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Comment by aarondong

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Comment by aarondong

This made me do a triple take. Here is the proposed logic as I follow it:

> AI models are dangerous. They can help bad people do dangerous things. They may be capable of autonomously executing dangerous things. They may cause unwanted effects on the labour market.

> More capable AI models are more dangerous, but require more money to train.

> Money requires investors with expectations that the model will generate a profit over its operational lifetime.

> The operational lifetime value of a model is decreased if every model is public and can be hosted on any infrastructure.

> If investors see less operational lifetime value from model companies, model companies receive less capital and therefore train more capable models at a slower rate.

Follow-ons: There are immediate risks in releasing capable cyber models that can be ablated and then launch cyberattacks. Concentration of power moves immediately into the infrastructure layer for inference.

Models will be kept private for longer, if not indefinitely, given there is less incentive to release them publicly.

Enforcement globally will occur because accessing the lucrative US market means using an open source model.

The entire argument hinges on strict enforcement of this policy, when AI model routing can already be opaque.

It also hinges on investors being rational and expecting free cash flow from AI companies, rather than reaching a criticality threshold of model capability for recursive self-improvement internally and parlaying that into a global mega-corporation.

New labs and companies without infrastructure connections will no longer be able to raise money, given investor expectations, and therefore not be able to increment AI progress.

So a win for the infrastructure layer, models being kept private for longer, new labs being unable to compete, immediate risks in the rollout (I suppose could be mitigated by a staged rollout i.e. policy active in 2030, pricing effects now), enforcement being tricky, and in the event that foreign competitors lead the AI frontier and then start closing models, just conceding the market to them if US consumers and companies find workarounds to pay for foreign AI.

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