In a future where open-source models commoditize the model layer itself, closed-source labs will likely adapt their business models. Monetization will move up the stack to the application layer (where the "last mile" value is) and down to the infrastructure layer (optimizing costs with custom chips).

Foundational AI models will commoditize into a utility layer where companies buy "intelligence on the fly." The real, sustainable profit will be captured by application companies that leverage various models to solve specific business problems, as most enterprises lack the expertise to use raw models effectively.

The market frets that cheaper open-source models cannibalize expensive frontier models. This is a misconception. Open source drives token elasticity, increasing total compute demand. It merely shifts high margins away from model providers to the underlying AI infrastructure players who provide the compute.

As customers increasingly adopt model orchestration—routing tasks to the most efficient model for the job—value shifts away from individual frontier models. This trend commoditizes the raw intelligence layer, posing a significant threat to companies focused solely on building the largest models.

Mobile networks built expensive global infrastructure with massive usage but captured little value as profits moved "up the stack" to apps. Foundation models, despite huge CapEx, face a similar risk of becoming a commoditized infrastructure layer with low pricing power.

An pro-open source stance can be seen as inherently "desalinationist" for the AI industry. By commoditizing models and lowering margins, it becomes harder for frontier labs to underwrite the massive capital expenditures for the next, larger training runs, thus reducing the insatiable demand for compute.

The 50-year supremacy of asset-light software may be an anomaly. If AI makes software creation nearly free, economic value will shift back to the historical mean: tangible assets like infrastructure, energy, and regulated, liability-bearing businesses that touch the physical world.

If AI makes intelligence cheap and universally available, its economic value may collapse. This theory suggests that selling raw AI models could become a low-margin, utility-like business. Profitability will depend on building moats through specialized applications or regulatory capture, not on selling base intelligence.

The core business of creating AI foundation models is weak due to a lack of compounding advantage and defensibility. With leads being fleeting and tools like Open Router allowing customers to switch models instantly, companies are forced into a brutal price competition, making regulatory capture their only viable long-term strategy.