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Dec 5, 2013 · 20m · mad

Mike Dauber, Battery Ventures // Data Driven NYC 19 // October 2013 (interviewed by Matt Turck)

Mike Dauber · 14m spoken Matt Turck · 3m spoken
0:00 / 0:00
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

In this Data Driven NYC interview hosted by Matt Turck, Battery Ventures investor Mike Dauber discusses shifting macro trends in tech innovation, his investment thesis favoring application-layer data startups over pure infrastructure, and key advice for early-stage enterprise founders.

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 18.4% of the talking time here. How this is scored →

Matt as informed peer 2.7 Guest teaching 4.7 Guest disagreement 1.8 Matt pushing back 0.3
05100:0010:0020:002:08–4:29 · Matt as informed peer 1/10 Macro Trends: Government vs. Consumer Tech Innovation The guest opens with a macro historical thesis on how technology operationalization flipped from government-led pre-2000 to consumer-led post-Google. The host yields the floor completely, allowing the guest to establish the analytical framework for the conversation.4:29–7:15 · Matt as informed peer 2/10 Investment Thesis: Application Layer vs. Pure Infrastructure The host asks a structured categorisation question about infrastructure versus applications. The guest elaborates extensive VC thesis points regarding SaaS versus cloud returns, educating the room on why invisible application-layer big data wins.7:15–10:04 · Matt as informed peer 3/10 Case Study: Duetto and Revenue Optimization The host prompts for concrete portfolio examples like Duetto. The guest delivers an engaging breakdown of hotel revenue management algorithms without host interruption.10:04–12:19 · Matt as informed peer 4/10 Pitching VCs and Developing an Unfair Advantage The host highlights the real-world friction of deep R&D cycles without early traction for non-famous founders. The guest agrees and explains the necessity of finding an unfair advantage to convince VCs.12:19–16:17 · Matt as informed peer 3/10 Series A Criteria and Enterprise Valuation Dynamics The host and audience ask about Series A valuation drivers and metrics. The guest explains how enterprise early-stage evaluation differs from consumer photo apps, warning against buzzword retrofitting using Splunk as an example.16:17–19:59 · Matt as informed peer 3/10 The Reality Gap: Grandiose Vision vs. Enterprise Execution The host prompts the guest to discuss the operational struggles of early startups. The guest candidly exposes the gap between grandiose vision pitching and the difficult last-mile engineering required to match incumbents like Oracle or Microsoft.2:08–4:29 · Guest teaching 5/10 Macro Trends: Government vs. Consumer Tech Innovation The guest opens with a macro historical thesis on how technology operationalization flipped from government-led pre-2000 to consumer-led post-Google. The host yields the floor completely, allowing the guest to establish the analytical framework for the conversation.4:29–7:15 · Guest teaching 5/10 Investment Thesis: Application Layer vs. Pure Infrastructure The host asks a structured categorisation question about infrastructure versus applications. The guest elaborates extensive VC thesis points regarding SaaS versus cloud returns, educating the room on why invisible application-layer big data wins.7:15–10:04 · Guest teaching 4/10 Case Study: Duetto and Revenue Optimization The host prompts for concrete portfolio examples like Duetto. The guest delivers an engaging breakdown of hotel revenue management algorithms without host interruption.10:04–12:19 · Guest teaching 4/10 Pitching VCs and Developing an Unfair Advantage The host highlights the real-world friction of deep R&D cycles without early traction for non-famous founders. The guest agrees and explains the necessity of finding an unfair advantage to convince VCs.12:19–16:17 · Guest teaching 5/10 Series A Criteria and Enterprise Valuation Dynamics The host and audience ask about Series A valuation drivers and metrics. The guest explains how enterprise early-stage evaluation differs from consumer photo apps, warning against buzzword retrofitting using Splunk as an example.16:17–19:59 · Guest teaching 5/10 The Reality Gap: Grandiose Vision vs. Enterprise Execution The host prompts the guest to discuss the operational struggles of early startups. The guest candidly exposes the gap between grandiose vision pitching and the difficult last-mile engineering required to match incumbents like Oracle or Microsoft.2:08–4:29 · Guest disagreement 2/10 Macro Trends: Government vs. Consumer Tech Innovation The guest opens with a macro historical thesis on how technology operationalization flipped from government-led pre-2000 to consumer-led post-Google. The host yields the floor completely, allowing the guest to establish the analytical framework for the conversation.4:29–7:15 · Guest disagreement 1/10 Investment Thesis: Application Layer vs. Pure Infrastructure The host asks a structured categorisation question about infrastructure versus applications. The guest elaborates extensive VC thesis points regarding SaaS versus cloud returns, educating the room on why invisible application-layer big data wins.7:15–10:04 · Guest disagreement 1/10 Case Study: Duetto and Revenue Optimization The host prompts for concrete portfolio examples like Duetto. The guest delivers an engaging breakdown of hotel revenue management algorithms without host interruption.10:04–12:19 · Guest disagreement 2/10 Pitching VCs and Developing an Unfair Advantage The host highlights the real-world friction of deep R&D cycles without early traction for non-famous founders. The guest agrees and explains the necessity of finding an unfair advantage to convince VCs.12:19–16:17 · Guest disagreement 2/10 Series A Criteria and Enterprise Valuation Dynamics The host and audience ask about Series A valuation drivers and metrics. The guest explains how enterprise early-stage evaluation differs from consumer photo apps, warning against buzzword retrofitting using Splunk as an example.16:17–19:59 · Guest disagreement 3/10 The Reality Gap: Grandiose Vision vs. Enterprise Execution The host prompts the guest to discuss the operational struggles of early startups. The guest candidly exposes the gap between grandiose vision pitching and the difficult last-mile engineering required to match incumbents like Oracle or Microsoft.2:08–4:29 · Matt pushing back 0/10 Macro Trends: Government vs. Consumer Tech Innovation The guest opens with a macro historical thesis on how technology operationalization flipped from government-led pre-2000 to consumer-led post-Google. The host yields the floor completely, allowing the guest to establish the analytical framework for the conversation.4:29–7:15 · Matt pushing back 0/10 Investment Thesis: Application Layer vs. Pure Infrastructure The host asks a structured categorisation question about infrastructure versus applications. The guest elaborates extensive VC thesis points regarding SaaS versus cloud returns, educating the room on why invisible application-layer big data wins.7:15–10:04 · Matt pushing back 0/10 Case Study: Duetto and Revenue Optimization The host prompts for concrete portfolio examples like Duetto. The guest delivers an engaging breakdown of hotel revenue management algorithms without host interruption.10:04–12:19 · Matt pushing back 1/10 Pitching VCs and Developing an Unfair Advantage The host highlights the real-world friction of deep R&D cycles without early traction for non-famous founders. The guest agrees and explains the necessity of finding an unfair advantage to convince VCs.12:19–16:17 · Matt pushing back 1/10 Series A Criteria and Enterprise Valuation Dynamics The host and audience ask about Series A valuation drivers and metrics. The guest explains how enterprise early-stage evaluation differs from consumer photo apps, warning against buzzword retrofitting using Splunk as an example.16:17–19:59 · Matt pushing back 0/10 The Reality Gap: Grandiose Vision vs. Enterprise Execution The host prompts the guest to discuss the operational struggles of early startups. The guest candidly exposes the gap between grandiose vision pitching and the difficult last-mile engineering required to match incumbents like Oracle or Microsoft.

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

0:00 · Matt 41.8% · guest 58.2%0:00 · Matt 41.8% · guest 58.2%3:00 · Matt 8.6% · guest 91.4%3:00 · Matt 8.6% · guest 91.4%6:00 · Matt 4.4% · guest 95.6%6:00 · Matt 4.4% · guest 95.6%9:00 · Matt 20.7% · guest 79.3%9:00 · Matt 20.7% · guest 79.3%12:00 · Matt 12.7% · guest 87.3%12:00 · Matt 12.7% · guest 87.3%15:00 · Matt 23.4% · guest 76.6%15:00 · Matt 23.4% · guest 76.6%18:00 · Matt 17.4% · guest 82.6%18:00 · Matt 17.4% · guest 82.6%
Sharpest disagreement ▶ 17:15 Dismissing grandiose startup vision vs incumbent reality

The guest bluntly critiques startup vision hype versus enterprise reality, arguing that incumbents cannot get away with pitching pure vision while startups frequently rely on empty rhetoric.

 Hardest push from Matt ▶ 10:04 Challenging VC expectations for early infrastructure pitches

The host actively pushes back on standard VC expectations by pointing out that complex infrastructure startups require long R&D cycles without early revenue or traction.

 Biggest teaching moment ▶ 2:09 Reframing government vs consumer technology development

The guest re-educates the audience on historical tech shifts, contrasting pre-2000 military-led innovation with modern NSA adoption of consumer big data architectures.

 Matt holds his own ▶ 10:04 Demonstrating deep knowledge of tech R&D cycles

The host demonstrates strong industry domain awareness by contrasting quick-to-market consumer products with deep enterprise infrastructure development timelines.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Macro Trends: Government vs. Consumer Tech Innovation 1520 The guest opens with a macro historical thesis on how technology operationalization flipped from government-led pre-2000 to consumer-led post-Google. The host yields the floor completely, allowing the guest to establish the analytical framework for the conversation.
Investment Thesis: Application Layer vs. Pure Infrastructure 2510 The host asks a structured categorisation question about infrastructure versus applications. The guest elaborates extensive VC thesis points regarding SaaS versus cloud returns, educating the room on why invisible application-layer big data wins.
Case Study: Duetto and Revenue Optimization 3410 The host prompts for concrete portfolio examples like Duetto. The guest delivers an engaging breakdown of hotel revenue management algorithms without host interruption.
Pitching VCs and Developing an Unfair Advantage 4421 The host highlights the real-world friction of deep R&D cycles without early traction for non-famous founders. The guest agrees and explains the necessity of finding an unfair advantage to convince VCs.
Series A Criteria and Enterprise Valuation Dynamics 3521 The host and audience ask about Series A valuation drivers and metrics. The guest explains how enterprise early-stage evaluation differs from consumer photo apps, warning against buzzword retrofitting using Splunk as an example.
The Reality Gap: Grandiose Vision vs. Enterprise Execution 3530 The host prompts the guest to discuss the operational struggles of early startups. The guest candidly exposes the gap between grandiose vision pitching and the difficult last-mile engineering required to match incumbents like Oracle or Microsoft.

Statements from this episode (10)

Assertion Not checkable as stated
From 1900 to 2000, government operationalized major tech before consumers
“Every technology that came out was really, even if it was invented by somebody else, It was operationalized by the military or some government entity.”
Mike Dauber Dec 5, 2013 ▶ 2:18
Assertion Not checkable as stated
Consumer companies have replaced government as the primary tech innovators
“The companies now that are coming up with the new technology are big consumer companies and not the government and not the military.”
Mike Dauber Dec 5, 2013 ▶ 3:53
Assertion Not checkable as stated
SaaS venture investments significantly outperformed cloud infrastructure tools
“If you look at the SaaS investments, they outperformed the cloud investments by a margin that was pretty significant, right?”
Mike Dauber Dec 5, 2013 ▶ 5:35
Assertion Not checkable as stated
Not enough data scientists exist to support all current data science tools
“There just aren't enough smart data scientists to buy all the technology that's being built today for data scientists, just as an example.”
Mike Dauber Dec 5, 2013 ▶ 6:23
Prediction Not checkable as stated
The 'big data' concept will disappear within 5 to 10 years
“And our belief is, five, 10 years from now, this notion of big data is going to disappear, and this is going to be this notion of data. And companies that incorporate big data capabilities into their applications are going to win relative to companies that don…”
Mike Dauber Dec 5, 2013 ▶ 6:57
Assertion Not checkable as stated
Steve Wynn's pricing rule was to charge $10 more than Caesar's Palace
“Steve Wynn personally had a single rule on how they ran what the price was for the hotel every night, and the rule was very, very, very simple. Look across the street at Caesar's Palace. Whatever they're charging, charge 10 dollars more.”
Mike Dauber Dec 5, 2013 ▶ 7:45
Insight
Startups without traction need a core market insight to raise venture capital
“A venture firm early on, if there's no data points, we have to have something that we can hold onto and say, Yeah, these guys haven't done this before, but, right, they have some core insight or some knowledge of the market where they've spent more time on thi…”
Mike Dauber Dec 5, 2013 ▶ 11:41
Disclosure
Battery Ventures saw 10 photo apps indistinguishable from early Instagram
“There were 10 companies that we saw on the photo sharing space that looked very similar to Instagram that were indistinguishable at the time, but ended up not being as successful.”
Mike Dauber Dec 5, 2013 ▶ 13:56
Disclosure
Splunk never mentioned 'big data' during its pitch to Battery Ventures
“So we were late investors in Splunk. Splunk was a great company. At no point during Splunk's pitch did they ever talk to us about big data.”
Mike Dauber Dec 5, 2013 ▶ 14:37
Insight
Completing the last mile of product execution kills many enterprise startups
“Creating feature functionality that goes across the board that works the enterprise scale, it actually proves to be pretty hard to do, and what happens, what ends up happening to a lot of startups is that, that last mile, if you will, proves to be the killer, …”
Mike Dauber Dec 5, 2013 ▶ 19:08
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