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.

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As technology made marketing tasks more efficient (e.g., Google Ads), it democratized access, causing a 5x increase in marketing jobs since the 1970s. Box's CEO argues AI will have a similar effect on all knowledge work by lowering costs, which will dramatically increase overall demand for that work.

The primary economic incentive driving AI development is not replacing software, but automating the vastly larger human labor market. This includes high-skill jobs like accountants, lawyers, and auditors, representing a multi-trillion dollar opportunity that dwarfs the SaaS industry and dictates where investment will flow.

Don't view AI through a cost-cutting lens. If AI makes a single software developer 10x more productive—generating $5M in value instead of $500k—the rational business decision is to hire more developers to scale that value creation, not fewer.

The economic incentive for VCs funding AI is replacing human labor, a $13 trillion market in the US alone. This dwarfs the $300 billion SaaS market, revealing the ultimate goal is automating knowledge work, not just building software.

In the current market, AI companies see explosive growth through two primary vectors: attaching to the massive AI compute spend or directly replacing human labor. Companies merely using AI to improve an existing product without hitting one of these drivers risk being discounted as they lack a clear, exponential growth narrative.

The true disruption from AI is not a single bot replacing a single worker. It's the immense leverage granted to individuals who can deploy thousands of autonomous AI agents. This creates a massive multiplication of productivity and economic power for a select few, fundamentally altering labor market dynamics from one-to-one replacement to one-to-many amplification.

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.

The narrative of AI destroying jobs misses a key point: AI allows companies to 'hire software for a dollar' for tasks that were never economical to assign to humans. This will unlock new services and expand the economy, creating demand in areas that previously didn't exist.

AI tools aren't just making employees more efficient; they are replacing human labor. This allows software companies to move from cheap per-seat pricing to a new model based on outcomes, like charging per support ticket resolved, capturing a much larger share of the value.

Elad Gil argues that the total addressable market for AI companies is not limited to traditional seat-based software pricing. Instead, it encompasses the multi-trillion dollar human labor market that AI can augment or automate.