Token ecomonics in Amsterdam: Inside the first Tokenomicon

The Linux Foundation's Tokenomics Foundation, launched in August 2026, held its first event, Tokenomicon + FinOps X, in Amsterdam on September 22–23, gathering more than 500 FinOps and tokenomics practitioners. The Foundation released Big-T Notation, a Tokenomics Mind Map, and a prompt cache explainer, while FOCUS 1.5 is planned for later this year to add model ID and family, input/output/cached token distinction, and PrincipalId tracking for AI spend. PointFive's Alon Arvatz presented a prompt compression test showing that less input led to more reasoning and a higher total cost, supporting Quesma's RTK research finding that smaller inputs can be offset by extra agent turns.

2026 is the year AI budgets started to burn fast – for many, faster than expected. Token spend became a separate cost category, and token economics turned into a discipline of its own. In August, the Linux Foundation launched the Tokenomics Foundation. Seven weeks later, on September 22–23, we went to Amsterdam for its first event: Tokenomicon + FinOps X https://www.tokenomicon.com/amsterdam/ . The conference gathered more than 500 FinOps and tokenomics practitioners – people who manage technology spend. Most talks focused on AI billing and providers. Agentic coding got less attention, even though it is the fastest-growing and least-understood cost for many engineering teams. Billing dashboards show how much was spent and when, but organizations still struggle to prove value behind burnt tokens. Below are the talks and projects we found most exciting, along with the Foundation’s projects, plus a little of Amsterdam outside the venue. What the Tokenomics Foundation is building The Tokenomics Foundation has released Big-T Notation and the Tokenomics Mind Map, and presented both in Amsterdam. Big-T Notation https://www.tokeneconomics.com/projects/big-t-notation/ describes how token consumption grows as workflows become more complex. Repeated context, additional model calls, and agents calling other agents can all increase the cost of a task. Similar to how Big-O notation helps estimate compute complexity, Big-T lets teams account for scaling patterns when designing a workflow. The Tokenomics Mind Map https://www.tokenomicon.com/amsterdam/agenda/tokenomics-mind-map/ organizes the core terms across the three pillars of the AI supply chain: production, consumption, and value. It gives engineers and finance teams a shared vocabulary for discussing AI costs and outcomes. Quesma contributed to both projects through the Foundation’s working groups on consumption and AI value. FOCUS and a common format for AI bills FOCUS https://focus.finops.org/ can already record token usage and cost in generic units, and AWS, Azure, and Google Cloud provide billing exports in that format. Planned for later this year, FOCUS 1.5 aims for broader support of AI spend https://www.tokeneconomics.com/projects/what-1-5-does-for-ai-cost-and-what-it-does-not/ , with: - model ID and family, - distinction of input, output and cached tokens, - PrincipalId to track the developer, team or agent behind the usage. We’d like to see the same approach adopted by Anthropic, OpenAI, and Cursor for their coding agents, so teams can compare what each agent costs without translating invoices first. Besides billing, there is room for a common format of agentic coding sessions: which model ran, at what effort, what the agent did, and how much it consumed. Connecting those records to billing data would help teams understand the work behind a charge. Getting caching right The Tokenomics Foundation released a prompt cache explainer https://www.tokeneconomics.com/cache-explainer/ that shows how prompt composition affects caching. You can reorder parts of a request and see how the input cost changes. The optimal order puts fixed tool descriptions, instructions, and context both retrieved information and accumulated session history first, and content that changes per request last. This enables caching to avoid recomputing identical tokens again. Talks we will return to PointFive and the cost of a completed task Alon Arvatz from PointFive shared a prompt compression test where less input led to more reasoning and a higher total cost. That matches what we found with our RTK research https://quesma.com/blog/does-rtk-make-ai-coding-cheaper/ : smaller inputs can be offset by extra agent turns. The useful measure is how much it costs to complete the task, including failed attempts. He also showed how to connect token usage to specific units of engineering work, with a simple chain: coding agent → programming environment → ticketing system. Whatever agent the engineer uses, its session cost is tied to the project, branch, or pull requests on their machine, which then links it to a closed ticket. That ticket becomes the unit of work behind the spend. UNICC and the usage behind a subscription The UNICC https://www.unicc.org/artificial-intelligence/ keynote raised a forecasting problem: a subscription invoice shows the price of the licenses, while the activity behind it stays hidden. Before moving that work to API billing, a team needs to know which models it used and how much input, output, and cached context those sessions consumed. Our practical takeaway is to keep the actual subscription-based usage alongside an API-equivalent cost estimate. Reprice the recorded usage by model and token category, then compare it with the subscription charge. That makes it easier to evaluate potential provider switching cost. Where the plan does not expose enough usage detail, the estimate needs to show that gap. Infracost and optimization for a specific workload Alistair Scott’s talk, Doubling Our Tokens, Keeping the Bill the Same , described Infracost’s internal AI cost reduction agent. Their dashboard compares provider bills with usage data collected through OpenTelemetry. Their own AI gateway gives them one place to apply optimizations. To test the changes, they use PromptFoo and Langfuse alongside custom tests for their agents. The State of Tokenomics In the closing keynote, JR Storment shared insights from the Foundation’s September 2026 State of Tokenomics https://www.tokeneconomics.com/state-of-tokenomics/ report. From the collected 472 responses across 11 industries, we’re getting an overview of AI usage across companies with $4.6 trillion in combined revenue. Proving value or ROI was the most frequently named challenge, at 43%. Visibility and attribution of spending followed at 27%. Only 7% named cost or pricing complexity. Clearly, the harder problem is showing what the tokens were spent on and who actually used them. A third of respondents claimed that a CTO, CIO, or technology function owned AI economics. A quarter shared ownership across the organization, while 12% had no defined owner at all.. Not surprisingly, those with clear ownership, are almost 4 times more likely to link the AI spend to a measurable outcome. Amsterdam outside the venue Our favorite part of Amsterdam came after the talks. Małgorzata was especially taken with the Herengracht, calling its canal houses the most beautiful apartments she had ever seen. In the evening, the lights come on behind their tall windows and reflect in the canals, and the whole street glows. In Dutch Windows https://www.researchgate.net/profile/Irene-Cieraad/publication/326462092 Dutch Windows Female Virtue and Female Vice/ , anthropologist Irene Cieraad links open curtains partly to postwar housing and ideals of openness. On streets with good lighting, large windows let residents watch life outside while remaining visible themselves. And yes, the bikes really are everywhere. What’s next We came to Amsterdam to see where Quesma fits, and we left with a clear direction: agentic coding sessions are least understood part of AI costs. It’s a problem we know well from building our benchmarks https://quesma.com/benchmarks/ . CompileBench, OTelBench, and BinaryAudit measure whether coding agents can complete real engineering tasks, like fixing builds, adding OpenTelemetry instrumentation, or finding backdoors in binaries. Every run also tells us how much each attempt cost, including the ones that failed. Two agents can reach similar result at very different prices, and the reason can be found in their sessions. We also work on measuring agentic AI. Quesma Shipper https://github.com/QuesmaOrg/quesma-shipper is our open-source collector for coding-agent sessions from Claude Code, Codex, and Cursor. We want to bring our experience to the Tokenomics Foundation. Our contribution to the Tokenomics Landscape is already confirmed. We’ll keep working in the Consumption and AI Value working groups, and we plan to contribute to future versions of FOCUS. If you’re trying to understand what your coding agents did, what it cost, and what your team got back – reach out to us https://quesma.com/contact/