Think about how much will change in the AI landscape during the lifetime of your next application. You can bet on improvements to code generation models, hardware, talent, review capabilities and more. This leads us to the need for a new metrics: something like TCO, but that also represents efficiency. I propose Value Per Token (VPT) as the buy-side equivalent to hyperscaler cost-per-million-token mindset.

VPT = (Business Value Delivered) / (Tokens Consumed)

Cost per token is and was never the real objective. It’s just the lowest common denominator right now, like compute power or memory. The real objective is value per token. If every token you issue does not generate enough business logic, maintainability or architectural clarity, you’re burning budget on noise. This is tokenomics in action.

The primary goal of tokenomics in software engineering is to collapse the costs and friction of the software development lifecycle: spec, iteration, debugging, integration, maintenance…all of it, by orders of magnitude. Let’s say a 1000x reduction in complete SDLC for a product. How do we achieve that when even the most forward thinking companies are settling around 10-30% productivity improvements using GenAI? One way is by lowering our material costs. Both labor and capital. This blog is about the capital side of the problem.

Continue reading “Tokenomics for Code: Value per Token in the Agentic Era” →

In the near future, it’s entirely plausible that your entire codebase will be written, refactored, and evolved by AI agents. But the “brownfield” you’ll inherit won’t be legacy human code, it will be legacy AI decisions. And those decisions will stem from choices you make today. For example, whether and when to adopt an AI-first development paradigm, and how to prepare your stack to reach that point.

Continue reading “Sowing the Agentic Brownfield” →