Tom Snyder: When AI helps create value, what does the platform get to learn from that process? :: WRAL.com

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A few weeks ago, Sam Altman walked into a Y
Combinator event and made the kind of offer that gets Silicon Valley talking.
OpenAI, he reportedly said, would provide $2 million worth of OpenAI API tokens
to every startup in the current YC batch, in exchange for future equity through
an uncapped SAFE agreement. The money was not cash, exactly. It was compute. In
the AI economy, that distinction matters less than it once would have. For many
young companies, access to models and inference capacity is quickly becoming as
important as access to cloud hosting, software tools, or even employees.

The easy way to understand the announcement is
as a market-share strategy. There is a market-share arms race happening now as
each platform tries to lock-in as many first-time users as they can. OpenAI
wants the next generation of startups building on OpenAI. Anthropic wants them
building on Claude. Google wants them building on Gemini. Meta, Microsoft,
Amazon, and others all understand that the early habits of builders can harden
into long-term dependency.

Once a startup builds its product
architecture, customer workflows, engineering talent, and business model around
a particular platform, moving away becomes expensive. That was true in the
cloud era. It was true in mobile and in enterprise SaaS. It will almost
certainly be true in artificial intelligence.

But AI introduces a more complicated question
than traditional platform lock-in. A startup building on Amazon Web Services
teaches AWS something about usage patterns, cost structures, and infrastructure
demand. A company building an iPhone app teaches Apple something about consumer
behavior and app categories. Those forms of learning matter, but they are still
mostly indirect. The platform sees where users go, how much they consume,…

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