5 Investors Share Insights on the Advantages and Disadvantages of Open Source AI Business Models

As the generative AI landscape evolves, startups are increasingly falling into two distinct categories regarding their business models.

Some firms believe that adopting a proprietary, closed-source model will give them an edge in a crowded market, choosing to keep their AI models and infrastructure confidential. Conversely, others are opting for an open-source approach, sharing their models, methods, and datasets to foster community engagement and collective growth.

Is there a definitive answer to which approach is better? Not necessarily. Yet, investors have varying opinions on the matter.

Dave Munichiello, general partner at GV (Alphabet's investment arm), argues that open-source AI fosters customer trust through transparency. He contends that while closed-source systems might provide better performance due to simplified documentation, they lack explainability, making it harder to sell to company boards and executives.

Ganesh Bell, managing director at Insight Partners, agrees with Munichiello but notes that open-source projects can lack polish compared to their closed-source rivals. He finds that front-end interfaces in open-source models may be less consistent and harder to maintain.

When it comes to early-stage startups, some investors believe that the choice between closed source and open source is less vital than the overall go-to-market strategy. Christian Noske, a partner at NGP Capital, suggests that startups should prioritize applying their models to practical “business logic” and proving value for their customers, regardless of whether they use open-source or closed-source models.

Many customers, as pointed out by Ian Lane, a partner at Cambridge Innovation Capital, are more focused on solutions to their business problems rather than the specific characteristics of the underlying AI model. Startups that understand this will find an advantage in the competitive AI landscape.

What about regulation? Will it influence how startups scale their businesses or publish their models? Some believe it might.

Noske warns that increased regulation could raise development costs and reinforce the power of big tech companies, making it harder for smaller AI startups to compete. He advocates for clearer policies that ensure responsible data usage in AI, address labor market implications, and prevent AI misuse.

Bell, however, views regulation as an opportunity; companies developing tools to help AI vendors comply with new rules might benefit economically while enhancing trust in AI technologies.

This exploration of open source versus closed source, strategic business models, and regulation is just the beginning. The respondents also discussed transitioning from open source to closed source, security concerns linked to open-source development, and the risks surrounding API-dependent AI models.

Continue reading to hear from:

- Dave Munichiello, General Partner, GV

- Christian Noske, Partner, NGP Capital

- Ganesh Bell, Managing Director, Insight Partners

- Ian Lane, Partner, Cambridge Innovation Capital

- Ting-Ting Liu, Investor, Prosus Ventures

The following insights have been edited for brevity and clarity.