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Is Coding Dead? How AI Is Changing the Future of Programming
How coding is evolving into a higher-level discipline where developers focus on designing, guiding, and validating AI-driven systems rather than just writing code. A question I get asked often is whether this is the end of coding. The first time I heard it was in the 1980s, when a low-level programmer working in assembly argued that higher-level languages and compilers would mark the end of efficient programming. Developers using those tools, he claimed, simply did not know how to code. Versions of this argument have repeated ever since. FORTRAN versus Pascal. C versus Java. Java versus no-code. And now, Python versus AI coding agents. The pattern is familiar. Each shift raises concerns about whether abstraction reduces the need for real programming skill. AI Is Changing How Code Is Written What is different now is the scale. AI tools are now used by 84% of developers, and many organizations – such as our own – track how much of their code is generated by AI. In our case, that number exceeds 30%. This echoes earlier transitions. At the time, similar attention was given to how much code was effectively written by compilers. For example, moving from assembly to FORTRAN reduced the number of lines needed for the same program by as much as 65–95%. At the same time, the market sends mixed signals. Entry-level developers are finding it harder to get hired, while overall demand for software developers is expected to grow. Universities are starting to question whether traditional coding curricula will remain relevant. Taken together, these trends make the question feel more urgent. But they still do not point to the end of coding. The Role of the Programmer Is Shifting What is changing is not whether we need programmers, but what we need them to do. Coding is becoming more collaborative, with AI handling an increasing share of implementation. But this does not reduce the importance of developers. I would argue that coders are playing an increasingly significant role that is not quantified by lines of code. This creates a growing need for developers who can interpret, validate, and refine AI-generated outputs, especially since such code often requires rework. The measure of a programmer is no longer how many lines they write, but how effectively they shape and control the system. This shift makes more sense when you step back and look at what programming has always been. Programming has never really been about rigid syntax or knowing how to compile and run code. A...
Think 2026: IBM Delivers the Blueprint for the AI Operating Model as ...
- Next-generation agent orchestration and agentic development give enterprises a unified way to plan, build, deploy, and govern AI agents at scale- Real-time, AI-ready data foundation gives enterprises the governed, connected information agents need to act with speed- AI-powered hybrid cloud management connects infrastructure, security, and operations across hybrid environments- Built-in governance and sovereignty controls let enterprises run AI at scale May 5, 2026 , /PRNewswire/ -- Today, at its annual Think conference, IBM (NYSE: IBM) announced its most comprehensive expansion of enterprise AI and hybrid cloud management capabilities to date. Products and capabilities unveiled today include the next generation of IBM watsonx Orchestrate for multi-agent orchestration, IBM Confluent to bring real-time data to AI, IBM Concert platform for intelligent operations, and IBM Sovereign Core for operational independence. The announcements address the defining challenge facing enterprises: many have invested heavily in AI, but only few believe it is paying off. The products and capabilities unveiled today address this gap for enterprises. "The enterprises pulling ahead are not deploying more AI – they're redesigning how their business operates," said Arvind Krishna, Chairman and CEO, IBM. "Running AI in the enterprise requires a new operating model, and IBM is enabling organizations to manage AI-driven systems with the same rigor, governance, and scale as their most critical infrastructure." A New Operating Model for the Agentic Enterprise AI requires a new operating model, built on four integrated systems working together: agents through coordinated AI that executes and adapts across the business; data through real-time, connected information that gives teams a shared view of what's happening; automation through end-to-end infrastructure and automated workflows that scale across processes; and hybrid through operational independence for sovereignty, governance, and security that allows AI to run consistently and with controls. Each is a separate priority that enterprises are chasing. Together, they represent a fundamental shift from improving parts of the business to changing how the business operates. Today's announcements represent the next evolution of IBM's portfolio, to deliver against the operating model. Agents: Orchestration and Development at Scale As organizations move from deploying a handful of agents to managing thousands, built by different tea...
AI coding tools with organizational context are quietly changing how ...
An AI coding tool with genuine organizational contextual intelligence changes that dynamic. The new engineer gets suggestions that reflect the actual codebase ...
The $570K canary: What AI coding agents reveal about enterprise ...
Software engineers are bank tellers, not toll booth workers. AI agents are eliminating routine implementation: The boilerplate, the CRUD endpoints, the standard ...
