Global AI Regulations and Their Impact on Industry Leaders - with Micheal Berger of Munich Re

Unknown Source April 15, 2025 21 min

artificial-intelligence generative-ai ai-infrastructure apple openai

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36 Key Quotes

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Comprehensive Summary: Global AI Regulations and Their Impact on Industry Leaders - with Michael Berger of Munich Re

This podcast episode features Michael Berger, Head of Ensure AI at Munich Re, discussing the maturation of enterprise AI adoption, driven significantly by the rise of Generative AI, and the critical role of global regulation and robust AI governance in managing associated risks.

1. Focus Area

The discussion centers on the evolving landscape of AI risk management and governance in the wake of widespread Generative AI adoption. Key topics include:

The impact of emerging global AI legislation (specifically the EU AI Act) on business strategy and insurance.
The shift from AI hype to grounded risk assessment, focusing on inherent model failures like hallucinations and probabilistic errors.
The necessity of formal AI governance frameworks to define risk tolerance and implement operational safeguards.
The changing nature of AI risk, particularly the systematic risk of discrimination when using foundational models.

2. Key Technical Insights

Probabilistic Nature of Failure: Business leaders must accept that AI models are inherently probabilistic and can fail (e.g., hallucinate) at any time; this risk cannot be entirely eliminated by technical means alone.
Systematic Discrimination Risk: The use of foundational models across many companies for sensitive use cases introduces a new, systematic risk of discrimination that can impact numerous consumer groups simultaneously.
Shift to Specialized Models: The future favors numerous, fine-tuned, task-specific AI models over large, general “master models,” as specialized models are easier to test, scope, and quantify risk for.

3. Business/Investment Angle

Regulatory Certainty Unlocks Potential: Legislation like the EU AI Act, despite industry criticism, provides necessary legal infrastructure and certainty regarding consequences, which can ultimately unlock further, more confident AI adoption.
Litigation as a US Regulatory Driver: In jurisdictions like the US, regulatory clarity is emerging through litigation (e.g., the Canadian airline hallucination case), forcing businesses to clearly define responsibility for model outputs.
Aggregation Risk Awareness: Companies must be aware of aggregation risk—where many entities rely on the same foundational model, leading to highly correlated error rates across the industry. A potential mitigation strategy involves deliberately choosing less optimal, but less correlated, model architectures.

4. Notable Companies/People

Michael Berger (Munich Re): Expert providing the insurance and risk management perspective on operationalizing AI governance.
EU AI Act: Highlighted as a primary example of proactive, risk-based regulatory infrastructure setting global standards.
Canadian Airline Case: Used as a real-world example where courts held an airline liable for a hallucinated policy generated by a ChatGPT-like model, emphasizing the liability shift to AI adopters.

5. Future Implications

The industry is moving toward a more mature, risk-aware phase of AI integration. The focus will be less on deploying the largest, most general models and more on operationalizing AI governance—defining clear risk tolerance thresholds and implementing granular mitigation strategies tailored to specific, fine-tuned use cases. AI insurance is expected to play a vital role as a safety net for residual, unmitigated risks.

6. Target Audience

This episode is highly valuable for Business Leaders, Chief Risk Officers (CROs), Legal Counsel, and AI Strategy Executives operating internationally, particularly those in regulated industries or those concerned with scaling AI adoption responsibly amidst evolving global legal frameworks.

🏢 Companies Mentioned

OpenAI ✅ ai_application

Emerge AI Research ✅ unknown

Daniel Fagella ✅ unknown

E M E R G E ✅ unknown

Apple Podcasts ✅ unknown

The Godfather ✅ unknown

Gen AI ✅ unknown

The Canadian ✅ unknown

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EU AI Act ✅ unknown

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💬 Key Insights

"one of the recommendations we have when it comes to that is also to think about whether to get very foundation models or very model architectures. So not just to build the most optimal and choose the most optimal model for a specific use case, but it might even be optimal not to choose the optimal model architecture, but a weaker model architecture which is less related to other model architectures which a company is using for similar use cases. Thinking on a complete level, this can help also to mitigate this kind of aggregation risk."

Impact Score: 10

"this reliance on foundational models introduces a new form of risk we need to be aware of, as do our insurance companies. As mentioned before, if many companies are utilizing the same foundation model for similar use cases, we see that hallucination rates, error rates of those kind of models, even if they are fine-tuned to the specifications of the individual company, those error rates and hallucination rates can be highly positively correlated. This is something to watch out for because this gives rise to aggregation risk."

Impact Score: 10

"as we have seen in the ChatGPT update from 2023, when it comes to GPT-4 update, prior to the update, the model was performing very well on some use cases, and the error rate was below 5%, but then suddenly on those same kind of use cases after the update, just by the nature of how the retraining was done, the error rate shot up to in excess of 90%."

Impact Score: 10

"I think this also already shows that the future might more lie into having many models for specific tasks and fine-tuned on specific tasks rather than one kind of general master model which is good for any use case. This was something what I believe that the future is, and I think this is also more risk-adequate."

Impact Score: 10

"This idea that you could get out-of-the-box very large models that will go through the entire organization, be collecting data, and not be meant to have direct contact with customers—these are models that can tell you the entire cast of The Godfather whether or not that's your business problem or not. They're very smart, but those tend to carry more risk versus smaller models that are developed for very specific purposes, very fine-tuned, and they don't need to tell you the cast of The Godfather."

Impact Score: 10

"thinking about foundational models, so if we would have a foundational model on this being trained and used for a specific use case by many companies, then yeah, and if it's found that this model discriminates in this sensitive use case, then suddenly many companies might be impacted by this kind of risk. And I believe this gives rise to a new form of aggregation risk on which companies need to look out for when considering their AI and generative AI use cases, especially when it comes to the use of foundation models."

Impact Score: 10

📊 Topics

#artificialintelligence 102 #generativeai 20 #aiinfrastructure 1