I Didn’t Think Much of Mira Murati Leaving OpenAI. I Was Wrong.

When Mira Murati left OpenAI in 2024, I’ll be honest, I didn’t think much of it. Executive departures from frontier labs had started to feel routine. Another founder, another round, another company promising to build “safe” or “collaborative” AI. I filed it away and moved on.

I was wrong to.

Over the past few weeks I’ve been reading Murati’s own posts and digging through Thinking Machines Lab’s product release docs, and I’ve come away genuinely impressed. Not by the hype, there’s plenty of that in this industry, but by the specificity of what they’re actually building. Back in February, when she first announced the company, Murati laid out the mission plainly: “I started Thinking Machines Lab alongside a remarkable team of scientists, engineers, and builders. We’re building three things: helping people adapt AI systems to work for their specific needs, developing strong foundations to build more capable AI systems, and fostering a…” That’s not the language of someone chasing a headline. That’s a product roadmap.

What they actually shipped

After 17 months of near-total silence, Thinking Machines shipped its first product on July 15: Inkling, a 975-billion-parameter multimodal model, released fully open-weight under Apache 2.0. No waitlist, no closed API. Anyone can download the weights from Hugging Face today. For a company that raised the largest AI seed round in history, $2 billion at a $12 billion valuation, choosing openness for the debut product instead of a walled garden is a real bet.

What impressed me most reading their materials wasn’t a performance claim, it was an admission. One early post about the launch noted that Thinking Machines says outright Inkling is not the best model on the market. In an industry built on superlatives, that kind of candor is rare, and it’s exactly the tone that made me trust the rest of what they’re saying.

The product I actually care about, though, is Tinker. It’s a fine-tuning platform that lets you take a frontier-scale model and train it on your own data, with your own algorithms, without standing up or renting a GPU cluster yourself. Thinking Machines runs the infrastructure; you keep the data and the resulting model. One early demo showed a full post-train-and-deploy loop finishing in about 27 minutes.

Fine-tuning itself isn’t new. Hugging Face has offered the tooling for years, PEFT, AutoTrain, the Trainer API, and a whole ecosystem has grown up around it. But most of that still assumes you’re wiring together your own infrastructure, managing your own training runs, and troubleshooting your own distributed setup once you’re working at any real scale. What Thinking Machines did with Tinker is take that same idea and remove the parts that used to require a dedicated ML infra team. Frontier-scale customization, in 27 minutes, without touching a GPU cluster, isn’t a new category. It’s the same category with the friction taken out. That’s the part that feels like an upgrade, not a reinvention.

Why I think the timing is right

Here’s my read on why this is landing now, and not two years ago: open source models have genuinely caught up, frontier API pricing from Claude and OpenAI keeps climbing, and corporations that want LLM capability increasingly do not want to hand their data to a frontier provider to get it. That tension is real, and it showed up in the research too. One X post replying to Thinking Machines’ launch put it better than I could: “The real story here: Thinking Machines built a model companies can customize for their own data, without shipping to an API provider. What does that change for how you’d use AI?” That’s the question every enterprise buyer I talk to is already asking themselves.

There’s a cost angle here too. One widely-shared post cited Forbes and Ookla’s Downdetector research showing major AI platforms logged 6 high-disruption days in Q1 2025, and 51 in Q1 2026, an 8.5x jump in a single year, concluding that “single-provider deployments are a P0 incident waiting to happen.” I’d take that stat with a grain of salt given who’s citing it, but the underlying point stands: betting your product entirely on one closed frontier provider is a real risk, not a hypothetical one.

The community reaction backs this up. The top comment on Inkling’s r/LocalLLaMA announcement thread, from u/Lost_Foot_6301 at 461 upvotes, put it simply: “from the former CTO of openai, thats pretty cool they are getting into opensource.” Another top comment, from u/HoppyD, was even more direct: “let’s make ai open again.” That thread pulled 1,290 upvotes total, which for a first product launch is not nothing.

I also noticed the tone coming from inside the company. New CTO Soumith Chintala, PyTorch’s co-creator, spent launch week publicly praising competitors rather than trash-talking them, calling Moonshot’s Kimi “a world-class model” and crediting Modal’s inference work in the same week Thinking Machines shipped its own model. That’s a small thing, but it’s the kind of small thing that tells you what a team actually values.

Where I land

I haven’t used Inkling yet. I want to be upfront about that. But I’ve carved out time to build a project on it, because the combination feels right: open weights for control, Tinker for speed, and a company that seems to be building for developers first and headlines second. We’re generally excited about where Thinking Machines is headed, and I’ll write up what we build once we’ve actually shipped something with it.

By Anthony Batt — media technologist, executive at Wevr, and co-founder of CO/AI.