Yet almost half of them are committing it anyway. That is not a confidence gap. That is a governance crisis hiding inside a productivity narrative. This week's edition of www.Datapro.News digs into what the empirical data actually shows about AI agents and code development, and the findings should give any data professional pause. Here is what caught our attention during the research: 👉🏼 A randomised controlled trial found developers with AI assistance felt 20% faster. Measured productivity showed a 19% slowdown on complex codebases. The gap between perception and reality is almost 40 percentage points. 🧐 Gartner projects that over 40% of agentic AI projects will be cancelled by 2027. The reason cited is not the technology. It is inadequate risk controls and a culture of measuring success through demos rather than production outcomes. 🧑🏽‍💻 Junior developer job postings are down 40 to 50% since early 2024. The apprenticeship pipeline that produced the senior engineers capable of verifying AI output is quietly being dismantled. If your organisation is building an AI-assisted engineering strategy, or governing the data that feeds one, this week's edition is worth your time 👇🏼

Every model now costs tokens. The free-at-point-of-use era for your most-used coding tool is over in 33 days. And it is not just GitHub. Anthropic has already made the move, quietly restructuring its enterprise agreements to replace flat-rate subscriptions with consumption-based billing, removing volume discounts at the same time. This week's datapro.news explains exactly why this was inevitable across every major vendor, what it means for your pipelines, and what you need to do before the next renewal lands. 👉 datapro.news

If you are already shipping RAG and agent workflows, this one is for you. The next wave is not just smarter models. It is models that run longer, call more tools, and require verification by design. Anthropic’s Mythos Preview is confirmed as restricted access under Project Glasswing, reinforcing the trend towards safety-bounded agentic systems. We break down practical moves you can make this quarter, from trust metadata in your catalogue to DAG-style verification loops and model-agnostic orchestration. Check out the video edition of this week's www.datapro.news below 👇

When comms drop out, the stack has to keep working. > Telemetry has to be trusted. > Anomalies have to be prioritised. > Decisions have to be made with partial information. That is distributed systems, observability, and reliability engineering under the harshest constraints imaginable. Artemis is basically a masterclass in building pipelines that survive latency, disruption, and zero tolerance for bad data. This week’s edition of Datapro.news goes deep on the Data and AI leaps that made Artemis-level ambition possible, and what it tells us about the future of data engineering on Earth. Full investigation in this week's DataPro.news 👇

I've seen this framing everywhere this quarter. And it's not wrong, exactly. It's just incomplete in a way that's genuinely misleading to early-career practitioners. The layoff waves of 2024 and 2025 was mostly a correction. Tech companies overhired by 25-50% during the pandemic and spent two years slowly unwinding that. AI got the credit (or the blame) because it made better copy for investors. The genuine AI-driven displacement is happening now, in 2026, and it is real. But it is not evenly distributed. It is concentrated in execution-heavy, process-repetitive roles. The engineers being squeezed are the ones whose primary value was running pipelines someone else designed. The engineers thriving are the ones who design them. Who govern them. Who can look at what a machine produced and say whether it is correct. That distinction matters enormously if you are deciding where to invest the next two years of your career. Full investigation in this week's DataPro.news 👇