AI won’t empty the software factory: Why the new engineers act like foremen

Human work has gone through several revolutions over the past few decades, each of them bringing a start to new careers and an end to others. Software careers, which seemed virtually untouchable a short while ago, have been touted as among the most prominent victims of artificial intelligence. On the surface, it’s easy to see why.

In the last few years, most enterprises have massively accelerated the rate at which they can produce code, while CIOs reported that tech industry layoffs hit their highest peak since 2024 earlier in the year. If I wanted to, I could now use AI to build a functioning CRM in a few days rather than weeks. With that in mind, it makes sense that senior executives may reconsider how much engineering headcount they really need to achieve their business goals. But would that CRM be fit for purpose, or secure enough to handle sensitive customer and business data? These are areas requiring skilled human judgment, so I’d encourage them to think twice.

AI has certainly changed the traditional path of software careers, but the value of knowledge, skills and experience in software engineering remains the same. As Faros AI found over its past two developer reports, AI speeds up individual coders, but review and rework swallow the gains before they reach the team. Poorly run projects will still lead to disappointing results, no matter how fast software teams can now churn out code. And at the rate companies need to be deploying to keep up with rivals, they need people with a sophisticated understanding of their organization to steward AI-generated software towards positive outcomes.

The conclusion that software engineers are under threat from AI reflects a confusion about what they do best. Code production is secondary to the ways that truly proficient software engineers think. That’s why I believe that demand for these minds will increase, not decrease, with AI.

The Jevons Paradox

When new resources increase efficiency, it’s instinctive to think that the consumption of that resource will drop; the Jevons paradox observes that the opposite tends to be true. Coal and steam made factories more efficient, prompting more tycoons to open factories and increasing overall consumption. As we’ve become better at generating electricity, we’ve found more uses for it, and consumption now dwarfs what was ever thought possible.

The Jevons paradox is not a promise that demand will always rise after an efficiency gain, but it is a useful lens through which to understand why software engineering will survive the AI boom. According to GitHub’s 2025 Octoverse report, developers pushed nearly one billion commits in 2025, a 25.1% year-on-year rise. Businesses are demanding more features and value as lower development costs make previously unviable levels of output more achievable.

So, it makes sense that software engineers capable of using AI to accelerate business value are going to be asked to create more of that value. Completing that loop, as my own business produces more value with more code, I’m inclined to hire more software engineers to manage more teams of agents, not fewer.

The employment outlook gives CIOs another reason to avoid making knee-jerk workforce decisions on the basis of short-term excitement. In July 2026, the U.S. Bureau of Labor Statistics projected that the number of software developers will grow 15.8% from 2024 to 2034, adding 267,700 jobs, even as it accounted for the spread of AI technologies. The shape of those jobs will change, but the need for people who can turn technical capacity into business value is not disappearing.

Lazy in, lazy out

AI will only be an accelerant for people who are able to apply it without outsourcing their thinking. There’s a big difference between asking an AI to write a strategy document for you, and asking it to challenge a base level of thinking that has already gone into the task. Treating AI as a ‘sparring partner’ to refine work rather than deferring to whatever it produces will naturally lead to far better results.

This is a mindset that those responsible for hiring, including myself, are looking to foster in software teams. AI amplifies the need for thinking that differentiates talented software engineers, who need to grasp why they’re doing the tasks they’re doing and what differentiates their organization in order to make a difference. Early career developer roles typically involve churning through tickets, but that skill falling down the order of priority doesn’t pull the rug out from under their development. It should instead free up more time for them to learn about their organization and grow into better problem-solvers.

I think about agents as virtual teammates. I wouldn’t hire a colleague and expect the first piece of work they return to be perfect. Colleagues need to be effectively onboarded and supported with context about the organization and its goals before they start producing value. A lazy prompt with incomplete context is simply a faster way to create something that looks plausible but does not solve the right problem.

Software engineers as foremen

Software engineers managing AI-assisted teams are becoming almost like ‘foremen’ — setting the direction of work, making sure agents have the right context and intervening when output falls short of requirements. Research from Gearset found that 82% of Salesforce teams trust AI to take on tasks in the build stage, dropping markedly to 58% at release. Routine configuration changes and writing tests used to be manual and time-consuming, but these are jobs teams feel most comfortable delegating to AI now. That leaves more space for engineers to manage the delegation of tasks and strategically build trust in AI.

AI has opened up more workstreams that can move at a faster pace at large organizations, meaning quality control is now the main imperative. This leaves an opportunity for software engineers to set intent, enforce standards and get closer to their business.

This change also has implications for junior engineers, who have traditionally learned a codebase through completing routine work. If AI performs all of this kind of work, leaders must be more deliberate in their approach to hiring early-career engineers: training them to explain the thinking behind projects and get more involved in architectural discussions. The objective is not to create more work for the sake of it, but to preserve and develop the problem-solving skills that help enterprises maintain quality at scale.

These skills aren’t new: the order of priority has simply shifted as the process of coding has become faster.

Deploy safely, or don’t deploy

Quality control is the primary challenge that has arisen from AI coding, and that is where engineers can make the most difference. They define what good looks like, for AI and human work alike, and make sure that all software changes are both safe to reach production and relevant to their business goals.

Software engineers supply the context and experience needed to make sure code is genuinely ready for production. An agent can generate a change that appears correct in isolation, but it does not necessarily reflect the surrounding architecture or the consequences of failure for customers and employees.

This is why demand will continue to improve for engineers who can turn higher output into safe, useful deployments. It’s crucial for AI-generated changes to pass the same version control, testing, security scanning, approval and auditing processes as human-written work. Engineers must set those standards and identify the right places where deterministic checks can replace manual reviews. Engineers will play a huge role in helping CIOs decide what work can sensibly be trusted to AI, while keeping teams accountable where human oversight is necessary.

The role therefore moves beyond writing each line of code and towards governing the whole path from intent to production. AI can accelerate the work between those points, but it cannot assume accountability for the outcome.

AI can be a force for good

I see AI as a positive force for the software industry if organizations give engineers the space to make more use of their critical thinking skills. When automation is used primarily for cost-cutting or headcount reduction, you disregard the critical thinking skills that make engineers so adept at improving the systems and products they build.

In an environment of psychological safety, where safeguards are prioritized over pure productivity, there is high potential for skilled software engineers to steward projects towards success at a much faster rate than was previously possible. Lazy inputs lead to bad outputs, but the best software teams are full of problem solvers with deep knowledge of their organization. You can’t outsource their thinking to AI, and the most successful companies in this era know that.