Use AI Today to Get a Better Job Tomorrow

(even if you think AI sucks, and especially if you’re worried about job security)

Dictation transcription in the 1970s

It's often unclear how new technology will eventually shape the workplace or create entirely new categories of jobs. When PCs first became available in the ‘90s, few could have predicted how pivotal they would become to businesses. Not many people knew how to use them – so much so that Microsoft shipped early versions of Windows preloaded with the Solitaire game as a clever way to teach customers how to use a mouse¹. Later, when the Internet became mainstream, many of its earliest use cases seemed purely for entertainment purposes. Enthusiasts and geeks built websites and used early versions of social media, but such novelties seemed to have no place in the professional world.

Many of those same geeks would become IT support workers, webmasters (how quaint of a term now!), or mobile app developers just a few years later. The combination of curiosity, experimentation, and willingness to try things out (even if they didn’t always work) made that possible. What if they had sat on the sidelines? Sure, they may have picked up on the new technology eventually – but some other early adopter probably would've swooped in and stolen the opportunity first.

The nearly-2025 flavor of this is AI. Like anything new, people fall into two categories: The early adopters are the folks who are actively building, using, or trying as many new AI tools as possible. Everyone else is in the bystander category, which ranges from the cautiously curious (“How can I use this stuff?”) to the stubborn skeptic (“I work 50 hours a week and need to clean my house, so unless the AI is going to take my kids to soccer, I don’t care.”) And, like those folks in the 90s and early 00s, many believe AI is a toy, unreliable and unsuitable for serious work. 

Such objections are understandable. Sometimes, the AI does hallucinate! And while this problem is improving, it does mean that good human judgment is still needed. Even so, the helpfulness and productivity gains are too dramatic to ignore. And for anyone who drew their conclusions on AI more than six months ago, the capabilities have improved significantly.

I’ll put it this way: If you haven’t used AI because you think its capabilities are overblown or won’t work for what you do, it’s worth a deeper look. 

Much of what is happening now with the application of AI rhymes with other historical, disruptive technology trends we’ve seen in the past. While the tools still have room to improve, there are useful things to learn now — I’m convinced using them now will provide a head start on what will become high-demand skills. The benefit of using AI today is similar to being among the first to fiddle with a Macintosh in the '80s or create a Facebook profile when you still needed a university email address. Figuring out how to interact with AI tools effectively (aka “prompt engineering”), developing an intuition about what works and where there are limitations, and knowing where the capabilities are improving isn’t just for fun. These are skills that will be career differentiators in the not-too-distant future. 

When companies realized they needed someone to manage their websites or run their social media accounts, the early adopters were not only the best equipped, but they were also in short supply! Learning these skills is like earning compound interest. The sooner you start, the more you’ll learn (and earn). 

Even if you’re not planning on changing jobs, technology is constantly transforming the nature of existing jobs. The day-to-day work of bookkeepers changed dramatically due to spreadsheets and accounting software. The job of machinists transitioned from using manual lathes to operating CNC machines. Architects no longer spend time drafting plans by hand, instead using CAD programs that allow them to spend time on more complex modeling. Becoming familiar with emerging technology is vital for career-switchers and career-lifers alike.

If you haven’t started using any AI tools, or if you’ve only been using tools like Microsoft Copilot in a work environment, it can be daunting to figure out where to start. The simplest advice is to use OpenAI’s ChatGPT 4o model or Anthropic’s Claude Sonnet 3.5 model. They’re both great, and as mentioned, tinkering when you're not sure how it works is part of the learning curve. If you have a more nuanced use case like software development or want to explore some of the newer tools, there are lots of other options². 

Sometimes people want to use these tools but feel stuck finding something to use them for. If that sounds familiar, think about all of the annoying, monotonous tasks in your personal life that you’d love to make magically disappear:

Do you struggle with meal planning and coming up with ideas for lunch or dinner based on what you have in your fridge and pantry? Ask AI. 

Do you have a long to-do list that needs to be broken down into smaller tasks and analyzed for the best place to start? Ask AI. 

When helping your kid with homework, do you find yourself thinking, “I haven’t studied this subject in forever. How do I explain this to a 13-year-old?” Ask AI!

These everyday, mundane personal life tasks are a great way to start getting your feet wet — the intuition and skills that come with using AI can be honed outside of a strictly work context. In fact, once you start identifying minor, annoying problems suitable for AI, you’ll probably find yourself keeping a tab in your browser or an app on your phone close by to ask questions. After these helpful but low-stakes uses become second nature, it’ll be much easier to imagine how these same tools can be used for more advanced or specialized problems.

When I talk to people about AI use cases, they often ask, “What are you using it for?” The honest answer is always schizophrenic sounding, like this sample from the last week:

• As a writing partner for this essay — so meta! (AI generated an outline, analyzed the structure, and helped me find sections to cut and clarify.)

• To classify a long list of companies based on the company description and specific category definitions. (Don’t ask; somehow, it’s more boring than you can even imagine.) 

• On several software development tasks, including writing Python code, database queries, and creating a configuration file from a 600+ page PDF document with API specifications. (Even with some AI-generated errors, this saved me hours!) 

• To help me explain a math homework problem to an eighth grader. (“How does exponent division work again?”)

Sure, getting AI help for each of these real-life problems was helpful. But more importantly, every one of these interactions was an opportunity to learn more about what AI tools do well and where there are gaps in their capabilities: Which AI tool is best for this problem? What style of prompts get the most accurate answers? How should I format my data to receive the best results?

This experience will accumulate over time, and as these tools aren’t going away any time soon, the most effective users of AI tomorrow will be the people starting to use the tools today. 

That’s why I plan to take the compound interest of learning every chance I get. You might want to consider cashing in, too.

1. Entertaining Training: Usability Guidance Built into Solitaire, Hearts & Minesweeper

2. Here is a list of the best tools organized by category if you want to dive deeper into the latest AI-related tools.