BlogStartup Economics
Startup Economics13 min read

From $0 to $10M ARR: Engineering Economics at Every Stage

What you measure at seed is different from Series A, which is different from growth.

By Richard Ewing·
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Stage-Dependent Framework

Seed ($0-500K): Track velocity to learning. Take debt aggressively. R&D: 60-80% of burn.

Series A ($500K-2M): Track cost to acquire/serve. Start paying down revenue-critical debt. R&D: 80-120% of revenue.

Series B ($2M-10M): Track engineering efficiency. Allocate 20-25% to debt remediation. R&D: 40-60%.

Growth ($10M+): Track R&D ROI. Full economic model. Maintain PDI below 1.5. R&D: 20-35%.


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Canonical Frameworks

The Software Phase Transition

The Software Phase Transition models the structural breakdown of traditional product management as the marginal cost of writing software approaches zero. In the pre-AI era, developer bandwidth was scarce and expensive. Organizations operated in the Solid state: managing 2-week sprints, grooming backlogs, and writing exhaustive PRDs to ration engineering hours. As tooling improved, organizations transitioned into the Liquid state of adaptive teams with fluid prototyping. With generative AI and autonomous agent pipelines, code generation costs collapse toward zero, propelling organizations into the Gas state. In the Gas state, developer capacity is no longer the rate-limiting constraint. Unbounded code generation creates exponential organizational complexity, coordination tax, and margin collapse. This forces a fundamental leadership evolution: product leaders must transition from managing feature velocity to becoming Product Economists who govern capital, system architecture efficiency, and uncertainty.

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Cost of Predictivity

The Cost of Predictivity measures the variable cost of AI accuracy. Unlike traditional software with near-zero marginal costs, AI features have significant variable costs that scale with both usage AND accuracy requirements. As AI correctness increases, cost scales exponentially - not linearly. This is the fundamental economic challenge of AI products. Traditional software follows a simple cost model: high fixed development cost, near-zero marginal cost per user. Build the feature once, serve it to millions for pennies. AI products break this model entirely. Every AI query costs compute. Every inference requires GPU cycles. Every improvement in accuracy requires either more sophisticated prompts (more tokens = more cost), retrieval-augmented generation (vector DB queries + embedding generation), or fine-tuned models (massive training costs amortized over queries). The cost structure looks more like a manufacturing business than a software business. The exponential curve is the killer. Moving from 80% accuracy to 90% accuracy might cost 2x. Moving from 90% to 95% might cost 5x. Moving from 95% to 99% often costs 10-20x. This is because the easy cases are solved by the base model, and each additional percentage point of accuracy requires increasingly sophisticated (and expensive) techniques to handle edge cases. This creates what Richard Ewing calls the AI Margin Collapse Point: the usage volume at which AI feature costs exceed the revenue they generate. Many AI features that work beautifully in prototype (low volume, don't need high accuracy) become economically devastating in production (high volume, users demand high accuracy). The AI Unit Economics Benchmark (AUEB) calculator at richardewing.io/tools/aueb helps companies calculate their Cost of Predictivity and identify their specific margin collapse point before it hits their P&L.

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Richard Ewing

The AI Economist - Quantifying engineering economics for technology leaders, PE firms, and boards.

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