📊 Full opportunity report: The Invisible Hand That’s Pushing AI Tokens Down on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
AI tokens have experienced a sharp decline of 40-60% from recent highs. Experts suggest this is due to margin shifts from frontier to open-source models, not demand loss. The real growth occurs in private labs and open inference clouds, which are hidden from public market metrics.
AI tokens have dropped by 40 to 60 percent from their recent peaks, according to industry analyst Thorsten Meyer, who attributes this decline not to demand destruction but to a redistribution of margins within the AI ecosystem. This shift is significant because it challenges common market interpretations and suggests underlying growth is still strong, although hidden from public view.
Thorsten Meyer explains that the sell-off in AI tokens is primarily due to a shift in margins from high-cost frontier models toward open-source inference models. This does not reduce overall compute demand; instead, it redistributes where the profit margins lie, moving them from oligopolistic labs to infrastructure providers like cloud services and chips, which charge uniformly for compute regardless of model type.
As a result, the total volume of tokens consumed actually increases because cheaper tokens enable more extensive use. Meyer emphasizes that this market behavior is misunderstood; the decline in token prices induces greater demand rather than suppresses it. This phenomenon is observable in his own operations, where switching to open models reduces costs and boosts token usage.
He further notes that much of this activity occurs in private AI labs and open inference clouds, which are invisible to public market metrics. These areas are the ‘dark matter’ of the AI economy, influencing supply and demand through factors like GPU availability and rising memory prices, but without direct reporting on their growth or financials.
Additionally, Meyer discusses the rise of multi-model routing systems, which combine open models with a smaller number of frontier models. This approach lowers costs and increases total token volume, while also elevating the value of the orchestrating frontier models, contradicting the narrative that cheaper inference reduces overall AI value.
The speculative AI names fell 40–60% from their highs in a month. Every fundamental I can measure accelerated in the same weeks. My view: the market is selling a layer of the stack it was never able to see — and panicking about the two risks that matter least.
▲ Opinion & analysis · not investment adviceOpen source taking share spooked the market as demand destruction. That’s backwards. Producing a token costs the same compute whoever emits it — so open weights don’t destroy demand, they move margin and grow the pie.
The acceleration is happening where public equities have almost no telemetry. You infer the layer from its gravitational pull on the gauges you can read.
- A handful of listed hyperscalers
- The chipmakers
- Quarterly filings, weeks late
- Private frontier labs
- Open-source inference clouds monetizing served tokens
- Its pull: GPU scarcity, rising rents, memory spot, token growth — none on a balance sheet
The two things everyone panicked about are the two I worry about least. The risks worth respecting are quieter.
For the buildout to pay for itself, trillions in new operating cash flow must appear. It can come from exactly two places.
The truth, as usual, is still getting its boots on.
Implications of Margin Redistribution in AI Markets
This analysis reveals that the recent decline in AI tokens does not indicate weakening demand but a shift in profit margins within the AI ecosystem. Investors and industry observers should understand that the growth in private labs and open-source inference clouds is largely hidden from traditional metrics, yet it drives substantial demand and infrastructure investment. Recognizing this can prevent misinterpretation of market signals and better inform strategic decisions in AI investments.
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The public AI market primarily tracks hyperscalers and chipmakers, but the fastest-growing demand occurs in private frontier labs and open inference cloud providers. These sectors are not reflected in public financial statements but influence supply, demand, and pricing through increased GPU usage, rising memory costs, and token growth. This 'dark matter' of AI is causing market whipsaws and mispricing of assets, as traditional metrics fail to capture the full picture of AI ecosystem expansion.
"The sell-off is reading the wrong layer of the stack. Demand isn’t falling; margins are shifting from frontier models to open-source inference, which redistributes profits but not demand."
— Thorsten Meyer
Unclear Extent of Private Sector Growth Impact
While Meyer emphasizes the importance of private labs and open inference clouds, the precise scale of their growth and impact remains difficult to quantify due to lack of public data. The full extent of how these sectors influence the broader AI economy is still emerging and subject to further analysis.
Monitoring Infrastructure Pricing and Private Sector Activity
Investors and analysts should watch for signs of continued margin shifts, rising GPU and memory prices, and increased token volumes in private and open-source AI sectors. Further data releases and industry reports will clarify the growth trajectory of these hidden layers, helping to refine market understanding and investment strategies.
Key Questions
Why are AI token prices falling if demand is increasing?
Token prices are falling due to margin redistribution from high-cost frontier models to open-source inference models, not because overall demand is decreasing. Cheaper tokens enable more usage, increasing total demand despite lower prices.
What is meant by the 'dark matter' of the AI economy?
The 'dark matter' refers to private AI labs and open inference cloud providers whose growth and activity are not reflected in public financial metrics but significantly influence supply, demand, and pricing in the AI ecosystem.
How does multi-model routing affect AI demand?
Multi-model routing lowers costs and increases total token volume by enabling orchestration across open and frontier models. It does not reduce demand but can actually enhance the value of frontier models.
Is the recent market decline a sign of weakening AI industry fundamentals?
No, according to experts like Thorsten Meyer, the decline reflects a shift in profit margins and market mispricing of unseen growth in private sectors, not a fundamental demand slowdown.
What should investors watch for to understand the true state of AI growth?
Investors should monitor infrastructure prices, GPU and memory demand, and token volume trends in private labs and open inference clouds, as these reveal the actual expansion of the AI ecosystem.
Source: ThorstenMeyerAI.com
