For the AI Native CFO

Token Ops Finance

Token Ledger Advisory helps CFOs manage enterprise AI usage. Turning cost and usage data into finance-ready views of margin, pricing, capitalization, capital preservation, and capital deployment.

“AI spend already has data. It lacks financial treatment.”

Finance Thesis

“Blunt caps preserve cash, but they erase the signal CFOs need to fund productive usage.”

Capital Thesis

Argument

Finance teams see AI through cloud bills, API projects, cost centers, tags, usage dashboards, and vendor exports. TLA adds the missing layer: a normalized Token Ops ledger that classifies activity by economic purpose, financial treatment, margin impact, and capital action.

Overview

Finance Practice

How finance teams see AI today.

Allocate, monitor, control — and the gap in treatment, margin, pricing, and capitalization.

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CFO Expectations

What CFOs need from Token Ops.

Ledger, margin, capital, control, and actionable decisions for funding, pricing, and routing.

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Research

Token spend is a CFO cost category.

Enterprise GenAI API spend, LLM budget growth, F1000 estimates, and the finance gap — sourced.

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Partners

Founding partner profiles.

AI systems, transaction advisory, and technical accounting expertise.

Meet the team →

Work Plan

Six-step engagement model.

From source inventory to CFO actions — inventory, connect, normalize, classify, evidence, act.

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Spend Sources

Where AI spend originates.

Model APIs, cloud platforms, enterprise data clouds, and AI work tools.

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First Engagement

Two to Four Weeks

Enterprise Token Ops Baseline.

Connect the core data sources, normalize usage, run classification rules, and produce the first CFO view of AI spend by financial treatment and business purpose.

Deliverables

Ledger plus action file.

Connector map, normalized ledger, treatment split, capitalization candidates, unit-economics view, exception report, and capital actions.

Schedule a Working Session

Allocate the cost. Classify the treatment. Fund the output. kripa@tlacap.com

Category	Fact	Source	Finance Relevance
Enterprise GenAI Spend	U.S. enterprise generative AI spend was estimated at $37B in 2025, up from $11.5B in 2024.	Menlo Ventures, State of Generative AI in the Enterprise	AI has moved from experimentation into a material enterprise spend category.
Model API / Token Spend	Menlo's 2025 enterprise GenAI estimate included roughly $12.5B of model API spend, the closest public proxy for direct token consumption.	Menlo Ventures, reported via secondary summaries and reconciliation analysis	Token consumption is already a multibillion-dollar vendor expense category.
Large-Enterprise LLM Budgets	Average enterprise LLM spend rose from about $4.5M to $7M over two years, with surveyed enterprises expecting roughly 65% growth to about $11.6M.	Andreessen Horowitz, Global 2000 CIO survey, 2026	Large enterprises are moving toward low-double-digit-million annual LLM budgets.
AI Investment Intensity	BCG's 2026 AI Radar found corporations expected AI investment to rise from about 0.8% to 1.7% of revenue.	BCG AI Radar 2026	Total AI budgets are much larger than token budgets, but token spend is a growing controllable input.
Token Pricing Basis	Frontier model vendors price usage per million input, cached-input, and output tokens. OpenAI, Anthropic, and Google publish token-metered API pricing.	OpenAI API pricing; Anthropic Claude pricing; Google Gemini API pricing	Tokens behave like a metered, fungible consumption unit that finance can allocate, monitor, and control.
Coding-Agent Cost Pressure	Anthropic states Claude Code enterprise deployments average about $150–$250 per developer per month, with wide variation by model, codebase, and usage pattern.	Anthropic Claude Code cost documentation	Agentic coding can create a large, department-level token cost pool.
Enterprise Cost Controls	OpenAI introduced enhanced ChatGPT Enterprise usage analytics and spend controls to help enterprises manage AI consumption and credit usage.	OpenAI; Reuters, June 2026	Vendors are adding controls because enterprise AI consumption has become financially material.
Spend Volatility	Recent research on agentic coding found token usage can vary significantly across tasks and runs, making cost forecasting difficult.	Bai et al., How Do AI Agents Spend Your Money?, 2026	Token spend requires finance-grade variance analysis, not just technical observability.
F1000 Annualized Token Spend	Fortune 1000 companies likely represent $10B–$25B of annualized direct model-token/API spend in 2026.	TLA estimate based on Menlo, a16z, vendor pricing, and coding-agent cost benchmarks	Direct token consumption is likely already a CFO-relevant spend pool across the F1000.
Core Finance Gap	Public data shows usage, pricing, and budget growth, but does not classify token usage by margin impact, capitalization potential, pricing recovery, or capital action.	TLA analysis	AI spend already has data; it lacks financial treatment.