Once, a college friend joked that coding was just solving digital puzzles all day—think endless coffee, late-night bugs, and the quiet thrill of launching something real. That’s why it’s weirdly jarring now to watch software engineering at Amazon pivot from caffeine-fueled marathons to something you could almost (almost!) automate with a click. The A.I. wave at Amazon isn’t so much cutting jobs on the spot as it’s pushing coders to run ever faster on a moving treadmill. But what happens to the spirit and substance of engineering when a machine, not a person, sets the pace?
From Artisans to Assembly Lines: Coding’s Unexpected Déjà Vu
There’s a strange sense of déjà vu sweeping through the world of coding. For many Amazon engineers, the arrival of Artificial Intelligence (AI) in coding feels eerily similar to the industrial revolution’s impact on factory floors. Back then, machines didn’t simply erase jobs—they transformed them, making work faster, more routine, and, some would say, less fulfilling. Today, AI in coding is doing much the same, but this time, the assembly line is digital.
Labor historian Jason Resnikoff calls this process work degradation: the shift from skilled, creative labor to segmented, pressured, and repetitive tasks. He’s documented how workers in industries like auto-making and meatpacking lamented the “speed-up” and loss of autonomy as technology advanced. Now, software engineers—once seen as modern artisans—are feeling the same squeeze.
At Amazon, this shift is especially pronounced. Coding teams have shrunk, but productivity targets haven’t budged. Instead, AI productivity tools like Copilot are picking up the slack. According to a Microsoft and university study, developers using Copilot saw their coding output jump by more than 25 percent. Amazon has leaned hard into generative AI, with CEO Andy Jassy touting “productivity and cost avoidance” in his latest shareholder letter. The message is clear: faster is better, and AI is the key.
But this acceleration comes at a cost. One Amazon engineer shared that his team was cut in half, yet output expectations stayed the same—thanks to AI. The pressure is real, and it’s not just about writing more code. It’s about hitting numbers, meeting deadlines, and keeping pace with a relentless, AI-driven workflow. As Lawrence Katz, a Harvard labor economist, puts it, it’s a “speed-up for knowledge workers.” He notes,
AI tools can make experienced programmers more productive, but they raise the bar and stress for newcomers.
This isn’t just an Amazon story. Shopify’s CEO recently declared that “AI usage is now a baseline expectation,” with performance reviews now including questions about AI integration. Google, too, is all-in: over 30 percent of code at Google is now AI-suggested and accepted by developers, and the company is running hackathons to push even more AI productivity tools into daily workflows.
For many engineers, the job has shifted from creative problem-solving to managing a faster, more fragmented process. Coding at Amazon is increasingly shaped by generative AI, which accelerates deadlines and breaks tasks into smaller, more routine chunks. Where once there was time to reflect, experiment, or explore alternative solutions, now there’s a constant push to deliver—quickly. Research shows that up to 80% of programming jobs will remain human-centric, but the nature of those jobs is changing fast. Work intensification with AI means coding is faster, more fragmented, and expectations are higher than ever.
The parallels to Amazon’s warehouse transformation are hard to ignore. Just as robots in fulfillment centers have replaced miles of walking with hyper-efficient item picking, AI in coding is ramping up the pace for engineers. Amazon claims robots haven’t replaced warehouse workers, but quotas have soared, and jobs have become more repetitive. The same dynamic is playing out in coding: fewer engineers, more output, less downtime.
Some Amazon engineers say that while using AI remains technically optional, it’s increasingly necessary to meet aggressive targets. Performance reviews now factor in AI usage, and the time to deliver features has dropped from weeks to days. The result? Less collaboration, more automation, and a growing sense that the job is about keeping up with the machine, not outsmarting it.
Not everyone is convinced this is progress. Simon Willison, a programmer and AI enthusiast, observes, “It’s more fun to write code than to read code.” With AI, the job often shifts from creation to review, leaving some feeling like bystanders in their own careers. Junior engineers worry that automating foundational tasks—like writing tests or drafting technical memos—could limit their chances for growth and promotion.
Still, there are bright spots. AI is lowering barriers and democratizing software creation, making it easier for entrepreneurs and prototypers to build new apps. But as workplace pressures mount, Amazon engineers are voicing anxieties about job automation, career paths, and even the environmental impact of AI. The echoes of the past are unmistakable: just as factory workers once fought for control over their pace and process, today’s coders are grappling with a new kind of assembly line—one powered by Artificial Intelligence.
When Creative Engineering Meets Algorithmic Pace: The Human Trade-Offs
The world of software development is changing fast, and nowhere is this more obvious than at Amazon. As AI tools and generative AI become embedded in daily workflows, the job of a software engineer is being redefined. The dream of machines writing code while humans focus on the “big picture” is here—but the reality is more complicated, especially when it comes to job satisfaction and professional growth.
For many engineers, the shift is subtle but profound. Tasks that once required deep thought and creativity are now handled by algorithms. As a result, more time is spent reviewing machine-generated output and less on hands-on coding. One Amazon engineer, who used to find joy in building new features, now spends most of his day double-checking AI suggestions and hunting for bugs. The creative slack—the breathing room to experiment and explore—has shrunk. As Simon Willison, a well-known programmer and AI enthusiast, puts it:
“It’s more fun to write code than to read code.”
This sentiment is echoed across the industry. Generative AI like Copilot can churn out code at lightning speed, but the human role often shifts to quality control. The thrill of creation is replaced by the grind of verification. Research shows that while task automation improves efficiency, it can also reduce job satisfaction and limit growth opportunities, especially for junior engineers.
- Less creative slack: Code review and bug-hunting now dominate, making the work less satisfying for many engineers.
- Professional growth pinch: Automating formative tasks means fewer opportunities for junior engineers to learn or impress.
- Performance reviews now ask: “How much A.I. did you use?”
At Amazon, the pressure to adopt AI tools is mounting. According to engineers, output expectations have soared, and deadlines are tighter than ever. One team saw its size cut in half, but the workload remained unchanged—AI was expected to fill the gap. This isn’t just a quirk of Amazon’s culture. Shopify’s CEO declared in April 2025 that “AI usage is now a baseline expectation,” and performance reviews have been updated to include questions about AI adoption. Google, too, is incentivizing AI-driven productivity, with over 30% of its code now suggested by AI.
The result? A new kind of workplace pressure. Performance metrics at Amazon and Shopify now formally track AI usage. For some, this is a nudge to embrace new technology. For others, it’s a source of anxiety, especially when the tools aren’t perfect. As one engineer described, the tools are “scarily good,” but still require extensive double-checking. The pace has quickened: features that once took weeks are now expected in days, thanks to code generation and automation.
This acceleration isn’t without cost. Junior engineers, in particular, are feeling the pinch. Tasks like software feature testing—once a rite of passage and a key to career advancement—are now automated. That means fewer chances to build expertise or impress managers. Harvard labor economist Lawrence Katz likens this to the shift from artisanal to factory work, calling it a “speed-up for knowledge workers.” Studies indicate that while seasoned programmers may benefit from automation, newcomers risk missing out on formative experiences.
There’s also a growing sense of being a bystander in one’s own job. As Willison notes, “It’s more fun to write code than to read code.” The joy of creation is replaced by the responsibility of oversight. For some, this is a welcome relief from repetitive tasks. For others, it’s a loss of autonomy and fulfillment.
Yet, there are upsides. Amazon CEO Andy Jassy reported that AI saved the company “the equivalent of 4,500 developer-years” by updating old software. That’s a staggering boost in efficiency. And as AI lowers the barrier to entry, it’s never been easier for entrepreneurs to build new apps—what Willison calls “a gift from heaven” for prototypers.
Still, the rapid transformation is stirring anxiety. Employee advocacy groups like Amazon Employees for Climate Justice are seeing more conversations about AI-related pressures and concerns over the future of AI and jobs. The question is no longer whether AI will replace engineers, but how it will reshape the very nature of software development—and what’s gained or lost along the way.
Beyond the Keyboard: New Pressures, New Possibilities—and a Dash of Dissent
The AI revolution at Amazon has pushed the boundaries of what it means to be a white-collar worker in tech. For software engineers, the arrival of generative AI tools—like Copilot and Amazon’s own in-house solutions—hasn’t triggered the mass layoffs many once feared. Instead, it’s quietly rewritten the rules of the game, reshaping job quality, workplace pressures, and even the very meaning of engineering expertise.
Engineers now find themselves racing against tighter deadlines, their workdays filled with a new kind of anxiety. It’s not just about keeping up with the pace; it’s about wondering what their careers will look like in a world where AI can write, test, and even review code. As research shows, while AI boosts productivity and opens doors for some, it also stirs real unease about job satisfaction and autonomy. The work has become faster and, for many, more repetitive—echoing the “speed-up” and “work intensification” that labor historians like Jason Resnikoff have documented in earlier industrial revolutions.
This acceleration isn’t unique to Amazon. Across the tech industry, companies like Google and Shopify are making AI usage a baseline expectation, weaving it into performance reviews and hackathons. At Amazon, CEO Andy Jassy has made it clear: generative AI is here to drive “productivity and cost avoidance,” and coding norms are changing. Some engineers have seen their teams cut in half, yet the output bar remains high—thanks, in part, to AI’s relentless efficiency.
But not everyone is cheering. The rise of AI-generated code has sparked a wave of employee activism inside Amazon. The Amazon Employees for Climate Justice group, once focused solely on environmental issues, now finds itself fielding concerns about AI’s impact on workplace stress and long-term career prospects. According to group spokesperson Eliza Pan, employees are increasingly worried about the “quality of the work” and the uncertainty of their future roles. The group maintains contact with several hundred employees, highlighting just how widespread these anxieties have become.
There’s a sense of déjà vu among Amazon engineers, many of whom have watched similar transformations unfold in the company’s warehouses. There, robots have made work faster but also more repetitive and physically demanding. Now, in the digital realm, AI is speeding up the pace, reducing the time for collaboration and reflection, and shifting the focus from creative problem-solving to rapid output and code review. As one engineer put it, “It’s more fun to write code than to read code”—but AI tools increasingly nudge developers toward the latter.
For junior engineers, the stakes feel especially high. Tasks like writing technical memos or testing software—once seen as stepping stones to career advancement—are now automated, raising fears that the path to promotion is growing murkier. Amazon insists that AI is meant to augment, not replace, human skills, and that promotion criteria remain clear. Yet, as Harper Reed, former CTO for Barack Obama’s re-election campaign, observes, “The deep understanding of code is less essential in an era where machines do the heavy lifting.”
Still, it’s not all gloom. One of the most profound AI impacts is the democratization of software creation. Generative AI is lowering barriers, making it possible for anyone—not just seasoned engineers—to prototype and build apps. As Simon Willison notes, this is “a gift from heaven” for entrepreneurs and tinkerers, who can now bring their ideas to life with unprecedented speed and affordability. Studies indicate that AI democratization is making software development more accessible than ever before.
Yet, as the pace quickens and expectations rise, engineers are left to grapple with a new reality: more output, less autonomy, and a workplace where the rules are still being written. The story unfolding at Amazon is a microcosm of the broader AI impact on white-collar work—a world where opportunity and anxiety walk hand in hand, and where the future of job quality, satisfaction, and professional growth remains very much in flux.
