Research-led essays, new evidence, arguments, interviews and artifact studies from a history that is still compiling. Every seeded essay ships with inline citations and a Works Cited record.
Open-weight AI spreads models rapidly but can make monetization harder. Closed AI captures usage directly but bears the serving burden. We compare both strategies.
Vertical AI narrows the problem and prices against domain value. General AI maximizes addressable market but carries broader research and serving costs.
Enterprise AI sells measurable business outcomes while consumer AI monetizes engagement. The margin tradeoff depends on pricing, support, inference, and scale.
Frontier labs absorb research and infrastructure costs. AI applications can price against specific workflows. This article compares the two economic layers.
A comparative look at the AI business models that have reached profit fastest: capital-efficient research, subscriptions, enterprise platforms, and infrastructure.
AI valuations can reach hundreds of billions before mature profit appears. This article explains revenue multiples, growth assumptions, margins, and capital intensity.
AI companies can report accounting profit while consuming enormous capital. Free cash flow helps reveal whether operations fund infrastructure and expansion.
A dollar of AI revenue can come from a subscription, API call, long-term cloud contract, advertisement, or services project. Those dollars are not economically identical.
AI customers, cloud providers, chipmakers, and model labs increasingly invest in one another. The resulting contracts can make revenue and capital flows hard to interpret.
GPU purchases are capital investments, but their cost reaches the income statement through depreciation. AI infrastructure makes asset life assumptions financially important.
AI companies can exclude stock compensation or treat research spending differently in adjusted metrics. Those choices materially change reported profitability.
Adjusted EBITDA can be useful, but AI companies may add back depreciation, stock compensation, and other large costs. GAAP and non-GAAP can diverge sharply.