Former OpenAI CTO Mira Murati Launches Thinking Machines AI Lab

Mira Murati, former Chief Technology Officer (CTO) of OpenAI, has launched Thinking Machines Lab, a new AI research and product company aimed at making artificial intelligence more accessible, customizable, and collaborative.

Bridging AI Knowledge Gaps

Thinking Machines Lab seeks to address critical gaps in AI development. While AI capabilities have rapidly advanced, public understanding and accessibility remain limited. The company’s mission is to create AI systems that are more transparent, adaptable, and useful across industries by:

• Expanding scientific understanding of frontier AI models

• Making AI systems easier to customize for individual needs and values

• Enhancing human-AI collaboration rather than focusing solely on full automation

According to Thinking Machines Lab, AI systems today remain difficult for people to tailor to their specific needs. The company aims to bridge this gap by making AI more widely understood, customizable, and generally capable.

A Team of AI Industry Leaders

Thinking Machines Lab brings together scientists, engineers, and builders who have contributed to some of the most widely used AI technologies, including:

• ChatGPT (OpenAI)

• Character.ai

• Mistral’s open-weight models

• Open-source projects such as PyTorch, OpenAI Gym, Segment, Anything, and Fairseq

With a team of experts—including Barret Zoph (CTO) and John Schulman (Chief Scientist)—the company aims to push AI development while maintaining a strong focus on safety and responsible deployment.

Core Principles of Thinking Machines Lab

The company’s vision is built on three key pillars:

• Human-AI Collaboration – Prioritizing multimodal AI that seamlessly integrates with human expertise across different fields, instead of solely relying on autonomous AI systems.

• Customization & Adaptability – Developing AI systems that are more flexible and tailored to individual or industry-specific needs in every field of work with a wider range of real-world applications, beyond just programming or mathematics.

• Scientific Advancement – Publishing research, sharing code, and supporting AI alignment and safety efforts to ensure responsible AI deployment.

Murati and her team emphasize that scientific progress is a collective effort. Thinking Machines Lab plans to regularly publish technical blog posts, papers, and code to advance AI research and development.

Building a Strong Foundation for AI Innovation

• Advancing Model Intelligence: At Thinking Machines Lab, model intelligence is the cornerstone of its work. In addition to prioritizing human-AI collaboration and customization, the company is focused on developing cutting-edge AI models that push the limits of science and programming. These advancements aim to enable breakthroughs in scientific discovery, engineering, and beyond.

• Prioritizing High-Quality Infrastructure: Research productivity depends on reliable, efficient, and scalable infrastructure. Rather than taking shortcuts, Thinking Machines Lab is committed to building long-term solutions that enhance productivity while maintaining the highest standards of security and performance.

• Emphasizing Advanced Multimodal Capabilities: Multimodal AI—which integrates information across text, images, audio, and more—is considered critical for improving communication, capturing intent, and enabling deeper real-world integration. Thinking Machines Lab aims to push the boundaries of practical AI applications by advancing this technology.

An Iterative Approach to AI Development

Research and Product Co-Design

The company takes an iterative approach to AI research and product development. By deploying AI products in real-world environments, the team gathers valuable insights that inform and refine its research. This continuous feedback loop ensures that advancements remain grounded in practical, high-impact solutions.

A Rigorous Approach to AI Safety

Thinking Machines Lab emphasizes that AI safety requires both proactive research and real-world testing. Its approach includes:

• Maintaining a high safety bar – Preventing misuse while ensuring user freedom.

• Sharing industry best practices – Providing guidelines for building safer AI systems.

• Supporting external alignment research – Openly sharing code, datasets, and model specifications to accelerate AI safety advancements.

By leveraging red-teaming, post-deployment monitoring, and continuous evaluation, the company aims to develop safeguards that will be essential for both current and future AI systems.

Focusing on Meaningful Impact

Rather than optimizing for conventional AI benchmarks, Thinking Machines Lab prioritizes real-world impact. The company aims to measure success based on how AI delivers tangible value in practical applications, rather than just improving existing metrics.

Looking Ahead

As Thinking Machines begins operations, it is actively recruiting AI researchers, engineers, and product builders to help shape the future of AI. The company is focused on developing cutting-edge AI models while maintaining a commitment to safety, security, and transparency.

For more information, follow @thinkymachines on X or visit their website for job opportunities.

What This Means

Murati's launch of Thinking Machines Lab arrives at a pivotal moment in AI's evolution. While companies like OpenAI, Anthropic, and Google DeepMind have dominated headlines with increasingly powerful foundation models, these breakthroughs have primarily benefited tech specialists rather than everyday users across diverse industries.

This venture represents a potential correction to AI's current trajectory. The industry has been caught in a capabilities race that prioritizes raw performance over practical usability, creating sophisticated systems that remain black boxes to most potential users. By emphasizing customization and human collaboration, Murati is challenging the implicit assumption that better AI simply means more autonomous AI.

The timing is particularly significant as enterprises increasingly struggle to extract real business value from generic AI systems. Organizations have discovered that deploying foundational models without customization often yields disappointing results that don't justify their implementation costs.

Murati's move also carries competitive implications for OpenAI. By assembling a team with expertise across multiple leading AI organizations, Thinking Machines Lab creates a new innovation hub that could accelerate progress in areas OpenAI may have deprioritized. This diversification of research focus across multiple companies likely benefits the field as a whole.

Perhaps most importantly, this approach addresses growing public skepticism about AI's benefits. By making systems more transparent and adaptable to individual values, Thinking Machines Lab offers a vision of AI that might earn greater trust and acceptance - potentially helping the industry navigate increasing regulatory scrutiny and public concern.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.