I’ve been experimenting with generative AI since 2020, and I have to say the progress we’ve seen in just a few short years is amazing. The things this technology can do today were almost unthinkable when I started using those early models.
I remember trying to get one of the early language models to help me build a simple “to-do list” application. It struggled to say the least. It didn’t really understand what I was aiming for, and to be honest, I had to hold its “hand” through every step of the process. I had to break things down, ask very specific questions, and already have a decent amount of subject knowledge myself. Back then, these models were mostly glorified search engines with a friendlier user interface great for ideas, but not quite partners in development.
But things began to change and over the years the models advanced, today these tools can really step up as a partner and even co-founders.
Over time, the interaction became less about guiding the AI and more about collaborating with it. I no longer needed to write most of the code myself or formulate perfectly-worded prompts. Today, I can build a far more advanced version of that original app in just a few hours, something that took weeks in 2020 with the original AI models I began working with. This is the type of leap in productivity that makes headlines scream about the future of jobs being at risk. And to be honest, I get it! I understand why some are sounding the alarms.
But here’s the thing: if you’re looking at the future of work through the same lens you used five or ten years ago, then yes, it’s going to feel terrifying. Of course, AI seems like a threat. After all, we no longer need interns and junior analyst spending hours manually cleaning datasets or scouring spreadsheets for “leading spaces” in cells (if you’ve ever done this task, you know the soul-sucking pain I’m talking about).
Let’s break down the problem, opportunities for those willing to learn, review two examples, and talk about what mindset will lead the future.
The Real Problem How we View Entry-level Work Needs to Change!
One of the most repeated criticisms I hear is that generative AI will eliminate “rite of passage” tasks the mundane early-career grunt work that, supposedly, teaches many things. But the truth is, of the things AI is replacing we learned very little other than to double-check everything and hit “save” obsessively (especially if you remember the horrors of the blue screen of death). I challenge the idea that mindless mundane tasks are a “rite of passage”. Because while those tasks may have taught us diligence, they didn’t exactly encourage innovation or creative thinking. In fact, they often stopped creativity in its tracks.
Most of us dreaded the parts of the job where we had our heads buried in spreadsheets, massaging messy data into something usable. We looked forward to the days when we could analyze, problem-solve, and add value. That’s what we were excited about and ironically, that’s the part many never got to, because they burned out in the grind before they had the chance.
The saddest part? That mind-numbing data cleanup process, done poorly and too quickly created lasting problems. Years later, many companies are sitting on mountains of inconsistent, poorly labeled, and unusable data because junior analysts weren’t incentivized to clean it properly. They wanted to move on to the “fun” stuff too quickly, and who could blame them?
The Opportunity Ahead
This is why I’m optimistic about the future with AI. If we use it correctly, we can eliminate meaningless tasks, free up brainpower, and give employees, especially new ones, the chance to start their careers doing meaningful, high-impact work.
This is why I strongly believe AI shouldn’t replace entry-level employees it should augment them.
Those junior analysts, associates and interns are still critical. They come in fresh. They have energy, idealism, and a drive to solve the “impossible.” The difference now is, they’ll actually have the bandwidth to try and do it.
We are at a unique moment in history where everyone from the new hire to the 30-year veteran can and should be given a personal AI assistant. But not just any assistant. These tools need to be tailored to role and context. Some will act as mentors and guides, others as research analysts or organizational experts. Some will summarize meetings. Others will design workflows or analyze code. The possibilities are endless.
A Tale of Two Companies: The AI Fork in the Road
Let me walk you through a simplified example I’ve been thinking about lately. It’s not meant to be perfectly realistic, but it illustrates the stakes of leadership decisions in this new world:
Company A has 100 employees and brings in $100 million in annual revenue, with 50% profit margins. Leadership sees an opportunity to automate low-level tasks and cut 25% of the workforce. Overhead drops, profits rise to $75 million, and shareholders celebrate.
Sounds smart, right?
Fast forward five years: the most experienced executives begin to retire. You promote mid-level managers, reorganize a bit, and add a few more automations to keep things running. But eventually, you hit a wall. There’s no bench strength. You’ve hollowed out your talent pipeline by eliminating the very roles that would’ve produced your future leaders.
Now you’re hiring externally, people who don’t know your culture, your vision, or your values. Innovation slows. Morale suffers. What was once a winning strategy now feels like a short-sighted mistake.
Company B same starting point takes a different approach.
Instead of replacing people, leadership introduces AI across the organization with a clear message: This is here to help you, not replace you. Every employee gets access to tools designed to remove drudgery and unlock creativity. You involve the team in the design process. You ask them what they need. You build together.
Now your 100-person team performs like a 200-person team. Productivity explodes. People are excited, not anxious. You start launching new products, entering new markets, and solving harder problems because your team isn’t burnt out, they’re inspired.
Which company would you rather be a part of?
The Infinite Growth Game
This is what I call the infinite growth game and companies that figure this out will win the next decade. Not just because they use AI, but because they use it intentionally.
The companies that view generative AI as a tool to eliminate headcount will see short-term gains and long-term decline. The companies that see AI as a lever for human potential will experience exponential growth not just in profit, but in culture, creativity, and resilience.
Because when you give humans better tools, they don’t become obsolete, they become unleashed.
What I Hope Every Leader Learns quickly
I’ve said this before, and I’ll say it again: Generative AI is nothing more than an advanced tool. It reflects the person wielding it. If you use it to replace people, you’re playing a dangerous and unsustainable game. But if you use it to support them to clear the clutter, unlock bandwidth, and give space for innovation you’re giving your team a gift.
We spent decades dreaming of technology that could help us “get more done.” That dream has now arrived.
And here’s what we must remember: We didn’t want a tool that would replace us. We wanted a tool that would empower us. One that would help us solve harder problems and create amazing things.
So let’s stop being afraid of the future.
Let’s stop measuring productivity in terms of bodies and hours.
Let’s stop thinking that the best use of AI is replacing entry-level employees with glorified spreadsheets that talk.
Let’s build organizations where humans and machines work together not in opposition, but in harmony. Where experience is valued, but imagination is celebrated. Where tools make us more human, not less.
Let’s build that future.