Why Proprietary AI Leads Over Open AI Models

In August, the White House hosted representatives from the nation’s top AI companies. In a closed-door meeting, the leaders reportedly discussed voluntary AI safety testing for advanced AI models. The regulations would only cover proprietary models that were deemed to be a “national security threat,” but would exclude open-source and open-weight AI models.

Higher scrutiny of proprietary models comes as demand for open-source and open-weight AI models is on the rise, driven by performance advancements and soaring commercial costs for U.S. frontier systems.

In a recent post on his long-running tech blog, MIT Initiative on the Digital Economy Digital Fellow Irving Wladawsky-Berger breaks down the evolving complexities surrounding the adoption and use of open-source AI models. Drawing from recent research from IDE Research Scientist and Microsoft Chief Economist Frank Nagle, as well as insights from AI tech leaders, Wladawsky-Berger offers his own theory of why proprietary models dominate today and where the technology is heading.

Below is an except from the post.

Proprietary vs Open AI Models: Experiment → Learn → Specialize → Scale

Open AI models present an intriguing economic puzzle. They can often deliver performance approaching that of leading proprietary models at a fraction of the cost. Yet enterprises continue to overwhelmingly favor closed, proprietary models — and by some measures, their share of enterprise AI spending is actually increasing. Why is that the case?

The answer may have less to do with the relative merits of open versus proprietary AI than with where enterprises currently find themselves on the AI adoption curve.

“Artificial intelligence is reshaping economic systems at a pace we have rarely seen in modern technological history,” wrote Frank Nagle, — research scientist at MIT’s Initiative on the Digital Economy and newly appointed chief economist at Microsoft’s AI Economy Institute, — in a November 2025 blog, “Revealing the Hidden Economics of Open Models in the AI Era.”

“Yet amid the excitement, a crucial part of the story has been missing,” he added. “Specifically, understanding the role that open models play in the AI economy, and how much value is being left on the table when organizations overlook open alternatives, are two topics requiring a closer look.”

In “The Latent Role of Open Models in the AI Economy,” Nagle and Georgia Tech professor Daniel Yue probed these questions by analyzing a comprehensive dataset of AI model usage, prices, and performance. Their research uncovered a striking economic puzzle.

Closed models account for roughly 80% of model usage and 96% of revenue, even though they cost, on average, six times more than competing open models. Meanwhile, open models routinely achieve 90% or more of the performance of proprietary models and typically close much of the performance gap within a few months after the release of a new closed model.

“The findings surprised even us,” Nagle wrote.”

So why are enterprises continuing to spend so much more on proprietary models?

A recent article, “Everyone Is Wrong About Open Source AI in the Enterprise,” by Decagon co-founder and CEO Jesse Zhang, offers a very interesting explanation.

“The prevailing story right now is that open source is eating the enterprise,” Zhang wrote. “The capability gap between the best closed and open models has shrunk to low single digits. A third of the Fortune 500 has verified accounts on Hugging Face.”

“And yet enterprise spend as a whole is moving the opposite direction. Open source models just fell to 11% of enterprise LLM spend, down from 19% a year ago. The trend is actually moving the other way compared to the popular narrative. Why is this and what does it mean for the future?”

Frontier Models for Discovery; Open Models for Production

Zhang offers a compelling resolution of this apparent paradox based on his own company’s experience. Decagon develops conversational AI agents for customer service, and roughly 90% of its workloads run on open models. “It wasn’t cost, and it wasn’t because our customers demanded it (though they don’t mind it),” he wrote. “It was because we had no other option.”

The reason has to do with where an AI application is in its development lifecycle.

“When a use case is new, you want the smartest general-purpose model you can get,” Zhang explained. “You don’t know the shape of the problem yet, so you pay a premium for intelligence you may not end up needing. That’s the right trade at that stage.”

But the economics change as the application matures.

“Once the use case is fully built out, when you know the distribution of inputs, the behaviors you need, and the failure modes to guard against, the trade flips. Now general intelligence is overhead, and you want the smallest, fastest model fine-tuned to do your specific thing extremely well.”

In other words, the AI deployment lifecycle might be summarized as:

Experiment → Learn → Specialize → Scale

During experimentation, the most capable frontier models offer significant advantages. Enterprises are still discovering what AI can do, which applications create real value, and how their workflows need to change. At this stage, paying a premium for the flexibility and broad intelligence of a proprietary frontier model can make considerable sense.

But once an application is well understood and operating at scale, the requirements change. Speed, latency, customization, control, and cost become increasingly important. At that point, smaller, specialized open models may become much more attractive.

Customer service is a particularly good example.

“Customer service happens to be one of the most obvious AI use cases in the industry,” Zhang wrote. It involves “well-understood workflows, enormous conversation volume, tight quality bars.”

Companies like Decagon may simply be further along the AI maturity curve than the average enterprise deployment. “When you’re running AI agents in production for customer service, latency makes or breaks the product,” Zhang explained. “A conversation where every turn takes 8 seconds is not a product anyone will use.”

That creates a need for smaller, faster models. But small models generally don’t meet demanding enterprise quality standards without significant customization. “They only get there through heavy fine-tuning on the exact task.”

And that, according to Zhang, is where open models have an important advantage.

“The frontier labs don’t really sell this combination. You can’t fine-tune their best models the way we need to, and their small models aren’t ours to shape. Small + fine-tuned means open weights.”

“The cost savings are real but secondary,” he added, “and enterprise comfort with self-hosted models is a nice side effect, not the reason.”

This suggests that the continued dominance of proprietary models doesn’t necessarily mean that open models are losing the competition. It may simply mean, as Zhang argues, that “enterprise AI as a whole is at the very beginning of the maturity curve.”

To read the rest of the post, go to Irving Wladaswky-Berger’s blog.

The post appeared first on MIT Initiative on the Digital Economy.