A software engineer writing on the community site dev.to has published a personal essay arguing that current predictions of the profession's decline under AI are the latest in a long series of premature obituaries, and that adaptability, not stability, has always defined the job. The piece is an opinion essay rather than a report of a product release, hiring study or policy change, so its claims should be read as one practitioner's account and interpretation, not as verified industry data.
The author's central historical argument rests on two cited episodes. He recalls a 2011 college event at the Dublin Convention Centre where a panel speaker told students they had little hope of good jobs because the market would be flooded with cheaper engineers from China and India, and that a computer science degree was comparable to training as a kitchen porter. He also cites an interview with Joyce Currie Little describing reactions to early high-level languages, in which people said of FORTRAN's arrival that programmers would no longer be needed. He further notes that in 1961 some worried abstraction itself would damage the profession.
These anecdotes are offered as evidence of a pattern rather than as systematic research. The essay presents no survey, sample or measurement of how often such predictions have been made or how they resolved, and the linked references are not shown to corroborate the broader claim. Readers should treat the historical framing as the author's synthesis of his own experience and reading, not as a documented trend analysis.
The author's own career path supplies the personal context. He writes that he worked roughly 15 years in hospitality, construction, farming, sales, landscaping and manual labour before landing his first software job, and that the change gave him the first work he genuinely loved and a sense of a good future. That background shapes his comparison between repetitive manual work and software's constant problem-solving, which he describes as the reason he finds the field rewarding despite its instability.
On the recent economics, the essay is candid about the correction that followed the zero-interest-rate period. The author calls the ZIRP-era demand for engineers obviously unsustainable, says he worried about what the correction would bring, and concedes it has been in some ways worse than he imagined. He still argues that being a software engineer today compares favourably with the other jobs he has held. This is a subjective comparison, not a claim about current hiring volumes or compensation.
The essay's most concrete external reference is a recent post by Gergely Orosz, which the author summarises as describing software engineering as substantially a matter of willingness to keep learning and to handle constant change. The author does not quote Orosz at length or report any data from that post, so it functions here as a supporting viewpoint rather than independent evidence for the essay's conclusions.
The mechanism the author proposes is attitudinal rather than technical. He argues that because the work is hard and always shifting, engineers should focus on getting better at what they do, turning change to their advantage, building things people like to use, and developing resilience against what he calls the constant attacks of the current period. He frames good engineers as problem solvers trying to make things better, and says the profession needs more such people, not fewer.
He also acknowledges that change can produce bad outcomes, naming enshittification as an example, and argues that this is a reason to want more decent people in the profession rather than a reason to abandon it. The essay does not define the term, quantify the phenomenon or cite evidence for it, so this remains an assertion of values rather than an analysed trend.
For freelancers, designers and developers, the practical takeaway is less a market forecast than a framing for how to respond to AI-driven change. The author's position implies that investing in continuous learning and in the quality of what you build is a more useful response than trying to predict whether the profession survives. That is editorial interpretation drawn from his argument, not a tested recommendation, and it offers no guidance on pricing, tooling choices or which skills to prioritise.
The essay's limitations are significant for anyone weighing it as career evidence. It is a single practitioner's reflection with no data on hiring, salaries, AI adoption or job displacement; its historical examples are recalled or quoted secondhand; and its optimism is explicitly a feeling rather than a forecast. The author states plainly that he cannot help feeling optimistic, which is an honest framing but not a basis for planning.
What remains genuinely unknown, by the essay's own account, is whether the current AI transition will follow the pattern of past alarms or break from it. The author asserts that doomers predicting software hopefuls are training for a dead-end job are wrong, but he presents no mechanism or evidence beyond historical analogy and personal conviction. The piece is therefore best read as a counterpoint to doom narratives rather than a rebuttal of them.
Its value to working developers lies in the reminder that the profession has repeatedly absorbed disruptive tooling changes and that resilience and continued learning have been durable responses. As a community opinion piece, it adds a perspective to the AI-and-jobs debate; it does not settle it, and readers should pair it with concrete labour-market evidence before drawing conclusions about their own prospects.