Successful AI startups from a VC perspective and how they are changing the mid-sized companies - with Max Bergmann
A conversation between Maximilian Hahnenkamp (Scavenger AI) and Max Bergmann (Investment Manager, HTGF)
From founder to early-stage investor: Max Bergmann manages a portfolio of tech startups at the High-Tech Gründerfonds (HTGF) – including Scavenger. In a conversation with Maximilian Hahnenkamp, he talks about the real state of AI beyond the buzzwords, why story, sales & trust are more important in fundraising than logos on the slide, how AI go-to-market is currently being restructured – and how he recognizes outstanding teams.
Change of Perspective: How Founder Experience Changes in VC
Tunnel vision vs. big picture: As a founder deeply involved in the problem, as a VC broadly and comparatively – the combination sharpens judgment.
Capital is the start, not the goal: After the round, the real work begins. Fundraising is sales (narrative, clarity, expectation management).
Empathy bonus: Those who know pains from the early phase coach more realistically – especially with first-time founders.
State of AI: Early Phase – with very low adoption
Early, not finished: In SMEs & traditional workflows, the utilization is partly <10 % – a huge gap.
Bubble unimportant – utility counts: Whether “hype” or not: What matters is which problem is being solved and how quickly the added value arrives.
From “wrapper bashing” to pragmatism: Lightweight AI wrappers can lower entry barriers – but differentiation requires more than an API.
What Makes AI Startups Successful (Today)
Precise problem fit in a clear target group (verticals/industries).
Go-to-market excellence: short time-to-value, pilot → rollout, references.
Trust & UX: explainable, secure, easy to deploy – not “just” AI.
Moats beyond the model: proprietary data, workflow embedding, distribution, domain knowledge.
“The model alone is not enough. The differentiation arises in the GTM, vertical focus, and real usage by the customer.”
Markets & Models: Where AI is Heading
Verticalization: No “next OpenAI” from Europe – instead, strong industry solutions.
New pricing logics: Moving away from pure seat subscriptions towards value/outcome pricing (paying for results).
Regulation as a framework: EU AI Act & co. slow down in places, but increase trust – relevant for corporates & SMEs.
Underserved, but full of opportunities: “Old” industries (insurance, construction, law), SMEs/hidden champions – a lot of know-how, little AI penetration.
SMEs in Focus: Why Barriers are Falling Now
In the past: Implementation hurdles (teams, integration time) were high.
Today: Fast testing is possible – AI makes entry approachable, teams can be specifically supplemented instead of being built from scratch.
Utilization levers: Workflow automation, decision support, reporting/controlling “on tap”.
Europe, Please Come Together
Opportunities: Universities, research, industrial base, regulatory expertise.
What’s missing: Scaling & bundling (European perspective instead of city smallness).
Vision: “Deep-tech made in Europe” – visible and globally relevant.
Investment Philosophy: How HTGF (Also) Measures
Team, team, team – but specifically:
Founder-market fit: access, credibility, learning curve.
Learning & pivot capability: pace in course changes (particularly central in AI).
Interaction in the founding team: respectful, clear, customer-centric.
Signals: history/linkedin (no dogmas, but anchors), industry sparring, initial traction/pilots.
Single founder? Possible. Complementarity can also be covered through advisors/early hires.
For AI Founders – 6 Succinct Do's
Problem > technology: Lead with business pain, not with “we use LLM X”.
Choose vertical: sharpen ICP, industrialize cases.
Trust by design: data path, GDPR, explainability, SLAs – clarify before the pitch.
Lead GTM by metrics: time-to-value, payback, expansion – make it visible.
Build moat: proprietary data/workflows/distribution, not just prompt glue.
Test pricing: from seats to value/outcome – verifiable ROI stories.
For SMEs – 5 Quick Starters
Choose a clear use case (e.g., offer/report automation).
Pilot with real data (4–8 weeks, defined KPIs).
Early integration of security & compliance (IT/legal on board).
Start change small: train power users, show success, then scale.
Track outcome KPIs (time savings, error rate, cash impact).
Looking Ahead
Deep-tech from Europe with visible impact.
AI as an enabler for hidden champions – less hype, more revenue and efficiency levers.
Fundraising remains important – as story & sales of a robust value proposition.