A few days ago I mentioned that I don’t feel comfortable making predictions in this unstable industry/world. However, I’ve noticed that there are people who do dare to make them (good for them!).
For this reason, I decided to create a compendium of those predictions, prioritizing those that align most across different sources.
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AI will work in multi-step loops: it will use tools, self-verify and correct errors until completion. This will surpass single-shot prompts, but will require solid guardrails like evaluations, permissions and audit logs. See [2], [4], [5].
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The developer’s role will shift from “writing code” to “orchestrating and auditing” AI-built systems. You’ll design the plan, choose tools and models, and verify results, deciding what not to automate. See [1], [2], [3].
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True speed will come from fast feedback, not from typing more: automated tests, canary deployments, instant rollbacks and good observability. With “verify first” loops, teams can realistically move 3–7× faster. See [5], [2], [3].
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Trust and security will be decisive: leaders will demand traceability, safe practices and proof that your AI won’t ship risky code. There will be more regulatory scrutiny and more incidents from unguarded AI code. See [3], [4], [2].
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You’ll make your codebases “AI-navigable” so agents can answer themselves: a single source of architectural truth, toolable interfaces, safe test environments and built-in evaluations. Small execution pods will move fast if the platform (observability, permissions, audit, test harnesses) is strong. See [2], [5].
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Products that replace entire workflows will be worth more than those that only add AI features. The “build vs buy” will flip: creating internal tools will be cheap; the real cost will be in maintenance and evaluation. See [2], [5].
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Executives will ask for hard proof of impact, not anecdotes. You’ll instrument time-to-value, code quality, cost per outcome and collaboration signals; plus maintain audits to scale pilots. See [3], [2].
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Junior hiring will tighten, so upskilling and alternative pathways will matter more. Cutting juniors risks a leadership vacuum; juniors with AI fluency, clear communication and good problem breakdown will stand out. See [1], [3].
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AI will write routine code; human advantages will be in architecture, performance, security and judgment. You’ll aim to be T-shaped: broad base with one or two depths and solid fundamentals. See [1].
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Boutique “agentification” consulting will grow by standardizing agent workflows for the mid-market. Leaders must allocate real budgets and learn these tools firsthand to stay ahead.
References:
[1] A. Osmani, “The Next Two Years of Software Engineering,” addyosmani.com, Jan. 5, 2026.
[2] Z. Wills, “My 2026 AI Bets (A Time Capsule),” zachwills.net, 2026.
[3] LeadDev, “5 uncomfortable predictions for engineering leaders in 2026,” leaddev.com, 2026.
[4] One Useful Thing, “Claude Code and What Comes Next,” oneusefulthing.org, 2026.
[5] V. Rufus, “Compound Engineering — The Next Paradigm Shift in Software Engineering,” vincirufus.com, 2026.