This is the story of a career spanning groundbreaking research, AI moonshots at Google X, building billion-dollar trading models at JPMorgan, and ultimately, a $60 million startup creating a new kind of AI designed to augment human thinking itself.
Most AI founders come from tech. Daniel George came from detecting gravitational waves from black holes as part of a Nobel-prize winning team to building TwinMind, one of Silicon Valley’s most promising AI companies.
Born in Kochi, India, Daniel studied engineering physics at IIT Bombay and then finished his PhD by age 24 in Astrophysics at the University of Illinois Urbana-Champaign (UIUC) in record time.
Physics to AI: The Moment Everything Changed
In 2015, during his first week at UIUC, a talk by futurist Ray Kurzweil reshaped his worldview, convincing him that AI would surpass human intelligence by 2029.
Motivated by this idea, he pivoted from traditional physics to the emerging intersection of deep learning and astrophysics, a move that would define the rest of his career.
He soon became the first researcher to apply AI to detect gravitational waves. His models helped isolate faint cosmic signals from noise, contributing to the discovery for which his team called LIGO won the Nobel Prize in Physics.
His work resulted in over 75 publications with more than 60,000 citations and he won over $200,000 in awards and fellowships, which established his reputation in both astrophysics and computer science.
A Career without Interviews: They Came to Him
Daniel’s research impact opened doors that most people only dream about. Without submitting a single job application, he was personally recruited by industry giants:
Sergey Brin invited him to present his thesis at Google X.
Jensen Huang highlighted his research during the NVIDIA GTC keynote.
Stephen Wolfram hired him and later invested personally in TwinMind.
At Google X, Daniel contributed to AI moonshot projects across robotics, audio processing, sensing, and genetic engineering. Later at JP Morgan, he led the development of AI trading models that had over $1 billion in revenue impact.
These experiences broadened his understanding of how AI is not just as a tool, but as a partner in human cognition.
The Monk Behind the Machine
Daniel’s journey is marked not just by accomplishments, but by discipline.
Even after earning millions of dollars, he kept his expenses the same as when he was a PhD student. He walked to work instead of buying a car and refused to own any material possessions except one backpack of items.
His philosophy is simple: reduce distractions, maximise focus. Every object you own ends up owning you.
By 29, Daniel had achieved financial independence. Without the pressure to earn a salary ever again, he could pursue what he was truly passionate about—becoming an entrepreneur.
The Blind Spot Everyone Missed
By 2024, the tech industry was locked in an arms race to build the most intelligent AI. Hundreds of billions of dollars has been spent training larger models on bigger datasets.
In the 1970s, there was a similar arms race between IBM, Intel, and others to build the most powerful CPUs. But the real winners ended up being Apple and Microsoft who took those processors, added memory, a mouse, a graphical interface, and built the operating system to create Personal Computers.
Daniel recognised the same opportunity. While everyone else is racing to build AI with higher IQ, Daniel realised memory and context are the real keys to create Personal Intelligence.
In early 2024, Daniel co-founded TwinMind with two former colleagues from Google X—who all had PhDs in AI—united by a shared belief: AI should not require constant prompting. It should be proactive, context-aware, and have perfect memory of your entire life. A memory operating system, a “Second Brain”, not just a chatbot.
TwinMind: The Memory Engine
The problem is that ChatGPT doesn’t know what’s happening around you, what you said to someone five minutes ago, or what you read this morning.
TwinMind is different. It listens continuously in your pocket, sees your screen, remembers your life, and acts before you even ask. Not a chatbot you prompt but an AI twin that prompts you.
It’s built around three core layers:
1. Context Capturing Layer:
The TwinMind mobile apps and browser extension transcribes audio around you in over 140 languages, capturing meetings, conversations, and your browser tabs.
2. Memory Organising Layer:
It organises information into multi-level memory—raw transcripts, images, summaries, and core insights, enabling the system to understand patterns across days, weeks, and months.
3. Proactive Intelligence Layer:
TwinMind anticipates needs, suggests actions, and recalls information proactively faster than the user even thinks to ask.
What makes this possible is a proprietary pipeline developed by Daniel’s team. Instead of training LLMs from scratch, TwinMind fine-tunes multiple open-source LLMs especially to run on-device across mobile and desktop apps.
This approach reduced operational costs dramatically, allowing the platform to run for under $1 per user per month—a breakthrough in the speech-to-text industry.
The Pirate House of Silicon Valley
TwinMind's founding team doesn't just work together. They all live together in a shared house in Menlo Park, famously called the “TwinMind Pirate House.” Just 5 minutes away from Sand Hill Road and Stanford.
Daniel lives in a cottage in the backyard with his wife Nina, an AI scientist previously at Google X and Oura Ring, now Director of AI at Bayesian Health. Her PhD in neuroscience and background in applied machine learning adds an additional layer of insight to TwinMind.
The setup reflects a belief the founders share: breakthroughs don't come from working 9 to 5 in the office. They come from a tight-knit crew working with hardcore obsession and relentless iteration.
The Endgame: When AI & Humans Think Together
Daniel believes this future is inevitable: a world where every human has their own personal AI companion always-on by their side. One that remembers everything you see, hear, and say, learns how you think, and genuinely becomes an extension of your mind.
His goal is not to replace human intelligence, but to augment it. To reduce the mental overhead of modern life while enabling people to operate at the highest capacity on what matters most.
As the world moves toward more intelligent, context-aware, personalised systems, Daniel’s work highlights an important shift: AI is no longer simply about answers. It’s about understanding, memory, and presence.
TwinMind and its founding team of missionaries aim to be at the centre of that transformation, so that humans and AI can think together.