How much will openness matter to AI?

Sometimes in tech we misunderstand our history. For example, because Linux eventually commoditized the Unix wars, and because Apache and Kubernetes became the standard plumbing of the web, we assume that “openness” is an inevitable force of nature. The narrative is comforting; it’s also mostly wrong.

At least, it’s not completely correct in the ways advocates sometimes suppose.

When open source wins, it’s not because it’s morally superior or because “many eyes make all bugs shallow” (Linus’s Law). It dominates when a technology becomes infrastructure that everyone needs but no one wants to compete on.

Look at the server operating system market. Linux won because the operating system became a commodity. There was no competitive advantage in building a better proprietary kernel than your neighbor; the value moved up the stack to the applications. So, companies like Google, Facebook, and Amazon poured resources into Linux, effectively sharing the maintenance cost of the boring stuff so they could compete on the interesting stuff where data and scale matter most (search, social graphs, cloud services).

This brings us to AI. Open source advocates point to the explosion of “open weights” models like Meta’s Llama or the impressive efficiency of DeepSeek’s open source movement, and they declare that the closed era of OpenAI and Google is already over. But if you look at the actual money changing hands, the data tells a different, much more interesting story, one with a continued interplay between open and closed source.

Losing $25 billion

A recent, fascinating report by Frank Nagle (Harvard/Linux Foundation) titled “The Latent Role of Open Models in the AI Economy” attempts to quantify this disconnect. Nagle’s team analyzed data from OpenRouter and found a staggering inefficiency in the market. Today’s open models routinely achieve 90% (or more) of the performance of closed models while costing about one-sixth as much to run. In a purely rational economic environment, enterprises should be abandoning GPT-4 for Llama 3 en masse.

Nagle estimates that by sticking with expensive closed models, the global market is leaving roughly $24.8 billion on the table annually. The academic conclusion is that this is a temporary market failure, a result of “information asymmetry” or “brand trust.” The implication is that once CIOs realize they are overpaying, they will switch to open source, and the proprietary giants will topple.

Don’t bet on it.

To understand why companies are happily “wasting” $24 billion, and why AI will likely remain a hybrid of open code and closed services, we have to stop looking at AI through the lens of 1990s software development. As I’ve written, open source isn’t going to save AI because the physics of AI are fundamentally different from the physics of traditional software.

The convenience premium

In the early 2010s, we saw a similar “inefficiency” with the rise of cloud computing. You could download the exact same open source software that AWS was selling—MySQL, Linux, Apache—and run it yourself for free. Yet, as I noted, developers and enterprises flocked to the cloud, paying a massive premium for the privilege of not managing the software themselves.

Convenience trumps code freedom. Every single time.

The $24 billion “loss” Nagle identifies isn’t wasted money; it is the price of convenience, indemnification, and reliability. When an enterprise pays OpenAI or Anthropic, they aren’t just buying token generation. They are buying a service-level agreement (SLA). They are buying safety filters. They are buying the ability to sue someone if the model hallucinates something libelous.

You cannot sue a GitHub repository.

This is where the “openness wins” argument runs into reality. In the AI stack, the model weights are becoming “undifferentiated heavy lifting,” the boring infrastructure that everyone needs but no one wants to manage. The service layer (the reasoning loops, the integration, the legal air cover) is where the value lives. That layer will likely remain closed.

The ‘community’ that wasn’t

There is a deeper structural problem with the “Linux of AI” analogy. Linux won because it harnessed a large, decentralized community of contributors. The barrier to entry for contributing to a large language model (LLM) is much higher. You can fix a bug in the Linux kernel on a laptop. You cannot fix a hallucination in a 70-billion-parameter model without access to the original training data and a compute cluster that costs more than any individual developer can afford, unless you’re Elon Musk or Bill Gates.

There is also a talent inversion at play. In the Linux era, the best developers were scattered, making open source the best way to collaborate. In the AI era, the scarce talent—the researchers who understand the math behind the magic—are being hoarded inside the walled gardens of Google and OpenAI.

This changes the definition of “open.” When Meta releases Llama, the license is almost immaterial because of the barriers to running and testing that code at scale. They are not inviting you to co-create the next version. This is “source available” distribution, not open source development, regardless of the license. The contribution loop for AI models is broken. If the “community” (we invoke that nebulous word far too casually) cannot effectively patch, train, or fork the model without millions of dollars in hardware, then the model is not truly open in the way that matters for long-term sustainability.

So why are Meta, Mistral, and DeepSeek releasing these powerful models for free? As I’ve written for years, open source is selfish. Companies contribute to open source not out of charity, but because it commoditizes a competitor’s product while freeing up resources to pay more for their proprietary products. If the intelligence layer becomes free, the value shifts to the proprietary platforms that use that intelligence (conveniently, Meta owns a few of these, such as Facebook, Instagram, and WhatsApp).

Splitting the market into open and closed

We are heading toward a messy, hybrid future. The binary distinction between open and proprietary is dissolving into a spectrum of open weights, open data (rare), and fully closed services. Here is how I see the stack shaking out.

Base models will be open. The difference between GPT-4 and Llama 3 is already negligible for most business tasks. As Nagle’s data shows, the catch-up speed is accelerating. Just as you don’t pay for a TCP/IP stack, you soon won’t pay for raw token generation. This area will be dominated by players like Meta and DeepSeek that benefit from the ecosystem chaos.

The real money will shift to the data layer, which will continue to be closed. You might have the model, but if you don’t have the proprietary data to fine-tune it for medical diagnostics, legal discovery, or supply chain logistics, the model is a toy. Companies will guard their data sets with far more ferocity than they ever guarded their source code.

The reasoning and agentic layer will also stay closed, and that’s where the high-margin revenue will hide. It’s not about generating text; it’s about doing things. The agents that can autonomously navigate your Salesforce instance, negotiate a contract, or update your ERP system will be proprietary because they require complex, tightly coupled integrations and liability shields.

Enterprises will also pay for the tools that ensure they aren’t accidentally leaking intellectual property or generating hate speech–stuff like observability, safety, and governance. The model might be free, but the guardrails will cost you.

Following the money

Frank Nagle’s report correctly identifies that open models are technically competitive and economically superior in a vacuum. But business doesn’t happen in a vacuum. It happens in a boardroom where risk, convenience, and speed dictate decisions.

The history of open source is not a straight line toward total openness. It is a jagged line where code becomes free and services become expensive. AI will be no different. The future is the same as it ever was: open components powering closed services.

The winners won’t be the ideological purists. The winners will be the pragmatists who take the free, open models, wrap them in proprietary data and safety protocols, and sell them back to the enterprise at a premium. That $24 billion gap is just going to be reallocated to the companies that solve the “last mile” problem of AI: a problem that open source, for all its many virtues, has never been particularly good at solving.