Nikolai Tangen: Warm welcome to this first AI seminar at Norris Bank. And this is just a huge moment because we have never seen a technology like this. And it's moving not in a straight curve. It's continuing to curve up and it's nearly vertical what this technology can do now.

Nikolai Tangen: And so the issue is what we call the technology overhang. Are we able to utilize all this technology? And that is really the tough part I think is to get the organizations to absorb and to utilize what we have. So we are as you know the most transparent fund in the world. And why is transparency good? Well, I think it's good because people can look in and see what we do. And we believe that that creates trust. But I think equally important is that we can look out and see what the world is doing.

Nikolai Tangen: And I have this dream of being able that we together, you know, the private and public can basically lift productivity in this country. That's a good ambition. I think we have a lot of user cases across the firm and we could have picked uh you know many many different ones. We picked 10 which we think gives uh a kind of a taste of different things we do. Some of them help us make more money, some make us save money. Some improve efficiency. Some improves accuracy and quality and some just prevents us from doing boring stuff.

Birgitte Bryne: So we've done many transformations in MBIM since 2015 but today I want to talk about three major steps that we have done that has built the foundation for our AI strategy. So the first thing we did was to insource operations. So before that we had an external vendor that took care of settlement, corporate actions, fund accounting, valuation, everything. But when we scaled into new markets, we wanted deeper expertise and richer data. So the solution for that was to bring it all home. We own the process and we also wanted to own the knowledge.

Birgitte Bryne: So the next big thing we did was to move all our IT infrastructure, all our IT systems to public cloud. So before that we used to rent space in an external data center, outsource the technology with it. But what we saw was that we had a data uh ceiling in a way. But what we wanted was to have a data horizon. We wanted instant scale on demand and we wanted to get away from server refresh cycles.

Nikolai Tangen: And is it fun to clean data? Birgitte Bryne: It's no fun at all. Nikolai Tangen: So it's the most it's the most kind of boring job there ever is. Does anybody thank you for cleaning your data? Birgitte Bryne: No. How do you get people to clean data? You basically tell them that 31st of January we are going to turn the old old data off. And if you sit there the day after and have no data, you are going to look very stupid. So we had a lot of late nights and an enormous work by the whole... that was a lot of tidy up and rewrite of code for basically everyone in the company but uh now we have one place for internal and external data with high quality and it's tidy up and it can be used for AI and it's called martium core.

Stian Peersen: So I'll I'll take you through our AI journey and having a good data warehouse and a lot of compute in the cloud has absolutely been necessary for us to get on with the AI journey. And it all started about two years ago when Nikolai had Sam Alman from OpenAI and Dario Amodai from Antropic on his podcast and he found out we should be 20% more efficient. So um to see how he actually got that working is that him being a a battery durel battery that never goes out of the energy on the top is pushing the organization throughout the last two years and he has pushed everyone.

Stian Peersen: If we're going to be 20% more efficient and we're going to start using AI, we have to change our habits. So, we needed to push everyone a lot and nudge them again and again and again. So, we created a uh add upskill program for everyone. And we also created an ambassador network to help people get going here. There were 20 uh volunteers from all over the organization. They were given the task of finding the one use case in your team that you think can be a valuable project with AI... with the help of entropic. So twice a week Antropic help us get started by doing uh creating training for these ambassadors and the AI team for two months.

Stian Peersen: We trained the ambassadors but we also had to train the rest of the organization. So we've created seven 30 minute sessions with different topics... and this was mandatory. Now do people like mandatory? They hate mandatory... It has to be mandatory and you have to be on them like a wasp. Everyone in NBIM is using cloud on a daily basis. With cloud comes cloud code and more than half of the employees are using cloud code to create solution. We just got Gemini this month uh to complement claude and already uh more than twothirds have signed on and started using it.

Lydia Toisuta: Now, at the fund, we're fully aware of the importance of ensuring the AI we use is always done responsibly. And that's why we've built a responsible AI framework. So, first we make the rules. Our responsible AI guideline sets requirements for every employee when we're buying, building, or using AI. The guideline aligns with law like the EU AI act as well as globally recognized AI standards. It addresses some key areas like protecting people's data and ensuring human involvement in all AI systems that help with investments or people related decisions.

Lydia Toisuta: Now the guideline takes a riskbased approach. That means the way we would handle a simple email filtering system is completely different to the way we would handle an AI system that impacts people. Our operating model... sets out key processes that we all need to follow from the development of an AI system all the way through to deployment and beyond. Now, at the very center of this governance structure, we have our AI governance working group.

Trond Grande: In our recently released uh strategy plan uh we mention AI a lot across every function... This is just one example of a pretty bold ambition if you ask me to cut all our manual processes in half by the end of 2028. So we'll take you through in rapid succession uh 10 different use cases three minutes each.

Ola Peter Gjessing: Imagine being contacted by Goldman Sachs. Ferrari's largest shelder, wants to sell shares worth 30 billion corners. They need an answer within one hour. Would you buy? In our team, we get about 200 these requests every year. The challenge is that we have very little time and the data is everywhere. We need both [code and LLM]. So we built agents, specialized AI programs with dedicated tasks and tools working together. When Goldman calls we spend less time gathering all data more time analyzing it which leads to better decisions that we make more money.

Safraz Farooq: Echo is a live overview of all of our communications activities across channels. In 2025, the fund was mentioned in almost 50,000 articles. That's why we built an AI powered sentiment analysis to help us do exactly that. So, this is an agent-based system where each article goes through a main agent that delegates to specialized sub agents... All of this data is stored directly into our data warehouse, Snowflake. Lastly, we built a chatbot sitting on top of Echo... Instead of digging through the dashboard, we can simply ask.

Geir Arild Tierland: We collect roughly a trillion data points about MBIM and MBIM's operation throughout the year. And then from those trillion, we surface down maybe somewhere in the range of a million to 100,000 things that might be considered suspicious. While I'm rolling out of bed uh this agent has started working immediately and it does the exact same process. It produces uh a report... which is very similar to what I would do. It does what would have taken me roughly half an hour in in 5 minutes.

Christina Wiggen: In 2025, MBIM held more than 3,000 company meetings. Each of these meetings take about 3 hours to prepare. So this is nearly 10,000 hours every year. The model loads data that only we have access to... one agent builds a plan. Then three to five sub aents go out and research... A final agent has been trained on our very best meeting prep examples. We'll soon add a simulation component. This will use podcasts, part past meetings... to predict what the counterpart is likely to tell you.

Oscar Bathen: Traditionally relying on the sell side banks... MBIM selected an external system in uh 2018. But this system, it doesn't know MBIM. Introducing our AI surveillance team. Uh we have six sub agents that are each reviewing all alerts... They all feed into a master agent which we call our enhanced vigilant agent or Eva. Uh she's always on, completes a full audit trail.

Martin Kulsrud: The challenge is to remove the bad apples from the fund. What we need to do is to remove the financial makeup that uh makes these companies look better than they really are. So we're making many different kinds of agents that is uh trying to spot this kind of financial makeup. We have made inhouse quite a unique data set where we have gone through every forensic accounting research shop and gotten all of their historic cases. The output of the model is uh the percentage likelihood that a company is going to develop into such a forensic accounting case.

Torjus Gylstorff: Every quarter we produce NBIM's financial statements... resource intensive. Think complex Excel workbooks. Our team of two are not developers. So we use cloud code and cursor to write the code. FX and tax at the push of a button on business day 2. Note 14... one person spent one full week producing this note. Now, it's done in only a couple of hours. AI enabled us to automatically produce note four... and notes 11... in the annual report 2025.

Christina Wiggen: As a responsible investor, we need to know whether any of these companies are linked to serious issues... child labor, deforestation... If a human analyst were to do that, we estimate we would need 3,000 analysts working an entire weekend and our team is only eight people. The screening process runs in two phases. Phase two, we deploy multiple AI agents, each responsible for researching the company from a different angle.

Kirsty MacGillivray: If AI can model language patterns, then it can model negotiation patterns. So, I created the negotiation simulator. We're able to predict over 80% of the arguments. Simulation is what sets this apart. In voice mode, I can simulate a live negotiation in a risk-free environment. I can even switch roles to see how AI would handle the situation if it were sitting in my seat.

Yngve: We are now in a place where we ourselves become our own worst enemy in the market. This footprint in the market, this market impact as we call it, we estimated last year to 14 billion of region. AI helps us find patterns in the market and give us probabilities... predictions build patience. Another thing we can do is to look at all the internal portfolios. Last year we parked [internally crossed] more than 120 billion and we don't go to the market. We have saved four, five, maybe even six billion croners in the way we trade for the fund.