The best barometer for AI's enterprise value is not replacing the bottom 5% of workers. A better goal is empowering most employees to become 10x more productive. This reframes the AI conversation from a cost-cutting tool to a massive value-creation engine through human-AI partnership.

To quantify the real-world impact of its AI tools, Block tracks a simple but powerful metric: "manual hours saved." This KPI combines qualitative and quantitative signals to provide a clear measure of ROI, with a target to save 25% of manual hours across the company.

A 'value premium' is emerging where users' reported value from AI grows faster than their usage time. Even users with flat usage hours report increasing value, demonstrating that skill development and learning curve payoffs are key drivers of AI ROI, independent of raw hours spent.

Dario Amodei quantifies the current impact of AI coding models, estimating they provide a 15-20% total factor speed-up for developers, a significant jump from just 5% six months ago. He views this as a snowballing effect that will begin to create a lasting competitive advantage for the AI labs that are furthest ahead.

Block's CTO quantifies the impact of their internal AI agent, Goose. AI-forward engineering teams save 8-10 hours weekly, a figure he considers the absolute baseline. He notes, "this is the worst it will ever be," suggesting exponential gains are coming.

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.

Instead of merely replacing jobs, AI will act as a force multiplier on the economy. AI companies will capture value by taking a small percentage—a 'tax'—on the significant productivity gains (e.g., 30-50%) they provide to knowledge workers. This model explains how AI platform revenues can scale to hundreds of billions.

The anticipated AI productivity boom may already be happening but is invisible in statistics. Current metrics excel at measuring substitution (replacing a worker) but fail to capture quality improvements when AI acts as a complement, making professionals like doctors or bankers better at their jobs. This unmeasured quality boost is a major blind spot.

While companies report low official adoption, about 50% of workers use AI and hide the resulting productivity gains. This 'shadow adoption' stems from fear that revealing AI's efficiency will lead to layoffs instead of rewards, preventing companies from capitalizing on the technology's full potential.