Speedinvest’s Oliver Holle & OpenOcean’s Ekaterina Almasque on how to identify the next AI investment sweet spot

A deep dive into how generalist and specialist VCs approach AI investing, adapt to rapid technological advancements and identify high-impact opportunities across industries.

In today’s episode, Andreas talks with Oliver Holle, Co-Founder and Managing Partner at Speedinvest, and Ekaterina Almasque, General Partner at OpenOcean, to unpack the current landscape of AI investing.

Speedinvest, a €500M generalist fund, and OpenOcean, a €150M fund specializing in AI and deep tech, offer contrasting yet complementary perspectives on how VCs are navigating the rapid advancements in artificial intelligence.

Together, we explore:

  • How SpeedInvest and OpenOcean approach AI investments and the strategic decisions behind focusing on specific layers of the AI stack.

  • The rise of AI agents and how this next wave of AI innovation shapes investment strategies.

  • Risk and opportunity in AI investing, particularly in foundational models versus applications and infrastructure.

  • Key AI disruption sectors include healthcare, enterprise solutions, and content creation.

  • Why VCs need to rethink traditional investment models when approaching AI startups.

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✍️ Show notes

1. Defining Investment Focus in AI

AI investments span multiple layers, from foundational models to applications. Oliver and Ekaterina share how their firms prioritize opportunities and navigate risks within this expansive space.

Oliver Holle:

  • SpeedInvest avoids foundational models due to capital intensity and infrastructure risks.

  • Focuses on adjacent opportunities like AI safety, transparency, and performance improvements.

  • Emphasizes vertical AI applications across sectors, treating AI as core infrastructure.

Ekaterina Almasque:

  • OpenOcean invests broadly but avoids capital-heavy foundational models.

  • Prioritizes scalable enterprise AI solutions addressing infrastructure gaps.

  • Sees high potential in GenAI applications that deliver immediate enterprise value.


2. Identifying the AI Investment Sweet Spot Between SMEs and Enterprises

The adoption of AI differs between large enterprises and SMEs. While large corporations often develop in-house solutions, SMEs require external, scalable tools. This creates unique investment opportunities across the spectrum.

Oliver Holle:

  • Large enterprises tend to develop internal AI solutions.

  • SMEs and mid-sized companies present scalable opportunities for external AI tools.

  • System integrators and solution providers effectively serve these segments.

Ekaterina Almasque:

  • Enterprises lack the necessary infrastructure to adopt AI at scale.

  • OpenOcean focuses on investing in tools that simplify AI adoption for enterprises.


3. The Rise of AI Agents

AI agents are emerging as a transformative force in automation, capable of performing tasks with minimal human oversight. Oliver and Ekaterina discuss the opportunities and challenges presented by this next wave of AI innovation.

Oliver Holle:

  • AI agents mark the "third wave," disrupting automation workflows.

  • Success depends on vertical specialization and customer-centric solutions.

Ekaterina Almasque:

  • AI agents have significant potential but require supporting infrastructure (orchestration, compliance, safety).

  • The rapid evolution risks making early investments obsolete.


4. The Impact of Rapid AI Evolution on Seed Investments

The fast-paced evolution of AI technology, especially with the rise of agents, has rendered some early-stage investments obsolete. Oliver and Ekaterina explain how startups can adapt and remain relevant.

Oliver Holle:

  • Investors risk chasing outdated trends like foundational models.

  • Startups with deep customer insight can adapt to evolving technologies.

Ekaterina Almasque:

  • Startups lacking infrastructure risk obsolescence.

  • Long-term success relies on sustainable solutions in AI safety and management.


5. Ensuring AI Accuracy for Enterprise Adoption

Accuracy and reliability are critical for enterprise AI adoption, especially in sectors where errors can be costly. The guests discuss how startups are addressing this challenge.

Oliver Holle:

  • Mission-critical sectors (finance, healthcare) require cautious AI deployment.

  • Focus on AI solutions that complement human oversight.

Ekaterina Almasque:

  • Enterprises struggle with AI accuracy due to limited proprietary models and talent.

  • Startups like LatticeFlow provide tools to verify and enhance AI performance.


6. Why VCs Must Adapt to AI's Unique Challenges

AI startups present unique challenges that differ from traditional SaaS models, requiring VCs to adjust their investment strategies. Oliver and Ekaterina highlight the need for belief-driven investing and specialized expertise.

Ekaterina Almasque:

  • AI startups often lack clear product-market fit, making KPIs less relevant.

  • Successful investing requires belief-driven decisions and deep technical insight.

Oliver Holle:

  • SpeedInvest built a specialized deep tech team to handle AI investments.

  • Different evaluation metrics are essential for AI versus SaaS models.

7. The Relevance of KPI-Driven Investing in AI

Traditional KPI-driven investment models face limitations in evaluating AI startups. The discussion covers whether these models remain effective in the AI space.

Oliver Holle:

  • KPI-based investing remains effective for SaaS and traditional sectors.

  • AI and deep tech require distinct evaluation frameworks.


8. Leveraging Deep Tech Expertise Across Investment Teams

SpeedInvest has developed specialized deep tech expertise. Oliver explains how this knowledge is selectively applied across teams to support AI-related deals.

Oliver Holle:

  • Deep tech expertise is selectively shared to avoid overextending resources.

  • Collaboration occurs when deals demand specialized insights, particularly in health.


9. High-Impact AI Investment Opportunities

AI offers disruptive potential across industries. Oliver and Ekaterina outline the sectors where they see the greatest opportunities for innovation and growth.

Oliver Holle:

  • Healthcare: AI-driven drug discovery and efficiency improvements.

  • Content/Consumer: AI-powered content creation (e.g., Kittl).

  • Deep Tech: Niche AI solutions with scalable impact.

Ekaterina Almasque:

  • Vertical AI: Protein design, drug discovery, resource optimization.

  • Enterprise Tooling: Simplified AI adoption for enterprises.

  • AI Safety: Tools ensuring ethical and accurate AI behavior.

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