20VC: The Biggest AI Leaders on What Matters More; Model Size or Data Size & Where Does The Value in AI Accrue; to Startups or to Incumbents
Richard Socher is the founder and CEO of You.com. Richard previously served as the Chief Scientist and EVP at Salesforce.
Douwe Kiela is the CEO of Contextual AI, building the contextual language model to power the future of businesses. Previously, he was the Head of Research at Hugging Face, and before that a Research Scientist at Facebook AI Research.
Alex Lebrun is the Co-Founder and CEO of Nabla, an AI assistant for doctors. Prior to Nabla, he led engineering at Facebook AI Research. Alex founded Wit.ai, acquired by Facebook in 2015.
Tomasz Tunguz is the Founder and General Partner @ Theory Ventures, just announced last week, Theory is a $230M fund that invests $1-25m in early-stage companies that leverage technology discontinuities into go-to-market advantages.
Sarah Guo is the Founding Partner @ Conviction Capital, a $100M first fund purpose-built to serve "Software 3.0" companies. Prior to founding Conviction, Sarah was a General Partner at Greylock where she made investments in the likes of Figma, Coda and Neeva.
Emad Mostaque is the Co-Founder and CEO @ StabilityAI, the parent company of Stable Diffusion. Stability are building the foundation to activate humanity's potential. To date, Emad has raised over $110M with Stability with the latest round reportedly pricing the company at $4BN.
Clem Delangue is the Co-Founder and CEO @ Hugging Face, the AI community building the future. To date, Clem has raised over $160M from the likes of Sequoia, Coatue, Addition and Lux Capital to name a few.
Cris Valenzuela is the CEO and co-founder of Runway, the company that trains and builds generative AI models for content creation. To date, Cris has raised over $285M for the company from the likes of Lux Capital, Felicis, Coatue, Amplify, and Nvidia to name a few.
Noam Shazeer is the co-founder and CEO of Character.AI. A renowned computer scientist and researcher, Shazeer is one of the foremost experts in artificial intelligence (AI) and natural language processing (NLP).
The Two Most Pressing Questions in AI:
What matters more the size of the model or the size of the data?
Where does the value accrue in the next 5-10 years; to startups or to incumbents?
20VC: Why No Models Today Will Be Used in a Year, Why Open Will Always Beat Closed in AI, Why Proprietary Data is Less Important Than Ever And Why EU AI Regulation is a Disaster with Alex Lebrun, Founder & CEO @ Nabla
Alex Lebrun is the Co-Founder and CEO of Nabla, an AI assistant for doctors. Prior to Nabla, he led engineering at Facebook AI Research. Alex founded Wit.ai, an AI platform that makes it easy to build apps that understand natural human language. Wit.ai was acquired by Facebook in 2015. Prior to Wit, Alex was the Founder and CEO of VirtuOz, the world pioneer in customer service chatbots, acquired by Nuance Communications in 2013.
In Today's Episode with Alex Lebrun We Discuss:
1. Third Time Lucky and Lessons from Zuckerberg:
How did Alex make his way into the world of startups with the founding of his first company?
What worked with Alex's prior companies that he has taken with him to Nabla? What did not work that he has left behind?
What were the single biggest takeaways for Alex from working with Mark Zuckerberg? How does Mark prepare for meetings? How does Mark negotiate so well?
2. Open vs Closed:
Why does Alex believe the winning AI models will always be open?
Why are open models not as transparent as people think they are?
What are the biggest downsides to both open and closed models?
Does Alex agree with Emad @ Stability that we will have "national data sets"?
3. Incumbent vs Startup:
Who wins in the AI race; startups or incumbents?
How important is access to proprietary data in winning in AI today?
How does Alex respond to many VCs who suggest so many AI startups are merely "a thin layer on top of a foundational model"? Is that a fair critique?
Which startups are best placed to challenge incumbents? Which incumbents have been most impressive in adopting AI into existing product suites?
4. Models 101: Size, Quality, Switching Costs:
Why will the best companies switch the models that they use often?
Will any models in action today be used in a year?
How important is the size of the model? How will this change with time?
In what way is new EU regulation around models going to harm European AI companies?
5. Location Matters: Who Wins:
When looking at China, US and Europe, who is best placed to win the AI war?
What are the biggest challenges Europe and China face?
Why is the US best placed to win the AI race? What does it have to overcome first?
If Alex were a politician, what would he do to ensure his country were best positioned?
