Sold first AI company to DeepMind in 2014 and now building his third AI startup: What do future AI companies need?

About this episode

Karl-Moritz Hermann has two AI companies behind him – a successful exit to Google's DeepMind and a failed financing that led his startup Saiga into insolvency. Today, he's building his third AI startup with Reliant AI and has already raised over 11 million euros in funding. His experiences offer valuable insights into the complex world of AI startups.

The Different Faces of AI Startups

"What's the difference between an AI startup and an AI startup?" This seemingly paradoxical question highlights an important insight: not all AI companies are created equal. Hermann clearly distinguishes between different types of AI startups, each with their own challenges in product development, go-to-market approach, and defensibility strategy.

Research-heavy high-tech companies face particular challenges in their development. Finding the balance between scientific excellence and commercial viability is one of the biggest hurdles for founders in this space.

Learning from Failure: The Saiga Insolvency

The path to a successful AI company isn't always linear. Hermann's experience with Saiga, which failed despite promising technology due to unsuccessful financing, reveals the realities of startup life. This experience shapes his approach at Reliant AI today and offers other founders important lessons about the risks and pitfalls in the AI space.

Defensibility Strategies for Application-Layer AI Startups

One of the biggest challenges for AI startups is the question of how to defend their position. Hermann discusses concrete strategies for how application-layer AI startups can protect themselves against competition. In a world where large tech corporations can quickly develop similar solutions, the right defensibility strategy is crucial for long-term success.

AI Model Development at Reliant AI

At Reliant AI, Hermann pursues a thoughtful approach to integrating new models. The challenge lies not only in technical implementation but also in the strategic decision of which models to integrate when and how. These decisions can determine the success or failure of an AI startup.

The Importance of Benchmarks and Quality Measurement

How do you measure the quality of AI solutions? Hermann emphasizes the importance of internal benchmarks for developing successful AI products. Without clear metrics, it's impossible to evaluate progress and make the right product decisions.

Strategic Selection and Execution of Pilot Projects

Choosing the right pilot customers is crucial for an AI startup's success. Hermann shares his strategies for selecting, acquiring, and managing co-development partners. The right pilot partner can not only provide valuable feedback but also serve as a reference customer for future business.

The Perfect AI Startup Founding Team and Hiring

Building the right team is particularly challenging for AI startups. The combination of technical expertise, business acumen, and the ability to communicate complex AI concepts is rare. Hermann provides insights into his experiences building AI teams and what founders should look for when hiring.

Location Choice for AI Startups

The question of the right location for an AI startup is complex. Factors such as talent pool, investor proximity, customer base, and regulatory environment all play a role. Hermann discusses the various considerations that AI founders must take into account when choosing a location.

From Research to Founding

The transition from AI research to company founding brings its own challenges. The academic world and the startup world have different success criteria and time horizons. Hermann's experience as both an AI researcher and founder offers valuable perspectives for others who want to take this path.

With his third AI company, Hermann demonstrates that persistence and the ability to learn from mistakes are crucial success factors in the AI space. His insights into various aspects of AI entrepreneurship – from technical development to business strategy – provide other founders with a valuable roadmap for building successful AI companies.