It’s quite clear that agentic coding has completely taken over the software development world. Writing code will never be the same. Shoot, it won’t be long before we aren’t writing any code at all because agents can write it better and faster than we humans can. That may already be true today.
But there is more to software development than merely writing code, and those areas—source control, documentation, CI/CD, project management—are ripe for some serious disruption from AI as well. Those areas may well be hit harder than coding itself.
I would imagine that if you were in the business of analyzing data and providing dashboard-level insights into that data, then you would be very worried indeed about what AI is going to do to your value proposition. Much of the SaaS industry is in the business of analyzing existing data, and that is exactly what AI agents can do well. When a simple question can get straight to the heart of what a pricey dashboard provides, then companies have to que
Writing code has always been the most time- and resource-intensive task in software development. AI is changing that, and faster than most engineering organizations are prepared for. Tools like Claude Code and Cursor are already handling significant parts of code construction, freeing developers to spend more time on requirements, architecture, and design.
But that shift creates a new challenge nobody is talking about enough. As AI takes on the heavy lifting, the skills that matter most are moving upstream: how to provide the right context for a prompt, how to evaluate what the model produces, and how to understand a problem deeply enough that you can’t be fooled by a confident but wrong answer.
This piece explores those three skills and why developers who master them will have a significant edge over those who don’t.
Beyond coding: Mastering the art of the prompt
Software translation tools such as compilers and assemblers map a high-level description of code to a lower-level represent
I’m not even remotely worried about AI eliminating software development jobs. In fact, I’m pretty sure there will soon be a boom in both software development jobs and the amount of software available to everyone.
People have always worried about automation causing massive unemployment. Each time a breakthrough happens, folks are sure that “it will be different this time.” Only it never is different.
But the worriers persist.
It’s paradoxical
You can tell them all about the Jevons paradox — the observation that as something becomes more efficient, demand for that more efficient thing increases rather than decreases. In the mid-19th century, William Jevons noticed that the use of coal became more efficient. Humans figured out how to get more heat and energy out of less and less coal. The common belief was that, because less coal was needed for the same amount of energy or heat, there would be less demand for coal as a result. Everyone was concerned that coal miners would lose their jo
Artificial intelligence has had an immediate and profound impact on software development. Coding practices, coding tools, developer roles, and the software development process itself are all being reimagined as AI agents advance on every stage of the software development life cycle, from planning and design to testing, deployment, and maintenance.
Download the May 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World and learn how to harness the power of AI-enabled development.
Artificial intelligence has had an immediate and profound impact on software development. Coding practices, coding tools, developer roles, and the software development process itself are all being reimagined as AI agents advance on every stage of the software development life cycle, from planning and design to testing, deployment, and maintenance.
Download the May 2026 issue of the Enterprise Spotlight from the editors of CIO, Computerworld, CSO, InfoWorld, and Network World and learn how to harness the power of AI-enabled development.
Alibaba’s Qwen Team has released Qwen3.6-27B, the first dense open-weight model in the Qwen3.6 family — and arguably the most capable 27-billion-parameter model available today for coding agents. It brings substantial improvements in agentic coding, a novel Thinking Preservation mechanism, and a hybrid architecture that blends Gated DeltaNet linear attention with traditional self-attention — all […]
The post Alibaba Qwen Team Releases Qwen3.6-27B: A Dense Open-Weight Model Outperforming 397B MoE on Agentic Coding Benchmarks appeared first on MarkTechPost.
Infosys said the integration will be used to help its clients modernize software development, automate workflows and deploy AI systems, initially focusing software engineering, legacy modernization, and DevOps.
It’s quite clear that agentic coding has completely taken over the software development world. Writing code will never be the same. Shoot, it won’t be long before we aren’t writing any code at all because agents can write it better and faster than we humans can. That may already be true today.
But there is more to software development than merely writing code, and those areas—source control, documentation, CI/CD, project management—are ripe for some serious disruption from AI as well. Those areas may well be hit harder than coding itself.
I would imagine that if you were in the business of analyzing data and providing dashboard-level insights into that data, then you would be very worried indeed about what AI is going to do to your value proposition. Much of the SaaS industry is in the business of analyzing existing data, and that is exactly what AI agents can do well. When a simple question can get straight to the heart of what a pricey dashboard provides, then companies have to que
