Divide Between Open-Source and Closed-Source AI Models Is Reshaping Tech Industry Dynamics – Report

With industry professionals divided in their approaches, CBInsights look at the winners and losers in the open-source vs. closed-source landscape and what enterprises “need to think about next for adoption.”

As part of their series on the generative AI divide, CBInsights has examined many of the key industry trends.

The divide between open-source and closed-source AI models is “reshaping tech industry dynamics.”

According to the update from CBInsights, companies building generative AI applications must understand this evolving landscape as it “has crucial implications for the infrastructure they adopt.”

Based on current trends, CBInsights researchers expect:

  • Consolidation around frontier models: Closed-source models from players like OpenAI, Anthropic, and Google will dominate the market. Only tech giants like Meta, Nvidia, and Alibaba are likely to sustain the costs of developing open-source models that can compete on performance with proprietary ones. Frontier model training costs are growing 2.4x annually, driven by hardware, staffing, and energy needs, according to Epoch AI.
  • Revenue and investment gaps threaten open-source model developers’ viability: While burning cash, closed-source leaders like Anthropic and OpenAI lead the private market in funding, revenue, and commercial traction. Open-source developers face similar costs but struggle to generate revenue or attract capital investment ($14.9B vs. closed-source’s $37.5B since 2020). This suggests they will move to commercialize their closed models (e.g., Mistral AI) and/or pivot to smaller, specialized offerings (e.g., Aleph Alpha).
  • Smaller models drive open-source adoption: Industry leaders, alongside a range of smaller players, are releasing smaller, specialized open-source models, as evidenced by Microsoft‘s Phi, Google’s Gemma, and Apple‘s OpenELM. This suggests a two-tier market for enterprises evaluating the landscape: closed-source frontier models for the most sophisticated applications and open-source smaller models for edge and specialized use cases.

CBInsights data has been used to map out the open-source and closed-source AI landscape.

Their analysis focuses on foundation models — the powerful, general-purpose AI systems that form a “critical infrastructure layer.”

As noted in the report from CBInsights, tech leaders have staked out clear positions: Meta and xAI are open-sourcing models like Llama 3.1 and Grok-1, while Google and OpenAI have “largely walled off their systems.”

Investment flows are also split between both approaches.

Since 2020, private open-source AI model developers have “attracted $14.9B in venture funding, while closed-source developers have secured $37.5B — reflecting different bets on how AI innovation will unfold.”

As explained in the report from CBInsights, the core difference lies in access: closed-source approaches “keep model details and weights proprietary, while open-source development makes these elements available so models can be more freely studied, run, and adapted.”

CBInsights pointed out that as LLM developers burn through cash, the focus has shifted to customer adoption — and revenue.

Based on CBInsights business relationship data, OpenAI is far “ahead of its peers in terms of its disclosed partnerships and client relationships.”

As clarified in the update, this business relationship analysis is “limited to publicly disclosed partnership, client, and licensing agreements for pure-play model developers to highlight adoption trends.”

Relationships are not exhaustive and are “directionally representative of trends across model developers’ partner and client relationships.”

In terms of revenue, CBInsights noted that OpenAI leads, with “projections of $3.7B in annual revenues for 2024 and $11.6B for 2025.”

But it’s also been burning cash: the company projected “midway through the year that it would lose $5B in 2024.”



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