It's Tokens All The Way Down

"The future belongs to those who consume the most tokens." This line, casually dropped by a friend, has been rattling around in my head for weeks. It's not just a clever play on Silicon Valley jargon—it's the most concise expression I've found of the economic transformation happening around us. He wasn't talking about cryptocurrencies, but computational inference—the resources required to run AI models at scale.

I've been trying to keep up with the pace of AI change. It's moving faster than crypto and is truly exponential. This creates a fundamental blindspot in how we plan and build. Most of us are still thinking in terms of steady progress when we're actually facing hockey stick curves.

We're at a moment similar to the early electrification of industry. In 1900, most factories still used a central steam engine with complex systems of shafts and belts to power individual machines. The visionaries weren't those who built bigger steam engines but those who recognized that distributed electric motors would change everything.

The token economy works similarly. Big companies are building massive, centralized AI models—the equivalent of those giant steam engines. But the real transformation is happening at the endpoints, where specialized applications precisely apply AI to specific problems.

I've watched the constraints shift over the years. First it was capital. Then technical talent. Now we're witnessing another transformation: computational leverage. The builders who win aren't merely those who write code, but those who masterfully orchestrate AI to write it for them.

This inverts traditional competitive dynamics. Good ideas were always abundant, but implementation was hard. Now implementation is increasingly commoditized. The constraint has moved upstream to problem selection and domain insight.

When everyone has access to the same AI tools, differentiation comes from somewhere else. I'm convinced the real value lies in model wrappers – specialized interfaces like Cursor for coding that create value by adapting general-purpose AI to specific verticals. What's remarkable is how this enables small teams to bootstrap highly specific products that would have required venture funding before. You can now build for narrower markets, keep larger equity percentages, and still create substantial value.

The edge isn't just in prompt engineering. It's in being bilingual – fluent in both human and machine communication, toggling effortlessly between them. The most valuable people I know can translate business requirements into machine instructions and then translate machine outputs back into human insights.

What's counterintuitive is how this shift makes system lock-in simultaneously more valuable and more attainable. The conventional wisdom says AI commoditizes everything, but I'm seeing the opposite. AI actually makes it easier to create sticky systems when your wrapper becomes the preferred interface to powerful models. Users who train on your interface develop muscle memory and dataset dependencies that are surprisingly hard to migrate.

Domain expertise becomes the big lock. When anyone can generate functional code through prompts, your unique insights into specific industries, user needs, and technical niches become the true competitive moat.

For those building products today, the metrics that matter have changed. "Token economics" isn't just about cryptocurrency—it's about how efficiently you convert computational resources into customer value.

The real opportunity isn't in raw token consumption, but in token arbitrage—finding those specific applications where small amounts of computation create disproportionate value. The electricity analogy holds: nobody cared about kilowatt-hours per se; they cared about what those kilowatt-hours enabled. The biggest fortunes went not to power companies but to those who found novel applications for electricity.

The projects that win won't be those burning the most tokens, but those creating the most value per token consumed. While big companies optimize for scale, small teams can optimize for specificity. This is the arbitrage opportunity of our time: being precisely useful in narrow domains where AI needs human expertise to be truly effective. In the economy of tokens, the craftsperson beats the factory.