In the world of enterprise artificial intelligence, the future is already here—but it is anything but evenly distributed. Two recent conversations in London illustrate this stark disparity. The head of engineering at a large hedge fund described teams with fleets of AI agents in full production, where nearly all code is now written by large language models (LLMs). Interestingly, junior hires in that organization are not permitted to use LLMs for code assistance, suggesting a deliberate strategy to protect foundational learning. In stark contrast, a data engineer at a major retail bank reported the opposite: no agents in use and only sporadic LLM adoption. While other divisions within the same bank may be moving faster on AI, his group clearly is not.

This is not a simple story of one company 'getting' AI and another falling behind. Rather, it reveals that even within a single organization, adoption curves can vary wildly. AI is widening the gap between teams that can operationally absorb new technologies and those that cannot. Recent data underscores this point. According to McKinsey, 88% of organizations now use AI in at least one business function, but only about one-third have begun scaling AI programs. When it comes to AI agents, just 23% report scaling an agentic system somewhere in the enterprise, while 39% are still in the experimentation phase. In any given business function, no more than 10% of respondents say they are scaling agents. Broad usage, therefore, is not the same as deep institutional change. There is still time for organizations to figure out AI—they are not behind.

Cue the engineering boom

A common narrative is that finance is cautious, regulated industries lag, and everyone is building with agents. None of these generalizations hold up completely. Some financial firms are moving aggressively, some are not, and some teams within the same firm are doing both simultaneously. Deloitte's 2026 enterprise AI research reinforces this point. Only 25% of respondents said they had moved 40% or more of their AI pilots into production. Just 34% claim to be using AI for deep business transformation—a figure that may be more aspirational than actual. Meanwhile, 37% are still applying AI at a surface level with negligible changes to core processes. This looks less like a tidal wave and more like a messy, uneven organizational test.

This unevenness is why predictions that AI will wipe out software jobs are likely wrong. The interesting effect of AI coding tools is not that they make software production cheaper, but what companies do with that lower cost. Box CEO Aaron Levie invoked Jevons paradox to explain this dynamic: when a capability becomes cheaper and easier to consume, demand for it often rises. Cloud computing did not lead to less compute usage; it spurred the creation of more applications that consumed compute. AI-assisted coding may be following a similar trajectory for software itself.

Data on engineering jobs supports this view. Lenny Rachitsky recently highlighted that engineering openings are at their highest levels in more than three years. The underlying TrueUp data shows 67,665 open engineering jobs as of March 2026, an increase of 78.2% from the recent low. Notably, this growth is not concentrated solely at the senior end. TrueUp's breakdown reveals that 44.6% of posted engineering roles within tech companies are entry- and mid-level, compared to 38.3% senior and 13.8% senior-plus. The data does not indicate that AI is eliminating roles for junior developers; instead, companies still want many engineers, even as AI tools spread throughout the profession.

What is happening is a shift in what enterprises want from engineers. Stack Overflow's 2025 survey found that 84% of respondents are using or planning to use AI tools in development, and just over half of professional developers use them daily. McKinsey's software development research found that the highest-performing AI-driven software organizations see 16% to 30% improvements in productivity, customer experience, and time to market, along with 31% to 45% improvements in software quality. However, McKinsey's crucial insight is that these gains do not come from merely sprinkling AI copilots over unchanged processes. They require reworking roles, workflows, and the entire product development system—a far more difficult organizational challenge than buying licenses for a coding assistant.

Software engineering is alive and well

Returning to the London conversations, the hedge fund leader may offer an early glimpse of where parts of enterprise engineering are headed: less time hand-authoring code, more time specifying, reviewing, steering, and orchestrating systems that increasingly generate code automatically. But that does not mean the retail bank division is irrationally lagging. In a heavily regulated environment, code generation is not the hardest part—governance is. Deloitte reports that only 21% of surveyed companies have a mature governance model for autonomous agents, and even those 21% may be overconfident. Additionally, 73% cite data privacy and security as a top risk, and 46% cite governance capabilities and oversight. This is not bureaucracy for its own sake; it reflects the messy challenge of plugging non-deterministic systems into deterministic, compliance-heavy environments.

Caution, however, is not free. Every quarter a team spends in pilot mode is a quarter in which more aggressive peers build operational muscle. OpenAI's enterprise usage data highlights how uneven that muscle-building already is. Frontier workers—those in the 95th percentile of adoption intensity—send six times more messages than the median worker. Frontier firms send twice as many messages per seat. According to OpenAI, the primary constraints are no longer model performance or tools, but organizational readiness and implementation. This rings true: the real divide is increasingly between teams that have learned to integrate AI into repeatable work and teams that still treat it as a promising but dangerous sideshow.

The distinction between task and job matters here. Writing a chunk of boilerplate code is a task; engineering is a job. Jobs bundle judgment, trade-offs, accountability, architecture, security, integration, testing, and the real-world operation of systems. AI can automate more tasks, but it has not eliminated the need for jobs—especially in environments where bad software decisions carry serious operational or regulatory consequences. McKinsey's broader AI survey found that most organizations are still navigating the transition from experimentation to scaled deployment. High performers stand out precisely because they redesign workflows and treat AI as a catalyst for innovation and growth, not just efficiency. That is a very different thing from saying, 'We gave everyone a chatbot and now we need fewer people.'

So no, AI is not plodding or rocketing toward a uniform enterprise future where software engineers quietly fade away. Instead, AI is splitting enterprises into fast-learning and slow-learning teams. It is rewarding organizations that redesign work, govern risk, and turn lower software costs into more software, not less. The code may be getting cheaper, but the ability to decide what should be built, how it should fit together, and how to keep it from breaking the business continues to increase in value. That is not the death of software engineering; it is the repricing of it. And every company, every team, is paying a different price.

Source:InfoWorld News