Fifteen years after Marc Andreessen declared that "software is eating the world," we are entering the next phase: intelligence itself is becoming abundant and nearly free.

Just as information became abundant through the internet, expertise and judgment — the scarce resource that was locked inside trained professionals — is now descending the cost curve at extraordinary speed. This descent is happening on two compounding curves at once: cheaper hardware and more efficient models. The result is that high-quality reasoning, once expensive and rationed, is rapidly becoming a commodity.

The dominant conversation in AI still revolves around which company has the smartest frontier model. However, the real economic transition underway is from maximizing raw intelligence to maximizing intelligence per dollar.

Enterprises are rapidly discovering that most workloads do not require the most expensive frontier models. As inference costs continue collapsing at an extraordinary rate, organizations are shifting from asking "Does AI work?" to "Does it work economically at scale?" This change favors cheaper, "good enough" models (including open-weight ones) for the vast majority of tasks, while reserving frontier intelligence only for high-value problems where additional capability creates outsized returns.

A new class of economic actor has arrived: autonomous agents that hold bank accounts, sign contracts, hire employees, and incur real financial obligations with no humans in the decision loop.

Traditional credit analysis collapses. The five classic pillars — management team, board oversight, audited statements, track record, and legal accountability — all assume human institutions. Remove the humans and the entire framework stops applying.

Frontier AI labs are not competing on the same playing field. Each has a unique identity that creates its own structural bottleneck.

OpenAI serves over 900 million monthly active users, mostly consumers. This produces a severe usage-mix problem: high volume of low-value prompts drives enormous inference costs with low revenue per token.