Ken Beinert is Chief Technology & Operations Officer at London Stock Exchange Group (LSEG), where he leads the development of its technical and operational architecture. The Goldman Sachs veteran joined the global markets infrastructure and data provider in summer 2025 – and is responsible for its AI transformation strategy.
The big focus? “Enabling sustainable growth and stability of our customers and their communities.” Among his tactical challenges: delivering that in an exceptionally fast-moving technology landscape – one where data discipline, innovation, and fiscal precision are all paramount. Beinert joined The Tokenomics Foundation’s Executive Director J.R. Storment to talk AI agents, economics, governance, and more.
The below is from the transcript of their conversation, lightly edited for brevity.
Q: Tell us about your AI strategy
“We think about our AI transformation strategy really in three pillars.
“The first is AI-ready data, where we're focused on making sure that our customers have access to our trusted high quality data in APIs and formats that are extremely accessible to AI. The second is transformative products where we embed AI directly into our existing products and evolve those products so that they're truly AI-native, and the third, of course, is what we call intelligent enterprise, where we're focused on enabling our internal colleagues with the right tools and practices.”
Q: When we think about AI economics, [The Tokenomics Foundation] finds that it's about the total cost of AI, because models and tokens are often a quarter of the spend. When we're talking about AI, what's included in that equation for you?”
15:31 When we think about the investment for an AI solution, I think really about it in two different phases. The first is [to…] focus on the problem that we're trying to solve, secondly on the success metrics that we'll use, the spend, the quality, the user experience are all part of defining the problem and defining the right solution.
We're not just planning for token spend. We're also planning for other infrastructure.. We're planning for data cleansing and preparation to make sure that the data is ready for AI solutions, and then of course we're also planning ahead for real-time guardrails, recurring evals, and comprehensive observability and telemetry, so that we can manage both the cost and the quality of our AI solutions over time.
Q: The Stake of Tokenomics survey showed that companies are increasingly investing in different model options [and building] governance layers in. How do you ensure that [your teams are] using the right tool for the job?
“One of the factors that I think is most key to model selection is doing comprehensive evals of a model in situ for each solution before we launch it, and then of course continuing to monitor those evals over time, and determining as new models become available, as new solutions become available, whether or not we get better outcomes, whether it is user experience, latency, or cost optimization…
Q: We've got frontier models that everybody's using. We've got open weight models. We've got SLMs. Are you using a mix of those? What does that look like?
We are using a mix of them. You can imagine quite a lot of our customer-facing solutions and our more advanced solutions do leverage frontier models, but increasingly we're finding that many of our solutions can be solved with non-frontier models, smaller models, in some cases custom models. We're beginning to see open weight models that really compete successfully with closed weight models, and that's both at the frontier level of model as well as a smaller level of model…
Q: Let's dig in a little bit about how the variability of AI models is cascading into potentially pricing model changes for you, or maybe just margin management. In the historic capex-heavy world, there was more straight predictability on how much you were going to spend. And with AI usage, both for developer productivity as well as the mission-critical AI, there's a lot less predictability. So, how are you seeing that variability cascade into how you're having to price for your customers?
I think the most important part of pricing AI-based solutions is to make sure we understand the business case upfront, and that we do very thorough evaling for quality and cost ahead of time, so we do understand the unit economics.
The second most important part of that is to bake in the right assumptions into those unit economics, so that we have the opportunity to continue to make them successful over time. Like the rest of the industry, certainly what we're seeing is that prices change very rapidly in multiple dimensions for potential AI solutions, but at the same time, what we're often seeing is that unit economics for an existing solution can improve over time, especially as newer models become available or as we tune our solutions based on our observations through our own telemetry and ongoing evals.
Overall cost, on the other hand, certainly can increase as we see customers and colleagues using those same products even more.
Q: You mentioned unit economics a few times. Is that a granular unit economic, like our cost to serve this customer, or is it a bigger concept?
The unit economics really can vary based on a specific use case. But for me, the example that comes most immediately, most accessibly to mind, is cost per task, where what we really want to understand are the typical tasks that a user uses a given AI solution for, so that we can develop the right evals for each of those tasks to measure both accuracy, quality, latency, feel and experience as well as cost.
Q: How do you enable those trade-offs between cost, value, speed, latency, and are there certain jobs or use cases where you heavily index toward one versus another?
Within LSEG, we have a number of use cases where we use AI for an input to our products, but we [also] have data preparation, data cleansing work that runs perhaps overnight and essentially is not latency sensitive, not part of the direct user experience. There, clearly we have an even greater range of models that we can select from, as opposed to use cases where a customer is engaging directly in an experience with a model. If we're talking again, 12 months from now, one problem that I'd love to see us as an industry solve is the challenge of running agents safely at scale. Today, when we select an agent platform, we're selecting solutions for identity management, entitlements, containerization, runtime infrastructure, sandboxes, and of course observability and telemetry. And each of the solutions that we could use has its own differences, its own potential gaps, which can make it quite hard to switch between platforms and between models and between providers. If we solve this problem, it will really help the industry as a whole to deliver better AI solutions.
Q: How are you driving accountability for AI spend in the firm?
“Well, first and foremost, we believe that the person who owns the P&L for the product also owns the AI spend. That aligns incentives and responsibility.
And secondly, we believe in providing central platforms and controls around AI, both for evals and cost controls, as a joint venture between enterprise AI and finance. And the reason for that is that while the accountability needs to remain with the P&L owner, centrally we have the opportunity to stay abreast of best practices and transformative AI solutions that can be rolled out across these use cases.”
Q: How does Tokenomics differ from FinOps for you?
I see AI economics really, in some ways, as an evolution of cloud FinOps, clearly it is more complex. The pace of change of model selection is much greater, with changes in new types of compute, storage, network infrastructure, etc. There's much more variability in cost, and there's just so many different dimensions of cost. For example, caching and fine tuning… These are things that FinOps didn't have to deal with!
Look, I think that the practices that we have in place for FinOps today, we're going to continue to have all those same practices. But we're going to raise the standard for how we do financial management so that it is more real-time… add an increased element of managing for user experience, because certainly what we find in AI, unlike compute, unlike storage, is that there's a much greater element of variable user
The thing that I'm most excited about is understanding the real potential of AI and putting it in place within our organization, it seems like every day there's new insights into practices that we can apply. Almost every day we're seeing new models that have capabilities in advance of what we expected, but entirely new model paradigms that enable use cases that literally did not seem feasible even a week ago.
Delivered in partnership with the Tokenomics Foundation. To learn more read the 2026 State of Tokenomics report.
