AI4SDLC
How AI is reshaping the software development lifecycle — data, practice, community
A research initiative and Telegram community on AI in SDLC. The annual AI4SDLC Research report synthesizes a meta-study of industry publications with our own engineer survey — to see real patterns past the hype.
What the community gives you
- 01See how AI is actually rolled out in large engineering orgs — cases and anti-cases
- 02Tell vendor marketing apart from what tools really do in a real team
- 03Get access to AI4SDLC Research data before it becomes common knowledge
- 04Discuss 'verify, don't trust', context engineering and AI-native processes with people building them
What the initiative is made of
AI4SDLC Research
Mixed design: meta-synthesis of 2023–2025 industry publications + a quantitative engineer survey (Sep–Dec 2025).
Telegram community
Live discussions of practices, tooling and AI-in-SDLC rollouts. No ads, focus on engineering substance.
Open findings
Eight stable patterns from the research and public slices by task type, trust, productivity and quality.
What's in focus
The initiative covers not 'AI in general' but specific stages and roles in the development lifecycle.
Key findings — AI4SDLC 2025
Eight stable patterns from the synthesis of meta-study and survey. Methodology and full context are in the public report.
AI as a daily tool, but not universal
58% use AI for generation/autocomplete often or always. But in high-context tasks (legacy, architecture) frequency drops noticeably.
Productivity rises, team-level effect is weaker
64% see productivity gains, 18% call them 'significant'. But bottlenecks shift downstream — to integration, review and releases.
Quality and trust need systemic validation
32% see quality improve, 14% see it drop. 49% don't trust AI-generated code; only 11% do. The 'verify, don't trust' norm becomes central.
Context and complexity define the outcome
AI's effect is unstable in complex, context-heavy tasks. This raises the value of formalized context and context engineering.
Who it's for
- Engineers and team leads rolling out AI in their dev process
- EMs, CTOs and Heads of Engineering designing AI-native processes
- Researchers and DevEx teams measuring tool impact
- Anyone who wants the data, not just vendor marketing
Become a part
Join the Telegram community and read AI4SDLC Research — to build AI-native development on data, not on vibes.
Open AI4SDLC on Telegram