AI Cost Methodology
The Bleeding Budget
$10,000Monthly
~$3,500Est. waste
~$42,000Recoverable/yr
At $10k/month, typical waste is 30-40%. Around $3,500 per month going nowhere.

AI spend is
easy to grow. Hard to
understand.

Every AI company has two numbers: what they pay, and what they should. Most know the first. Almost none have calculated the second.

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AI spend with no assigned owner or cost center
Sits outside finance team visibility entirely. Zylo SaaS Management, 2024.
Cannot forecast AI costs accurately
Of growing companies, within 10% accuracy. Benchmarkit, 2025.
Recoverable, per dollar of AI spend
35–55¢

The gap is not linear. Early-stage companies lose around a third. At scale the percentage compounds: redundant calls, idle capacity, misclassified spend accumulating faster than anyone models. The methodology calculates the exact number before the investor does.

AI spend grew fast.
The financial discipline around it did not.

The invoice tells you what you spent. It does not tell you what you should have spent, where that spend belongs in your accounts, or what it is doing to your gross margin. Most companies have never calculated those numbers. The gap is already in their financials.

Productive
Recoverable
Output-generating calls 52%
Redundant or over-batched 18%
Idle or overprovisioned 20%
Misclassified in accounts 10%
Total AI bill · tap or hover to explore · $10k–$30k/month

This is a strategic reevaluation of how a company allocates its most growth-critical spend. Rush of Read works from spend data and workflow descriptions. No code access. No infrastructure review. The output is a decision-ready view of where AI spend is working, where it is not, and what a corrected structure does to the numbers that matter.

84%
of companies report AI costs cutting into gross margins by more than 6 points
AI Cost Governance Report, 2025
16x
difference in cost between the most and least efficient model choices for identical tasks
Redis LLM Token Report, 2026
15%
of growing companies can forecast AI costs within 10% accuracy
Benchmarkit / Mavvrik, 2025
What the methodology surfaces
01
The exact recoverable cost
Per tool, per classification bucket, per month. Not an estimate. A number that holds up in a due diligence conversation.
02
A corrected cost structure
What belongs in cost of goods, what belongs in operating expense, what generates no output. In terms a CFO and an investor can read in the same sitting.
03
AI unit economics
The ratio that defines model efficiency, calculated from your actual spend data. The metric most founders cannot produce before a raise.
04
A foundation for what comes next
The methodology is the engine. The output can be a one-time report, an ongoing audit process, or the data layer behind a live cost dashboard. It fits the company, not the other way around.
Nikith Adigopula Founder, Rush of Read LinkedIn

The idea came from watching the same pattern repeat across early-stage companies. AI spend was scaling. The financial discipline around it was not. Founders heading into raises could tell investors what they paid. Almost none had calculated what they should have paid, or what the gap was doing to their gross margin.

So I built the methodology to calculate it. Twelve financial modules. Four classification buckets. A calculation engine validated across synthetic cases before asking anyone to test it on real data. The work was done before asking for anything in return.

What I am looking for now is the right environment to prove it. A small number of companies willing to work through the methodology together, and the context to make the output sharper.

Every company has recoverable cost.
The only question is who calculates it first.

Design Partners Selective cohort · 4–6 companies

The design partner relationship is not a beta program and not a consultation. It is a small, intentional collaboration built around a few conversations. The methodology is run on real spend data and the output is used in real decisions. The purpose is to build close to the ecosystem rather than apart from it. That is what makes the methodology sharper for every company that comes after.

01
Discovery call 30 minutes. We confirm fit, align on scope, and agree on what a useful output looks like for your context.
02
Spend overview You share what you are spending on AI tools and a brief description of how they are used across the business. That is the entire input. No system access, no integrations, no code.
03
Methodology run The engine classifies every line item, generates findings, and produces the report. Typically one to two weeks. You receive the output before any debrief.
04
Debrief and close One call to walk through the findings. The relationship ends here unless you want to continue. No ongoing obligation.

Every engagement is covered by a mutual NDA before any data is shared. Your spend data and findings are not stored beyond the engagement, not shared, and not used for any purpose outside of it.

Book a discovery call Rush of Read. nikith@rushofread.com
A rush of read.
An end to red.