Perfect Competition Will Crush AI Profits

The AI investment craze is suffering from the same flawed assumptions that sparked past bubbles. Without a course correction, the U.S. economy faces a self-inflicted bust. The mid-2000s saw Wall Street's best and brightest packaging subprime loans into complex securities, assuring the world they were safe, with regulators nodding in approval and rating agencies giving them a clean bill of health. Investors snapped up these instruments, few asking if the assumptions behind them were wrong.

Today, a similar blind spot is spreading through the economy – not through mortgages, but through artificial intelligence (AI). Everyone from Big Tech giants to startup founders, institutional investors, and Washington policymakers believes that artificial intelligence is a revolutionary technology, and that big capital expenditures (capex) today will lead to countless industrywide profits in the future. But there's a problem: demand in value-added, real-world applications is unlikely to be large enough to justify all this investment.

The demand highlighted by AI promoters is drawn from Large language model (LLM) training sessions and queries – neither of which is associated with much sustainable, real-world revenue. Much of the revenue being booked in this ecosystem ultimately comes from a limited pool of venture capital (VC) funds. According to recent estimates, LLM companies generate roughly $18.5 billion in annualized revenue.

This ironical fact is highlighted by a story from The Information that estimated 95% of businesses are seeing no return on AI pilot projects. It's ironic because the $18.5 billion in annualized revenue is peanuts relative to the astonishingly large amount of capital poured into these companies. The revenue streams also show that LLM companies are not showing positive operating leverage, indicating that costs scale alongside revenue.

There are two key blind spots in today's AI investment mania: the fallacy of composition and adverse selection. The fallacy of composition assumes what's true for one player is true for all – if Microsoft makes money selling AI products, investors assume the entire AI sector will do the same. However, success in a competitive market often comes at the expense of rivals.

Adverse selection is also at play: heaviest users of AI chatbots and tools, those demanding the most computing resources, are often not paying enough subscription fees to cover their costs. Casual users, who make up the vast majority, may never convert to paying customers. This undermines the very business model these companies are betting on.

What happens when the market discovers this? You get a doom loop: companies overspend on infrastructure, hoping for future demand that never materializes, while investors keep piling in – until someone finally notices the emperor is minimally clothed.

Why Small Businesses, Not Big Tech, May Be the Real Winners

The so-called "Magnificent 7" (Microsoft, Google, Apple, Amazon, Meta, Nvidia, and Tesla) are expected to capture most of the economic value created by AI. However, past tech revolutions have shown that the biggest winners weren't always the biggest companies. The advent of the internet helped millions of small and mid-sized businesses gain customers and compete globally.

Low-cost e-commerce tools like Shopify and advertising platforms like Google and Facebook gave them the reach they never had before. It's possible that AI's biggest beneficiaries won't be the firms building the models, but the dentists, restaurant owners, and local manufacturers who use those tools to accelerate mundane processes and serve customers better.

The Myth of Lock-In and the Illusion of Pricing Power

One of the most dangerous assumptions in AI capex is that these platforms will enjoy pricing power – meaning they can charge enough to cover their costs and then some. However, this assumption crumbles when looking closely at the AI chatbot market.

The AI chatbot market looks a lot like perfect competition, where many providers offer nearly identical products, and prices fall toward the cost of production. Unlike the broadband boom of the 2000s, where companies had to rip up streets to lay wires and customers had few broadband alternatives, switching from one AI model to another takes a few clicks.

If you're a free user of ChatGPT, and it suddenly costs $20 a month to use it at all, but Claude or Gemini is free and just as good, nothing is stopping you from switching. That's the nightmare scenario for the AI providers: they've built massive infrastructure assuming recurring subscription revenue, but the customer base may have no loyalty and little willingness to pay.

AI Infrastructure: A Recipe for Disaster

AI infrastructure is being built largely on faith. Companies are scaling up compute power without clear signs of sustainable demand. Unlike oil and gas, where prices adjust second-by-second, AI companies operate in a fog.

China's More Pragmatic Approach

Companies like DeepSeek are building AI tools designed to solve narrow problems in energy, logistics, and manufacturing. That's closer to how the human brain works – selectively activating regions based on the task at hand. And it's proving to be more effective than trying to create superintelligence in a box.

The Dark Side of AI Hype

Carl Brown, a veteran developer, shredded the idea that large language models (LLMs) will replace human coders anytime soon. He explained how writing code is only a small part of the job – long-term thinking, fixing bugs, and figuring out what the software should do.

AI tools can crank out lines of code fast, but they often do it sloppily, duplicating functions and ignoring best practices. The result is software that's harder to maintain, easier to hack, and more prone to crashing.

The Consequences of AI Hype

Big Tech companies may be tapping the brakes on Nvidia chip orders to avoid an accelerating decline in their free cash flows – a trend already looking scary. A recession or bust in venture capital funding for AI LLM companies could drive operating cash flow lower.

This would make the BofA chart surge higher than they project in the dotted lines. And higher capex relative to cash flow means free cash flow would dive toward zero. To avoid that scenario, hyperscale data center operators would cut AI chip orders.