This article is crafted based on publicly accessible information and is intended purely for the purpose of information exchange, not as any form of investment advice.

On July 24, 2026, a seemingly ordinary headline in The Wall Street Journal signaled a pivotal moment: U.S. corporations are collectively halting their lavish spending on large AI model calls.

Over the past two years, companies prided themselves on 'maximizing token usage.' Internal leaderboards crowned those with the highest API call costs as pioneers of digital transformation. Today, this narrative has completely reversed—frugality has become the new norm, and thriftiness the new virtue.

Cursor, a company soon to be acquired by Musk for $60 billion, has coined a precise term for this trend: tokenomics, the economics of token usage. Its former investment banker and current on-site CTO, Mike Saeks, encapsulated the essence of this shift with a vivid metaphor: 'Using the most powerful model for daily tasks is akin to driving a Lamborghini to the grocery store for milk—that car is designed for the racetrack.'

All indicators suggest that this is not just a witty remark but the commencement of a reassessment of the AI bubble narrative of the past two years.

01 From Conspicuous Consumption to Calculated Survival

The rapidity of this shift has even caught the most astute observers off guard.

Just months ago, companies were competing to outdo each other with exorbitant AI bills. 'Token maximization' was a term deeply ingrained in Silicon Valley's ethos: it was about packaging waste as progress and misinterpreting spending as a sign of faith. The prevailing logic then was that higher usage indicated deeper AI integration, and steeper spending signaled clearer technological leadership.

Now? Marty Kausas, CEO of AI customer service platform Pylon, puts it bluntly: 'I see zero customer loyalty. It's a real bloodbath out there.'

Pylon itself has benefited from this 'bloodbath.' So far this year, it has secured approximately $1.6 million in token credits for free from one AI vendor, $65,000 from another, and $10,000 from a third. Kausas's summary is cold and precise: AI companies are frantically offering discounts to customers to prevent them from leaving.

This abrupt change in tone is driven by one key factor: an oversupply of AI models.

While OpenAI was still boasting about GPT-5.5's reasoning capabilities and Anthropic was hyping Fable's ethical alignment, companies uncovered a truth that both angered and relieved them: the vast majority of tasks simply do not require the most expensive, cutting-edge models.

Cursor conducted an experiment: building a web browser from scratch. Using GPT-5.5 throughout cost just over $10,000. Using Cursor's own Composer model paired with Anthropic's Opus 4.8 cost $1,339—a significant difference.

Mike Saeks's observation cuts deeper: 'In the past, the best model for a task changed every few months. Now, it feels like it changes several times a week.'

Hidden within that statement is a bombshell of information: the pace of commercialization of model capabilities has outstripped all expectations. The practical usability gap between cutting-edge and ordinary models is narrowing by the week. Corporate CFOs' Excel spreadsheets have sensed this trend long before any technical white paper.

02 China's Models: A 'Flanking Maneuver'

Silicon Valley's AI cost-cutting movement validates a more fundamental prediction: the future of AI models is commoditization.

This means that AI models themselves will become cheaper and more interchangeable, with true value lying in sustained intelligence breakthroughs rather than in model sales.

Chinese companies are precisely the most aggressive suppliers of these commoditized models. Names like DeepSeek, Kimi from Moonshot AI, Zhipu's GLM, MiniMax, and Mimo are appearing on U.S. corporate procurement lists at an alarming rate.

Barry McCardel, CEO of AI data analytics platform Hex, reveals that in the past two weeks, roughly 50% of clients have integrated Kimi from Moonshot AI into their workflows.

His attitude reflects the consensus among a new generation of corporate decision-makers: 'Any lab could release a frontier-level model at any time. We need to stay flexible.'

Note that word: flexible.

In the past, choosing an AI vendor was akin to a near-marital commitment. Selecting OpenAI or Anthropic meant long-term binding, deep integration, and a technology stack that was difficult to migrate.

Now, marriage has become dating. Companies freely switch between models, using the priciest for planning, cheaper ones for execution, open-source options for customization, and Chinese models to fill gaps.

Legal AI startup Harvey's approach is a textbook demonstration: it uses China's GLM-5.2 for routine tasks, equipped with a tool to judge if a task is too difficult; if so, it automatically calls in Anthropic's Fable 5 to take over.

President Gabe Pereyra sums it up casually: 'We partner with all models. We're exploring a bunch of similar schemes to slash costs while maintaining or boosting performance.'

This is the flanking maneuver unfolding in U.S. corporate circles.

On the front lines, OpenAI and Anthropic continue to burn money to push the intelligence ceiling. On the flanks, Chinese companies are gobbling up market share layer by layer with open-source or low-cost models.

03 The Cross-Contamination of Two Narratives

Zooming out, deeper undercurrents are converging.

Over the past two years, the U.S. AI industry has constructed dual narratives.

The first is the Holy Grail narrative represented by OpenAI and Anthropic: pricier research, larger models, and every breath closer to Artificial General Intelligence (AGI) justify investment at all costs. This narrative underpins their astronomical private valuations and looming Initial Public Offering (IPO) expectations.

The second is the Fear Of Missing Out (FOMO) narrative among corporate clients: fear of missing the AI revolution, fear of competitors pulling ahead, leading to frenzied purchases of top-tier model services and soaring token spending. This narrative fuels AI companies' skyrocketing revenue curves.

Now, cracks are appearing in both narratives simultaneously.

Cursor's experimental data, Pylon's zero-loyalty declaration, Hex's 50% client shift to Chinese models—these fragments point to a single conclusion: corporate clients are waking from FOMO. They realize the Lamborghini is gorgeous, but they're just going to the grocery store.

This awakening directly undermines the Holy Grail narrative. If the most profitable application-layer clients start diverting, start using cheaper alternatives, the flywheel of OpenAI and Anthropic—charging premium fees for advanced models to fund R&D—will begin to stall.

The irony is that this movement coincides with the U.S. government contemplating restrictions on Chinese AI models.

OpenAI and Anthropic executives publicly accuse Chinese startups of copying their technology, with some in the Trump administration even proposing a total ban on Chinese models.

Yet corporate actions are far more honest than policy rhetoric: Microsoft is already considering integrating Chinese models like DeepSeek into its platform. Last Friday, a coalition of tech firms including Nvidia, Microsoft, and Palantir signed an open letter supporting open models, urging policymakers to exercise restraint on potential restrictions.

This seemingly accidental alliance is, in essence, capital's instinctive rebellion against geopolitics. When API bills can be slashed by an order of magnitude, ideological concerns tend to be shelved temporarily. Because the saved money is more concrete and urgent than possible threats.

04 The Wisdom of Three Cobblers

Among all cases, Zoom's story is most symbolic.

The video-conferencing giant began using Meta's open-source model Llama three years ago, saving a fortune through fine-tuning. Today, it simultaneously uses Anthropic, OpenAI, and multiple open-source models.

Zoom CTO Xuedong Huang quotes a Chinese proverb: 'Three cobblers with their wits combined equal Zhuge Liang the mastermind.' He believes this model-mixing methodology is the corporate secret sauce.

The quote carries deep meaning. In OpenAI and Anthropic's narrative, the path to intelligence is linear and hierarchical: the smartest people build the smartest models, and others pay to use them.

Zoom and its peers prove with actions: we can build a smart system ourselves, piece by piece. This is less a technical rebellion than a philosophical divergence.

One pursues depth, the other breadth. One seeks the ascetic penance of intelligence ceilings, the other the actuarial calculation of cost floors. They don't contradict; together, they form the complete picture of AI implementation.

In July 2026, this collective shift among U.S. corporations is quietly spreading—a reassessment of the AI bubble narrative of the past two years. Companies now calculate Return On Investment (ROI) for every API call in Excel, CTOs assess the absurdity of 'Lamborghinis for milk runs' like Saeks, and AI industry valuation logic faces a comprehensive reevaluation.

This doesn't mean OpenAI and Anthropic lack value. On the contrary, their frontier explorations remain irreplaceable. Cursor's experiments also prove that the most complex tasks still require the most powerful models for planning, review, and oversight. But the 'irreplaceable top layer' and the 'assemblable bottom layer' are entirely different businesses. The former is a Holy Grail, the latter a pipeline. The former demands extreme focus and long patience; the latter, extreme efficiency and flexible adaptation. Zoom and Cursor have chosen the latter with 'three cobblers' wisdom—this is the market's sagacity. They're not enemies; they merely stand on opposite sides of the same coin. And now, the entire U.S. corporate world is flipping that coin. The Holy Grail still gleams, but people are starting to calculate gas money for grocery runs.