Paraphrasing William Gibson, the future of artificial intelligence is already here, but it is far from evenly distributed. This observation rings especially true in the enterprise world, where AI adoption remains a deeply uneven phenomenon, varying not just across companies but even within the same organization. Two recent conversations in London perfectly illustrate this divide. In a meeting with a hedge fund engineering leader, AI was fully embedded: entire teams of agents in production, all code written by large language models (LLMs), and a ban on junior hires using LLMs for code assistance. On the other hand, a data engineer at a large retail bank reported no agents and sparse use of LLMs, with little visibility into whether other divisions were moving faster.
This is not a story of one company 'getting' AI and another not. Rather, it is a reminder that even within a single enterprise, wildly divergent adoption curves can coexist. AI is widening the gap between teams that can absorb it operationally and those that cannot. Recent data from McKinsey reinforces this picture. The consulting firm 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 systems, just 23% have scaled them somewhere in the enterprise, while 39% are still experimenting. In 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 to figure out AI—no team is irrevocably behind.
Cue the engineering boom
Common narratives suggest that finance is cautious, regulated industries lag, or everyone is building with agents. None of these hold universally. Some financial firms are moving aggressively, others are not, and some teams inside the same firm are doing both at once. Deloitte’s 2026 enterprise AI research reinforces this messiness: only 25% of respondents had moved 40% or more of their AI pilots into production, and just 34% said they are using AI to deeply transform their businesses—a number that likely reflects aspiration more than reality. Fully 37% still use AI at a surface level with little or no change to core processes. This pattern resembles a messy, uneven organizational test rather than a tidal wave.
This unevenness helps explain why predictions that AI will wipe out software jobs are misguided. The interesting thing about AI coding tools is not that they make software cheaper to produce, but what companies do with that lower cost. Box CEO Aaron Levie invokes Jevons paradox: when a capability becomes cheaper and easier to consume, demand for it often rises rather than falls. Cloud computing did not lead companies to need less compute; it made them build more compute-intensive applications. AI-assisted coding is likely to do the same for software itself.
The data on engineering jobs supports this view. Lenny Rachitsky recently noted that engineering openings are at their highest in more than three years. Underlying TrueUp data shows 67,665 open engineering jobs as of March 2026, up 78.2% from the recent low. Importantly, this is not concentrated at the top: 44.6% of posted engineering roles within tech companies are entry and mid-level, versus 38.3% at senior level and 13.8% at senior-plus. This indicates that AI is not eliminating roles for junior developers; rather, it is changing what enterprises want from engineers.
Software engineering is alive and well
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 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 do not come from simply sprinkling copilots over unchanged processes. They require reworking roles, workflows, and the full product development system. That is a much harder organizational challenge than buying licenses for a coding assistant.
Returning to the London conversations: the hedge fund leader may be 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 generate code. But the retail bank division is not irrationally lagging. In heavily regulated environments, code generation is not the hard part; governance is. Deloitte reports that only 21% of surveyed companies have a mature governance model for autonomous agents, and 73% cite data privacy and security as a top risk, with 46% citing governance capabilities and oversight. This is not bureaucracy for its own sake—it is a recognition that plugging non-deterministic systems into deterministic, compliance-heavy environments gets messy fast.
Caution, however, is not free. Every quarter spent in pilot mode is a quarter in which more aggressive peers build operational muscle. OpenAI’s enterprise usage data shows how uneven that muscle-building already is: frontier workers (the 95th percentile of adoption intensity) send six times more messages than the median worker, and frontier firms send twice as many messages per seat. OpenAI notes that 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 those still treating it as a promising but dangerous sideshow.
This is why the distinction between task and job matters. 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 reality of operating systems in the real world. AI can automate more tasks, but it has not 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 high performers stand out precisely because they redesign workflows and treat AI as a catalyst for innovation and growth, not just efficiency.
AI is not plodding or rocketing toward a uniform enterprise future where software engineers quietly fade away. Instead, it is splitting enterprises into fast-learning and slow-learning teams, 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 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