AI Tokenomics: How to tokenmin while ROImaxxing | MMC

AI Tokenomics: How to tokenmin while ROImaxxing

user<br>Advika Jalan<br>, Simon Menashy<br>, Prakriti Roy

icon-yoi 30.06.26

Enterprise AI

Insights Hub

AI Tokenomics: How to tokenmin while ROImaxxing

A company accidentally spent $500 million on tokens. We’re not making this up; fact is stranger than your AI agent’s wildest hallucination. And they managed to spend it all in a single month because they hadn’t set employee limits on Claude usage. In a similar vein, Uber and Service Now burned through their entire annual AI budgets in the first few months of this year.

These extraordinary levels of expenditure are driven by dramatic developments along two axes: price and quantity. Major AI providers like Anthropic and GitHub Copilot are moving from generous flat-rate subscriptions to usage-based billing. For example, GitHub Copilot’s June 2026 pricing changes increased model costs for legacy Pro and Pro+ users by as much as 9x to 18x. Also, enterprises are increasingly deploying AI agents at scale, and multi-agent systems typically use about 15x more tokens than chat interactions (meanwhile, agentic coding systems can use >1000x more tokens than chat). And let’s not forget the less obvious pricing/quantity changes, as seen when Anthropic’s Opus 4.7 shipped with a tokenizer which kept per-token rates identical, but generates up to 35% more tokens from the same input.

The good news is that 50-80% of your token spend is unnecessary. There is a multitude of (hidden) ways in which you’re bleeding tokens: everything from using frontier models for trivial tasks, to re-processing the same context repeatedly, to having “gossipy” AI agents who send vast quantities of superfluous information back-and-forth. Reassuringly, all of these problems have fixes, which we discuss in this report.

Here’s our blueprint for token-efficient AI:

Invest in context and memory management solutions . Input tokens, rather than output tokens, account for the larger share of token spend. The best context and memory management solutions feed AI agents the precise information they need (not too little, not too much) in an efficient manner whilst improving accuracy. Also, because these solutions exist independently of model providers, it’s easier to switch models.

Build multi-model systems . Model capabilities are evolving too quickly to bet on a single provider, and Anthropic’s “Fable Fracas” showed that access to frontier intelligence can vanish overnight for geopolitical reasons. In this report, we’ve focused on 3 key components of multi-model systems:

Open source models. As open and closed model performance increasingly converge, ensembles of budget models can rival or exceed frontier intelligence at around half the cost.

AI routers and gateways. For most of the use cases you don’t need frontier intelligence, so routers and gateways direct your traffic to the lowest cost model that can meet your performance requirements.

Inference providers. Agentic AI demands a new inference stack because unlike chat, agentic workloads are long-running, asynchronous, context-heavy, and dominated by tool use. That’s where async and batch inference can tremendously lower costs.

Make verification fast and cheap. About 60% of the cost of agentic software engineering isn’t in initial code generation, but in automated refinement and verification. The “fully loaded” cost of tokens should also include the cost of human verification and re-work.

Focus on workflow design . Even if you run the same agent on the same task, the cost can vary by 30x, and this unpredictability necessitates two actions:

Building real-time visibility into costs and ways to control that expenditure; and

Reserving LLMs for reasoning, not tasks that SQL, rules, or templates can handle. If something can be handled accurately and cheaply using deterministic methods, it’s best to avoid using agents.

Introduce spend controls without stifling adoption, by factoring in actual adoption patterns. Use pooled budgets rather than fixed per-user limits to balance experimentation with cost discipline – e.g. 5% of users may account for 40% of token consumption, and this shifts around over time depending on the pace and depth of adoption.

Don’t optimise for token cost alone. Security, governance and sovereignty are equally important considerations when designing AI systems.

We’ve also featured various startups and scaleups providing solutions (for everything from context and memory management to inference provision) in a market map. If you’re building in this space – or you’re an AI-native company cleverly ‘tokenminning’ while ‘ROImaxxing’ – please reach out to Advika, Simon or Prakriti – we’d love to chat. We’ve been researching on and investing in AI companies for over a decade, and today we have one of the largest portfolios of AI companies in Europe – so we’re clearly very keen on the space.

Note: Certain startups and scaleups may fall...