Paraphrasing William Gibson, the future of AI is here, but it's nowhere close to evenly distributed yet. This observation was vividly confirmed by two contrasting conversations in London last week. In one meeting, the head of engineering at a large hedge fund described teams with fleets of AI agents in full production, where all code is now written by large language models. Interestingly, junior hires are not allowed to use LLMs for code assistance. In another meeting, a data engineer at a large retail bank painted the opposite picture: no agents, sparse use of LLMs, and no clear indication that other parts of the bank were moving any faster.

This isn't a simple story of one company 'getting' AI while another lags behind. It's a reminder that even within the same organization, adoption curves for new technologies can be wildly divergent. AI is widening the gap between teams that can absorb it operationally and teams that cannot. Recent data from McKinsey underscores this point: 88% of respondents say their organizations use AI in at least one business function, yet only about one-third report scaling AI programs. For agentic AI, 23% have scaled such systems somewhere in the enterprise, while 39% are still experimenting. And within any given function, no more than 10% claim to be scaling agents.

Broad usage, in other words, is not the same thing as deep institutional change. There is still time for enterprises to figure out AI. They are not behind, but the window of opportunity is narrowing.

Cue the engineering boom

The notion that 'finance is cautious' or 'regulated industries are behind' is an oversimplification. Some financial firms are moving aggressively; others are not. Some teams inside the same firm are doing both at once. Deloitte's 2026 enterprise AI research reinforces this: only 25% of respondents had moved 40% or more of their AI pilots into production. Just 34% say they are using AI to deeply transform their businesses (a number many suspect is more aspirational than actual), while 37% still use it at a surface level with little or no change to core processes. This looks less like a tidal wave and more like a messy, uneven organizational test.

This unevenness explains why predictions that 'AI will wipe out software jobs' miss the mark. The interesting thing about AI coding tools is not that they make software cheaper to produce; it's 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 didn't lead companies to need less compute; it made them build more things that consumed compute. AI-assisted coding may be doing something similar for software itself.

Data on engineering jobs is instructive. Lenny Rachitsky recently highlighted that engineering openings are at their highest levels in more than three years. TrueUp data shows 67,665 open engineering jobs as of March 2026, up 78.2% from the recent low. And this is not concentrated at the very top: 44.6% of posted roles within tech companies are entry or mid-level, versus 38.3% at senior level and 13.8% at senior-plus. So AI is not eliminating roles for junior developers; rather, companies still want a lot of engineers, even as AI tools spread throughout the profession.

A cleaner way to understand what is happening is that AI is not killing the need for engineers but changing what enterprises want from them. 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 shows 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. But these gains don't come from sprinkling copilots over an unchanged process. They come from reworking roles, workflows, and the full product development system.

Software engineering is alive and well

Returning to the London conversations: the hedge fund leader may represent 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 for you. But that does not mean the retail bank division is irrationally lagging. In a heavily regulated environment, code generation is not the hard part—governance is. Deloitte reports that only 21% of surveyed companies currently have a mature governance model for autonomous agents (and those 21% are probably kidding themselves). At the same time, 73% cite data privacy and security as a top risk, and 46% cite governance capabilities and oversight. That's not bureaucracy for its own sake; it's a recognition that plugging non-deterministic systems into deterministic, compliance-heavy environments gets messy fast.

Still, caution is not free. Every quarter a team spends in pilot mode is a quarter in which more aggressive peers are building operational muscle. OpenAI's enterprise usage data shows how uneven that muscle-building already is. Frontier workers—defined as the 95th percentile of adoption intensity—send six times more messages than the median worker. Frontier firms send twice as many messages per seat. OpenAI says the primary constraints are no longer model performance or tools, but rather organizational readiness and implementation.

This rings true. The real divide is increasingly not between companies that have access to AI and those that don't; it's between teams that have learned how to integrate AI into repeatable work and teams that are still treating it as a promising but dangerous sideshow.

This is also why the distinction between task and job matters. Writing a chunk of boilerplate code is a task. Engineering is a job, bundling judgment, trade-offs, accountability, architecture, security, integration, testing, and the ugly reality of operating systems in the real world. AI can automate more tasks, but it hasn't eliminated the need for jobs, especially in environments where bad software decisions carry real operational or regulatory consequences. McKinsey's broader AI survey found that most organizations are still navigating the transition from experimentation to scaled deployment, and high performers stand out precisely because they redesign workflows and treat AI as a catalyst for innovation and growth, not just efficiency.

So no, AI isn't plodding or rocketing toward one uniform enterprise future in which software engineers quietly fade away. Instead, AI is splitting enterprises into fast-learning and slow-learning teams, and 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 keeps increasing in value.

That's not the death of software engineering. It's the repricing of it, and every company and every team is paying different prices.

Source:InfoWorld News