FWIW I do think that availability of competitive open weight and other non-frontier models, along with improvements in harnesses that can get good results out of these models, will result in less concentration and a healthier marketplace.

However, these frontier labs are also making moves that could let them capture a disproportionate share of the upside. One possibility is a situation analogous to the smartphone manufacturing space, where there are dozens of players but just a handful (e.g. Apple, Samsung in smartphones) capture the lion's share of the revenue.

▲

skeptic_ai 3 hours ago | parent [-]

Apple you can’t exit the ecosystem.

Samsung the same. And is the best android device.

If tomorrow comes a Nokia os will be dead in the water: it has no apps.

But with a new llm that doesn’t matter. There is nothing sticky about typing Gemini, Claude or codex in a cli.

	▲	keeda an hour ago \| parent [-]
		There's nothing sticky today but you can bet they're working maniacally to fix that. These companies will make most of their money in the enterprise space and there are probably unlimited ways to engineer stickiness in an enterprise setting. Like, MSFT still rakes in those billions despite pretty much every one of their products having commodity competitors. The AI labs are also making moves to secure long-term enterprise presence, such as their Forward Deployed Engineer strategy. I think that is a trojan horse play that could make enterprises dependent on them forever, much like so many companies are still dependent on IBM's mainframes. As an extreme example, you could imagine a company's core business logic encoded in the weights of a proprietary model custom-trained and hosted by one of these model providers, something even more inscrutable and sticky than ancient COBOL codebases.