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Sep 30, 2016 · 28m · mad

Venture Capital Investor Panel: Investing in Big Data (Data Driven NYC / FirstMark)

Mike Dauber · 6m spoken Jeff Chung · 5m spoken Jake Flomenberg · 5m spoken Matt Turck · 2m spoken
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At a FirstMark Data Driven NYC event, host Matt Turck moderates a venture capital panel featuring investors from Accel, Amplify, Foundation Capital, and Ame Cloud Ventures as they analyze Big Data market cycles, artificial intelligence investments, tech giant competition, and regulatory bottlenecks.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 11.7% of the talking time here. How this is scored →

Matt as informed peer 1.3 Guest teaching 2.0 Guest disagreement 1.5 Matt pushing back 0.2
05100:0010:0020:001:46–4:44 · Matt as informed peer 1/10 Macro Trends and Cycles in Big Data Matt Turck opens the panel with a broad macro question about where big data sits in the hype cycle. The guests harmoniously discuss market consolidation, open source proliferation, and the shift toward application-layer software without conflict.4:44–10:07 · Matt as informed peer 1/10 Investment Theses and Specific Big Data Applications Matt prompts the panelists for specific recent investments and sector theses. The guests exchange lighthearted banter about buzzwords and agree on searching for 10x technical teams.10:07–16:07 · Matt as informed peer 3/10 Evaluating the Reality and Promise of Artificial Intelligence Matt asks if the current wave of AI is real or overhyped. He demonstrates specific knowledge of Jeff Chung's portfolio by citing Color Genomics and connecting it to personalized medicine data crunching.16:07–22:50 · Matt as informed peer 3/10 Navigating Tech Giants and the AI Talent War Matt frames a challenging topic regarding startup viability against tech incumbents like Google and Facebook. The panelists explain data moats, TensorFlow strategies, and the intense talent acquisition war.22:50–26:43 · Matt as informed peer 0/10 Audience Q&A: Venture Capital Value Add Beyond Capital An audience member turns the tables on the VCs, questioning how they differentiate given that capital is cheap and abundant. Mike Dauber directly pushes back on the premise that smart scaling capital is easy to come by.26:43–28:15 · Matt as informed peer 0/10 Audience Q&A: Healthcare Regulations and Data Access An audience question about HIPAA and healthcare regulatory barriers leads Mike Dauber to explain why regulatory friction hinders US healthcare AI training data relative to international markets.1:46–4:44 · Guest teaching 2/10 Macro Trends and Cycles in Big Data Matt Turck opens the panel with a broad macro question about where big data sits in the hype cycle. The guests harmoniously discuss market consolidation, open source proliferation, and the shift toward application-layer software without conflict.4:44–10:07 · Guest teaching 2/10 Investment Theses and Specific Big Data Applications Matt prompts the panelists for specific recent investments and sector theses. The guests exchange lighthearted banter about buzzwords and agree on searching for 10x technical teams.10:07–16:07 · Guest teaching 1/10 Evaluating the Reality and Promise of Artificial Intelligence Matt asks if the current wave of AI is real or overhyped. He demonstrates specific knowledge of Jeff Chung's portfolio by citing Color Genomics and connecting it to personalized medicine data crunching.16:07–22:50 · Guest teaching 2/10 Navigating Tech Giants and the AI Talent War Matt frames a challenging topic regarding startup viability against tech incumbents like Google and Facebook. The panelists explain data moats, TensorFlow strategies, and the intense talent acquisition war.22:50–26:43 · Guest teaching 3/10 Audience Q&A: Venture Capital Value Add Beyond Capital An audience member turns the tables on the VCs, questioning how they differentiate given that capital is cheap and abundant. Mike Dauber directly pushes back on the premise that smart scaling capital is easy to come by.26:43–28:15 · Guest teaching 2/10 Audience Q&A: Healthcare Regulations and Data Access An audience question about HIPAA and healthcare regulatory barriers leads Mike Dauber to explain why regulatory friction hinders US healthcare AI training data relative to international markets.1:46–4:44 · Guest disagreement 1/10 Macro Trends and Cycles in Big Data Matt Turck opens the panel with a broad macro question about where big data sits in the hype cycle. The guests harmoniously discuss market consolidation, open source proliferation, and the shift toward application-layer software without conflict.4:44–10:07 · Guest disagreement 2/10 Investment Theses and Specific Big Data Applications Matt prompts the panelists for specific recent investments and sector theses. The guests exchange lighthearted banter about buzzwords and agree on searching for 10x technical teams.10:07–16:07 · Guest disagreement 0/10 Evaluating the Reality and Promise of Artificial Intelligence Matt asks if the current wave of AI is real or overhyped. He demonstrates specific knowledge of Jeff Chung's portfolio by citing Color Genomics and connecting it to personalized medicine data crunching.16:07–22:50 · Guest disagreement 2/10 Navigating Tech Giants and the AI Talent War Matt frames a challenging topic regarding startup viability against tech incumbents like Google and Facebook. The panelists explain data moats, TensorFlow strategies, and the intense talent acquisition war.22:50–26:43 · Guest disagreement 3/10 Audience Q&A: Venture Capital Value Add Beyond Capital An audience member turns the tables on the VCs, questioning how they differentiate given that capital is cheap and abundant. Mike Dauber directly pushes back on the premise that smart scaling capital is easy to come by.26:43–28:15 · Guest disagreement 1/10 Audience Q&A: Healthcare Regulations and Data Access An audience question about HIPAA and healthcare regulatory barriers leads Mike Dauber to explain why regulatory friction hinders US healthcare AI training data relative to international markets.1:46–4:44 · Matt pushing back 0/10 Macro Trends and Cycles in Big Data Matt Turck opens the panel with a broad macro question about where big data sits in the hype cycle. The guests harmoniously discuss market consolidation, open source proliferation, and the shift toward application-layer software without conflict.4:44–10:07 · Matt pushing back 0/10 Investment Theses and Specific Big Data Applications Matt prompts the panelists for specific recent investments and sector theses. The guests exchange lighthearted banter about buzzwords and agree on searching for 10x technical teams.10:07–16:07 · Matt pushing back 0/10 Evaluating the Reality and Promise of Artificial Intelligence Matt asks if the current wave of AI is real or overhyped. He demonstrates specific knowledge of Jeff Chung's portfolio by citing Color Genomics and connecting it to personalized medicine data crunching.16:07–22:50 · Matt pushing back 1/10 Navigating Tech Giants and the AI Talent War Matt frames a challenging topic regarding startup viability against tech incumbents like Google and Facebook. The panelists explain data moats, TensorFlow strategies, and the intense talent acquisition war.22:50–26:43 · Matt pushing back 0/10 Audience Q&A: Venture Capital Value Add Beyond Capital An audience member turns the tables on the VCs, questioning how they differentiate given that capital is cheap and abundant. Mike Dauber directly pushes back on the premise that smart scaling capital is easy to come by.26:43–28:15 · Matt pushing back 0/10 Audience Q&A: Healthcare Regulations and Data Access An audience question about HIPAA and healthcare regulatory barriers leads Mike Dauber to explain why regulatory friction hinders US healthcare AI training data relative to international markets.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 19.5% · guest 80.5%0:00 · Matt 19.5% · guest 80.5%3:00 · Matt 12.9% · guest 87.1%3:00 · Matt 12.9% · guest 87.1%6:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%9:00 · Matt 14.4% · guest 85.6%9:00 · Matt 14.4% · guest 85.6%12:00 · Matt 1.2% · guest 98.8%12:00 · Matt 1.2% · guest 98.8%15:00 · Matt 31.3% · guest 68.7%15:00 · Matt 31.3% · guest 68.7%18:00 · Matt 0.7% · guest 99.3%18:00 · Matt 0.7% · guest 99.3%21:00 · Matt 17.8% · guest 82.2%21:00 · Matt 17.8% · guest 82.2%24:00 · Matt 2.4% · guest 97.6%24:00 · Matt 2.4% · guest 97.6%27:00 · Matt 21.4% · guest 78.6%27:00 · Matt 21.4% · guest 78.6%
Sharpest disagreement ▶ 23:59 Mike Dauber challenges audience Q&A premise

Mike Dauber forcefully rejects the audience member's assertion that access to capital is easy, noting that smart capital capable of scaling companies is actually in very short supply.

 Hardest push from Matt ▶ 16:07 Matt Turck questions startup viability against tech giants

Matt pushes back against optimistic AI startup narratives by challenging the panel on whether small startups can survive against giants like Google and Facebook.

 Biggest teaching moment ▶ 18:07 Jake Flomenberg breaks down Google's open source strategy

Jake educates the panel and audience on how Google open-sources tools like TensorFlow to commoditize algorithms while preserving their true moat, which is proprietary data.

 Matt holds his own ▶ 14:57 Matt Turck demonstrates domain and portfolio knowledge

Matt shows active mastery of the panel's investments by unprompted naming of Color Genomics and framing its connection to deep learning and personalized medicine.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Macro Trends and Cycles in Big Data 1210 Matt Turck opens the panel with a broad macro question about where big data sits in the hype cycle. The guests harmoniously discuss market consolidation, open source proliferation, and the shift toward application-layer software without conflict.
Investment Theses and Specific Big Data Applications 1220 Matt prompts the panelists for specific recent investments and sector theses. The guests exchange lighthearted banter about buzzwords and agree on searching for 10x technical teams.
Evaluating the Reality and Promise of Artificial Intelligence 3100 Matt asks if the current wave of AI is real or overhyped. He demonstrates specific knowledge of Jeff Chung's portfolio by citing Color Genomics and connecting it to personalized medicine data crunching.
Navigating Tech Giants and the AI Talent War 3221 Matt frames a challenging topic regarding startup viability against tech incumbents like Google and Facebook. The panelists explain data moats, TensorFlow strategies, and the intense talent acquisition war.
Audience Q&A: Venture Capital Value Add Beyond Capital 0330 An audience member turns the tables on the VCs, questioning how they differentiate given that capital is cheap and abundant. Mike Dauber directly pushes back on the premise that smart scaling capital is easy to come by.
Audience Q&A: Healthcare Regulations and Data Access 0210 An audience question about HIPAA and healthcare regulatory barriers leads Mike Dauber to explain why regulatory friction hinders US healthcare AI training data relative to international markets.

Statements from this episode (11)

Prediction Not checkable as stated
Flomenberg: Future software will be either data-driven or 'shitty'
“In the future there's gonna be two types of software, data-driven software and shitty software, and I prefer strictly to invest in the data-driven type.”
Jake Flomenberg Sep 30, 2016 ▶ 0:45
Insight
Flomenberg: Application-facing enterprise software can accommodate 100 big winners
“There's room for, you know, a hundred winners across every different facet of application-facing enterprise software.”
Jake Flomenberg Sep 30, 2016 ▶ 3:05
Insight
Dauber: Technical founders very rarely understand the vertical markets they seek to disrupt
“If people know markets, they're somewhere over here, and people know the technology are somewhere over by that exit sign, and they very, very rarely run into each other. And so the number of companies you meet that truly understand the verticals that they're t…”
Mike Dauber Sep 30, 2016 ▶ 3:19
Insight
Flomenberg: Startups cannot maintain a fundamental algorithmic advantage in perpetuity
“Do you think you're gonna have a fundamental algorithmic advantage in perpetuity or not? And in the vast majority of cases the answer is almost demonstrably no.”
Jake Flomenberg Sep 30, 2016 ▶ 6:30
Insight
Dauber: Back 10x technical teams and figure out the market later
“If you're really, really, really honest, it's finding the 10 X teams, and you can kind of figure out the markets later, and so that's not like a very tweetable line, but I think it's the truth.”
Mike Dauber Sep 30, 2016 ▶ 8:59
Assertion Not checkable as stated
Dauber: Portfolio AI analyzes medical cases 10% more accurately than humans
“We have a portfolio company in the med tech space where, you know, my dad was a pathologist, and when I was a kid, it would take him 20 minutes to analyze a case to determine whether or not you had cancer. You know, I think in a minute, ah, these guys can anal…”
Mike Dauber Sep 30, 2016 ▶ 10:34
Opinion
Flomenberg: Competing directly against Google in AI is a terrible strategy
“Competing with Google is, is a terrible idea.”
Jake Flomenberg Sep 30, 2016 ▶ 18:07
Opinion
Dauber: Google is the only tech giant successfully investing outside core business
“Google's the only company that seems to go out and invest in areas that have nothing to do fundamentally with their core business, and then do a good job of it.”
Mike Dauber Sep 30, 2016 ▶ 20:14
Assertion Not checkable as stated
Chung: Tech giants offer seven-figure salaries to undergraduate AI recruits
“If you go straight to academia and try to pull them out of, even if it's, you know, undergrad, they're getting seven figure offers from Google, Facebook to go work on, whether it's core, Deep learning tech, AI, or better ad targeting.”
Jeff Chung Sep 30, 2016 ▶ 21:16
Disclosure
Dauber: Amplify portfolio company looked internationally for early healthcare data
“I mean, for one of our companies, one of the things they did was they looked internationally first, which is not a typical strategy for us, but it was finding data partners who had access to kind of what Jake was talking about, right, kind of this unfair acces…”
Mike Dauber Sep 30, 2016 ▶ 27:23
Prediction Not checkable as stated
Dauber: HIPAA puts the US at a material disadvantage in healthcare AI
“And I think the United States is gonna be at a material disadvantage on the healthcare side for this reason, where I think, ah, as a patient, and as, ah, a son or a father, You know, I appreciate the HIPAA laws, and I think they're, they have all these good in…”
Mike Dauber Sep 30, 2016 ▶ 27:37
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