The industry has already exhausted the public web data used to train foundational AI models, a point underscored by the phrase "we've already run out of data." The next leap in AI capability and business value will come from harnessing the vast, proprietary data currently locked behind corporate firewalls.

As AI tools enable millions of amateur creators to produce professional-quality content, platforms like YouTube and Spotify become less reliant on a small number of mainstream media giants. This diffusion of content creation shifts bargaining power away from traditional studios and labels to the platforms themselves.

The primary bottleneck for advancing AI is high-quality, tacit data—skills and local insights that are hard to digitize. Individuals can retain economic value by guarding this information and using it to train personalized AI tools that work for them, not their employers.

New technologies perceived as job-destroying, like AI, face significant public and regulatory risk. A powerful defense is to make the general public owners of the technology. When people have a financial stake in a technology's success, they are far more likely to defend it than fight against it.

Like railroads, AI promises immense progress but also concentrates power, creating public fear of being controlled by a new monopoly. The populist uprisings by farmers against railroad companies in the 1880s offer a historical playbook for how a widespread, grassroots political movement against Big Tech could form.

Previously, data privacy concerns were abstract for most, leading only to worse ads. Now, giving AI companies unfettered access to your professional data provides them with the exact material needed to train models that will automate your job.

The Writers' Guild of America strike offers a sophisticated model for labor unions navigating AI. Instead of an outright ban, they negotiated a dual approach: winning protections against AI-driven displacement while also securing guarantees for their members to use AI as an assistive tool for their own benefit.

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 concept of data colonialism—extracting value from a population's data—is no longer limited to the Global South. It now applies to creative professionals in Western countries whose writing, music, and art are scraped without consent to build generative AI systems, concentrating wealth and power in the hands of a few tech firms.