Every so often, you come across a company that feels less like a job move and more like a conviction.
For me, Revenium was that moment.
I've seen what happens when infrastructure scales faster than financial accountability. Smart teams ship powerful new capabilities and then struggle to answer what should be simple questions:
- Which feature actually made money?
- Which workflow burned cash?
- Which customer segment is profitable?
- Are we scaling intelligently, or just scaling spend?
Cloud forced companies to rethink how they manage cost.
AI is forcing them to rethink economics entirely.
Most aren't ready for that shift.
AI Doesn't Work Like Normal Software
Here's what happens when a customer uses an AI feature in your product. They ask a question. Seems simple. But behind the scenes, an agent wakes up, pings three models, calls a couple external APIs, stores context, and generates an answer.
Four seconds later, the customer has a response. And you have a cost somewhere between $0.80 and $8.00, depending on what just happened under the hood.
That's the core issue: AI is non-deterministic. One query can trigger 10 API calls or 10,000, which breaks traditional forecasting.
Multiply that by 10,000 users. By the time finance asks why the AI bill doubled, your engineering team has already shipped two more features.
Most teams still treat AI like an R&D project. They measure accuracy, optimize latency, and count tokens.
But leadership doesn't care about token counts. Leadership needs to know whether this is making money or burning it, and whether the business can afford to scale it.
That gap between "the AI works" and "the AI is economically viable" is the problem Revenium directly solves.
The Ledger, Not a Dashboard
Revenium isn't another analytics tool. It's not observability. It's not FinOps with a fresh coat of paint.
It's an AI Economic Control System—the system of record for AI usage, cost, and unit economics, with the controls to enforce pricing and economic guardrails in real time.
It captures every model call, every agent action, and every API request, and attributes them to the specific customer, feature, workflow, or team that caused them.
That level of attribution gives you cost-to-outcome visibility.
Dashboards tell you what happened. A ledger lets you govern it.
A ledger enables governance because it ties cost to a responsible unit. Once you can assign spend to a feature and a customer, you can set budgets, detect runaway loops, and enforce guardrails in real time. Without that, teams argue from averages and ship fixes after losses.
Instead of reports like "our AI costs are up 40%," you get:
- Feature X costs $0.42 per interaction and drives conversions
- Workflow Y is margin-positive
- Customer segment Z is underwater—every interaction loses money
- Agent A just crossed the profitability threshold
The Team
I've been doing this long enough to know that markets get shaped by teams, not just ideas.
What sold me on Revenium was not only the technology but also the founders.
They have all built and sold successful companies before. They know what it takes to go from zero to acquisition. They've done the hard work of defining categories, earning customer trust, and building products that actually solve real problems.
But more than that, they're just genuinely good people. Kind. Funny. The kind of people you actually want to spend time with, not just work alongside.
I'm at a point in my career where I get to be selective about who I work with. Life's too short to spend it with people you don't like, even if the opportunity looks good on paper. I'm lucky that this team is both incredibly sharp and a pleasure to be around.
Why Now
In 2026, AI economics is moving to the board agenda because the spending curve is no longer a rounding error.
Enterprises are not struggling to access models. They are struggling to keep margins intact as usage grows and autonomy increases.
Observability shows failures in runtime. FinOps shows spend by account. Neither shows profitability by feature and customer, which is what pricing, roadmap, and go-to-market decisions require.
You need a real-time economic control system built specifically for how AI actually behaves.
That's the opportunity.
What I'm Here to Do
My job as VP of Marketing comes down to three things:
- Make "AI Economic Control System" the standard. Help teams understand that attribution at the transaction level isn't a nice-to-have—it's how you make AI sustainable.
- Build the narrative that matches the ambition. Show why AI activity is economic activity, and why the tools companies use today weren't built for that reality.
- Create a demand engine that reflects the problem we're solving. Turn technical depth into business clarity. Make it obvious why governable, attributable, profitable AI isn't optional anymore.
We're not making cost visibility prettier.
We're building the control layer that keeps AI economics sustainable.
And we're just getting started.
If you're thinking about how AI economics will reshape your industry or building in this space and want to compare notes, let's connect.
Find me on LinkedIn: https://www.linkedin.com/in/jstaretorp/
