Justus Lumpe, Konstantin Vinogradov, Nils Eiteneyer & Stephan Wirris
The AI Hype Filter: What’s Real, What’s Investable, What’s Noise?
#1about 3 minutes
How venture capitalists use AI for deal sourcing
VCs leverage AI to analyze diverse data sources like company registries, GitHub, and LinkedIn to identify promising investment opportunities earlier.
#2about 4 minutes
Finding defensible business models beyond software
As AI commoditizes software, defensibility shifts from code to moats like open source communities, strong brands, and rapid go-to-market execution.
#3about 3 minutes
The strategic value of vertical AI solutions
Verticalization in AI allows for superior user experience and deep domain context, creating stickier products than broad horizontal platforms.
#4about 3 minutes
How AI forces portfolio companies to adapt
AI acts as both an accelerator for new companies and a disruptive force for established ones, requiring them to reinvent business models to survive.
#5about 3 minutes
Looking past the hype to find real AI opportunities
Investors are focusing on startups that solve tangible business problems rather than chasing fleeting trends and buzzwords in the rapidly changing AI landscape.
#6about 4 minutes
Navigating AI regulation and open source
While regulation like the EU AI Act is debated, Europe's strength in open source AI models offers a path to building foundational infrastructure.
#7about 2 minutes
The most critical metrics for investing in AI
Beyond revenue, investors evaluate AI startups on commercial traction, user adoption rates, and the founding team's ability to build trust with customers.
#8about 2 minutes
Common red flags in an AI startup pitch
Investors are wary of pitches that promise a one-size-fits-all solution, lack a clear defensible moat, or hide behind abstract promises instead of pragmatic plans.
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Matching moments
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Increasing the value of talk recordings post-event
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Why corporate AI adoption lags behind the hype
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The future of recruiting beyond talent acquisition
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Incentivizing automation with a 'keep what you kill' policy
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Automating formal processes risks losing informal human value
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Moving beyond headcount to solve business problems
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Rapid-fire thoughts on the future of work
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