computer science

Thinking Machines Lab

Thinking Machines Lab is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Thinking Machines Lab rather than just read about it. In short: Thinking Machines Lab Inc. is an American artificial intelligence (AI) startup founded by Mira Murati, the former chief technology officer of OpenAI. Their flagship product is Inkling, an open weights large language model.

Key takeaways

Thinking Machines Lab belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Thinking Machines Lab to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Thinking Machines Lab from memory before moving on to harder problems.

Reference excerpt

Thinking Machines Lab Inc. is an American artificial intelligence (AI) startup founded by Mira Murati, the former chief technology officer of OpenAI. Their flagship product is Inkling, an open weights large language model. The company was founded in February 2025, and by July had completed an early-stage funding round led by Andreessen Horowitz, raising $2 billion at a valuation of $12 billion overall from investors such as Nvidia, AMD, Cisco, and Jane Street. The company is based in San Francisco and structured as a public benefit corporation. Inkling released under the Apache License on July 15, 2026, with 975 billion parameters; the company released Inkling Small, a distilled 276-billion parameter model with similar performance, the same month. The model was partially developed using Chinese open weights models DeepSeek-V3 for its foundation and Moonshot AI's Kimi K2.5 for post-training synthetic data. At the time, Inkling was considered the largest and most powerful open weights model released from the outside of China, exceeding Nvidia's Nemotron 3 Ultra.

History By its launch in February 2025, Thinking Machines Lab was reported to have hired about 30 researchers and engineers from competitors including OpenAI, Meta AI, and Mistral AI. Its founding team members include Barret Zoph, former OpenAI VP of Research (Post-Training), Lilian Weng, former OpenAI VP, and OpenAI cofounder John Schulman, who joined after a brief stint at the lab's competitor Anthropic. In January 2026, it was reported that Barret Zoph and Luke Metz, departed the startup to return to OpenAI. Other former OpenAI employees who have been hired include Jonathan Lachman and Andrew Tulloch (although Tulloch departed after getting recruited for Meta Superintelligence Labs). Thinking Machines Lab's advisers include Bob McGrew, previously OpenAI's chief research officer, and Alec Radford, who was a lead researcher for OpenAI. In March 2026, Thinking Machines Lab announced a strategic partnership with NVIDIA involving an undisclosed investment and a multi-year agreement to deploy one gigawatt of Vera Rubin computing capacity. In August 2026, the company moved its headquarters to the office building 2300 Harrison St, in San Francisco's Mission District.

Products On October 1, 2025, Thinking Machines Lab announced Tinker, an API for fine-tuning language models. Users would submit jobs through the API for fine-tuning one of the various open-weight models supported. The Lab would run the jobs on its internal clusters and training infrastructure. Thinking Machines Lab released its large language model Inkling on July 15, 2026, under the Apache License, with 975 billion parameters. The model drew from Chinese open weights models DeepSeek-V3 for its architecture and Moonshot AI's Kimi K2.5 for post-training synthetic data. Media outlets described Inkling the leading non-Chinese open-weights model, ahead of Nvidia's Nemotron, Mistral AI's models and, Google DeepMind's Gemma, but noting a lag behind China's open-weights releases by Z.ai, DeepSeek, MiniMax Group, and Xiaomi MiMo. Inkling Small, a 276-billion parameter model with comparable performance to Inkling, was released by the company on July 31, 2026.

Business structure Thinking Machines Lab grants Mira Murati a deciding vote on board matters, weighted to provide her with a majority decision-making capability. Additionally, founding shareholders possess votes weighted 100 times greater than those of regular shareholders. In July 2025, Andreessen Horowitz was reported to have led the company's initial funding round, raising "about $2 billion at a valuation of $12 billion". The government of Albania (Murati's country of origin) was also included in this round, making a $10 million investment which required an amendment to the country's 2025 budget.

References

External links Official website

Illustrations

Worked examples

Example 1 — a first encounter with Thinking Machines Lab

Start with the simplest possible case. Write down what Thinking Machines Lab claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Thinking Machines Lab before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Thinking Machines Lab ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Thinking Machines Lab

In research: Thinking Machines Lab appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Thinking Machines Lab in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Thinking Machines Lab is common in secondary-school and first-year university syllabi. It links to neighbouring topics 2025 establishments in California, AI safety, AI software, so understanding it makes those chapters shorter.
In everyday life: Look for Thinking Machines Lab outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Thinking Machines Lab in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Thinking Machines Lab means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Thinking Machines Lab out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Thinking Machines Lab in simple terms?

Why does Thinking Machines Lab matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Thinking Machines Lab?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Thinking Machines Lab.