The current discussion centers on the idea that “AI will replace software engineers at some point in the future.” This assumption is flawed because technological advancement rarely works in such discrete leaps. Instead, innovation typically unfolds gradually—starting slowly, then accelerating rapidly until it becomes undeniable. Often, people claim that something will happen in the future, even though the process has already begun. By the time the change is fully entrenched, most simply accept it as inevitable—as seen during the dot-com bubble.
Today, AI is already impacting software employment. Opportunities are becoming increasingly competitive; soon, competing with sophisticated large language models (LLMs) trained on programming language documentation will be nearly impossible. We see this trend in the tech industry as companies lay off staff and people like Mark Zuckerberg openly discuss efforts to replace developers with AI. This disruption is not speculative—it is occurring right now.
Technological disruption works by rapidly destabilizing long-held notions of value. We once believed that libraries and physical mail were indispensable. However, while libraries persist as repositories of knowledge, they have become largely unnecessary for simple knowledge access, and physical mail has been supplanted by email for speed and efficiency. Similarly, new technology comes to dominate gradually while older methods become redundant.
So the key question becomes: Just as email transformed communication and rendered physical letters secondary, what impact will highly capable LLMs have on programming and mathematics? Humans may continue to code manually, but AI will simultaneously generate valid code and mathematical solutions on a massive scale. The societal shift will be subtle at first—a slow realization of redundancy that will eventually force us to ask: Why perform tasks manually when automated systems can produce results more efficiently?
This inquiry is critical. If we fail to ask these questions and adapt to rapid AI advancements, we risk being left behind and facing a more disruptive future. It is in everyone’s interest to gradually adjust to the notion that technology can now perform tasks once thought to require years of specialized education and experience.
In a capitalist system, supply and demand dictate that if you produce something of high value that is currently scarce, people are willing to pay a premium. But what happens when those rare skills become abundant? What happens when someone with minimal training can generate the same outcomes as seasoned professionals, thanks to advanced AI? The economic and societal implications are profound, and addressing these questions is essential as we navigate this transformative era.
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