AI Economics & Subsidy Exposure

Know the full cost before the subsidy fades.

Explore your engineering and product AI spending, stress-test profitability, and compare commercial pricing exposure with a predictable Sage cost schedule. Start with your business profile or try the labeled worked example.

Choose your scenario

Founder financial intelligence

AI economics dashboard

Modeled USD planning · version V3.2 · pricing reviewed October 13, 2026

Choose a business profile and explore what changes. No financial data is required. This free assessment shows illustrative scenarios; an engagement validates them with your actual costs and usage.

Business profile

3 engineers modeled · prefilled illustrative scenario.

Illustrative financial profile · Modeled.

Profile evidence and assumptions

Example financial profile · Modeled, not typical company data or a verified forecast. Edit these sample assumptions or replace them with your invoices and usage logs. Stress settings are selectable sensitivities, not expected growth or probability forecasts.

Founder takeaway · illustrative scenario

What this could mean for your business

Operating budget exposure

$728 / month

Month-12 AI costs before incentives. Baseline: $603 / month.

Profit margin exposure

32.0% → 26.4%

Current to month 12 under selected usage, revenue, expense and incentive assumptions.

Dependency exposure

Provider dependence needs review

Which workflows must keep running if a provider changes prices, limits or terms?

Planning signals, not findings about your company. An engagement validates the exposure and tests whether private or hybrid intelligence fits your work.

Validate this scenario with PG Consulting

Explore the financial dashboard below.

Monthly revenue

$20,000Example / entered assumptions · Modeled

Engineering AI spending

$375Seats + net extra usage + separate API

Product AI spending

$228Inference + supporting costs, before credits

Total AI operating cost

$603Cash-like planning basis, after entered credits

Projected profit margin

32.0%Modeled operating profit proxy

Current assumptions: 3 engineering seats · Codex / ChatGPT Business · monthly · 2,000 product tasks/month · 1 calls/task.

Evidence: Modeled profile; client invoices and usage logs needed. Engineering uses seat and usage assumptions.

Planning next step: AI Economics Optimization

Customize this scenario (optional)

Revenue basis

Entered monthly revenue ($)

Non-AI operating expenses / month ($)

Engineering subscription plan

Engineering seats

Invoice override / month ($; optional)

Product supporting expenses / month ($)

Product tasks / month (team total)

Billable model calls / task (include retries)

Provider change details

Provider changes restore plan-specific seat pricing and allowance assumptions. Your invoice override, separate API spending, operations costs and paid-usage preference are retained.

Engineering cost assumptions

Engineering is calculated from seats and additional usage below. An entered invoice overrides the complete engineering total. Supporting expenses are modeled as volume-variable, without a token-price multiplier; include tools, retrieval, storage, monitoring and review. Unknown expenses stay unknown.

Engineering subscriptions and additional usage

Published seat price; additional usage is a modeled budget. Official billing reference. Reviewed October 13, 2026. Enterprise contract rates are not inferred.

Additional usage AFTER included allowance / seat ($)

Separately billed engineering API use ($/month)

Engineering AI operations ($/month)

Enable paid subscription overage (Copilot)

Seats $75; extra usage $300; included monthly AI credits no verified convertible pool.

The $100 additional-use budget per developer is a Modeled sensitivity, not an industry average. Copilot defaults use 11,900 credits per developer, including the 1,900-credit Business allowance. Codex included subscription use is never added again as API cost. Cursor documentation references $40 seats but conflicts with its current marketing page; confirm the applicable plan. Gemini requires an existing agreement or replacement quote. Taxes and enterprise commitments require client terms.

Token usage and applicable rates

uncached input tokens / call

uncached USD / million tokens

write input tokens / call

write USD / million tokens

cached input tokens / call

cached USD / million tokens

Output tokens / call (includes reasoning)

output USD / million tokens

Published Standard Sol reference. Published reference: gpt-6.1-sol, Standard tier, input $2, cache write $2.50, cache read $0.10, output $10 per million. Input categories are disjoint. Above 272k total input per call the reference input rates double and output is 1.5×. Split different request classes into separate assessments; this reference is not a suitability recommendation. Cache defaults of zero are illustrative, not measured.

Financial exposure

Where monthly costs can move

Current$603

Post-subsidy$603

Month 12 · growth-only path$717

Month 12 · selected stress path$728

Forecast assumptions

Month 12 paths use the same dated workload and finite-credit ledger as Sage. Price stress affects inference, not fixed seats or supporting expense. Growth is a one-time volume step, not compounded monthly.

Interactive stress test

Test your exposure

Stress-test assumptions

Selectable Modeled stress assumptions. These are not expected growth or price forecasts.

Usage growth (%)

Effective token-price change (%)

Revenue change (%)

Customize scenario timing (optional)

Usage / revenue change starts in month

Price change starts in month

Selected stress: 50% usage, 25% token price, 0% revenue. Temporary incentives retain their entered schedule.

Annual forecast

See the cost path over twelve months

Baseline twelve-month AI spending

$7,236

Selected twelve-month AI spending

$8,730

Chart methodology

Solid navy: selected stress. Dashed slate: baseline. Monthly values include finite credits and scheduled changes; this is not current-month cost × 12.

Monthly forecast and profit values

Month	Tasks	Baseline AI	Selected AI	Credit	Revenue	Non-AI expense	Profit / loss
1	3000	$603	$728	$0	$20,000	$14,000	$5,273
2	3000	$603	$728	$0	$20,000	$14,000	$5,273
3	3000	$603	$728	$0	$20,000	$14,000	$5,273
4	3000	$603	$728	$0	$20,000	$14,000	$5,273
5	3000	$603	$728	$0	$20,000	$14,000	$5,273
6	3000	$603	$728	$0	$20,000	$14,000	$5,273
7	3000	$603	$728	$0	$20,000	$14,000	$5,273
8	3000	$603	$728	$0	$20,000	$14,000	$5,273
9	3000	$603	$728	$0	$20,000	$14,000	$5,273
10	3000	$603	$728	$0	$20,000	$14,000	$5,273
11	3000	$603	$728	$0	$20,000	$14,000	$5,273
12	3000	$603	$728	$0	$20,000	$14,000	$5,273

Temporary incentives · explicitly modeled, not verified grants

Remaining eligible credit balance ($)

Inference coverage (0–1)

Months until expiry

These inputs are hypothetical, scoped to product inference only. Balance depletes month by month; no credit applies after expiry. Default zero means a no-incentive planning scenario, not a conclusion about eligibility. Grant terms require an engagement.

Profitability comparison

Current versus month-12 operating results

Current

Revenue: $20,000
Non-AI expenses: $13,000
AI operating costs: $603
Operating profit / loss proxy: $6,397
Margin: 32.0%

Month 12 · selected stress

Revenue: $20,000
Non-AI expenses: $14,000
AI operating costs: $728
Operating profit / loss proxy: $5,273
Margin: 26.4%

Modeled margin change: -5.6 percentage points.

Profitability assumptions

Cash-like planning proxy, not audited accounting profit. Revenue changes independently from usage. Customer-variable delivery costs grow once within non-AI expenses; the remaining non-AI expense stays fixed. Missing material expenses keep profit unavailable.

Customer contribution economics

Is customer usage earning its way?

Customize customer assumptions (optional)

Monetization model

Paying customers

Price per billable unit ($)

Billable units / customer / month

Successfully completed AI tasks / month

Other customer-variable delivery costs ($/month)

Variable share of product supporting expense (0–1)

Heavy-user share of paying customers (0–1)

Heavy-user share of delivered workload (0–1)

Revenue = paying customers × billable units × unit price when customer monetization is selected. A subscription normally uses one monthly unit; usage, transactions and services use entered billable units. Enterprise is an entered account price, not a provider quote. Customer-variable costs are already part of non-AI business expenses; they are not added twice. Fixed engineering and supporting expenses are excluded from contribution. Credits are excluded from structural customer profitability.

Current

AI variable cost / paying customer: $1.14
AI cost / successful task: $0.1267
Product contribution: $17,772
Contribution margin: 88.9%
Heavy-user AI cost / customer: $5.70
Heavy-user contribution / customer: $44.30
Typical-user AI cost / customer: $0.6333

Month 12 · selected stress

AI variable cost / paying customer: $1.7625
AI cost / successful task: $0.1306
Product contribution: $16,648
Contribution margin: 83.2%
Heavy-user AI cost / customer: $8.8125
Heavy-user contribution / customer: $16.1875
Typical-user AI cost / customer: $0.9792

Heavy-user variable delivery costs remain below their modeled revenue on these assumptions.

Customer contribution assumptions

Utilization distribution is a two-cohort planning model, not measured customer behavior. Revenue is allocated equally per paying customer; variable delivery costs follow workload share. Replace this approximation with customer-level billing and traces in an engagement. Stress changes usage and revenue independently while retaining paying-customer count.

Sage comparison

Cost predictability and financial independence

Explore Sage Organizational Intelligence and implementation options

Comparison horizon

Explore a Sage scenario without entering quotes or benchmarks.

Illustrative costs and capacity, not a client quote or capability promise.

Sage cost predictability

Sage is ready for client-specific implementation. Its cost schedule is predictable within the quoted capacity and support scope. Commercial token prices fluctuate; Hybrid retains cloud exposure.

Unverified feasibility · hypothetical financial comparison.

Customize Sage scope, capacity and evidence (optional)

Workload replaced by SageAssume equivalent delivery for this product workload

The assumption checkbox is not hardware or workload evidence. Benchmarks for quality, latency, concurrency, accuracy and operations require engagement review before a validated financial case.

Hardware benchmark / workload evidence references (optional)

No benchmark evidence supplied.

On-Prem capacity (local tasks / month)

Hybrid capacity (local tasks / month)

Hybrid share of tasks served locally (0–1)

This is scoped product-workload TCO: all entered product expenses are assumed displaced. Engineering is retained in business operating results and excluded equally from these three comparison totals. Include every local supporting expense in Sage operations. Partial replacement or retained product costs need a separately scoped comparison. Equivalent quality, throughput and latency are planning assumptions until measured. Residual cloud expense scales with the same monthly usage and price factors as commercial. Enter an independently supported local routing share for capacity; cost share does not imply task share.

Unverified feasibility; conditional modeled financial break-even. Growth, price timing and commercial credits come from the same month-by-month assessment ledger. No universal Sage price or savings promise. Sage credit eligibility is not assumed; only the entered commercial counterfactual receives modeled credits.

Commercial API

Variable pricing · incentive dependence

$4,230

12-month cash cost under selected price path

Price change: 25% from month 1.

Sage On-Prem

About Sage On-Prem

A private organizational second brain that maintains governed knowledge, institutional memory and contextual retrieval.

Predictable within quoted scope

Customize On-Prem costs (optional)

Installed upfront quote ($)

Local operations / month ($)

Client-specific

Unverified feasibility.

Local capacity not evidenced; hypothetical payback only.

Explore the worked example to see how break-even can change.

Baseline payback comparison

Baseline: Quote needed: enter implementation and monthly operating costs.

Sage Hybrid

About Sage Hybrid

The same Organizational Intelligence foundation, extended by authorized agents to retrieve information and invoke specialist capabilities when needed.

Local predictability + residual cloud exposure

Customize Hybrid costs (optional)

Installed upfront quote ($)

Local operations / month ($)

Residual cloud / month ($)

Client-specific

Unverified feasibility.

Local capacity not evidenced.

Explore the worked example to see residual cloud-price exposure.

Customize capacity expansion (optional)

Scheduled month (both Sage options)

Incremental cash expense ($)

Additional local task capacity / month

Additional ongoing operations / month ($)

Synthetic sensitivity event applied to both alternatives. Use separate client schedules in the engagement.

What is needed to calculate break-even

Enter client quote and operating costs, define the workload and confirm the equivalent-delivery assumption to calculate cumulative TCO and break-even.

Sage Organizational Intelligence · Your organization’s second brain

Persistent organizational context

Keep the context behind decisions available across work and time.

Governed knowledge retrieval

Retrieve organizational knowledge within the client’s configured access and governance boundaries.

Knowledge retention and hybrid agents

Preserve organizational memory locally and extend selected workflows with hybrid agent capabilities.

Organizational Intelligence assumptions

Client-specific implementation scope; these strategic benefits are not assigned an invented cash value.

Recommended next step

Validate the exposure before committing

Provider concentration: high impact / high likelihood · Modeled.

Provider concentration assessment

Founder strategic assessment (Modeled), without a numeric probability or risk surcharge. Review provider dependence, changing terms and migration options.

AI Economics Optimization

Why this recommendation

Engineering accounts for at least half of modeled AI cost. Reconcile seats, included allowances, agent usage and separately billed API use.

Recommendation assumptions

Modeled decision rules, not an industry benchmark or probability forecast. Thresholds: customer variable-delivery loss → Transformation; at least 25% modeled cost exposure or 20% incentive dependence, or local capacity gap → Independence Pilot; otherwise → Optimization. Recommendation rules do not derive the founder’s strategic concentration assessment or a monopoly probability.

Strategic Sage benefits

Organizational memory, knowledge retention and control are strategic Sage benefits; no cash value has been assigned.

Request an economics review

Executive report assumptions and evidence

All consumption and business inputs are Modeled unless replaced with client evidence. No confidential scenario is sent automatically. Profile: Early-stage SaaS; engineering plan: Codex / ChatGPT Business · monthly; workload: not defined.

Stress: one-time usage step 50% from month 1, price 25% from month 1, revenue 0%. Horizon 12 months. Credit balance $0, coverage 0%, expiry 0 months. Rates: 2 / 2.5 / 0.1 / 10 USD per million, Published Standard Sol reference.

Monthly tasks 2000; calls/task 1; uncached / write / cached / output tokens: 3000 / 0 / 0 / 800. Output includes reasoning. Unknown material expenses are not zero.

Engineering seats 3; subscription consumption 35700 AI credits/month; post-allowance budget $100 / seat; separate API $0; operations $0; invoice override Needs input. Revenue basis customers; monetization Subscription; paying customers 200, price $100, units/customer 1; successful tasks 1800. Capacity unknown On-Prem / unknown Hybrid; local task share 0.7; evidence none. Unverified feasibility.

Report version V3.2, annual forecast $8,730; all printed sections use this same scenario. Recommendation: AI Economics Optimization — Engineering accounts for at least half of modeled AI cost. Reconcile seats, included allowances, agent usage and separately billed API use..

Evidence reviewed October 13, 2026: Standard model pricing; FTC competition and switching-cost discussion. These sources do not validate modeled consumption or quantify small-business monopolization probability. Client invoices and 2–4 weeks of usage logs are required for a defensible engagement estimate.

Possible next steps

Choose the work the evidence warrants.

AI Independence Pilot

Start with one knowledge domain, approved sources and a bounded Sage environment. Evaluate the usefulness of its answers and define whether to expand.

See the Sage pilot scope and deliverables

AI Economics Optimization

Validate subscription and usage assumptions against invoices, representative workloads, model mix, usage limits and contract terms before scaling.

AI Economics Transformation

Connect economics to operating model, governance, procurement and long-term strategic choice.

These are unpriced engagement directions. Dashboard recommendations use explicit planning rules and require evidence review. See the full AI Transformation Assessment.

Consultation

Turn planning assumptions into an engagement brief.

An AI Economics review reconciles your invoices and representative usage, identifies the cost drivers, and scopes practical options. The form sends only the contact details you enter after you consent; your scenario is not attached.