Paraphrasing William Gibson, the future of AI is here, but it's nowhere close to evenly distributed yet. This stark unevenness was on full display during two recent conversations in London. In one meeting, the head of engineering at a large hedge fund described teams with fleets of agents in full production, where all code is written by LLMs—though junior hires are curiously barred from using them for assistance. In another, a data engineer at a large retail bank painted the opposite picture: no agents, sparse use of LLMs, and little movement even as other departments may accelerate. These anecdotes underscore a critical reality: even within the same company, AI adoption curves can diverge wildly.

This isn't about one company 'getting' AI and another not. Rather, it's a reminder that technology adoption is deeply uneven, and AI is widening the gap between teams that can absorb it operationally and those that can't. The best recent data supports this view. McKinsey found that 88% of respondents say their organizations use AI in at least one business function, but only about one-third report scaling AI programs. For agentic AI, 23% have scaled an agent system somewhere in the enterprise, while 39% are still experimenting. In any given function, no more than 10% report scaling agents. Broad usage, in other words, is not deep institutional change. There is still time to figure out AI; no one is behind.

Financial firms: cautious or aggressive?

The narrative that 'finance is cautious' or 'regulated industries are behind' is oversimplified. 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 point: only 25% of respondents had moved 40% or more of AI pilots into production. Just 34% say they're using AI to deeply transform their businesses (a likely aspirational number), while 37% still use it at a surface level with little change to core processes. This sounds less like a tidal wave and more like a messy, uneven organizational test.

This unevenness explains why fears that 'AI will wipe out software jobs' are misguided. The interesting thing about AI coding tools isn't that they make software cheaper, but what companies do with that lower cost. Box CEO Aaron Levie invoked Jevons paradox: when a capability becomes cheaper and easier to consume, demand often rises. Cloud computing didn't reduce compute needs; it led to more things consuming compute. AI-assisted coding may do the same for software. The data on engineering jobs is telling: Lenny Rachitsky highlighted that engineering openings are at their highest in over three years, with TrueUp data showing 67,665 open jobs as of March 2026, up 78.2% from the recent low. Notably, 44.6% of posted roles are entry- and mid-level, versus 38.3% senior and 13.8% senior-plus. Companies still want many engineers, even as AI tools spread.

What AI changes: tasks, not jobs

AI isn't killing the need for engineers; it's changing what enterprises want from them. Stack Overflow's 2025 survey found 84% of respondents use or plan to use AI in development, with half of professional developers using it daily. McKinsey's software development research shows high-performing AI-driven organizations see 16-30% improvements in productivity, customer experience, and time to market, along with 31-45% improvements in software quality. But these gains come not from sprinkling copilots over unchanged processes, but from reworking roles, workflows, and the full product development system—a much harder organizational challenge than buying licenses.

Consider the hedge fund leader's experience: less time hand-authoring code, more time specifying, reviewing, steering, and orchestrating systems that generate code. But the retail bank division is not irrationally lagging. In heavily regulated environments, governance is the hard part. Deloitte reports only 21% of companies have a mature governance model for autonomous agents (and likely overstate). Meanwhile, 73% cite data privacy and security as top risks, and 46% cite governance capabilities and oversight. That's recognition that plugging non-deterministic systems into deterministic, compliance-heavy environments gets messy. Caution, however, isn't free. Every quarter in pilot mode allows aggressive peers to build operational muscle.

OpenAI's enterprise usage data shows how uneven this muscle-building is: frontier workers (95th percentile of adoption intensity) send six times more messages than the median worker; frontier firms send twice as many messages per seat. The primary constraints are no longer model performance or tools, but organizational readiness and implementation. This rings true: the real divide is between teams that have integrated AI into repeatable work and those still treating it as a promising but dangerous sideshow.

Task vs. job: a crucial distinction

The distinction between a task and a job is key. Writing boilerplate code is a task; engineering is a job. Jobs bundle judgment, trade-offs, accountability, architecture, security, integration, testing, and real-world operations. AI can automate more tasks, but it hasn't eliminated the need for jobs, especially in environments where bad software decisions carry consequences. McKinsey's broader AI survey found most organizations still navigating from experimentation to scaled deployment, and high performers redesign workflows and treat AI as a catalyst for innovation and growth—not just efficiency. Saying 'we gave everyone a chatbot and now need fewer people' is naive.

So AI isn't plodding or rocketing toward one uniform future where software engineers fade away. Instead, it splits enterprises into fast-learning and slow-learning teams, rewarding organizations that redesign work, govern risk, and turn lower software costs into more software. The code may be getting cheaper, but the ability to decide what to build, how to fit it together, and how to keep it from breaking the business keeps increasing in value. That is not the death of software engineering; it is the repricing of it, and every company and every team is paying different prices.

Source: InfoWorld News