There's a seductive promise in AI-first development: ship in an afternoon what used to take a quarter. The data says it's half-true — and the missing half is where budgets die.
Starting has never been cheaper
Per Stanford's 2025 AI Index, GPT-3.5-level inference fell from $20 to $0.07 per million tokens between late 2022 and late 2024 — a 280-fold drop in 18 months. Prototyping is genuinely cheap.
Keeping it is where the bill arrives
- Most pilots never pay off: MIT found 95% deliver no P&L impact; McKinsey found only 39% see any EBIT impact, mostly under 5%.
- Inference scales with usage forever, and the IEA expects AI data-centre power to more than quadruple by 2030.
- Models drift, prompts rot, evals need upkeep — none of it is one-time.
Traditional development front-loads the pain: high upfront cost, low tail. AI-first development inverts it — cheap to start, and the meter never stops.
The pattern that works
- 1.AI at the edges, deterministic code at the core.
- 2.Build the eval harness before you scale.
- 3.Abstract the vendor so swapping models is config, not a rewrite.
- 4.Budget the tail, not just the launch.
Sources
- Stanford HAI — 2025 AI Index
- MIT NANDA — The GenAI Divide
- IEA — Energy and AI
Written by ivector
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