Over the past week, I have been exploring the changing landscape of AI the Chinese brought out free open-source AI.

The paradigm suddenly shifted from the AI hyperscalers monopolizing AI with expensive, full-capability “frontier” AI to a business model where small, customized, “distilled” AI programs can handle >90% of routine applications cheaply and often in-house.

wsj.com - China US AI model costs


Corporate America Has Suddenly Decided to Stop Blowing Money on AI
Companies big and small are mixing models and it’s changing the economics and power players of the industry

By Angel Au-Yeung,Katherine Bindley and Tina Li, The Wall Street Journal, July 24, 2026

Companies across the country are coming around to a radical idea with the potential to upend the industry powering the global economy: They don’t have to blow their budgets on AI.

Fed up with ballooning costs, companies big and small are starting to use lower-priced models, including some built in China. In many cases, they are adding the new, cheaper models alongside OpenAI and Anthropic’s products, shopping a la carte for their artificial intelligence…

The most powerful and expensive AI models aren’t necessary for relatively mundane tasks.

“It’s like driving a Lamborghini to go to the grocery store to pick up milk when that was designed to be raced around a track…”

Being economical—or tokenomical—is a dramatic reversal in mindset. Just a few months ago, it was a badge of honor to be using AI so much that you spent a lot on tokens. Companies rewarded employees for tokenmaxxing, flashing leaderboards that showed who had spent the most. Now they are thrift-maxxing.

The shift in their budgeting isn’t just about how much U.S. companies are spending on AI. It’s also a geopolitical issue that pits the world’s economic superpowers against each other. Generally, the best-known U.S. models are closed, which means they are strictly controlled by the companies developing them. China is known for cheaper and open-weight models, which means they can be downloaded and customized…

Strategies to lower AI costs include limiting access to top models for new hires and using the most advanced AI systems to plan how tasks will be completed before turning to cheaper models for the execution… [end quote]

This new paradigm is already causing the hyperscalers to try to lock in customers, offering partnerships, tens of thousands of dollars in incentives and heavily subsidized AI usage. That will cut into the high earnings growth expectations that justify the high P/E multiples.

The new paradigm also cuts into the expected use forecast since much of the AI “inference” will be done by distilled models instead of frontier models. Giant data centers that were built to satisfy the demand for frontier AI could have excess capacity, driving down price. (Typical of the pricing cycles of semiconductor fabs.)

Responsible CTOs and CFOs will immediately jump on this opportunity to save their company money while improving productivity with AI that’s designed for their specific needs.

This thrifty paradigm disrupts the business model that supported the AI bubble by bridging the wide moat the hyperscalers (and their investors) counted on…only a week ago.
Wendy