Conviction
A working set of convictions on where software design is headed, how teams should operate with AI, and what changes when the IDE stops being the center of gravity. These are living notes — updated as experiments and writing sharpen the edges.
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.