Unlike traditional software that optimizes for time-in-app, the most successful AI products will be measured by their ability to save users time. The new benchmark for value will be how much cognitive load or manual work is automated "behind the scenes," fundamentally changing the definition of a successful product.

A technology like AI can create immense societal value without generating wealth for its early investors or creators. The value can be captured by consumers through lower prices or by large incumbents who leverage the technology. Distinguishing between value creation and value capture is critical for investment analysis.

Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").

Traditional metrics like GDP fail to capture the value of intangibles from the digital economy. Profit margins, which reflect real-world productivity gains from technology, provide a more accurate and immediate measure of its true economic impact.

During major platform shifts like AI, it's tempting to project that companies will capture all the value they create. However, competitive forces ensure the vast majority of productivity gains (the "surplus") flows to end-users, not the technology creators.

Marks warns against conflating a technology's societal impact with its investment potential. Fierce competition among AI service providers or their customers could pass all productivity gains to consumers through lower prices. This would result in little to no profit for the underlying companies, echoing a similar warning from Warren Buffett during the dot-com era.

Marks questions whether companies will use AI-driven cost savings to boost profit margins or if competition will force them into price wars. If the latter occurs, the primary beneficiaries of AI's efficiency will be customers, not shareholders, limiting the technology's impact on corporate profitability.

The true commercial impact of AI will likely come from small, specialized "micro models" solving boring, high-volume business tasks. While highly valuable, these models are cheap to run and cannot economically justify the current massive capital expenditure on AGI-focused data centers.

Traditional SaaS metrics like 80%+ gross margins are misleading for AI companies. High inference costs lower margins, but if the absolute gross profit per customer is multiples higher than a SaaS equivalent, it's a superior business. The focus should shift from margin percentages to absolute gross profit dollars and multiples.