20VC: Who Wins in AI; Startup vs Incumbent, Infrastructure vs Application Layer, Bundled vs Unbundled Providers | From 150 LP Meetings to Closing $230M for Fund I; The Fundraising Process, What Worked, What Didn't and Lessons Learned with Tomasz Tunguz
Tomasz Tunguz, Founder and General Partner at Theory Ventures, dives into the future landscape of AI and its impact on startups versus established companies. He shares his journey from Redpoint to founding a $230M fund, offering insights on the intricacies of fundraising and the lessons learned through extensive LP meetings. The conversation also explores the distinction between foundational AI models and application layers, highlighting investment strategies and the economic implications of AI in code generation. Tune in for valuable takeaways on venture capital and emerging technologies!
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volunteer_activism ADVICE
Fundraising Data Room
Use a data room with track record, pitch deck, bio, and metrics for fundraising.
Qualify LPs beforehand based on their mandates and preferences, like solo GP or stage focus.
volunteer_activism ADVICE
Don't Pre-Qualify
Don't send pitch decks beforehand, as LPs might find reasons to reject without meeting you.
Send materials after a meeting to showcase your brilliance and create follow-up opportunities.
insights INSIGHT
The Myth of the Single Close
The "first and only close" is a vanity metric for VCs and doesn't guarantee success.
Closing dates are arbitrary and depend on auction strength and LP relationships.
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In this book, Tetlock and Gardner present the results of the Good Judgment Project, a massive forecasting tournament that identified a small group of 'superforecasters' who are exceptionally good at predicting future events. The authors explain that good forecasting does not require powerful computers or arcane methods but involves gathering evidence from various sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. The book uses stories of forecasting successes and failures, as well as interviews with high-level decision makers, to illustrate these principles and demonstrate how anyone can improve their forecasting abilities[3][4][5].
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. Prior to founding Theory, Tom spent 14 years at Redpoint as a General Partner where he made investments in the likes of Looker, Expensify, Monte Carlo, Dune Analytics, and Kustomer to name a few. Tom also writes one of the best blogs and newsletters in the business which can be found here.
In Today's Episode with Tomasz Tunguz We Discuss:
Founding a Firm: The Start of Theory:
Why did Tom decide to leave Redpoint after 14 years to found Theory?
What are 1-2 of his biggest lessons from Redpoint that he has taken with him to his building of Theory?
What does Tom know now that he wishes he had known when he started investing?
2. From 150 LP Meetings to Closing $230M: Raising a Fund I
How would Tom describe the fundraising process?
How many meetings with LPs did he have? How many did he know previously?
What documents did he share with LPs? Did he have a dataroom? How did he use it?
How did Tom create a sense of urgency to compel LPs to come into the fund?
How does Tom feel about the debate between one close and multiple closes?
What was the #1 reason LPs said no to investing?
What worked and Tom would do again for the next raise? What did not work and he would change for the next raise?
3. Where Will Value Accrue in the Next Decade of AI:
Startup vs Incumbent: Will incumbents embrace AI before startups are able to acquire distribution?
Infrastructure vs Application Layer: Where will the majority of value accrue in the next decade; infrastructure or application layer?
Bundled or Unbundled: Will bundled services be the dominant consumer and enterprise choice or will unbundled specialized solutions win?
4. AI and The World Around It:
How does Tom believe AI could save the US economy?
Why does Tom believe Google are the losers in the AI race?
Which incumbents have responded best to AI?
Why does Tom believe we will be in a worse macro place at the end of the year than we are now?
The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 
