From OpenAI to Thinking Machines: Mira Murati’s Bold Leap and the Opportunities Opening for New Tech Founders

In the high-stakes world of AI, one name that has resonated powerfully in recent years is Mira Murati. She played a key role at OpenAI and is now co-founding Thinking Machines Lab, a new startup that could change the way people interact with smart machines forever. Murati’s journey from OpenAI to the world of AI entrepreneurship gives us a glimpse into what’s possible for new tech founders in a field that is both exciting and unexplored.

A Humble Start: Early Life & Education

Ermira “Mira” Murati was born on December 16, 1988, in Vlorë, Albania. Her early life was shaped by curiosity, resilience, and academic ambition. At 16, she won a scholarship to Pearson College UWC in Canada, where she completed her International Baccalaureate. 

She then moved to the U.S. for higher education, earning a Bachelor of Arts from Colby College and a Bachelor of Engineering in Mechanical Engineering from Dartmouth College (Thayer School). This dual‑degree background gave her a rare combination: a liberal-arts mindset plus strong technical discipline — a foundation that would serve her well in the interdisciplinary world of AI.

Building Foundations: Early Career (Goldman Sachs, Aerospace, Tesla)

She landed a prestigious internship at Goldman Sachs and got to see the high-pressure world of finance, logical problem-solving, and forward thinking when Murati started. She then moved to Zodiac Aerospace, where she worked on engineering the bleeding edge of new concepts, which is where she really honed her technical skills and got to see the inner workings of hardware innovation. 

In 2013, she joined Tesla as a Senior Product Manager for the Model X program. At Tesla, she brought together the hardware and software teams, making sure the product is developed in harmony, and showed her ability to tackle and complete massive, high-stakes projects that would become the hallmark of her career in AI.

Leap Motion: Immersive Tech & Human-Computer Interaction

From 2016 to 2018, Murati served as VP of Product & Engineering at Leap Motion, a pioneering AR/VR startup focused on hand- and finger-tracking technology. This role deepened her understanding of human-computer interaction and gestural interfaces. 

Her work there appears to have shaped a fundamental philosophy: AI should feel like a partner, not a black box. That belief would echo in her later work at OpenAI and now at Thinking Machines.

At OpenAI: From Applied AI to CTO

Murati joined OpenAI in 2018 as VP of Applied AI & Partnerships, a role that put her at the intersection of research and real-world application. Over time, she rose to Senior Vice President, overseeing research, product, and strategic partnerships. 

She became CTO in May 2022 and led technical teams on some of OpenAI’s biggest projects, like ChatGPT, DALL·E, Codex, and the voice model Sora. She also spoke out in public about AI safety, ethics, and alignment, calling for responsible development even as the technology grew quickly.  

In November 2023, during a dramatic board shake-up, Murati was appointed interim CEO when Sam Altman was temporarily removed. This showed how trusted and powerful she had become at OpenAI. 

The Turning Point: Stepping Away to Explore

Murati announced her departure from OpenAI in September 2024, framing it as a deeply personal decision to explore her own vision. She chose a more deliberate path instead of immediately taking another corporate job. This path would let her build on her ideas without the limits of an established institution.

Thinking Machines: Vision, Mission, and the $12 B Bet

In February 2025, Murati launched Thinking Machines Lab, based in San Francisco. By July 2025, the startup closed a massive $2 billion seed round, and as a result, its value is estimated at a staggering $12 billion, according to multiple sources. The round was co-led by Andreessen Horowitz, with participation from Nvidia, Accel, Cisco, AMD, and Jane Street.

Murati has said the first product will launch in the “next couple of months,” and notably will include a significant open-source component for researchers and startups. She also promised that Thinking Machines will publish research to help the broader research community better understand “frontier AI systems.” 

Ambitious Plans Behind the Size

• Thinking Machines is working on “multimodal AI that works with how you naturally interact … through conversation, through sight, and through the messy way we work together,” according to information that is available to the public.
• According to a company datasheet, it is working on custom “memory-first” AI processors and a software stack that is supposed to be very energy efficient. This includes a 5-nanometer tape-out with TSMC and a 250-petaflop pilot cluster that is set to be released in the second quarter of 2026.

Strategic Moves Behind Her Success

Murati’s path reflects calculated, visionary choices — and the latest funding round underscores how her reputation and strategy are translating into real capital. Here are a few key enabling factors:

1. Exceptional Credibility and Network: he seed round wasn’t just big; it was the biggest ever. Wired says that the $2 billion seed is “the largest seed funding round in history.”
   
2. Talent Magnetism: Her core team includes well-known former OpenAI researchers like John Schulman, Barrett Zoph, Lilian Weng, Andrew Tulloch, and Luke Metz.
   
3. Long-Term Vision + Openness: Her commitment to releasing research and open-source tools suggests she’s building not just a company — but also a platform for more AI innovation.
   
4. Capital Efficiency & Hardware Focus: In addition, cutting-edge research into capital efficiency and hardware is another feature of Thinking Machines. They’re creating in-house hardware and cutting down energy consumption with the goal of halving the runtime costs of training and inference, something that’s a massive problem for AI models today.

What the Seed Round Means for Founders & the Broader Ecosystem

Murati’s $2 billion seed round, which valued the company at $12 billion, sends a strong message to founders, VCs, and the AI community. Here’s what it tells us:

• Investor Appetite for Frontier AI Is Exploding: The size of the round shows how sure VCs are about next-generation AI labs, especially those run by people who have already proven themselves.
  
• Talent & Vision Are Worth Billions: Backing isn’t just about current products — it’s about future potential. Murati’s network and vision convinced top-tier investors to place a massive bet.
  
• Open Research Matters: Her promise of open-source and shared science could reshape how new AI labs operate, making openness a competitive advantage rather than a risk.
  
• AI Hardware Still Matters: The focus on custom chips and computing efficiency shows that the AI race isn’t just about models; it’s also about the infrastructure.
  

Broader Trends & Context: Why Murati’s Timing Is Powerful

To fully appreciate Murati’s leap, it’s helpful to look at more general data and research in the AI and startup ecosystem:

1. AI Investments Continue to Surge
   
   • The Stanford HAI AI Index Report (2025) says that private AI investment around the world grew by 26%, and corporate AI investment reached $252.3B in 2024. 
   • Generative AI funding reached $33.9B in 2024, up 18.7% from 2023. Stanford HAI 
2. AI’s Share of VC Funding Is Soaring
   
   • PitchBook says that AI companies got 53% of all VC funding in the first half of 2025. 
   • That concentration shows that more and more investor money is going to a small number of promising AI labs instead of being spread out across many early-stage startups.
     
3. Effects of AI Adoption on Structure
   
   • A recent academic study (“Follow the money: a startup-based measure of AI exposure …”) introduced the AI Startup Exposure (AISE) index. It shows that while many high-skilled occupations are theoretically exposed to AI, the actual adoption by startups is more uneven.
     
   • Another study (“AI Investment and Firm Productivity …” in Japanese enterprises) found that younger executives (under 50) are more likely to adopt AI, and that AI adoption is associated with ~2.4% productivity gains, driven by cost reduction, innovation, and revenue enhancements.
     
   • A different research (“Artificial Intelligence, Lean Startup Method, and Product Innovations”) looked at ~1,800 Chinese startups and found that startups using AI + Lean Startup principles produced more innovative products faster.
4. Challenges and Efficiency Trade‑offs
   • A natural-experiment study on India’s knowledge-intensive startups (2016–2025) revealed that while AI-era companies secure greater funding and higher absolute valuations, their per-employee productivity and efficiency ratios may be inferior, indicating that initial AI investments frequently prioritise capacity building before efficiencies are fully materialised.
     

Risks & Challenges

Even with the incredible funding and strong team, Thinking Machines faces significant headwinds:

• Execution Risk: Building frontier multimodal AI systems is hard, and the product hasn’t been released to the public yet.
  
• Competition: Labs like OpenAI, DeepMind, Anthropic, and others are major competitors — and many have vast resources.
  
• Regulatory & Ethical Risk: As AI gets better, there will be more rules and public worries about safety, bias, and alignment.
  
• Capital Intensity: The hardware goals (like custom AI processors) need a lot of capital and advanced partnerships (like with TSMC), which come with execution risk.

Lessons & Takeaways for New Tech Founders

Mira Murati’s journey (and current move) offers powerful lessons for aspiring tech founders, especially in deep tech / AI:

1. Invest in Domain Credibility
   Building domain expertise, like Murati did, earns the trust of investors and talent over time.
2. Build a Vision That Resonates
   A bold, future-focused mission, especially in cutting-edge AI, can attract a lot of capital, even before the product-market fit is found.
3. Leverage Openness as a Differentiator
   Murati’s plan to include open-source components and publish research is not just altruistic — it’s strategic. It helps build credibility, community, and a research network.
4. Bridge Tech & Hardware
   Founders who think beyond models and into efficient computing, hardware, and architecture may gain a competitive edge.
5. Be Prepared for Risk
   Massive capital, moonshot vision, and high-profile talent also bring pressure: to deliver, scale responsibly, and maintain ethical guardrails.
   

Looking Ahead: What This Means for the Future of AI

Mira Murati’s transition from OpenAI CTO to founder of a high-valuation, deeply ambitious AI lab is a significant moment in the AI ecosystem. Her path suggests that:

• AI entrepreneurship is maturing, and independent, well-funded startups can compete, not just big tech labs.
  
• Open research and multimodal AI could be the next big thing in making AI that really understands what people are doing.
  
• Hardware innovation (efficiency, custom chips) is far from dead; it’s still a big part of the future of AI scale.
  
• Vision from the founder is more important than ever. Investors are putting more money into companies led by experienced AI leaders who have been on the front lines.
  

A Data-Driven Founder’s Leap

Mira Murati’s story is compelling not just because of her personal journey, but because it is deeply intertwined with macro trends and cutting-edge research. Her move to start Thinking Machines Lab, backed by $2 billion in seed funding at a $12B valuation, shows how much people believe in her vision.

But it’s not all about the money. The timing is right because big changes are happening in AI research, investment, and use. Her method, which uses open science, high-performance hardware, and multimodal AI, could have a big impact on how the next generation of AI systems is made and run.

For new founders, Murati’s story can be an invaluable inspiration and guide for founders. Invest in your strengths, dare to dream big, build with responsibility and utilise both open-source and proprietary approaches, as she did. It’s clear in her journey that she is not just building the future of AI, but shaping it.

Empowering New Tech Founders: Global Talent at Your Fingertips

When building a new tech company, like Mira Murati’s Frontier AI Lab, Thinking Machines Lab, you need more than vision and funding to get started. You need the right people with the right skills, and that can be difficult to find, particularly in a global talent market. Well-known platforms like Kinetic Innovative Staffing provide a solution for founders and small businesses to find, attract and deploy talent all over the world.

1. Accessing the Best Talent Globally

Accessing top talent is a challenge, especially for early-stage founders and engineers in need of highly skilled engineers, developers, data scientists or AI specialists, and don’t want to break the bank hiring them locally, when launching a startup. Coming hotfooting off the plane to Silicon Valley won’t be an option either, due to the financial strain, so Kinetic Innovative Staffing gives them a way into a massive pool of over nine million (9 Million + ) international professionals, with specialists in”

• Software development and AI/ML engineering
• Digital marketing and creative services
• Accounting, payroll and business operations
• Customer support and virtual assistance. 

Well-known for its system of pre-screening and approving its talent, Kinetic Innovative Staffing sends startups directly to the most suitable and high-calibre workers, enabling them to build top-notch teams, even when they don’t know many people in the area. 

2. Reducing Staffing Costs by up to 76%

One of the biggest obstacles for small businesses and AI startups is financial, and hiring local talent is expensive, especially in tech-heavy cities. Kinetic Innovative Staffing offers a solution: hiring remote workers in the Philippines, which slashes staffing costs by up to 76% and frees up funds that can be better used for investments in

• Hardware infrastructure, servers, or GPU clusters
  
• Marketing, research, and development
  
• Accelerating product launches without financial strain
  

It’s basically what large AI labs do at scale, concentrating resources on high-impact areas and minimising overheads. 

3. Scaling Quickly with Flexibility

The AI startup landscape moves fast. For founders, delays in staffing can slow product development, research, and iteration. By leveraging Kinetic Innovative Staffing, startups can:

• Onboard multiple team members in weeks, not months
  
• Flexibly adjust team size based on project milestones
  
• Access specialised expertise (AI, engineering, analytics) without long-term employment commitments
  

This agility is crucial for founders looking to capitalise on market timing, much like Murati did in securing her massive seed funding while aligning her team and vision.

4. Maintaining Quality While Expanding

Cost-cutting doesn’t have to mean dropping the standard of your workforce. Kinetic Innovative Staffing’s plan guarantees businesses:

• Hire professionals with strong academic and technical backgrounds
  
• Gain access to candidates experienced in startup and remote work environments
  
• Benefit from ongoing training and support, improving retention and performance
  

For AI founders,  this allows them to scale up their research teams, software development, or operational assistance without exhausting all their resources. 

5. From Vision to Execution: Supporting Your AI Journey

Just as Murati’s success relied on assembling a team of expert engineers, researchers, and product managers, startups can replicate this on a global scale. Kinetic Innovative Staffing bridges the gap between vision and execution:

• Founders can focus on building products, refining AI models, and securing investment
  
• Remote teams handle critical functions like software development, testing, customer support, and data management
  
• Startups can expand internationally without opening multiple expensive offices, leveraging global talent from day one
  

Often face the challenge of finding the right talent and scaling up to meet their demands when ambitious tech founders want to turn their idea into a fully operational AI startup. Platforms like Kinetic Innovative Staffing provide a practical solution to the high costs and logistics associated with global-scale staffing for tech startups.  Kinetic Innovative Staffing brings the founder’s vision to life by supplying them with the necessary personnel. It’s no longer necessary to take a gamble on talent from international markets. Kinetic’s streamlined hiring process, comprehensive training and better operational efficiency bring results much faster. The company makes sure to work at a sustainable cost.

Important Things to Remember for AI and Tech Founders

1. Vision + Talent = Execution: Big ideas require capable teams. Even Murati’s $12B seed round wouldn’t matter without her expert team.
   
2. Global Staffing Reduces Risk: Remote teams can significantly lower costs, enabling startups to allocate funds toward research, infrastructure, and product development.
   
3. Scale Fast, Smartly: Rapid hiring through a vetted platform like Kinetic lets founders meet deadlines, build faster, and pivot as the market evolves.
   
4. Maintain Operational Discipline: Access to affordable, skilled staff allows startups to balance growth and efficiency, avoiding early-stage overburn.
   
5. Leverage Global Networks: By connecting with international talent, founders can bring diverse expertise and perspectives — often a differentiator in frontier AI development.
   

By combining visionary leadership like Mira Murati’s with practical operational support via Kinetic Innovative Staffing, new founders can navigate both the exciting opportunities and the daunting challenges of AI entrepreneurship. Whether it’s launching a deep-tech lab, a SaaS startup, or a research-heavy AI venture, the right team — sourced globally and cost-effectively — can make all the difference.

References (APA Style)

1. Wikipedia. (2025). Mira Murati. Retrieved November 16, 2025, from https://en.wikipedia.org/wiki/Mira_Murati 
2. TechCrunch. (2025, July 15). Mira Murati’s Thinking Machines Lab is worth $12B in seed round. Retrieved from https://techcrunch.com/2025/07/15/mira-muratis-thinking-machines-lab-is-worth-12b-in-seed-round 
3. TechCrunch. (2025, June 20). Mira Murati’s Thinking Machines Lab closes on $2 B at $10 B valuation. Retrieved from https://techcrunch.com/2025/06/20/mira-muratis-thinking-machines-lab-closes-on-2b-at-10b-valuation 
4. Bloomberg. (2025, June 23). Murati’s Thinking Machines Raises Cash at $10 Billion Valuation. Retrieved from https://www.bloomberg.com/news/articles/2025-06-23/murati-s-thinking-machines-raises-cash-at-10-billion-valuation 
5. Dataconomy. (2025, July 16). Mira Murati’s AI startup lands massive $2 B seed. Retrieved from https://dataconomy.com/2025/07/16/mira-muratis-ai-startup-lands-massive-2b-seed 
6. BrandVM. (2025). Thinking Machines Lab Hits $12 B Valuation… custom AI processors + software stack. Retrieved from https://www.brandvm.com/post/thinking-machines-12b-valuation-2025 
7. Fortune. (2025, July 28). Mira Murati’s $2 B seed round inspires female founders. Retrieved from https://fortune.com/2025/07/28/mira-murati-2-billion-seed-round-thinking-machines-lab-openai-female-founders 
8. Economic Times. (2025). Mira Murati’s Thinking Machines Lab raises $2 B seed round at $10B valuation. Retrieved from https://economictimes.indiatimes.com/tech/funding/mira-muratis-thinking-machines-lab-raises-2-billion-seed-round-at-10-billion-valuation/articleshow/122003222.cms 
9. FinancialIT. (2025). Mira Murati’s Thinking Machines Lab Hits $12B Valuation in Seed Round. Retrieved from https://financialit.net/news/fundraising-news/thinking-machines-raises-2-billion-funding-round-led-andreessen-horowitz 
10. TechCrunch. (2025, April 10). Mira Murati’s AI Startup Aiming for $2 B Seed Round. Retrieved from https://techcrunch.com/2025/04/10/mira-muratis-ai-startup-is-reportedly-aiming-for-a-massive-2b-seed-round 
11. CNBC. (2025, July 22). AI startups raised $104B in first half of 2025, exits lag behind. Retrieved from https://www.cnbc.com/2025/07/22/ai-startups-raised-104-billion-in-first-half-exits-different-story.html 
12. PractoMind. (2025). AI startups raised $104B in H1, exits different story. Retrieved from https://practomind.com/blogs/ai-startups-raised-104-billion-in-first-half-of-year-but-exits-tell-a-different-story 
13. Jafari, Mobini Dehkordi, Chitsaz & Yaghoobzadeh. (2025). SAISE Framework for Startup Evaluation. arXiv. Retrieved from https://arxiv.org/abs/2508.05491 
14. Arxiv. (2025). AIs Structural Impact on India’s Knowledge Intensive Startup Ecosystem. Retrieved from https://arxiv.org/abs/2507.19775 
15. Arxiv. (2025). Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits. Retrieved from https://arxiv.org/abs/2510.09465 
16. Arxiv. (2025). Reasoning‑Based AI for Startup Evaluation (R.A.I.S.E.). Retrieved from https://arxiv.org/abs/2504.12090 

FAQs

1. Why is Mira Murati’s move from OpenAI to founding Thinking Machines Lab significant?

Murati’s transition marks a major shift in the AI landscape. As OpenAI’s former CTO and the leader behind ChatGPT, DALL·E, and Codex, her decision to launch her own AI lab — backed by a record-breaking $2B seed round — shows that independent founders with strong vision and credibility can now compete directly with major AI labs like OpenAI, Anthropic, and DeepMind.

2. What makes Thinking Machines Lab different from other frontier AI startups?

Thinking Machines focuses on multimodal AI, custom memory-first processors, and an energy-efficient software stack. Its commitment to open-source components and publishing research sets it apart from closed labs. This mix of frontier research + open science + hardware innovation is rare among early-stage startups.

3. How does this massive $2B seed round impact new tech and AI founders?

It signals that investors are aggressively backing frontier AI — especially founders with deep experience, strong networks, and bold visions. Murati’s success shows that early-stage founders can raise meaningful capital if they combine technical credibility with long-term ambition, even before launching a product.

4. Why should AI and tech founders consider using global talent platforms like Kinetic Innovative Staffing?

AI startups need highly skilled engineers, researchers, and operators — but hiring locally is expensive and slow. Kinetic Innovative Staffing gives founders access to 9M+ global specialists while reducing staffing costs by up to 76%, allowing them to redirect more capital toward GPUs, infrastructure, and R&D — just like major AI labs do.

5. What key lessons can founders learn from Mira Murati’s journey?

Murati’s path highlights five core principles:

• Credibility attracts capital. Deep expertise builds investor trust.
• Vision matters. Frontier ideas can unlock massive funding early.
  
• Openness is strategic. Publishing research builds community and influence.
  
• Talent is everything. Breakthroughs happen when the right people work together.
  
• Think beyond software. Hardware efficiency and architecture will define the next era of AI.
  

These lessons offer a roadmap for founders building ambitious AI or deep-tech companies.