Dan Amoss: Perfect Competition Will Crush AI Profits

“Gradually, then suddenly.”

– Hemingway

December 18, 2025 — Today’s AI investment craze suffers from the same flawed assumptions that sparked past bubbles.

Without a course correction, the U.S. economy faces a self-inflicted bust.

In the mid-2000s, Wall Street’s best and brightest were packaging subprime loans into complex securities and assuring the world they were safe. Regulators nodded. Rating agencies blessed them. Investors snapped them up.

Few asked the obvious question: What if the assumptions behind these instruments are 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 is convinced 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 that AI promoters constantly highlight 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.

A recent story from The Information estimated that these LLM companies generate roughly $18.5 billion in annualized revenue.

Ironically, The Information was trying to rebut the claims from an MIT Media Lab report that concluded 95% of businesses are seeing no return on AI pilot projects.

It’s ironic because $18.5 billion in annualized revenue is peanuts relative to the astonishingly large amount of capital poured into these companies.

The revenue streams in the chart above are also not very valuable because LLM companies are not showing positive operating leverage.

Plenty of evidence indicates that costs scale alongside revenue at LLM companies. Heavy users burn lots of electricity and shorten Nvidia chips’ lives in their usage of the models. And free users will just switch to a competing LLM if ever required to pay a subscription fee.

From Groupthink to Boom-Bust

Today’s AI investment mania has two key blind spots: the fallacy of composition and adverse selection.

Let’s break those down.

The fallacy of composition is the mistaken belief that what’s true for one player is true for all. If one company like Microsoft makes money selling AI products, then investors assume the entire AI sector will do the same.

But that’s not how competitive markets work. In fact, success in a competitive market often comes at the expense of rivals. There can be winners, but not everyone can win.

This fallacy infected the telecom boom of the late 1990s. Just because one broadband provider had profitable demand didn’t mean every fiber-optic network was worth building. Billions were spent laying cables that no one would use for another decade.

Then there’s adverse selection. In insurance, this happens when sick people are more likely to buy health insurance than healthy people, leading to losses for the insurer that doesn’t attract enough healthy customers.

In AI, something similar is unfolding: the heaviest users of AI chatbots and tools – those demanding the most computing resources – are often not paying enough in subscription fees to cover their costs to the LLM provider.

The 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

There’s a popular narrative that the so-called “Magnificent 7” (Microsoft, Google, Apple, Amazon, Meta, Nvidia, and Tesla) will capture most of the economic value created by AI.

But history suggests otherwise.

In past tech revolutions, 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.

Similarly, 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 irony? While these SMBs (small- and medium-sized businesses) benefit, the AI platforms enabling them may struggle to get paid. If Google’s new Veo video model helps a dentist craft a great local ad, will that dentist pay Google more?

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.

But look closely, and this assumption crumbles.

The AI chatbot market looks a lot like perfect competition, a textbook term for markets 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.

It’s like investing billions into oil wells only to find out your oil sells for $10 a barrel instead of $70.

Where’s the Feedback?

In a healthy economy, production and consumption communicate constantly. If a company builds something useful, customers respond by buying it. If they overbuild, inventories pile up and prices fall, sending a signal to slow down.

AI infrastructure, by contrast, 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. They release tools, collect usage stats, and hope that paid conversions will follow.

But hope is not a business model.

In fact, today’s AI leaders resemble 2006-era banks more than healthy, feedback-driven enterprises. They’re in an unhealthy relationship with venture capital (VC) firms that need higher and higher valuation “marks” to justify their fees.

VC firms don’t want transparency about customer conversion rates, churn, or operating losses that scale with revenue. That would kill the narrative before the VCs can cash out in IPOs to the public market.

Best Regards,

Dan Amoss, CFA
Strategic Intelligence & Grey Swan Investment Fraternity

P.S. from Addison: Dan Amoss provided an excellent, on-the-spot analysis of what’s actually going on in the accounting department of today’s marquee AI companies. Frankly, the conversation exceeded my expectations and yielded insights I’d not heard before – from Dan or anyone else.

Notably, Mr. Amoss described how they’re juggling inventory, capital expenditures and depreciation of goods on the books to make their prospect look rosier for Wall Street’s big investment firms.

For good measure, he walked through how he takes that deep analysis and selects trades for the Strategic Intelligence franchise he writes with Jim Rickards.

It’s helpful as an investor to have intelligent analysis articulated clearly and then translated into actionable advice.

If you’re looking for a more value-oriented approach to AI companies, Dan’s views provide the perfect outline. He’s a natural resource bull, too, so he made a few quick recommendations at the end of the call regarding gold and silver plays he likes. It’ll be worth your time to listen to the replay if you weren’t able to join us in person.

As always, we’ll have the replay up shortly for paid-up members.

We’ll be back to Grey Swan Live! after the holiday season – with more old friends and new colleagues joining us. There are a host of new names on our target list as well.

In short, as we wrap up our first full year building this community of free thinkers, we’re just getting started! We got some new tools on the way for you, too. Thank you for joining us! And stay tuned…

If you have requests for new guests you’d like to see join us for Grey Swan Live!, or have any questions for our guests, send them here.