Friday Wrap-Up: Lessons from Google’s Former CEO and Europe’s AI Regulation Problem
Episode
10 min
Read time
2 min
Topics
Productivity, Investing, Fundraising & VC
AI-Generated Summary
Key Takeaways
- ✓AI Model Depreciation Risk: New AI models take roughly six months to build but can become obsolete within five months, creating a critical valuation challenge for investors. Assessing how quickly model costs depreciate is now a core due diligence question for AI company investments.
- ✓Europe's AI Application Window: Europe has lost ground in energy, chips, cloud, and foundational models, leaving application-layer AI as its remaining competitive opportunity. Draghi's Financial Times piece reinforces that deploying AI broadly across European industries is the primary lever for maintaining global competitiveness.
- ✓Corporate Culture as AI Bottleneck: Most companies will fail to adapt their culture quickly enough to capture AI productivity gains. Organizations must prioritize speed, agility, and role flexibility now, treating cultural transformation as an operational risk rather than a long-term HR initiative.
- ✓Gender AI Usage Gap: Internal token-usage data at Norges Bank Investment Management shows men consume 2.5 times more AI tokens than women overall, and 1.6 times more when excluding power users. Closing this gap requires targeted, division-level interventions since productivity gains depend on consistent adoption across all staff.
What It Covers
Nicolai Tangen and Stille Matyskaard recap their Eric Schmidt interview, covering AI model depreciation cycles, Europe's regulatory disadvantage, corporate culture adaptation speed, and a gender gap in AI tool usage inside Norges Bank Investment Management.
Key Questions Answered
- •AI Model Depreciation Risk: New AI models take roughly six months to build but can become obsolete within five months, creating a critical valuation challenge for investors. Assessing how quickly model costs depreciate is now a core due diligence question for AI company investments.
- •Europe's AI Application Window: Europe has lost ground in energy, chips, cloud, and foundational models, leaving application-layer AI as its remaining competitive opportunity. Draghi's Financial Times piece reinforces that deploying AI broadly across European industries is the primary lever for maintaining global competitiveness.
- •Corporate Culture as AI Bottleneck: Most companies will fail to adapt their culture quickly enough to capture AI productivity gains. Organizations must prioritize speed, agility, and role flexibility now, treating cultural transformation as an operational risk rather than a long-term HR initiative.
- •Gender AI Usage Gap: Internal token-usage data at Norges Bank Investment Management shows men consume 2.5 times more AI tokens than women overall, and 1.6 times more when excluding power users. Closing this gap requires targeted, division-level interventions since productivity gains depend on consistent adoption across all staff.
Notable Moment
Internal data revealed that nine out of ten of the fund's heaviest AI users are men, prompting leadership to treat the gender adoption gap as a measurable productivity risk requiring active management across every division.
Episode Transcript
Hi everyone and welcome back to our Friday wrap up. I'm Stille Matyskaard. I'm Nicola Tanen. This week you spoke to Eric Schmidt, he's former CEO of Google and I would say one of the loudest voices on AI. Absolutely, I mean he was very important at Google but he remains very important in the tech society, extremely well plugged in and very, very knowledgeable and having seen the whole development and continues to be involved in AI also within weapon production and weapon development and very involved also in Ukraine. A new AI model takes about six months to build and then it might be out of date in five months. How should we as an investor think about a business model like that? Well, that's the really big question when these companies come to the market. What's the sustainability of the business model? How long time does it take to depreciate the costs they put into it? How long is the model valid for? And I guess the same goes for the chips companies, what's the length of time one can use these chips for and how do you depreciate? It's really one of the biggest questions when it comes to valuing these companies. What do you think about the changes to the labor markets we might see here? Well, think it's very interesting. You know, we have always said that we are not going to lay off people in the fund due to AI use. We rather use it to make the company more competitive, better, we can do more with the same amount of people and so that's how we are looking at it. And some other companies are doing that too, for instance IKEA are saying they're not going to lay off people, they're going to reskill whoever is not needed in their current jobs and that's an interesting way of doing it. And then you've got other companies again which are going to lay off people. I think the jury's out, we really don't know how this is going to pan out, we really don't. So it's one of the biggest societal questions we are facing just now. Yeah, and then he also talked about how we should think about this when it comes to regulation. He says Europe regulates before it innovates and it's something we heard other people say as well. What do you think about that? Well, think that's for sure a fact. We regulate a lot in Europe. For instance in this country, we were the first country to regulate away meta glasses in public places. So we do regulate a lot, we have more regulations than AI whilst in The US they have more AI than regulations. I think over regulating AI in Europe is a potentially very unfortunate thing because you can really stop the development before it started and if there is one thing we need in Europe now, it's really implementation of AI. Draghi mentioned it …
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