Jevons paradox

The talk argues that AI will transform work and the economy without making human labor obsolete. It uses radiology and historical examples like containerization and cloud computing to explain Jevons paradox: when technology makes a service cheaper, demand for it can grow and create new work. AI is already automating routine tasks, but many roles may shift toward supervision, coordination, and higher-value work. The speaker urges founders to take AI’s impact seriously and build with it rather than expect either no change or the collapse of work.

Aaron Levie argues that enterprise AI value comes from software that delivers complete business outcomes, not from the underlying model or its tokens. As models improve and their costs fall, companies that connect them to workflows, data, governance, and business systems can build durable products and margins. Enterprises will buy software for common business functions while building AI capabilities for work that defines their competitive edge. Levie expects AI to expand software markets and help companies do more work, with efficiency gains reinvested in growth and better services.

The talk explores whether AI will let tiny teams build billion-dollar companies and whether people should still learn to code. The speakers compare coding benchmarks such as SWE-bench with ImageNet, noting that benchmarks can accelerate progress but that fixing small bugs is far from designing and building a complex product. They argue that AI can automate some programming tasks, while product design, data modeling, and understanding messy real-world needs still call for human judgment and craftsmanship. They conclude that coding remains valuable for building technical taste and problem-solving skills, and that AI may help more people turn ideas into prototypes and companies.