AI Snake Oil-Part 3: The Rise of Open-Source Models
In Part 1 of AI Snake Oil, I pulled back the curtain on the massive wave of AI hype being driven by tech CEOs and the media and introduced a number of credible skeptics on the mainstream narratives. In Part 2: Where Are All the Data Centers?, I explained how the largest industrial over-build in American history is hitting a wall.
Today I want to explain why I believe that smaller, more efficient AI models will play a major role in the future of AI.
The Shift Toward Lower-Cost, More Efficient AI Models
The massive frontier Large Language Models (LLMs) are set to lose significant market share to more flexible AI firms riding on their coattails. This raises a critical question for investors: When will the markets realize that the current AI capital expenditure boom is a money furnace, and begin shifting their capital to more productive uses?
Currently, the margin of safety for tech investors is razor-thin. As a whole, the global semiconductor industry trades at a price-to-earnings (P/E) ratio of about 55. This means investors are paying $55 upfront for every $1 of current earnings.
What does this imply about future expectations? Dan Rasmussen of Verdad Capital, utilizing a valuation process developed by Michael Mauboussin and Alfred Rappaport, calculated that roughly 75% of the global semiconductor industry's current value is derived from cash flow projections priced more than 10 years into the future.
The Cracking Pricing Power of Frontier AI
In 2016, OpenAI and Anthropic didn’t even exist. Yet, both are poised to go public this fall at valuations nearing $1 trillion each—a combined market value that exceeds the entire U.S. energy sector. To justify these staggering valuations, they must achieve the enduring pricing power of tech giants like Google and Microsoft—and sustain it for decades.
But there is a glaring problem: their pricing power is already cracking.
Open-source models from around the world are emerging as fierce competitors, and not just in the low-end retail segment. Agile AI firms are aggressively targeting high-end enterprise users. The corporate moat is evaporating fast.
As enterprises move from experimental AI projects to large-scale implementations, they are asking two logical business questions:
Are OpenAI and Anthropic’s premium models worth the cost?
Can we achieve the same results for less money elsewhere?
Consider a company with a $10 million AI budget running on Anthropic's top-tier model, Claude Opus. At current rates, they could burn through that budget in a matter of weeks. By switching to an alternative like DeepSeek, that same $10 million budget could last a year.
According to data from AI benchmarking firm Artificial Analysis, Anthropic’s Opus costs nine times more than its cheapest Chinese counterpart to perform the exact same work. Furthermore, models that are six to twelve months old are proving perfectly capable of handling the vast majority of enterprise tasks. And many tasks can be run by models run on in-house networks or even a single computer.
The Rise of Alternative Models
Chinese research labs—including Moonshot, Xiaomi, DeepSeek, and Zhipu—have all shipped open-source models over the last four months that match or nearly match American frontier models on core benchmarks.
Even Nvidia CEO Jensen Huang has noted this shifting tide: “American models are better overall. OpenAI's is better. Anthropic's is better. Gemini is better. However, Chinese open source models are well ahead of us.” And last month, in an interview with Dwarkesh Patel, Huang said that the Chinese army of AI researchers is a fundamental advantage and they are the best in the world.
The market is already responding. According to data from OpenRouter, three of the top five most-utilized models this month are Chinese. In 2024, they represented just 1% of platform usage; today, they command over 40%.
Meanwhile, American frontier LLM companies continue to burn billions on infrastructure, training ever-larger models on the most expensive chips, all while straining a power grid that can barely keep up. These massive costs must be absorbed—the only question is by whom? Consumers have a choice; it’s the investors who will be left holding the bag.
The Rise of Trusted, Low-Cost Alternatives
The technical edge that once justified premium pricing is eroding. For governments, banks, healthcare providers, and other regulated entities that avoid Chinese software, American start-ups are stepping up.
Cohere: Outfits like Cohere build efficient models tailored specifically for highly regulated industries. According to CEO Aidan Gomez, Cohere’s revenue has increased sixfold over the past year.
Nvidia: Nvidia is now shipping its own open-source models, called Nemotron, positioning them as direct alternatives to both Chinese models and overpriced Western frontier LLMs. Nvidia understands that AI software will commoditize; their goal is simply to democratize the software so they can sell as many semiconductor chips as possible to a highly diversified customer base. Already, enterprise giants like Palantir, Salesforce, ServiceNow, and CrowdStrike are deploying Nvidia’s open-source models.
Reflection: Capitalizing on this same trend, American startup Reflection recently raised capital at a multi-billion-dollar valuation, positioning itself as a domestic, trusted alternative to DeepSeek.
Nvidia, Cohere, and Reflection are all targeting the same lucrative market: enterprises seeking highly capable models at a fraction of the cost, built on secure infrastructure.
The Bottom Line for Investors
This fall, when OpenAI and Anthropic launch their initial public offerings, we’ll see what investors have to say. For now, they’re priced as if they own enterprise AI. The data, however, suggests their slice of the pie is shrinking by the day.
At Alpha Rock, we recently established a position designed to profit from the AI infrastructure over-build and what the market has yet to realize: smaller, specialized and more efficient models are taking market share from the frontier LLMs and will play an important role in the future of AI.
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