No other advice is offered more to shell-shocked workers blitzed by the lattice of layoffs, the ghosted interviews and the non-response responses than that of "focusing on AI skills".
Workers are to believe that being "skilled at AI" will make them instantly more attractive candidates to companies.
These are the same companies that, often enough, are rewarded by Wall Street for firing workers and pepper phrases like "LLM", "artificial intelligence" and "generative AI" into every earnings call, interview, think piece and forecast they create.
Workers are to believe these same companies will pursue anyone "sufficiently skilled" rather those who are most responsible for the creation of the top-tier AI models on the market.
The fiction of ‘sufficient skill’ masks the reality that AI’s biggest breakthroughs come from the few while the many are told to catch up.
If AI ends up being as transformational as its most ardent promoters claim, it is ridiculous to imagine that the same business leaders who have steered the steady cascade of layoffs over the past three years, in spite of record profits, will maintain their most consequential cost-centers rather than trim them in pursuit of improved margins.
But let's assume this isn't true.
Upskilling in AI as sound advice for today's working professional requires another assumption: that the business leaders calling for more AI skills are even arguing in good faith.
Remember, the people peddling this advice are the same people overseeing the injection of generative AI into every conceivable corner of every major product we can name, not because users and customers are clamoring for this and not because it addresses stated user needs.
Generative AI isn’t answering demand, it’s manufacturing it. One product update at a time.
The "upskilling on AI" trend is a necessary pre-condition for the mass sales and enterprise integration of AI "solutions". Without it, all the hawkers of such tools and services have is the biggest speculative bet in history, without any takers. Given the billions that have been invested and the billions more planned for AI, a narrative justifying all that spend is not only necessary but required.
Anybody remember "learn to code"? This was only the most recent major upskilling effort workers were advised to undertake.
Why?
Because software was eating the world and software-as-a-service was the knife and fork. We needed tons of engineering talent to build and scale massive SaaS platforms. The 2010s were the era when the biggest, most influential internet companies we can name were on the "come up".
In the 2010s, software ate the world and engineers set the table...
Now that the platforms have been built and the key scaling challenges resolved, armies of engineers are no longer required to maintain these multi-billion dollar businesses.
Now that the platforms have been built, where does all that engineering talent go?
Now workers are to believe that AI will be different, that we'll need all these people to build AI systems and platforms in the same way we "needed" all those engineers during the SaaS boom.
But AI and associated disciplines like Machine Learning, Deep Learning and Data Science are specialist competencies. We'll need far fewer practitioners in order to make revolutionary advances that produce outsize value for companies.
Unlike the SaaS boom that required scaling teams of engineers, AI is a high-leverage domain where a small number of specialists can generate disproportionate value. This makes broad workforce demand unlikely.
Even if most business leaders understood AI as a precision solution to a well-defined business problem rather than an efficiency hack, where would they have the willing upskiller direct their attention? Building their own models from scratch? Mastering the intricacies of fine tuning the output of pre-trained models with LoRA? Building the data pipelines that feed machine learning models? Or do they simply mean better prompt engineering?
Realistically how much of that fits into a single self-financed online course or certification program, what is the lifetime value of knowledge that has such a high degree of obsolescence and what are the competitive prospects of a holder of such certifications against a genuine specialist with a terminal degree? What really determines the amount of value either will add to a company, if any?
These are the most important and sadly unanswered questions.
Whatever else it may be, upskilling in AI will not be a vehicle for widespread or even modest employment any more than coding was before it. Cloud companies are designed for massive output relative to modest labor inputs; their ability to scale outputs infinitely is what drives their profitability.
For all the noise around "upskilling in AI", we should expect the same employment boom and bust cycle from the last decade and prepare financially, professionally and emotionally for whatever comes after the AI hype.
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