Original Title: Owning vs. Renting Intelligence
Original Author: Lin Qiao
Translation: Peggy, BlockBeats

Editor's Note: Mythos was shut down this week, reminding many AI entrepreneurs of a question obscured by cost discussions: when a product's core capability is built on external models and platforms, what does the company truly own?

In recent years, open-source models have often been discussed within the framework of "cheaper alternatives to cutting-edge models." But this article argues that cost is not the most critical variable; control is. For an AI company, calling an API for cutting-edge models can quickly launch products and lower technical barriers, but it also means that core capabilities may be subject to the rules, prices, strategic adjustments, or even takedown decisions of model providers.

The article further suggests that "owning intelligence" does not mean abandoning cutting-edge models, but rather that companies should embed their data, workflows, domain knowledge, evaluation standards, and edge cases into a controllable model system. The future AI competition may not be dominated by a single largest model, but by multiple "frontiers": general-purpose frontier models, enterprise-specific fine-tuned models, vertical niche models, and routing systems composed of multiple models working together.

The shutdown of Mythos serves as a reminder: the true moat in the AI era is not just how powerful a model you can call, but whether you can turn intelligence into your company's own asset.

Below is the original text:

Mythos was shut down this week. Whether you agree with this decision or not, that’s not the point.

What really stings many people is: a company built on intelligence it cannot control suddenly finds itself under a set of decisions it cannot influence. Many founders, after seeing this, ask themselves the same question: which parts of my business are actually just "rented"?

In recent years, discussions about open-source models have mostly focused on cost: can they really perform tasks? If so, how much cheaper are they compared to calling cutting-edge model APIs?

By now, we have quite clear answers. We have worked with companies like @RampLabs, @cursor_ai, @harvey, and the typical approach is similar: start with a powerful open-source model, fine-tune it on the tasks that truly matter to the company, and continuously compare it with cutting-edge models through strict evaluation.

The results are consistently surprising. For the most critical tasks, a fine-tuned open-source model can often approach or even match the quality of cutting-edge models at very low cost.

But what became clear this week is that cost has never been the most important issue.

The deeper issue is control. Who truly owns the intelligence your product depends on?

Many recent discussions boil down to the analogy of "rent" versus "own." The comparison isn't perfect, but it’s very useful.

Renting Intelligence

Renting has always worked well before problems arise. Apartments are move-in ready, lights work, water pipes are functional, repairs are handled. That’s why most companies choose this path initially.

Cutting-edge model APIs are excellent products. They enable startups to build things that seemed impossible just a few years ago.

But renting also means restrictions. Landlords can raise rent, decide what modifications you can make, change the rules. Occasionally, for reasons unrelated to you, they can also tell you: it’s time to move out.

You haven't done anything wrong. You’re just operating on someone else’s turf.

That’s why Mythos’s story resonates with so many. When your core capability depends entirely on someone else’s platform, you expose yourself to a set of decisions beyond your control.

Most of the time, this isn’t critical. But sometimes, it can become extremely important in an instant.

Owning Intelligence

The lesson isn’t that companies should stop using cutting-edge models. Far from it. Cutting-edge model labs have achieved extraordinary technology. Most products should use them. We do too.

In many ways, cutting-edge models are becoming infrastructure. But infrastructure and ownership are two different things.

You can use public infrastructure while still owning what truly creates value for your business. In AI, "owning" means starting from an advanced open-source model and shaping it around your company's most unique parts.

Your data.

Your workflows.

Your domain knowledge.

Your edge cases.

Your evaluation standards.

Your definition of "good."

Over time, this model will become less general and more reflective of the actual work your company handles daily. That’s where value is created.

Think of it as a house. Moving furniture is easy, repainting a wall is easy. But if your future depends on the layout of the house itself, you’ll eventually want the ability to move walls. The same applies to intelligence.

When intelligence truly belongs to you, no one can silently remove the floor beneath your product.

That’s also why we build Fireworks this way.

We place training and inference within the same system, allowing companies to adopt the best open-source models, shape them around their most important issues, and deploy steadily into production.

Not just consuming intelligence. Owning intelligence.

There is no single frontier

There’s also an optimistic lesson this week: the future of AI isn’t determined by one model winning everything.

There is no single frontier. There are many kinds of frontiers.

A frontier model is one kind of frontier.

A model fine-tuned on years of proprietary company knowledge is another kind of frontier.

A specialized model that solves a narrow problem better than any other model is yet another frontier.

A system that routes requests to multiple models, enabling them to collaborate and outperform individual models on many tasks, is also a frontier.

The most interesting change in AI isn’t that one model is becoming smarter, but that intelligence is becoming more customizable.

The companies that ultimately succeed won’t necessarily own the largest models, but those that can turn intelligence into their own unique assets.

Looking Ahead

Much of this week was spent reacting to news, but we continue to release products: @Kimi_Moonshot K2.7 Code, @MiniMax_AI M3, @Alibaba_Qwen 3.7 Plus.

My hope for the future isn’t a model quietly consuming everything it sees.

It’s that many teams can own their own part of the frontier.

If Mythos’s shutdown prompts you to reconsider the trade-offs involved, we’re happy to chat.

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