Conviction
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Table of contents
The future of software design
Design is shifting from artifacts to outcomes. The work moves upstream: defining systems, interfaces, and constraints that enable software to be assembled, not just designed. AI accelerates this shift by compressing the distance between intent and implementation.
- Systems over screens: Designers specify behaviors, contracts, and data models that span products and platforms.
- Proof over prototype: Real, runnable proofs replace static mockups; fidelity is functionality.
- Human-in-the-loop: Designers become editors and directors of AI-generated variants, curating toward taste and performance.
Further reading: Proof of Concept and selected writing on design and engineering.
AI-native management
Teams are evolving from maker-only to maker–model ensembles. Management shifts from headcount planning to capability planning—what combination of people, data, and models delivers outcomes reliably and safely.
- Orchestration: Managers design workflows where agents handle repetitive work and people handle ambiguity.
- Quality as a product: Evaluation data, benchmarks, and red-teaming become first-class assets.
- Ethics and controls: Guardrails, consent, and attribution are operating requirements, not post-facto checks.
Related essays on leadership and AI: Proof of Concept.
Post IDE world
As coding shifts from manual keystrokes to conversational and agentic workflows, the IDE becomes a collaboration surface rather than the primary tool. The primitives are problems, tests, and contracts; the output is orchestrated by agents and reviewed by humans.
- Intent-first: Problem decomposition, specs, and evaluation drive generation.
- Continuous verification: Tests, types, and instrumentation are the new UI.
- Composable agents: Tool-using agents coordinate across repos, clouds, and runtimes.
See experiments and notes at Proof of Concept.
Human x AI collaboration
Great outcomes emerge when people and models play to their strengths. Humans set direction, define taste, and handle ambiguity; AI accelerates exploration, handles repetition, and scales evaluation.