In Just Two Years, AI Turned Software Development on Its Head
Two years ago, AI coding tools felt experimental. Today they’re part of the everyday toolkit. Teams ship faster, solo devs punch far above their weight, and “prompting” is now as normal as Googling. The biggest signal? A majority of professional developers now use AI every day.
In the latest Stack Overflow survey, 51% of professional developers say they use AI tools daily, and 84% are using or planning to use AI in their workflow. That’s a dramatic jump from 2024, when adoption (using or planning to use) sat at 76% and “currently using” was 62%. Stack Overflow+1
GitHub’s Octoverse shows how quickly the ecosystem moved: contributions to generative-AI projects surged and new AI projects nearly doubled year over year, reflecting how work (and open source) reorganized around AI assistance. The GitHub Blog
Industry studies echo the shift: Google’s 2025 DORA report finds a significant majority relying on AI for software tasks, not just code generation but testing, documentation, and ops. blog.google
What Actually Changed (Day to Day)
1) Scaffolding & boilerplate vanish.
Copilots spin up project skeletons, data layers, and CRUD flows in minutes. Engineers focus attention on architecture, data modeling, and business logic.
2) Code review moves earlier.
LLM assistants flag bugs, smells, and security issues inline as you write. Human review still decides — but with far better “first pass” quality.
3) Tests stop being an afterthought.
Generating unit and integration tests is now a keystroke. Coverage improves without blowing up timelines, and flaky tests get triaged faster.
4) Docs keep up with reality.
Auto-summaries of PRs, changelogs, and READMEs keep knowledge current. New hires onboard faster because the docs match the code.
5) DevEx compounds.
From SQL queries to Terraform, shell scripts to YAML, assistants unblock the tiny things that used to stall flow. The net effect is higher velocity with less context switching.
6) Product work gets tighter.
Engineers prototype UI, copy, and even small backend services directly with AI, shortening loops with design and PM.
New Responsibilities for Teams
Guardrails & policy. Decide what code and data AI can see; keep PII out of prompts; log prompts/outputs for audits.
Quality gates. Require human review for risky changes; track acceptance rates of AI suggestions.
Skills & standards. Teach prompt patterns, establish code-style constraints, and version prompts like you version code.
Measure impact. Look at PR cycle time, defect rates, test coverage, and incident regressions — not just “LOC generated.”
What Won’t Change
Great engineering still means clear thinking, sound architecture, and empathy for users. AI accelerates good practice; it doesn’t replace it. Teams that win pair taste + judgment with AI leverage.
What to Do Next (90-Day Plan)
Pick two workflows to automate end-to-end (e.g., tests + docs), prove time saved, then expand.
Add policy & logging for AI usage (repo allowlists, redaction, prompt/response logs).
Set quality targets (min test coverage, max escaped bugs) and review monthly.
Upskill the team with short playbooks: prompting patterns, secure context, acceptance criteria.
Instrument your DevEx (PR lead time, review latency, deploy frequency) so improvements are visible.
The Bottom Line
Between late-2023 and late-2025, AI moved from novelty to norm. With over half of professional developers using AI tools daily and most teams adopting them in some capacity, software development has permanently shifted toward human-guided, AI-accelerated work. The winners aren’t those who generate the most code — they’re the ones who apply AI to build the right thing, faster, with higher quality.
At abZ Global, we’re continuing to build on these trends—from Squarespace & Shopify to Angular and AI-powered custom web apps. If you’re planning a project, reach out via our contact form or email marian@abzglobal.net