Revenue scale
Reported revenue scale relative to this tool's $20B reference ceiling.
Free educational planning tool
AI economics scorecard: revenue quality, cost pressure and compute exposure
Turn a frontier-AI revenue headline into a structured diligence conversation. Adjust the inputs, see the transparent score drivers and email yourself a personalised two-page scorecard.
Transparent 100-point modelCompute sensitivityNo valuation claims
Educational planning tool only - not investment, credit or valuation advice.
Choose then edit a scenario
OpenAI economics scenario
Choose a source-qualified preset or start a blank comparator. Every number remains editable; a zero in a preset indicates an undisclosed or deliberately un-inferred value, not a real zero.
Preset disclosure note
Reported inputs only: annualised recurring revenue and planned compute spend are reported; current gross margin, operating expenses, cash and enterprise mix are not treated as disclosed here and remain at zero until you replace them.
Visual breakdown
Score drivers
The bars below make each score component visible. Thresholds are explained in the downloadable scorecard and should be challenged against primary-source evidence.
| Driver | Maximum already on the page | What the default view says |
|---|---|---|
| Revenue scale | 17 | Scored against this tool's $20B reference ceiling. The default view shows 17/17. |
| Gross margin | 15 | Not scored until a comparable gross-margin input is supplied. |
| Operating discipline | 17 | Not scored until comparable revenue and operating-expense inputs are supplied. |
| Liquidity buffer | 14 | Not scored until cash and a comparable cost or compute input are supplied. |
| Revenue growth | 12 | Not scored until a like-for-like growth input is supplied. |
| Enterprise mix | 15 | Not scored until an enterprise-mix input is supplied. |
| Capital visibility | 10 | A yes/no visibility input. Not an assessment of funding quality. The default view shows 10/10. |
token calculator - A model API rate is not this scorecard.
AI governance - Operating credibility is a governance question, not a score.
17/17
Gross margin
Not scored until a comparable gross-margin input is supplied.
Operating discipline
Not scored until comparable revenue and operating-expense inputs are supplied.
Liquidity buffer
Not scored until cash and a comparable cost or compute input are supplied.
Revenue growth
Not scored until a like-for-like growth input is supplied.
Enterprise mix
Not scored until an enterprise-mix input is supplied.
Capital visibility
A simple yes/no visibility input; it is not an assessment of funding quality.
Sensitivity
What changes the picture?
This simple stress check asks how the planning score changes when margins compress and operating costs rise, or when compute-spend needs exceed the original assumption.
Baseline inputs
Margin −10 pts + operating costs +20%
Compute spend +50%
Personalised resource
Email my scorecard
Research context
OpenAI: source boundaries and comparison limits
Reported 2026 press coverage; ARR/run-rate is not recognised revenue and forecast spending is not audited actual spend.
Reuters: reported $70B annualised recurring revenue
Reuters reported that OpenAI's annualised recurring revenue was nearing $70B, citing Axios and a source familiar with the company financials. ARR is not the same as recognised revenue.
Read sourceReuters: projected compute and cash-flow pressure
Reuters reported an FT account of a presentation projecting $856B of compute and infrastructure spending by 2030 and negative free cash flow over 2026–2030. These are forecasts, not booked expenses.
Read sourceThe selector does not independently validate information or generate a company rating. It only populates linked, qualified planning inputs; all values should be checked and replaced for the purpose you are assessing.
Next step
Turn an economics question into an operating decision
A credible AI plan connects compute, data rights, authority boundaries, content visibility and real commercial measurement. The free audit is an appropriate start when the question is how ready your own site and growth operations are for AI-mediated discovery.
AI VisibilityFAQ
Questions about the AI economics scorecard
It is a transparent educational model that combines the figures you enter for revenue scale, gross margin, operating expenses, liquidity, compute-spend exposure, growth, enterprise mix and funding visibility. It produces a planning label and sensitivity checks; it does not value a company or predict financial performance.
No. The scorecard is not investment, credit, accounting, tax, legal or valuation advice. It uses simplified inputs and threshold bands to help users frame diligence questions. Use audited or primary-source material and consult qualified advisers for any investment or financing decision.
Growth and resilience are different questions. A company can show rapid revenue expansion while still having high operating costs, limited liquidity coverage or substantial compute commitments. The model makes those trade-offs visible rather than treating a revenue headline as a complete economic picture.
Enter a disclosed or carefully researched multi-year compute commitment. The model spreads it across a five-year planning horizon solely to create a comparable sensitivity input. It does not forecast payment timing, depreciation, contractual flexibility or actual capacity utilisation.
The email gate delivers the personalised two-page scorecard you requested. Integrated.Social stores the minimum information needed to deliver and deduplicate that resource. It is not a marketing subscription and the form makes no commercial follow-up promise.
The default scenario is an illustrative OpenAI planning input based on publicly reported 2026 press coverage and company statements. It should not be read as audited financial reporting. The page links to the reported sources and labels estimates, annualised run-rates and private-market reporting as such.
Yes. Replace the illustrative inputs with disclosed or independently reported figures for the company and period you want to explore. For a fair comparison, define whether you are using recognised revenue, ARR/run-rate, gross margin and cash on the same basis for each company.
The presets only auto-fill figures that are clearly attributable in their linked source. A zero field in a preset means that input is undisclosed or intentionally not inferred; it does not mean the company has zero cash, expense or margin. Presets are comparison starting points, not company ratings.
Treat the result as a diligence prompt. Check the primary financial source, distinguish revenue from run-rate, investigate compute obligations, test margin sensitivity and connect model costs to a specific governed workflow. Then use the Free AI Visibility Score or a discovery call if you need an accountable operating plan.