The SRE to developer ratio matters more than most engineering organizations realize. Learn how to properly size your Site Reliability Engineering team for scale.
Google's guideline: 1 SRE per 5-10 developers for mature services. Reality for most: 1 SRE per 15-20 developers is common but leads to reliability debt.
Under-staffed SRE costs: more incidents (each costing $10K-500K), slower incident recovery, burn-out and attrition ($200-400K per departure), and reliability debt that compounds.
Right-size by: services per SRE (max 5-7), on-call rotation (min 5 people), toil budget (<50% of time), and automation investment (30%+ of time).
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Read Concept →The executive discipline bridging engineering velocity, financial P&L contribution, and product margin strategy to prevent technical debt and AI COGS from destroying business valuation.
Read Concept →A degenerative process where AI models experience severe performance degradation after being iteratively trained on synthetic data generated by other models.
Read Concept →The financial discipline of managing, projecting, and optimizing the per query token costs associated with running large language models in production.
Read Concept →The compounding maintenance burden and operational friction incurred when new technology is deployed without decommissioning legacy systems, effectively taxing all future engineering velocity.
Read Concept →The non-linear increase in communication overhead, alignment meetings, and process friction that occurs when scaling engineering organizations, ultimately degrading per-capita execution capacity.
Read Concept →The systemic masking of growing software maintenance liabilities (OpEx) behind inflated velocity metrics and new feature launches, creating a fragile engineering economy that requires constant new capital to sustain.
Read Concept →The analytical framework for determining the precise point where the ongoing maintenance cost of a software feature exceeds its marginal revenue value, necessitating immediate deprecation.
Read Concept →The systemic erosion of traditional SaaS gross margins caused by the integration of generative AI features, as variable compute and API costs scale linearly or exponentially with user engagement, fundamentally altering software unit economics.
Read Concept →The principle that heavily AI-augmented teams of ten elite engineers can now achieve execution parity with traditional enterprise engineering organizations of over one hundred, fundamentally altering the economics of software creation.
Read Concept →The architectural pattern of storing and reusing similar LLM query results using vector embeddings to bypass redundant frontier model API execution and eliminate variable COGS.
Read Concept →The strategic shift of migrating high-volume inference tasks from commercial Frontier APIs (OpenAI, Anthropic) to local Small Language Models (SLMs) to achieve financial breakeven on variable COGS.
Read Concept →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.
Read Definition →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.
Read Definition →The AI Economist - Quantifying engineering economics for technology leaders, PE firms, and boards.
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Richard Ewing: AI Economist & Capital Auditor