Ana Megawati
Software engineering, long hailed as one of the most lucrative and stable professions in the United States, has been dramatically disrupted by the rapid ascent of artificial intelligence, leading to a wave of layoffs, underemployment, and profound uncertainty across the industry. In 2022, the profession stood as a beacon of economic opportunity, with its practitioners earning salaries often exceeding $200,000 annually, double the national median. Yet, barely two years later, the landscape has fundamentally shifted, challenging the very definition of what it means to be a software engineer.
The transformation is starkly embodied by individuals like Matt, a software engineer who, to protect his employment, requested anonymity. His daily four-hour train commute to Pawling, New York, once a mundane journey, has become an unexpected sanctuary. It is here that Matt dedicates himself to a personal passion project: a browser-based video game, every line of code meticulously written by his own hand. "I am actively trying to keep my axe sharp," Matt explained, reflecting a growing sentiment among his peers. Over the past six months, Matt’s professional duties have increasingly veered away from traditional coding, intricate problem-solving, and architectural design towards the less stimulating task of reviewing code generated by artificial intelligence. Convinced that this shift will erode his core competencies, he consciously endeavors to minimize his reliance on AI in his personal work, stating, "I am trying not to leverage AI where I can." His career, once a perceived bedrock of stability and upward mobility, now feels precarious, shadowed by a layoff last summer and a recent directive from his current boss to embrace AI more fully.
The Golden Age of Code: A Retrospective
For a generation of workers, software engineering represented not just a career, but a promise of security and rapid advancement. The allure was undeniable. A decade ago, the call to "learn to code" echoed across the nation, championed by political leaders and tech titans alike. In December 2013, then-President Barack Obama launched the "Computer Science for All" initiative, a $4 billion endeavor aimed at integrating computer science education into American schools, declaring it the "new basic" skill essential for economic opportunity. This governmental push was amplified by the private sector, with industry luminaries such as Mark Zuckerberg and Bill Gates featuring in coding tutorial videos for educational non-profits, further cementing coding’s status as a gateway to success.
The burgeoning demand for skilled programmers fueled an unprecedented "tech talent war" throughout the 2010s. Companies fiercely competed for top-tier talent, offering lavish salaries, signing bonuses often in the hundreds of thousands of dollars, and unparalleled perks. This era saw the proliferation of coding bootcamps, which exploded in popularity, graduating over 2,000 students in 2013 alone, a figure that surged by over 1,000% by 2020, according to Course Report data. By 2022, the US Bureau of Labor Statistics reported 1.5 million software engineers in the country, with nearly 50 million developers working worldwide, according to market research firm SlashData. The profession seemed invincible, a clear path to prosperity for those willing to master its intricacies.
The AI Inflection Point: Disruption and Downsizing
The seemingly unassailable trajectory of software engineering began to falter with the public release of OpenAI’s ChatGPT in late 2022. This pivotal moment ushered in a new era, demonstrating AI’s unprecedented capability to generate human-quality text and, critically, code. The impact was swift and severe. Since ChatGPT’s debut, over 600,000 US tech workers have lost their jobs, as tracked by Layoff.fyi, a significant portion of which includes software engineers. Major tech companies, once symbols of relentless growth and hiring, including Google, Amazon, Microsoft, and Meta, initiated sweeping layoffs, citing various factors including economic headwinds and, increasingly, the efficiencies offered by AI.
The consequences extend beyond job losses. The unemployment rate for computer science graduates saw an increase, reaching 7% in 2024, up from 6.1% the previous year, according to data from the New York Fed. Even more concerning is the underemployment rate, which surpassed 19%, indicating that a substantial number of recent graduates are working in roles that do not fully utilize their skills or educational background. Compounding this, US tech job postings on Indeed experienced a steep decline, plummeting 36% between 2020 and 2025, signaling a contraction in hiring demand across the sector.
The shift in industry dynamics is profound. Google, a pioneer in software development, now reports that 75% of its internal code is written by AI, a staggering figure that underscores the transformative power of these new tools. This rapid adoption by industry giants sends a clear message: AI is not merely an assistant but a fundamental force reshaping the core mechanics of software development.
Redefining the Craft: From Coder to AI Conductor
The consensus among experts is clear: the traditional value of writing code is diminishing, while the ability to evaluate and manage AI-generated code is rapidly gaining prominence. Bouke Klein Teeselink, an assistant professor of economics at King’s College London, articulated this evolving reality starkly, stating, "It’s hard to say what exactly the profession will look like in two years, but it’s clear that the skill of writing code is over. AI is hugely augmenting what it means to be a software engineer, and the reasonable yardstick for success depends on how well engineers use the tech."
Ethan Mollick, an associate professor of management at the University of Pennsylvania’s Wharton School and author of the upcoming book Co-Existence, echoed this sentiment, emphasizing that the role of a software engineer is not disappearing but rather changing. "Now it’s not about who can write the most code," Mollick noted. Instead, the focus has shifted towards higher-order skills: defining problems, designing robust systems, and effectively directing AI tools to generate solutions. "It shifts the skills around, so suddenly that’s where the value is," he added.
This redefinition presents a significant challenge for experienced engineers like Matt, who once considered himself a "leading voice" in solution execution. Now, he observes, "the line between what I am deciding and what AI provides has certainly blurred." The creative, problem-solving aspects that once drew many to the profession are increasingly being handled by machines, leaving human engineers with tasks that often feel less fulfilling, such as code review and validation.
Adapting to the New Reality: Strategies for Survival
Software engineers are grappling with an existential choice: adapt to an increasingly unpredictable industry or seek alternative paths. Many are doubling down on fundamental computer science principles, recognizing that a deep understanding of underlying logic and architecture is crucial for evaluating AI-generated code. Others are actively chasing new skills, such as prompt engineering, AI model selection, and machine learning operations, to stay relevant.
George Dover, a software engineer with six years of experience in Portland, Oregon, exemplifies this adaptive spirit. After being laid off from Intuit Mailchimp in late 2024, he temporarily became a substitute kindergarten teacher while navigating a bleak job market. "It’s very difficult to let go of something that was a large part of your personality for a good number of years," Dover reflected, questioning, "What else is there for me?" Yet, he did not surrender. Recognizing the imperative to understand AI, he immersed himself in generating code for websites using AI tools, then rigorously evaluated the output to discern its strengths and limitations. His process involved meticulously checking for errors, redundancies, unusual AI decisions, bugs, and visual glitches. "The quality has to be rigorously tested," he affirmed. "Sometimes that tradeoff is a good one, other times it leads you down rabbit holes that take longer than coding it yourself." His perseverance paid off: after nearly two years, 400 applications, and numerous interviews, Dover secured a new software engineering position, specifically oriented towards AI.
The burgeoning need for meticulous code review and validation is a critical aspect of the new paradigm. Shriram Krishnamurthi, a professor of computer science at Brown University, cautions that while it’s too early for definitive declarations about the profession’s ultimate form, especially given AI’s recent leap in code quality, the increased demand for expert code reviews will likely "weed out some professionals." He noted, "Some software engineers trained well for this, and many did not. Those who did will thrive; those who didn’t are going to have to retool."
Furthermore, the economic equation of AI itself suggests a continued need for human oversight. David Malan, a computer science professor at Harvard University, points out that the immense computational costs associated with building and running large AI models, exemplified by OpenAI’s reported $8 billion expenditure and Anthropic’s estimated $3 billion burn last year, will eventually be passed on to customers. This financial reality makes it unlikely that companies will rely entirely on AI without human engineers to optimize, manage, and validate its output. Malan anticipates a "healthier balance of software engineers being supported by AI" rather than outright replacement.
Psychological Toll and Educational Shifts
Beyond the technical and economic shifts, the AI revolution is exacting a significant psychological toll on software engineers. The emotional impact of job insecurity, the loss of creative satisfaction, and the feeling that years of specialized training might be devalued are widespread. Sam, a Los Angeles resident who also requested anonymity, embodies this distress. A decade ago, burdened by $130,000 in student loan debt from a dropped music degree and a string of unsatisfying odd jobs, he pivoted to software engineering, drawn by its promise of stability. Now, after five years, he feels professionally unstable and fears a layoff could end his career, forcing him to compete with a deluge of displaced talent from tech giants. He laments that AI has usurped "the creative, fun part of the job," reducing his role to the "worst part: reviewing code I didn’t write." The anxiety is so profound that he finds himself contemplating drastic career changes, musing, "What if I opened a food truck? What if I got into forestry?"
This pervasive malaise is reflected in educational trends. The once-booming interest in computer science is waning. Enrollment in computer and information science programs at four-year universities saw an 8.1% drop in undergraduate enrollment and a 14% decline in graduate enrollment during the 2025-2026 school year, according to the National Student Clearinghouse. This downward trend, if sustained, could reshape the pipeline of future tech talent, potentially leading to a shortage of the specialized human skills needed to manage and innovate with AI. Universities and coding bootcamps are now under pressure to adapt their curricula, focusing less on rote coding and more on AI interaction, system design, and ethical considerations.
The Rise of Collective Action
In the face of these profound disruptions, software engineers are increasingly turning to collective action to protect their livelihoods and define their future. Kaitlin Cort, an engineer with eight years of experience, left her corporate role to establish "What We Will," a resource center operating under the umbrella of the Tech Workers Coalition. Her decision was driven by a stark realization: "I’m not a very senior-level engineer. I can see that the pace at which AI was getting better was faster than the pace at which I was getting better. That definitely made me really anxious."
Cort initially pursued software engineering to alleviate student loan debt and witnessed firsthand its transformative power for individuals from diverse backgrounds through her work at coding bootcamps. However, as her role shifted from creation to review, she felt compelled to make a greater impact. Launched in February, "What We Will" provides crucial support to tech workers navigating layoffs, salary negotiations, unemployment benefits, up-skilling opportunities, and union organizing. Its inaugural campaign targeted Amazon workers advocating against rapid AI adoption, while subsequent efforts aided Oracle employees with severance negotiation and fostered support groups. In May, the center collaborated with Meta workers to address concerns about employee surveillance.
The anxieties surrounding AI and mass layoffs are fueling a surge in membership and interest in collective action. Cort reports that whenever "What We Will" conducts mass outreach, over 100 people join within a week, and at least ten workers reach out daily to organize through the Tech Workers Coalition. Lacking a historical roadmap for such an unprecedented industrial shift, Cort has sought guidance from established organizations like the Alphabet Workers Union and the Washington Post Tech Guild. "There’s a lot of need right now," she emphasized. "We just, as an industry, don’t have a guild – we don’t have regulations or standards that are really shared." This collective organizing signals a growing awareness among tech professionals that individual adaptation alone may not suffice against the systemic forces of AI, necessitating broader industry standards and worker protections.
The journey of software engineering, from a niche discipline to a cornerstone of the modern economy and now to a profession in flux, mirrors historical industrial transformations. While the "learn to code" mantra may be fading, the underlying human capacity for innovation, critical thinking, and complex problem-solving remains indispensable. The future of software engineering is not one without engineers, but rather one where engineers, empowered by AI, will focus on higher-level strategic thinking, ethical considerations, and the intricate art of human-machine collaboration. The challenge for the current generation lies in embracing this evolution, mastering the new tools, and collectively shaping an industry that balances technological progress with human well-being.
