Companies claim AI is revolutionary for productivity, yet economic studies, including one by OpenAI itself, show no correlation between spending on AI and increased revenue per employee. The hype about transformative efficiency is not reflected in actual economic output.

OpenAI and Anthropic are presenting a version of profitability that excludes their largest expenses: model training and inference. Critics compare this to an airline ignoring the cost of its jets. This financial engineering aims to create a positive outlook for potential IPOs but masks their true cash burn rate.

A significant portion of AI industry revenue is illusory, consisting of circular payments. For instance, NVIDIA invests in a company like OpenAI, which then uses the funds to buy NVIDIA's chips. This creates the appearance of strong revenue growth while masking the industry's financial fragility.

The AI ecosystem appears profitable but is often a circular cash flow. Tech giants invest in AI startups, which then use that money to buy services (chips, cloud) from the same investors. This creates the illusion of a robust market without requiring significant outside customer revenue.

It's increasingly difficult to gauge the true profitability of cloud businesses due to circular investments. Tech giants invest in AI startups, which then use that capital (often in the form of cloud credits or vouchers) to pay for compute on the investor's platform, inflating reported revenue growth without a corresponding cash transaction.

Companies like Microsoft and Google invest in AI firms like OpenAI, which then spend that capital on their cloud services. This creates the illusion of diverse, organic demand when it's heavily concentrated and effectively self-funded, masking underlying weakness.

Unlike standalone competitors OpenAI and Anthropic, Google's DeepMind financials are not reported separately because its AI is deeply integrated across products like YouTube and Search. Value is captured through engagement boosts rather than direct monetization, obscuring its true growth and profitability compared to rivals.

By claiming AI chips have a 5-6 year lifespan instead of a more realistic 3, companies can manipulate accounting figures like EBITDA. This hides massive upfront costs and pushes the appearance of profitability further into the future, masking the true scale of their losses.

Revenue figures for AI companies can be misleading. The same dollar is often counted multiple times as it moves from the end customer through a SaaS provider and a cloud platform before reaching the model provider, creating a "margin stacking" effect that obscures the true net revenue.