I spent the first two weeks of this year reading every AI prediction I could find, then stopped. Not because the writing was bad. Some of it was sharp. But the forecasts were expiring faster than I could finish them. A new model would drop, a vendor would announce something, a benchmark would get shattered, and whatever someone had written in January felt like archaeology by February. The half-life of a one-year AI prediction is shorter than a sprint cycle.
The deeper problem was what they were measuring. They counted automatable tasks, ran benchmarks, estimated job exposure. None of them asked how organisations actually decide to restructure, or what happens when the cost curves shift but the org chart doesn't. They treated software engineering as a set of tasks to optimise rather than a function embedded in institutions with their own logic and friction.
So instead of betting on what a model release does to your Q3 velocity, this piece looks at what five years of compounding AI adoption does to the organisations building software. To answer that properly, you have to start somewhere most predictions skip: how companies actually work.
How Software Organisations Work
The org chart on the company website is not wrong, exactly. It's just incomplete in the ways that matter.
Organisations don't function as pyramids. They function as translation machines, and the translation happens in layers, each with a different time horizon and a different kind of work.
The C-suite operates on the longest horizon. They own the three-to-five year bets: market position, regulatory programmes, platform migrations that span multiple budget cycles. Crucially, they often don't know every project currently running in the organisation. They don't need to. Their job is to set direction and constraints, not track the work. A CEO who knows the details of every active sprint has bigger problems than an inefficient org chart.
Below them sits the first middle layer. Directors, senior managers, heads-of. These are the translators. They take a strategic initiative like "reduce infrastructure costs by 30% over three years" and decompose it into programmes that can actually be staffed, scoped, and delivered. This is not mechanical work. It requires understanding both the strategic intent and the operational reality well enough to know when the two are in tension. A director who can't push back on an initiative that's been scoped unrealistically isn't doing the job. McKinsey's research found that just over half of companies regularly translate strategic goals into three-to-seven year financial plans, meaning the long-horizon bet always exists in tension with quarterly cost pressure. The first middle layer lives in that tension every day.
Below them sits the second middle layer: managers, project managers, delivery leads, scrum masters. These are the executors. They take programmes and make them happen, coordinating across teams, tracking dependencies, translating requirements into tickets, escalating blockers, reporting status upward. The work is real and the pressure is constant. But the nature of the work is fundamentally different from the layer above it. The first layer exercises judgment about what to build. The second layer exercises coordination to make sure it gets built.
That distinction — judgment vs coordination is the one the org chart doesn't show you. And it's the one that matters for everything that follows.
One important caveat before we go further: none of this describes small businesses or lower-medium sized organisations. In a 20-person company, the founder is the C-suite, the first middle layer, and often the second. Decision-making is fast, hierarchy is flat, and the layers described above either collapse into one person or don't exist at all. The dynamics here apply to organisations large enough to have grown the full stack, typically from around 200 people upward, where the coordination burden has grown large enough to justify dedicated roles for it.
What AI Changes
The second middle layer doesn't grow because companies are wasteful. It grows because visibility has a cost, and that cost scales with complexity.
When a director hands a programme to an engineering team, they need to know if it's on track. Not in real time, but reliably enough to escalate before something becomes a crisis. Status reports flow upward. Risk registers get updated. Sprint ceremonies create checkpoints. The system works because human communication between layers requires human intermediaries to manage it.
As organisations grow, the surface area requiring visibility expands faster than the programmes themselves. Each new team is another node. Each new dependency is another handoff that needs tracking. The answer companies have consistently reached for is more coordination: more people whose job is to make sure other people know what is happening. This is how you end up with what Zuckerberg described when restructuring Meta: managers managing managers managing managers managing the people actually doing the work. His diagnosis was right even if the fix was blunt. You cannot remove a coordination layer without replacing the coordination function. The question is whether that function still requires humans to perform it.
For most of the current second middle layer, it doesn't. Not entirely, and not immediately, but directionally.
The project manager's work splits into two functions that rarely get separated. The first is synthesis: reading across Jira, GitHub, budget trackers, and vendor systems to assemble a coherent picture of programme health for the director. The second is planning: working with directors on next quarter's capacity, budget allocation, and programme scope. Today both require a human because the underlying systems don't talk to each other in any meaningful way. Someone has to read each one, reconcile the signals, and produce the output.
Agentic AI systems are beginning to make the synthesis function redundant. The early harnesses like OpenClaw that genuinely execute across tools rather than simply respond to queries point at what this looks like in practice: an agent that reads your Jira board, watches your GitHub PRs, tracks budget burn, and surfaces a coherent programme picture without a human assembling it. When that synthesis becomes reliable, the project manager's time reclaims itself. What remains is the judgment work: scope tradeoffs, stakeholder management, pushing back on unrealistic timelines, working with directors on planning cycles that currently consume weeks of manual data assembly. That work was always the more valuable half of the role. It just got buried under the synthesis overhead.
For engineering managers the change cuts deeper, and in a different direction. The constraint on how many direct reports a manager can effectively handle has never been the number of humans they could track. It has been the quality of attention they could give each one. Career conversations, technical mentorship, performance calibration, helping someone work through a problem they are stuck on. These are not coordination functions and they cannot be delegated to a dashboard. What AI changes is the overhead around them: the status chasing, ticket grooming, and ceremony facilitation that consumes the hours that should be spent developing people. In the 1980s the average managerial span was 1-to-4 direct reports. Information technology moved that closer to 1-to-10. AI moves it further still.
But span widening is the smaller part of the story. The bigger part is what engineers are now being asked to become.
For most of the past decade, engineers were reduced to implementers. Handed tickets, kept away from stakeholders, insulated from the business context that would have made their work meaningful. AI is reversing that. The engineer who only closes tickets is being displaced by tooling. What survives is the engineer who can engage with the problem domain, work directly with stakeholders, own outcomes rather than tasks. That is a significantly harder job to grow someone into than the one the industry settled for. Career conversations get more complex. Mentorship requires more than code review. Performance calibration becomes about judgment and domain understanding, not velocity metrics. The engineering manager who was already stretched thin on four direct reports, spending most of their time in ceremonies and status updates, now has more reports, deeper development conversations, and engineers whose scope of responsibility has expanded substantially. The coordination overhead coming down is what makes that possible. It is not optional relief. It is the condition that makes the expanded role survivable.
For directors the change is about bandwidth. Their job is translating strategy into programmes and making judgment calls about priority and scope. AI doesn't do that. What changes is the fidelity and speed of the information they are working from. A director who currently waits for a weekly status report will instead have a live synthesis across the programme portfolio. The planning cycle that currently takes weeks of manual data assembly collapses to days. The director constrained by information velocity becomes constrained by their own judgment speed, which is as it should be.
For the C-suite the change is about calibration. They don't need to know every project, but they do need to sense when strategic intent and execution are drifting apart. AI-assisted synthesis makes that drift visible earlier. The quarterly business review becomes less about assembling the picture and more about interrogating it. The leaders who thrive will be those who use the freed bandwidth to engage more deeply with the long-horizon bets that actually determine their company's position, not those who use it to meddle in execution they never needed to own.
The through-line across all layers is the same. Coordination work that was performed by humans because the systems didn't talk to each other becomes automated. Judgment work that was always the point becomes the primary occupation. The hierarchy doesn't disappear. It shrinks. The same organisational function gets performed by fewer people, each with a wider span and a sharper focus on the work that actually matters.
What shrinks is not value. What shrinks is the overhead that was obscuring it.
Opportunities, Competition and New Industries
The scripted work goes away first. Password resets, order status, appointment scheduling, tier-one support. It should. That work was never the interesting part of any job. What remains is what was always hardest to scale: the customer disputing a charge they don't understand, the stakeholder who needs someone to work through a problem that fits no category in any decision tree. Those interactions need someone who can listen past the surface to the actual problem, make a judgment call, and leave the other person feeling heard rather than processed. The role doesn't disappear. It sheds what it was never good at.
The WEF's 2025 Future of Jobs report puts numbers on the shape of this: 92 million jobs displaced by 2030, 170 million new ones created. Over 85% of employment growth since 1940 came from technology-driven job creation and the pattern has been consistent across every wave. This cycle is no different in that respect. What changes is where the new jobs concentrate: toward complexity, toward domain knowledge, toward the work that requires someone who understands the problem well enough to exercise genuine judgment about it.
The mechanism driving the expansion is inference cost. GPT-3.5-level performance dropped from $20 per million tokens in November 2022 to $0.07 by October 2024, a 280-fold reduction in eighteen months. Projects that failed an ROI calculation in 2021 need recalculating at 2025 prices. The long tail of businesses that enterprise software never properly served — too small for SAP, too complex for off-the-shelf tools is now economically addressable. A 15-person logistics company can build custom route optimisation. A regional accountancy firm can offer AI-powered client tools that would have required a dedicated engineering team two years ago. The assumption that custom software was for enterprises is becoming false faster than most small business owners realise. The constraint has shifted from cost to capability: not "we cannot afford this" but "we need someone who understands our domain well enough to build this right."
That space also includes a significant amount of bad code. Humans have been shipping debt-laden software since long before AI arrived. AI didn't invent the problem, it inherited the tendency and gave it a faster engine. GitClear tracked an eightfold increase in duplicated code blocks during 2024, with 46% of code changes consisting entirely of new lines while refactored and moved code dropped sharply. MIT professor Armando Solar-Lezama called it a brand new credit card for accumulating technical debt in ways we were never able to before. The expansion creates its own counter-demand: the backlog of systems needing someone who can read them, diagnose them, and make principled decisions about what to fix grows alongside the new projects. Who fixes the slop matters more than who created it.
Healthcare is the clearest domain that has crossed a viability threshold. Buying cycles have compressed from 12-18 months down to under six, yet 80% of the market remains untapped. Prior authorisation systems that trap clinical staff in paperwork producing no patient value. Voice interfaces for patient engagement a two-doctor practice could never previously afford. Diagnostic support tools surfacing patterns across patient records at a scale no clinician could maintain unaided. These are not incremental improvements. They are categories of software that didn't exist as commercially viable products three years ago. The engineering required to build them correctly — integrating with legacy clinical systems, navigating data governance constraints, designing for safety-critical failure modes — is hard in ways no amount of AI assistance substitutes for. That hardness is the opportunity.
Legal and compliance follows the same pattern with added regulatory tailwind. The EU AI Act, the EU Cyber Resilience Act, evolving data sovereignty requirements across jurisdictions: each a new surface area of compliance work organisations need software to manage. Contract review, regulatory change monitoring, audit trail generation. Work that previously required expensive specialist time, or simply wasn't done rigorously, is now viable at a cost that makes sense for organisations of all sizes. Regulation is a forcing function for software investment, and the current environment is generating more of them than the industry has seen in a decade.
Manufacturing and industrial software is crossing a different threshold entirely. Predictive maintenance systems previously requiring expensive specialist integration can now run on existing sensor infrastructure at a fraction of the cost. Digital twins for factory floors, simulations that let operators model consequences before making changes, are moving from enterprise-only to mid-size manufacturers. The engineering problems are genuinely difficult: real-time control loops, safety-critical systems, integration with legacy industrial hardware designed before the internet existed. That difficulty is not a barrier. It is a moat for engineers who can navigate it.
Security sits in a category of its own because AI is simultaneously creating the problem and generating the demand for people to solve it. The AI cybersecurity market is projected to reach $86 billion by 2030, driven by accelerating attack surface expansion and a talent shortage that already stood at 4.8 million unfilled positions before agentic AI began proliferating across enterprise stacks. Attackers use the same foundation models, the same code generation tools, the same agentic frameworks that defenders do. The threat surface expands every time a new agent ships, every time a developer uses AI to generate integration code without understanding the security model of the library they're calling. What the next five years demand is the engineer who reasons adversarially: who identifies how a system might be exploited before it is, who treats security as a design constraint from the first conversation rather than a compliance checkbox before launch. Every IT position is becoming a cybersecurity position.
The competitive consequence of all this is the part most organisations haven't fully priced in. The inference cost collapse doesn't just create new markets. It compresses the time advantage incumbents used to enjoy. A category of software that took three years and a dedicated team to build in 2021 now takes months for a well-scoped team with domain knowledge and the right tools. The window between a new entrant identifying an opportunity and being able to ship something real into it has shortened dramatically. Organisations that have restructured around speed — flatter hierarchies, engineers with broader scope, faster decision cycles — will move through that window. Organisations still running the full coordination stack will still be assembling the business case.
Evolution, Not Revolution
The next five years of AI in software organisations will disappoint everyone waiting for a dramatic moment.
There will be no single announcement, no model release, no product launch that draws a clean line between before and after. The AI-augmented software of 2025 — features bolted on, LLM wrappers shipped as products, "powered by AI" in every marketing deck will quietly give way to something less visible and more consequential. AI-native organisations: built around what the technology actually does well, with engineers who understand the domain before they touch the model, and hierarchies structured around judgment rather than coordination. The transition will feel unremarkable as it happens and significant in retrospect.
The hype will persist throughout. Every new model release will generate another wave of disruption predictions, another round of job displacement headlines, another set of quarterly forecasts that expire before anyone finishes reading them. The noise is structural. The signal is slower and less exciting. DORA's finding that a 25% increase in AI adoption produces a 2.1% productivity lift is not a headline. It is the honest starting point for a compounding argument that plays out over years, not quarters.
What compounds is not the technology. It is the organisational adaptation. The companies figuring out how to work AI natively rather than additively. The engineers expanding their scope rather than defending their tickets. The managers using freed coordination overhead to actually develop their people. The directors making faster, better-informed decisions because they are no longer waiting for a human to assemble the picture. Those advantages are invisible quarter by quarter. Over five years, they are structural.
The organisations that get this right will not look like they did something dramatic. They will look like they quietly got better at the things that always mattered: judgment, speed, and people who understand the problem deeply enough to solve it. The ones that get it wrong will still be running the same coordination overhead with a layer of AI tools bolted on top, wondering why the productivity gains never arrived.
Not disruption. Evolution & Accumulation.
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