Three surveys put build-instead-of-buy between 32% and 92%, and Fibery's founder explains why the curve stops

McKinsey's State of AI 2026 asked more than 1,700 respondents whether they skipped a software purchase because they could build it internally with agentic coding tools. 32% said yes. In technology it was 41%, among healthcare payers and providers 39% (CIO Dive).

Temporal's State of Development report, out August 25 from 554 engineers, found 92.3% had tried building an app they would previously have bought, with 25.6% reporting substantial impact. Retool's February 2026 Build vs Buy report already had 35% of enterprises having replaced SaaS with custom software. Three instruments, seven months apart, measuring displacement that already happened rather than forecasting it.

The spread between 32% and 92% is not a contradiction, it is the difference between "we skipped a purchase" and "I tried building something." Temporal's own 25.6% substantial-impact figure is the honest middle. Something like a quarter to a third of the buying decisions in these samples have moved.

Michael Dubakov, Fibery's founder, published the sharpest counter-argument I have read on this, and it surfaced on Hacker News on August 31 (mdubakov.me). His claim: interfaces are now cheap to generate with Claude Code, Lovable and v0, so the durable asset is the base underneath. Database, permissions, collaboration, audit trails. Custom code is only practical when it inherits that base and its protections automatically. He names Retool, Softr, Notion and Fibery as converging on an 80/20 model, 80% solid base and 20% AI-written custom code, and cites building a mushroom-farm management system in about an hour on Fibery Custom Apps.

His timing argument is the one that will hold up: adding customization to a solid base takes quarters, building a solid base takes years. That is a concrete mechanism for why these survey numbers plateau instead of running to 100%.

I have shipped three solo products in the past year and I believe both halves of this. The UI is not where the time goes. The time goes into the permission model, the audit trail, the migration you did not plan, and the six weeks after launch where you discover what your data model got wrong. An agent writing your CRUD screens does not touch any of that.

DoltHub gave us a data point on the other side. DoltLite reached Beta at v0.50.0 on August 31, a SQLite fork that swaps the B-tree for a Prolly Tree over a single-file chunk store, giving an embedded database Git-style branch, merge, diff and conflict-aware sync. Tim Sehn frames it as an agent-team experiment: "It only took about 2,000 pull requests but DoltLite going Beta proves a team of agents certainly could" (DoltHub). The benchmarks are published and unflattering, 10% slower reads and 60% slower writes in-memory, 3.1x slower autocommit writes. Two thousand agent PRs over five months produced a real database with real regressions, which is roughly what I would expect and more than I would have predicted a year ago.