The Technology Adoption Curve, Twenty Years On

InfoQ 20th-anniversary editorial (Renato Losio & Dio Synodinos, 2026-06-08) that uses the technology-adoption-curve (Rogers’ diffusion of innovation) as a lens over 20 years of software technology — and the founding source of this wiki’s cluster F (diffusion & adoption of ideas/technologies), ingested when the domain was broadened on 2026-06-09.

Thesis

A developer publication’s lasting value is identifying ideas in the innovator / early-adopter stages and sharing practitioner experience before the hype — “the most valuable thing a software developer publication could do was identify ideas in the innovator and early adopter stages.” Good trend-calling came not from clairvoyance but editorial discipline: stay close to practitioners and listen to what was getting harder, not what was getting hyped.

Twenty years placed on the curve (as of 2026)

Agile — late majority / laggard: “won so completely that the word has lost most of its specificity.”
SOA — laggard as a brand, but its problems live on in microservices & agent orchestration.
Cloud — late majority (early proof via Netflix / Chaos Monkey, 2012).
DevOps — early→late majority; platform engineering its current evolution.
Containers / Kubernetes — a rare “clean win”: the de-facto cloud-native substrate.
Microservices — late majority, with a “healthy and overdue counter-current.”
ML as an engineering discipline — early majority (the bet to treat ML as engineering, not research, proved prescient).
AI engineering / agentic systems — the innovator/early-adopter band: context engineering, spec-driven development, reliability frameworks for non-deterministic systems.

Five predictions for 2036

Agentic systems follow microservices’ arc — over-application, then “a more honest conversation about when they actually make sense.”
Spec-driven development’s impact is uncertain but worth early-adopter tracking.
“Reliability engineering for AI systems will become its own discipline,” mirroring SRE’s emergence.
Green IT / compute sustainability advances from innovator to early adopter.
The technologies that matter most are unknown — hence editorial humility.

Why it’s here (cluster F)

Beyond KM and formal methods, this is the wiki’s first source on how ideas & technologies diffuse — the technology-adoption-curve as an analytical/thinking framework. It is also reflexive: InfoQ’s “track the innovator band, listen to practitioners over hype” method is the editorial cousin of this wiki’s own continuous-curation discipline (llm-wiki/gbrain). Cross-spoke adjacencies (not duplicated): its agentic-systems and “reliability engineering for AI” threads sit next to agentic-tooling-wiki and platform-ops-wiki respectively.

Caveat

A publication’s anniversary editorial — self-referential about InfoQ’s own track record, and the curve placements are the authors’ qualitative judgment, not measured adoption data.

technology-adoption-curve · tools-for-thought · llm-wiki · gbrain