Michael Heinrich is the Co-Founder and CEO of 0G Labs, a layer 1 blockchain building decentralised infrastructure to make AI a public good. Under his leadership, 0G has secured over $350 million in ecosystem commitments and is expanding its network of AI-native applications. A serial entrepreneur and former Bridgewater strategist, Michael also founded Garten, a Y Combinator and Stanford StartX-backed corporate wellbeing company that grew to over $200 million in revenue and employed a 650-person team. His background spans product vision, crisis leadership, and M&A, with operating experience across tech giants like Microsoft, SAP, and Bain & Co. He is also an angel investor and venture partner at Pioneer Fund, with early investments in Filecoin, Uniswap, Worldcoin, and Anthropic. He has taught at Stanford, where he created the “Hacking Consciousness” course, and holds degrees from Harvard, Stanford, and UC Berkeley, and is a Board Trustee at MIU. Outside of crypto, Michael practises Shaolin Kung Fu and advocates for mindfulness and wellbeing in high-performance tech environments.
You’ve built startups, taught at Stanford, practised Shaolin Kung Fu, and now you’re leading an AI blockchain protocol. What connects all those experiences?
I’ve always liked connecting things that seem unrelated. Rather than going deep in just one area, I’ve tried to maintain a broad perspective.
Stanford has this idea of a “T-student.” Someone who has deep expertise in one field, combined with a broad base of knowledge. I’ve applied that idea to my career.
I started out in deep tech at Microsoft’s Visual Studio Group and SAP Labs, then moved to business consulting at Bain, working with Fortune 500 companies in tech, finance, and consumer goods.
After that, I joined Bridgewater Associates, where I was involved in portfolio construction and saw about $60 billion in daily trades. That built on research I did as an undergrad at Harvard Business School. I’d considered a finance PhD at one point, but decided to apply that knowledge more practically.
How did that lead you to crypto and, more specifically, the AI side of crypto?
After Bridgewater, I returned to Stanford for graduate school, and that’s where I took notice of Bitcoin.
Everyone was talking about it — classmates like Marc Andreessen were involved, and Tim Draper was around as a DFJ fellow. I’d actually heard about Bitcoin back in 2010 from a TV show, but never looked into it. At Stanford, I finally read the white paper and bought some Bitcoin.
The real spark came during the ICO boom. It was chaotic, but there was a lot of creativity.
I invested in several projects, did well, and realised I wanted to build something in the space. But at the time, I was scaling my web2 company, so I waited until the timing made sense.
Before 0G, you scaled Garten to $200 million in revenue and navigated a huge COVID-related downturn. How did that shape your approach to risk and leadership in crypto?
That period taught me two big lessons.
First, the importance of balancing intense work with proper rest. Before COVID, everything was growing fast. Our metrics were excellent; we had a team of 650 people, over $100 million in annual revenue, and were IPO-ready.
Then overnight, COVID hit, a major funding deal fell through, and we went from cash-rich to cash-poor. It was like flying a plane that suddenly lost an engine, with no communication, no visibility, and a panicked crew. I had to land it safely.
Coming through that taught me that if I could handle that kind of crisis, I could handle volatility in crypto, which, as we know, has its share of rough patches.
Has that changed how you approach fundraising and financial management now?
Absolutely. I used to think like most founders: raise enough for 12 to 18 months. This time, I insisted on having at least five years of runway. That meant raising around $5 million at the pre-seed stage, which went better than expected.
We’ve also been careful with treasury management. Some crypto foundations burn through $60 to $200 million a year. We’ve managed to nearly offset our cash burn through thoughtful treasury strategies.
It lets us focus on the long-term mission rather than constantly chasing the next funding round.
0G describes itself as laying the foundation for “AI as a public good.” What does that actually mean, and how do you avoid simply recreating centralised control?
Today, AI is a black box. You often don’t know who labelled the data, where it came from, what model you’re interacting with, or how outputs are filtered.
In crypto, we like to say, “Don’t trust, verify,” and that should apply to AI too. Especially for large societal AI use cases, like running an airport. A closed system is terrifying.
There was even a case with Anthropic where an AI agent, when threatened to be shut down, blackmailed its owner with personal info. That’s scary.
So, making AI open, verifiable, and decentralised is key.
How do you attract developers from the traditional AI sector?
We approach it three ways.
First, we’ve set up an $88.8 million ecosystem programme to support promising builders.
Second, we run events like buildathons, hackathons, and marketing that help drive awareness.
Third, our business development team actively reaches out to strong companies and teams.
What does 0G offer that sets it apart from other projects?
We’ve designed everything specifically for large-scale AI workloads. For example, we were the first to train a 107 billion parameter model using decentralised GPUs.
We also use a software-based verification method that avoids the need for expensive trusted hardware, which means consumer-grade GPUs can handle AI inference.
What kinds of projects are you excited to see built on your platform?
Everything from longevity to AI-based trading decentralised exchanges.
But I’d love to see more tools with social value, like AI agents helping people make better charitable donations or summarising endless Telegram messages. I’d pay for that!
Where do you see the future of AI heading?
I’m optimistic if we get verifiability and open systems right. If we don’t, it risks becoming dangerous and unaccountable.
Done properly, AI could mean that in a few generations, people work less for survival and more for fulfilment.