The AI Demand Bubble

3 hours ago

Hyperscalers like Amazon, Microsoft, and Google are misleading investors by not disclosing that over 70% of their AI revenues come from OpenAI and Anthropic, two unprofitable AI labs.
The vast majority of cloud growth attributed to AI is actually driven by compute spending from OpenAI and Anthropic, not diverse customer demand.
Hyperscalers are engaging in circular financing: they invest billions into OpenAI and Anthropic, which then spend that money on their cloud services, inflating revenues artificially.
Analyst estimates show that without OpenAI and Anthropic, the AI businesses of Amazon, Microsoft, and Google are catastrophic failures, with negligible revenue relative to massive capex.
The AI industry's demand is highly concentrated; there is insufficient non-OpenAI/Anthropic demand to justify the trillions spent on data centers and GPUs.
If either OpenAI or Anthropic fails, a large portion of AI compute demand and hyperscaler revenues would evaporate, leading to massive impairments and economic fallout.
Microsoft 365 Copilot and other AI software products have struggled to generate meaningful revenue, indicating weak demand for AI-powered software.
The AI bubble is built on hype and deception, with investors being sold a lie about the true state of AI demand and hyperscaler growth.

The author introduces a versioning system for updates on the AI bubble instead of volumes.
The AI bubble is described as a psyop, melodrama, financial crisis, and exploitation of ignorance for trillions in investment.
LLMs are real but don't fulfill promises like replacing 50% of white-collar jobs, as claimed by boosters like Dario Amodei.

The author argues that the multi-trillion-dollar AI capital expenditure boom is unsustainable, driven by reckless spending from hyperscalers on speculative assets, with little to no actual demand outside of subsidized AI labs like OpenAI and Anthropic.
The BIS annual report confirms that hyperscalers' capex commitments outpace earnings and free cash flow, warning that disappointment in returns could trigger a sudden pullback, leading to a protracted investment bust with knock-on effects on borrowers across the supply chain.
OpenAI and Anthropic are deeply unprofitable, with massive losses and compute costs, representing the majority of AI revenue (68% or more) and being subsidized by hyperscalers, making the industry's apparent revenue figures misleading.

NVIDIA overstated Blackwell GPU efficiency gains, and AI nonetheless fails to make startups profitable.
Hyperscalers have dramatically increased debt and capital expenditure for AI, with no disclosed AI revenue and growing cash-flow negativity.
AI data center spending creates a vicious cycle: more demand raises hardware prices, requiring more debt, which becomes more expensive.

The AI industry requires $3 trillion or more in revenue by 2030 to sustain its infrastructure, with planned data centers costing $9.5–15 trillion.
Anthropic and OpenAI have made massive compute commitments ($770 billion+ and $330 billion+ respectively) that demand exponential revenue growth—$174 billion/year by 2029 for Anthropic and $184 billion for OpenAI.
Token-based billing has exposed AI's lack of ROI: companies are capping spend (e.g., Uber at $1,500/month per user) and struggling to measure value.

LLMs have broken economics because their token-based costs are metered and unpredictable, unlike typical software subscriptions, and users often pay for wasted tokens.
AI companies like OpenAI and Anthropic are unprofitable; they subsidize consumer subscriptions by giving away far more tokens than the subscription cost, and enterprise token billing has led to budget overruns (e.g., Uber).
Most AI services are not differentiated or reliably useful, leading to low adoption and revenue concentration at OpenAI and Anthropic, with many startups unprofitable.

The author introduces a versioning system for updates on the AI bubble instead of volumes.
The AI bubble is described as a psyop, melodrama, financial crisis, and exploitation of ignorance for trillions in investment.
LLMs are real but don't fulfill promises like replacing 50% of white-collar jobs, as claimed by boosters like Dario Amodei.

The author argues that the multi-trillion-dollar AI capital expenditure boom is unsustainable, driven by reckless spending from hyperscalers on speculative assets, with little to no actual demand outside of subsidized AI labs like OpenAI and Anthropic.
The BIS annual report confirms that hyperscalers' capex commitments outpace earnings and free cash flow, warning that disappointment in returns could trigger a sudden pullback, leading to a protracted investment bust with knock-on effects on borrowers across the supply chain.
OpenAI and Anthropic are deeply unprofitable, with massive losses and compute costs, representing the majority of AI revenue (68% or more) and being subsidized by hyperscalers, making the industry's apparent revenue figures misleading.

NVIDIA overstated Blackwell GPU efficiency gains, and AI nonetheless fails to make startups profitable.
Hyperscalers have dramatically increased debt and capital expenditure for AI, with no disclosed AI revenue and growing cash-flow negativity.
AI data center spending creates a vicious cycle: more demand raises hardware prices, requiring more debt, which becomes more expensive.

The AI industry requires $3 trillion or more in revenue by 2030 to sustain its infrastructure, with planned data centers costing $9.5–15 trillion.
Anthropic and OpenAI have made massive compute commitments ($770 billion+ and $330 billion+ respectively) that demand exponential revenue growth—$174 billion/year by 2029 for Anthropic and $184 billion for OpenAI.
Token-based billing has exposed AI's lack of ROI: companies are capping spend (e.g., Uber at $1,500/month per user) and struggling to measure value.

LLMs have broken economics because their token-based costs are metered and unpredictable, unlike typical software subscriptions, and users often pay for wasted tokens.
AI companies like OpenAI and Anthropic are unprofitable; they subsidize consumer subscriptions by giving away far more tokens than the subscription cost, and enterprise token billing has led to budget overruns (e.g., Uber).
Most AI services are not differentiated or reliably useful, leading to low adoption and revenue concentration at OpenAI and Anthropic, with many startups unprofitable.

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