Yes, writing code is easier than ever.
AI assistants autocomplete your functions. Agents scaffold entire features. You can describe what you want in plain English and watch working code appear in seconds. The barrier to producing code has never been lower.
And yet, the day-to-day life of software engineers has gotten more complex, more demanding, and more exhausting than it was two years ago.
This is not a contradiction. It is the reality of what happens when an industry adopts a powerful new tool without pausing to consider the second-order effects on the people using it.
If you are a software engineer reading this and feeling like your job quietly became harder while everyone around you celebrates how easy everything is now, you are not imagining things. The job changed. The expectations changed. And nobody sent a memo.
The Baseline Moved and Nobody Told You
There is a phenomenon happening right now that most engineers feel but struggle to articulate. The expected output of a software engineer in 2026 is dramatically higher than it was in 2023. Not because anyone held a meeting and announced new targets. Not because your manager sat you down and explained the new rules. The baseline just moved.
It moved because AI tools made certain tasks faster. And when tasks become faster, the assumption follows immediately: you should be doing more. Not in the future. Now.
A February 2026 study published in Harvard Business Review tracked 200 employees at a U.S. tech company over eight months. The researchers found something that will sound familiar to anyone living through this shift. Workers did not use AI to finish earlier and go home. They used it to do more. They took on broader tasks, worked at a faster pace, and extended their hours, often without anyone asking them to. The researchers described a self-reinforcing cycle: AI accelerated certain tasks, which raised expectations for speed. Higher speed made workers more reliant on AI. Increased reliance widened the scope of what workers attempted. And a wider scope further expanded the quantity and density of work.
The numbers tell the rest of the story. Eighty-three percent of workers in the study said AI increased their workload. Burnout was reported by 62 percent of associates and 61 percent of entry-level workers. Among C-suite leaders? Just 38 percent. The people doing the actual work are carrying the intensity. The people setting the expectations are not feeling it the same way.
This gap matters enormously. If leadership believes AI is making everything easier while engineers are drowning in a new kind of complexity, the result is a slow erosion of trust, morale, and eventually talent.
A separate survey of over 600 engineering professionals found that nearly two-thirds of engineers experience burnout despite their organizations using AI in development. Forty-three percent said leadership was out of touch with team challenges. Over a third reported that productivity had actually decreased over the past year, even as their companies invested more in AI tooling.
The baseline moved. The expectations rose. And for many engineers, no one acknowledged that the job they signed up for had fundamentally changed.
The Identity Crisis Nobody Talks About
Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it. The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended. That is what drew most of us to this profession. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
Now they are being told to stop.
Not explicitly, of course. Nobody walks into a standup and says “stop writing code.” But the message is there, subtle and persistent. Use AI to write it faster. Let the agent handle the implementation. Focus on higher-level tasks. Your value is not in the code you write anymore, it is in how well you direct the systems that write it for you.
For early adopters, this feels exciting. It feels like evolution. For a significant portion of working engineers, it feels like being told that the thing they spent years mastering, the skill that defines their professional identity, is suddenly less important.
One engineer captured this shift perfectly in a widely shared essay, describing how AI transformed the engineering role from builder to reviewer. Every day felt like being a judge on an assembly line that never stops. You just keep stamping those pull requests. The production volume went up. The sense of craftsmanship went down.
This is not a minor adjustment. It is a fundamental shift in professional identity. Engineers who built their careers around deep technical skill are being asked to redefine what they do and who they are, essentially overnight, without any transition period, training, or acknowledgment that something significant was lost in the process.
Having led engineering teams for over two decades, I have seen technology shifts before. New frameworks, new languages, new methodologies. Engineers adapt. They always have. But this is different because it is not asking engineers to learn a new way of doing what they do. It is asking them to stop doing the thing that made them engineers in the first place and become something else entirely.
That is not an upgrade. That is a career identity crisis. And pretending it is not happening does not make it go away.
The Expanding Role: When Everything Becomes Your Problem
While engineers are being asked to write less code, they are simultaneously being asked to do more of everything else.
More product thinking. More architectural decision-making. More code review. More context switching. More planning. More testing oversight. More deployment awareness. More risk assessment.
The scope of what it means to be a “software engineer” expanded dramatically in the last two years, and it happened without a pause to catch up.
This is partly a direct consequence of AI acceleration. When code gets produced faster, the bottleneck shifts. It moves from implementation to everything surrounding implementation: requirements clarity, architecture decisions, integration testing, deployment strategy, monitoring, and maintenance. These were always part of the engineering lifecycle, but they were distributed across roles. Product managers handled requirements. QA handled testing. DevOps handled deployment. Senior architects handled system design.
Now, with AI collapsing the implementation phase, organizations are quietly redistributing those responsibilities to the engineers themselves. The Harvard Business Review study documented this exact pattern. Product managers began writing code. Engineers took on product work. Researchers started doing engineering tasks. Roles that once had clear boundaries blurred as workers used AI to handle jobs that previously sat outside their remit.
The industry is openly talking about this as a positive development. Engineers should be “T-shaped” or “full-stack” in a broader sense. Nearly 45 percent of engineering roles now expect proficiency across multiple domains. AI tools augment generalists more effectively, making it easier for one person to handle multiple components of a system.
On paper, this sounds empowering. In practice, it means that a mid-level backend engineer is now expected to understand product strategy, review AI-generated frontend code they did not write, think about deployment infrastructure, consider security implications of code they cannot fully trace, and maintain a big-picture architectural awareness that used to be someone else’s job.
That is not empowerment. That is scope creep without a corresponding increase in compensation, authority, or time.
From my experience building and scaling teams in fintech and high-traffic platforms, I can tell you that role expansion without clear boundaries always leads to the same outcome: people try to do everything, nothing gets done with the depth it requires, and burnout follows. The engineers who survive are the ones who learn to say no, to prioritize ruthlessly, and to push back when the scope of their role quietly doubles without anyone acknowledging it.
The Supervision Paradox
There is an irony at the center of the AI-assisted engineering workflow that nobody wants to talk about: reviewing AI-generated code is often harder than writing the code yourself.
When you write code, you carry the context of every decision in your head. You know why you chose this data structure, why you handled this edge case, why you structured the module this way. The code is an expression of your thinking, and reviewing it later is straightforward because the reasoning is already stored in your memory.
When AI writes code, you inherit the output without the reasoning. You see the code, but you do not see the decisions. You do not know what tradeoffs were made, what assumptions were baked in, what edge cases were considered or ignored. You are reviewing someone else’s work, except that someone is not a colleague you can ask questions. It is a statistical model that produces plausible-looking code without any understanding of your system’s specific constraints.
A survey by Harness found that 67 percent of developers reported spending more time debugging AI-generated code, and 68 percent spent more time reviewing it than they did with human-written code. This is not a failure of the tools. It is a structural property of the workflow. Code review without shared context is inherently more demanding than reviewing code you participated in creating.
Yet the expectation from management is that AI should be making everything faster. So engineers find themselves in a bind: they are producing more code than ever, but the quality assurance burden has increased, the context-per-line-of-code has decreased, and the cognitive load of maintaining a system they only partially built is growing with every sprint.
This is the supervision paradox. The faster AI generates code, the more human attention is required to ensure that code actually works in the context of a real system with real users and real business constraints. The production bottleneck did not disappear. It moved from writing to understanding, and understanding is harder to speed up.
The Acceleration Trap
What makes all of this especially difficult is the self-reinforcing nature of the cycle.
AI makes certain tasks faster. Faster tasks create the perception of more available capacity. More perceived capacity leads to more work being assigned. More work leads to more AI reliance. More AI reliance leads to more code that needs review, more context that needs to be maintained, more systems that need to be understood, and more cognitive load on engineers who are already stretched thin.
The Harvard Business Review researchers described this as “workload creep.” Workers did not consciously decide to work harder. The expansion happened naturally, almost invisibly. Each individual step felt reasonable. In aggregate, it produced an unsustainable pace.
Before AI, there was a natural ceiling on how much you could produce in a day. That ceiling was set by thinking speed, typing speed, and the time it takes to look things up. It was frustrating sometimes, but it was also a governor. A natural speed limit that prevented you from outrunning your own ability to maintain quality.
AI removed the governor. Now the only limit is your cognitive endurance. And most people do not know their cognitive limits until they have already blown past them.
This is where many engineers find themselves right now. Shipping more code than any quarter in their career. Feeling more drained than any quarter in their career. The two facts are not unrelated.
The trap is that it looks like productivity from the outside. Metrics go up. Velocity charts look great. More features shipped. More pull requests merged. But underneath the numbers, quality is quietly eroding, technical debt is accumulating faster than it can be addressed, and the people doing the work are running on fumes.
What Junior Engineers Are Facing
If the picture is difficult for experienced engineers, it is even harder for those starting their careers.
Junior engineers have traditionally learned by doing the simpler, more task-oriented work. Fixing small bugs. Writing straightforward features. Implementing well-defined tickets. This hands-on work built the foundational understanding that eventually allowed them to take on more complex challenges.
AI is rapidly consuming that training ground. If an agent can handle the routine API hookup, the boilerplate module, the straightforward CRUD endpoint, what is left for a junior engineer to learn from? The expectation is shifting toward needing to contribute at a higher level almost from day one, without the gradual ramp-up that previous generations of engineers relied on.
Entry-level hiring at the 15 largest tech firms fell 25 percent from 2023 to 2024. The HackerRank 2025 Developer Skills Report confirmed that expectations are rising faster than productivity gains, and that early-career hiring remains sluggish compared to senior-level roles. Companies are prioritizing experienced talent, but the pipeline that produces experienced talent is being quietly dismantled.
This is a problem that extends beyond individual career concerns. If junior engineers do not get the opportunity to build foundational skills through hands-on work, the industry will eventually face a shortage of senior engineers who truly understand the systems they oversee. You cannot supervise what you never learned to build.
As I have written before, code is for humans to read. If the next generation of engineers never develops the fluency to read, understand, and reason about code at a deep level, no amount of AI tooling will compensate for that gap.
What Good Leadership Looks Like Right Now
If you lead engineering teams, the most important thing you can do right now is acknowledge that this transition is genuinely difficult. Not theoretically. Not abstractly. For the actual people on your team.
The career they signed up for changed fast. The skills they were hired for are being repositioned. The expectations they are working under shifted without a clear announcement. Acknowledging this reality is not a sign of weakness. It is a prerequisite for maintaining a team that trusts you.
Start with empathy, but do not stop there.
Give your team real training. Not a lunch-and-learn about prompt engineering. Real investment in the skills that the new engineering landscape actually requires: system design, architectural thinking, product reasoning, security awareness, and the ability to critically evaluate code they did not write. These are not trivial skills. They take time to develop, and your team needs structured support to build them.
Give them space to experiment without the pressure of immediate productivity gains. The engineers who will thrive in this environment are the ones who have room to figure out how AI fits into their workflow without being penalized for the learning curve. Every experienced technologist I know who has successfully integrated AI tools went through an adjustment period where they were less productive before they became more productive. That adjustment period is normal, and it needs to be protected.
Set explicit boundaries around role scope. If you are asking engineers to take on product thinking, planning, and risk assessment in addition to their technical work, name it. Define it. Compensate for it. Do not let it happen silently and then wonder why your team is burned out.
Rethink your metrics. If your engineering success metrics are still centered on velocity, tickets closed, and lines of code, you are measuring the wrong things in an AI-assisted world. System stability, code quality, decision quality, customer outcomes, and team health are better indicators of whether your engineering organization is actually producing value or just producing volume.
Protect the junior pipeline. If you have stopped hiring junior engineers because AI can handle entry-level tasks, you are solving a short-term efficiency problem by creating a long-term talent crisis. The senior engineers you rely on today were junior engineers who learned by doing the work that AI is now consuming. That path still matters.
And finally, keep challenging your team. I have never met a good engineer who did not love a good challenge. The engineers on your team are not fragile. They are capable, intelligent people who signed up for hard problems. They can handle this transition. Just make sure they are set up to meet it.
What Engineers Can Do for Themselves
If you are an engineer navigating this shift, here is what I would tell you based on two decades of watching technology cycles reshape this profession.
First, do not abandon your fundamentals. The pressure to become an “AI-first” engineer is real, but the engineers who will be most valuable in five years are the ones who deeply understand the systems they work on. AI is a tool. Understanding architecture, debugging complex systems, reasoning about performance and security: these skills are not becoming less important. They are becoming more important because someone needs to be the adult in the room when AI-generated code breaks in production at 2 AM.
Second, learn to set boundaries with the acceleration trap. Just because you can produce more does not mean you should. Sustainable pace matters. The engineers who burn out trying to match the theoretical maximum output AI makes possible are not the ones who build lasting careers. The ones who learn to work with AI deliberately, choosing when to use it and when to think independently, are the ones who will still be thriving in this profession a decade from now.
Third, embrace the parts of the expanded role that genuinely interest you. If the engineering role now includes more product thinking, more architectural decision-making, more cross-functional communication, treat that as an opportunity rather than an imposition. These are skills that senior engineers and technical leaders need. You are being given access to a broader set of capabilities earlier in your career than any previous generation of engineers. That is not a burden. It is a head start.
Fourth, talk about what you are experiencing. The isolation of feeling like you are the only one struggling with this transition is one of the most damaging aspects of the current moment. You are not the only one. The data confirms it. Two-thirds of engineers report burnout. The expectation gap between leadership and engineering teams is well documented. Talking openly about these challenges, with your team, with your manager, with your broader network, is not complaining. It is professional honesty.
And fifth, remember that this profession has survived every prediction of its demise. COBOL was supposed to eliminate programmers. Expert systems were supposed to replace them. Fourth-generation languages, CASE tools, visual programming, no-code platforms, outsourcing. Every decade brings a new technology that promises to make software engineers obsolete, and every decade the demand for skilled engineers grows. AI will not be different. The tools change. The fundamentals endure.
The Paradox We Need to Name
AI made writing code easier and made being an engineer harder. Both of these things are true at the same time, and pretending that only the first one matters is how organizations lose their best people.
The engineers who are struggling right now are not struggling because they are bad at their jobs. They are struggling because their jobs changed underneath them while the industry celebrated the part that got easier and ignored the parts that got harder.
Expectations rose without announcement. Roles expanded without boundaries. Output demands increased without corresponding increases in support, training, or acknowledgment. And the engineers who raised concerns were told, implicitly or explicitly, that they just needed to adapt faster.
That is not how you build a sustainable engineering culture. That is how you build a burnout machine.
The industry needs to name this paradox honestly. AI is an incredible tool. It is also placing enormous new demands on the people using it. Both things can be true. Both things need to be addressed.
The organizations that get this right, that invest in their people alongside their tools, that acknowledge the human cost of rapid technological change while still pushing forward, those are the organizations that will attract and retain the best engineering talent in the years ahead.
The ones that do not will discover something that every technology cycle eventually teaches: tools do not build products. People do. And people have limits that no amount of AI can automate away.
If this resonated with you, I would love to hear your perspective. What has changed most about your engineering role in the last year? Drop me a message or connect with me on LinkedIn. I write regularly about the intersection of AI, software engineering, and leadership at ivanturkovic.com. Follow along if you want honest, experience-driven perspectives on how technology is actually changing this profession.