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.

The genre sibling

developers-history-of-ai runs the same retrospective move over a longer arc and a narrower subject: seventy years of AI, sorted by where the "intelligence" sat at each stage — the programmer's explicit rules, then learned parameters, then features the machine derives itself, then the transformer lineage, then agents. Both pages are practitioner history rather than measured adoption, and both should be read with the caveat above; the placements are judgments. Set side by side they say something neither says alone, which is that each era's automation moved up a layer of the developer's own work rather than replacing the worker at the bottom of it.

Tier: T4 → T3 (2026-08-25)

An anniversary editorial in which a publication assesses its own twenty years of trend-calling. The load-bearing claim is we spotted these ideas early, and here is the editorial discipline that did it — the maker on how well their own thing worked, which is exactly T3. Not T4: it is argued, dated and attributed, and it is the founding source of this wiki's cluster F. The placements on the curve are one publication's readings, not survey data, and that was already flagged below.

technology-adoption-curve · developers-history-of-ai · tools-for-thought · llm-wiki · gbrain