public, it is believed that in some cases the companies are providing versions of these models to the government that contain fewer guardrails than the version they release to the general public. Read more from the <\/span>New York Times<\/span><\/i> here<\/span>.\u00a0<\/span><\/p>\n

Read more The \u2018imminent\u2019 oil crisis isn\u2019t at the pump\u2014it\u2019s under your hood<\/a><\/p>\n

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AI CALENDAR<\/h3>\n

Fortune Brainstorm Tech, Aspen, Colo. Apply to attend\u00a0here.<\/p>\n

VivaTech, Paris.<\/p>\n

International Conference\u00a0on<\/span> Machine Learning (ICML), Seoul, South Korea.<\/p>\n

AI<\/span>\u00a0for Good Summit, Geneva, Switzerland.<\/p>\n

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Ai4 2026, Las Vegas.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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