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

Editor’s Note: Mythos was shut down this week, prompting many AI entrepreneurs to revisit a question that cost discussions have overshadowed: when a product’s core capabilities are built on external models and platforms, what exactly does a company truly own?

In the past few years, discussions about open-source models have often been framed as “cheaper alternatives to frontier models.” But this article argues that cost is not the most critical variable—control is. For an AI company, calling a frontier model API can quickly launch a product and lower technical barriers, but it also means that core capabilities may be constrained by the model provider’s rules, pricing, strategy changes, or even decisions to take the model offline.

The article further suggests that “owning intelligence” does not mean giving up frontier models. Instead, enterprises should store their own data, workflows, domain knowledge, evaluation standards, and edge cases within a controllable model system. The future competition in AI may not be dominated by a single largest model; there will be multiple “frontiers”: general frontier models, enterprise-specific post-training models, vertical specialized models, and routing systems composed of multiple models working together.

Mythos’s shutdown, therefore, feels like a reminder: in the AI era, the real moat is not just how powerful a model you can call, but whether you can turn intelligence into an asset that belongs to your company.

Below is the original text:

Mythos was shut down this week. Whether you agree with this decision or not, it’s no longer the point.

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

In the past few years, discussions about open-source models have mostly focused on costs: Can they really accomplish tasks? If so, how much cheaper are they compared to calling frontier model APIs?

Now, we already have fairly clear answers. We have worked with companies like @RampLabs, @cursor_ai, @harvey, and so on. The basic path is similar: start from a strong open-source model, fine-tune it on the work that truly matters to the company, and continuously compare it with frontier models through rigorous evaluation.

The results are surprising again and again. On the tasks that enterprises care about most, a tuned open-source model often manages to come close to—or even reach—the quality of frontier models at very low cost.

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

The deeper problem is control. Who actually owns the intelligence that your product depends on?

Recently, many discussions have been summarized as the difference between “renting” and “owning.” The analogy isn’t perfect, but it’s very useful.

Renting Intelligence

Renting has always worked well before it goes wrong. Apartments are move-in ready—you can carry your bags in and live there. The lights can turn on, the water pipes work, and repairs are handled. That’s why most companies choose this route at the beginning.

Frontier model APIs are excellent products. They enable startups to build things that, a few years ago, would have seemed unbelievable.

But renting also comes with limitations. Landlords can raise rent, decide what kinds of renovations you can make, and change the rules. And occasionally, for reasons that have nothing to do with you, they can even tell you: you need to move out.

You didn’t do anything wrong. You’ve just been operating on someone else’s turf.

That’s also why the story of Mythos resonates with so many people. When your core capabilities depend entirely on someone else’s platform, you end up exposed to a set of decisions beyond your control.

Most of the time, that’s not a big deal. But sometimes, in an instant, it becomes extremely important.

Owning Intelligence

The lesson here is not that companies should stop using frontier models. Far from it. Frontier model labs have already achieved extraordinary technology. Most products should use them. We use them ourselves too.

In many ways, frontier 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 the AI field, “owning” means starting from a cutting-edge open-source model and shaping it around the most unique parts of your company.

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 able to reflect the actual work your company handles every day. Value is created right here.

You can think of it like a house. Moving furniture is easy, and painting a wall is easy. But if your future depends on the house’s layout itself, you will eventually want the ability to move walls. Intelligence is the same.

When intelligence truly belongs to you, no one can quietly pull the floor out from under your product.

That’s also why we built Fireworks in this way.

We put training and inference into the same system, enabling companies to adopt the best open-source models, shape them around the most important questions in their own business, and deploy them steadily into production.

Not just consuming intelligence. Owning intelligence.

No single frontier

There’s also an optimistic takeaway this week: the future of AI doesn’t depend on 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 based on years of a company’s proprietary knowledge is another kind of frontier.

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

A system that routes requests to multiple models, allowing them to collaborate and outperform a single model across many tasks, is also a frontier.

The most interesting change in the AI field isn’t that one model is getting smarter and smarter; it’s that intelligence is becoming more and more customizable.

In the end, the companies that win out may not be the ones with the largest models, but those that can turn intelligence into their own unique assets.

Looking Ahead

A lot of this week was spent reacting to news, but we chose to keep releasing products: @Kimi_Moonshot K2.7 Code, @MiniMax_AI M3, @Alibaba_Qwen 3.7 Plus.

The future I’m looking forward to isn’t one where a single model quietly consumes everything it sees.

It’s one where many teams can own their own portion of the frontier.

If Mythos’s shutdown causes you to start reconsidering the trade-offs involved, we’d be happy to chat.

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