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Former OpenAI CTO Mira Murati has officially launched a new AI startup called Thinking Machines Lab, which emerged today. The company is dedicated to make AI more customizable, widely understood, and generally capable. As expected, Thinking Machines Lab, the startup is focused on artificial intelligence.
Murati, now CEO of Thinking Machines Lab, has brought together an impressive team. OpenAI co-founder John Schulman is the Chief Scientist, and former OpenAI research leader Barret Zoph is the CTO. In total, about 30 researchers and engineers from companies like OpenAI, Google DeepMind, and Meta have joined the startup.
What Thinking Machines Lab is Building
Murati’s startup is focused on AI that collaborates with humans, rather than just fully autonomous systems. The team is working on multimodal AI—technology that can process and generate information across multiple types of data, such as text, images, and code.
AI safety is also a major focus. The company promises to prevent misuse of its models, share best practices, and support research on AI alignment by providing access to code, datasets, and model specifications.
The team includes scientists, engineers, and builders who have contributed to some of the most widely used AI products and open-source projects.
Murati was instrumental in developing ChatGPT, DALL·E, and GitHub Copilot at OpenAI before leaving in 2023. Her move to start Thinking Machines Lab signals a growing trend: former OpenAI leaders launching new AI ventures.
The startup is currently hiring machine learning scientists, engineers, and research program managers. Murati is reportedly raising significant venture funding, though details remain undisclosed. Soon, Murati may raise over Millions in venture capital, though the company hasn’t confirmed this.
Mira Murati serves as CEO of Thinking Machines Lab, joined by John Schulman (OpenAI co-founder) as chief scientist and Barret Zoph (former OpenAI chief research officer) as CTO.
Thinking Machines Lab aims to build AI tools that: Adapt to people’s unique needs and goals. Create more widely understood and customizable AI systems. Develop “multimodal” systems that collaborate effectively with humans. Push the frontier of capabilities in domains like science and programming.
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