A number of AI enthusiasts are advising soon-to-be and recent graduates to skip the internship and instead become proficient with AI because that’s how they are going to get a job. And, as you should know by now, it’s bullcr@p. Being able to write a prompt for a Gen-AI LLM that will return a convincing (but not necessarily sound) result is not going to get you a job. The only skill that’s going to get you a job is competence!

As with every over-hyped tech-du-jour that came before ([predictive] analytics, the fluffy magic cloud, SaaS, the WWW, etc), AI is not a silver bullet that’s going to solve all of an organization’s problems and grant magical status to those who have mastered it.

The only thing you’ll master with Gen-AI is the art of the con since whatever it spits out is so well written (compared to the average literary skill of an average high school, and even University, graduate these days) and so convincing that, without expert guidance, an average person is convinced that it must be right when they don’t know better. But that’s not a skill most organizations are going to hire you for (outside of sales and marketing), even if the organization is known for questionable ethics.

Organizations don’t need clueless idiots. They need experts who can assess situations, determine options, decide on the best option, and implement the decision. Someone who knows the analysis to run, the data to collect, the tools to use, the reports to create, the logs to keep, and the contracts to write.

And while you can’t graduate an expert, you can graduate with the skills to start you on the path to becoming one — the traditional skills of math, logic, critical reasoning, project planning, project management, and relevant domain knowledge — not creative crafting of perilous prompts for a flakey LLM that will eventually fail you no matter how much time and effort you put into that prompt.

And if you get get an internship and prove yourself, maybe that will lead to full time job where you can apprentice under a master in the real world and gain the experience you need to go from an adept (with the core knowledge and skills but not the wisdom needed to succeed in the real world) to practitioner (who has gained enough wisdom and experience to manage standard tasks and functions on their own, and who only needs guidance for new or complex situations not yet encountered) and, eventually, to expert where you become the new organizational mentor and the one that new hires turn to for help.

And organizations need (future) experts because only an expert knows when

• it only has wrong/incomplete data (which will prevent an AI from ever working)
• an analysis/outcome is wrong based on math fundamentals
  (and when an LLM-based AI multiplied by -1 because you told it to deliver savings vs. find the best opportunities based on price variability, lowest price, market trends, and differential analysis)
• reasoning is correlative, not causative (which is a failure of not just LLMs, but many people as well)
• an analysis is incomplete (because only they have specific insight that was not available to the machine or another analyst)
• etc.

That’s why, if you want to become a true master of your craft, you need to forget the AI mastery and instead land an internship where you can apply the mastery of the real skills you learned in your degree program to stand out, get an apprenticeship, and learn how things work in the real world and acquire the real world mastery you need to get the job you want. Only then will you be able to work your way up to becoming the leader, and expert, you want to be.

There is no Artificial Intelligence (just Artificial Idiocy) and organizations will always need top talent. Automation, and well designed applications that solve real problems efficiently and effectively, will reduce the number of back-office employees that an organization needs and any employee who’s only skill is pushing bits will be eliminated. However, the need for talented employees will only increase to not only oversee the tools and handle the exceptions, but correctly analyze increasingly complex real-world situations and make the right decisions.

At the end of the day, AI tool mastery is meaningless if you can’t logically and holistically analyze the outputs with respect to math fundamentals and a real-world scenario!