Is AI actually coming for STEM careers?
Seth Price | January 26, 2026Artificial Intelligence (AI) has dominated the news cycles in recent years. Everything from music and art to spoof videos of celebrities and politicians to new chemical compounds have been generated through the clever use of AI. As with all disrupting technology, there are many questions about ethics, legislation, careers and other such topics that are currently in debate. Perhaps one of the most fundamental questions is: “If AI can do all of this, what does that mean for science, technology, engineering and math (STEM)-related careers?” Or, maybe, “how can I position myself to get or keep a STEM career even with AI’s impact?”
The role of automation and its ties to AI
Automation is not an end goal; it is an iterative process. Repetitive tasks get replaced by robots, controlled by a human. Then, the robot is controlled electronically, based on human input. Eventually, AI handles most of the process control decisions, leaving only design and the occasional process excursion for a human.
As a whole, automation has improved human existence exponentially. From the early days of designing machine tools that made interchangeable parts possible to the affordable, self-driving car that will be a reality in the very near future, human lives are generally safer, happier and more productive thanks to automation.
Current debates on agriculture have been focused on nutrient content, not starvation and safety, thanks to food and beverage automation. Mass-produced steel is only affordable due to the scale up driven by automation. One could even argue that the American Civil War was won, and thus ended legal slavery, thanks to the mass-production of equipment in the industrialized north over the agricultural south.
There are, however, growing pains. Poor or incomplete AI, such as a voice-recognition in some phone trees, accidents with self-driving cars, and other such widely publicized problems make it tempting to throw out AI altogether. Some jobs will be replaced. Those positions will join the job titles left behind in history, such as candle makers, spraggers (otherwise known as someone who jammed blocks of wood into ore cart wheels to stop them), ice cutters (who cut ice from ponds to fill “ice boxes” before refrigerators) and many others.
Moore's Law
In terms of job stability, one should consider Moore’s Law. In 1965, Gordon Moore theorized that the number of transistors on a chip will double, meaning computing power will double about every 18 months. So far, this has held true, even though every electronics journal has theorized the end of this trend since its inception.
Given that Moore’s law continues to hold, and computing power has been exponentially growing for decades, why would one expect to do the exact same job for the exact same company at the exact same desk for a 30- or 40-year career?
This computer might be in the dustbin of history, but technological advances like AI will lead to careers that haven't been dreamed up yet. Source: James Thew/Adobe Stock
The future of STEM careers
Building a future in the STEM fields means being adaptable to whatever situation or challenge appears next. The good news is that many STEM-related folks already excel at this. They read about the latest scientific discoveries and the newest technology. They are willing to experiment and are constantly asking questions like “how can I make this more efficient?” The downside is that there is no stability. Just when one system seems to be working correctly, along comes something new that will improve performance, and the learning begins once again.
Because of this, the trick is to never stop learning. It sounds cliche, but it holds true. Many workplaces might have one person who refuses to upgrade, refuses to advance, refuses to learn the new technology. Thirty years ago, this person refused to get on the internet. Forty years ago, they refused to use an operating system outside of DOS. Seventy years ago, they thought transistors were a neat toy but will never replace vacuum tubes. Refusing to learn means becoming replaceable quickly.
A lack of stability does not have to mean a lack of employability. Look for repetitive, dangerous, boring or high precision operations; every one of these is ripe for automation and thus is ready for AI control. AI also has the potential to speed up training operations, remove mental load of simple, mundane problem-solving tasks, data analysis and similar tasks. This frees up the human mind to perform actual, creative thought – something a machine can only mimic, not natively perform.
AI, even with some of its mistakes, makes it easier for a technician to upskill and build confidence in their knowledge. As such, it has the potential to raise the base understanding of the average person and help them generate new ideas and designs. From there, who knows where those new ideas will lead, and what careers they might create.
Tomorrow is here for engineers
The bottom line is that AI isn’t coming; it is already here. Certain jobs will be replaced with AI, and yet there will always be a need for human decision-making processes. The division between tasks that can be automated and tasks that should be performed by a human will always be dynamic, changing with markets and new technology.
The short answer: yes, some jobs will be lost, while others will be created. The job titles that will be lost will largely be ones that are repetitive or dangerous. The bright side to this is that many of the job titles that are created may not have been invented yet.
This essay reads like something I might have written myself—perhaps with some chatbot assistance. I’m retired from a software career but have stayed closely engaged with developments in STEM, and with AI in particular. Over the past few years, I’ve watched chatbot behavior evolve in ways that are unmistakable. Early on, even basic or “softball” questions could yield bizarre or unreliable responses. Today, queries about fairly niche technical topics are often answered accurately and coherently—something
What has not changed, however, is the apparent inability of these systems to plan, anticipate, or offer genuine insight. When I ask for help with a truly unique project—one that is not already well represented in blogs, papers, or online discussions—the responses tend to flatten out. They read like the output of someone with limited creative experience: competent within familiar patterns, but unable to reason forward into uncharted territory. In that regard, the answers resemble those of an engineer constrained by outdated assumptions rather than one actively grappling with a new problem.
What gives me pause is not merely that this limitation exists, but that it appears largely unchanged despite intense effort. The last several years have brought dramatic improvements in knowledge synthesis, recall, and presentation. If LLMs were naturally trending toward genuine creativity or future-looking insight, one might expect at least some parallel progress there. Instead, the gap remains.
That raises an uncomfortable possibility: perhaps large language models are fundamentally incapable of the kind of anticipatory reasoning and creative adaptability that many hope for—and fear. Humans are notoriously susceptible to extrapolating long-term trajectories from short-term trends. It may be that recent gains reflect optimization within a narrow paradigm rather than movement toward general intelligence. If so, LLMs may ultimately fall well short of the transformative promise projected onto them—unable to do what humanity both hopes they can achieve, and worries they might.
(Full disclosure - this was written with the help of ChatGPT 5.2. The thoughts are mine, the wording is "AI")