AI agents are doing real work inside real companies. But the CFO's financial model still assumes all work is done by people. Forum AI Studio is looking for a finance operator who wants to build the accounting infrastructure that doesn't exist yet.
Co-Build With Us →Three years ago, AI was a line item in the innovation budget. Today it's showing up in engineering, operations, customer service, finance, and legal simultaneously. AI agents are completing tasks that used to appear on payroll. Token costs are spiking unpredictably. GPU assets are sitting on the balance sheet with no depreciation framework. And when the board asks what the company is getting for its AI spend, the honest answer at most companies is: we don't know.
Cloud FinOps took a decade to mature into a real discipline and a $4B+ software category. AI workforce economics is two years behind that curve, and the problem is structurally harder, because it sits at the intersection of technology costs, labor costs, and business outcomes simultaneously. The incumbent tools track tokens as another compute line. They don't connect cost to business outcome, and they don't model the workforce transition. Nobody has built the product that fits this problem yet.
98% of FinOps teams are now managing AI spend, up from 31% two years ago. The problem graduated from emerging concern to quarterly board agenda item in 18 months. CFOs who couldn't get budget approval to address this in 2024 are getting it now.
Cloud FinOps platforms track tokens as another compute line. They don't connect cost to business outcome. They don't model workforce transition. The software that exists was built for the cloud problem, not the AI workforce problem. The shape is wrong.
AI labor and AI liability frameworks are being written now. The companies that have clean attribution and audit trails before the mandates land won't need to retrofit. The CFOs who have already solved this internally are already asking for tooling.
All three are fundable. What matters is which one matches your lived experience with the problem.
The system that tells a CFO how much work their AI agents completed this quarter, what it cost per task relative to human equivalent, and where the ROI is actually showing up. Not token tracking. Business-level attribution connected to P&L.
FP&A infrastructure for modeling mixed human and AI headcount. What happens to our cost structure if we replace 20% of this function with agents? What does that do to margin? Forward-looking modeling, not retrospective reporting.
The product that finally answers whether the AI investment is working, connecting model costs, agent task completion rates, and business outcomes into a board-ready reporting layer. The first company to make AI ROI legible to a non-technical CFO owns an enormous distribution channel.
You've been a CFO, VP Finance, or FP&A lead at a company that started deploying AI at scale. Maybe you built the spreadsheet that tries to attribute AI spend to real output, or the headcount model that no longer holds up now that agents are doing the work, or the board deck that couldn't answer a straight question about ROI. Whichever version of this you've lived, you understand the financial model well enough to know what product would actually fit the buyer. That's who we build with.
Founders who join our AI Studio get a $250K investment and a full co-building team that includes product, engineering, GTM, and more from day one, plus the agentic playbook to build fundable AI companies way faster than doing it alone.
Co-Build With Us →