In the rapidly evolving world of software engineering, artificial intelligence is no longer an intriguing novelty at the periphery of the industry. It is now an omnipresent force, quietly and profoundly reshaping the daily realities of developers everywhere. At the helm of this technological transformation stands Thomas Dohmke, CEO of GitHub, the largest and most influential platform for code collaboration on the planet. His recent remarks about the future of software engineering reveal not just a new direction for the craft, but also a challenge to the very identity of those who call themselves engineers in the age of AI.
To understand the magnitude of the change, one need only look at the meteoric rise of AI coding tools such as GitHub Copilot. These platforms, powered by large language models, are capable of generating, reviewing, and even refactoring code at a speed and scale previously unimaginable. For some, this innovation inspires excitement; for others, a ripple of anxiety. Will the machines replace the human touch in programming? Will years of hard-won expertise be rendered obsolete by a machine that never tires, never forgets, and never hesitates?
Dohmke does not mince words about the seismic shift underway. In a recent interview, he laid out what he believes to be the “key for winning” for software engineers in this new AI-augmented landscape. The message is clear: adapt or risk being left behind. But adaptation, according to Dohmke, is not about learning to out-code the machine or memorizing ever more arcane syntaxes. The true advantage, he argues, will belong to those who can wield AI as a partner rather than view it as a rival.
“The future belongs to the engineers who can ask the best questions,” Dohmke asserts. In his view, the value of a software engineer is shifting from the ability to write lines of code from scratch to the ability to frame problems, design solutions, and orchestrate the work of both humans and machines. The skill set that once defined the profession—meticulous attention to syntax, encyclopedic knowledge of programming languages, the solitary hours spent debugging—will soon be augmented, if not superseded, by a new suite of abilities: critical thinking, creativity, and above all, the discernment to know what to ask and how to ask it.
This reimagining of the engineer’s role may sound radical, but it is hardly unprecedented. Throughout the history of technology, tools have always changed the nature of work. The introduction of the compiler liberated programmers from the drudgery of assembly language. High-level languages made it possible to think in terms of complex systems rather than individual instructions. The rise of open-source platforms, with GitHub at the forefront, transformed coding from a solitary pursuit into a global collaborative effort. Each leap in technological sophistication has forced engineers to evolve, emphasizing new skills while rendering others less essential.
Yet the arrival of AI represents something different in both degree and kind. Unlike previous tools, AI does not simply automate repetitive tasks or enable new efficiencies. It encroaches on the creative and cognitive territory that once seemed the exclusive preserve of the human mind. For the first time, the act of writing code itself—a task that defined generations of engineers—is being shared, and sometimes led, by machines.
Dohmke’s perspective is not just philosophical; it is grounded in the realities he observes at GitHub, a platform with more than 100 million developers and a front-row seat to the changing dynamics of software creation. He points to the way AI tools like Copilot are already being adopted by programmers around the world—not as replacements for human effort, but as powerful amplifiers of it. In organizations that have embraced these tools, productivity is soaring, code quality is improving, and engineers are freed from the tedium of boilerplate tasks to focus on higher-level design and problem-solving.
There is, of course, an undercurrent of unease beneath this narrative of progress. For every engineer who eagerly integrates Copilot into their workflow, there is another who wonders whether their core skill set is being devalued. The question is not just economic—Will there be enough jobs for human programmers?—but existential: What does it mean to be a software engineer when machines can code?
Dohmke’s answer is both reassuring and demanding. The future, he argues, will always have a place for humans who can see the bigger picture, empathize with users, and inject ethical considerations into the digital tools that shape our world. AI, for all its impressive capabilities, remains a tool—one that needs guidance, oversight, and, crucially, vision. The best engineers, therefore, will be those who can harness AI’s power to expand their own capacity for innovation.
The implications extend far beyond individual careers. As AI tools become more sophisticated, the barrier to entry for software development will inevitably lower. This democratization of coding has the potential to unlock creativity in millions who previously lacked the training or confidence to participate. It also promises to accelerate the pace of technological change, with all the opportunities—and risks—that entails.
For educational institutions, the challenge is clear. Curricula that focus solely on traditional coding skills may find themselves increasingly out of step with industry needs. Instead, there is growing recognition of the importance of teaching students not just how to code, but how to think, question, and collaborate—both with other humans and with intelligent machines.
For tech companies, the stakes are even higher. Those that succeed in fostering a culture of curiosity and adaptability will thrive in the AI era. Those that cling to old paradigms risk being overtaken by nimbler competitors who can leverage AI not just as a tool, but as a catalyst for reinvention.
In the end, Dohmke’s advice for software engineers is a call to arms for anyone navigating the uncertainties of an AI-driven future. The winners will not be the fastest typists or the most prolific coders, but those who can imagine new possibilities, articulate compelling questions, and orchestrate the talents of both people and machines. The age of AI has arrived. To flourish in it, engineers—and the institutions that train and employ them—must embrace not just new technologies, but new ways of thinking. The coding revolution, it seems, is only just beginning.
