Open AI models are attracting enterprises, governments, and startups with the promise of lower costs, greater control, and more flexibility. But their wider availability raises practical questions for companies: how can they build businesses around models others can access, and what choices should they make before committing to an open model?
What startups should consider before building
Choosing an open model is only one part of the decision. Startups also need to consider the tools and infrastructure they will use to build on it. Those choices shape how a model fits into a product and what a company needs to operate that product.
Irene Solaiman, Chief Policy Officer and SVP of Strategy at Hugging Face, discussed these questions with Rebecca Bellan on TechCrunch’s Equity podcast. The conversation focused on what startups need to know before building on open models, including the trade-offs that come with the approach.
For a startup, the appeal of lower costs and more control may be weighed against the work involved in selecting and supporting the right tools and infrastructure. The source does not prescribe a single route; instead, it frames open model adoption as a set of decisions that depend on what a company is trying to build.
The challenge of capturing value
As capable models become more freely available, access alone may be less of a distinction for companies building with them. That leaves a central business question: how does a company capture value when the underlying model is not exclusive?
The question matters to startups as well as established organizations. Lower costs, control, and flexibility may make open models attractive, while broad availability can make it harder to rely on access itself as the source of a company’s advantage. The podcast discussion places that tension among the key considerations for businesses building on open AI.
Competition adds another layer
The spread of Chinese open models raises a further question for American players: can they keep up? The source presents this as part of the broader debate around open AI, alongside the business choices that startups face.
For companies deciding what to build, competition affects the context in which open models are becoming more mainstream. The choices around tools, infrastructure, and trade-offs are therefore connected to a wider shift in who can develop and use increasingly capable models.
A practical decision, not a single answer
The conversation with Solaiman is framed around understanding the costs, risks, and trade-offs involved in building on open models. For startups, the useful starting point is to examine the benefits they are seeking—lower costs, control, or flexibility—and then consider the tools and infrastructure needed to pursue them.
Open models offer a compelling promise, but the questions of business value and competition remain. The decisions companies make as they adopt these models will help determine how they turn broad access into products and lasting value.