Navigating a Tech Career in the Age of AI
As I visit colleges and speak with aspiring entrepreneurs, one topic keeps coming up: computer science majors are finding it increasingly difficult to land entry-level jobs in the age of AI.
I’ve spoken with several CEOs of major tech firms, and they all echoed a similar sentiment: they are no longer hiring entry-level engineers at the pace they once did. Companies don’t want the hassle of training fresh graduates or meeting the high expectations many new hires have about how the workplace should operate. Instead, they’re replacing many entry-level roles with AI tools while hiring more senior engineers who can fully harness AI at scale.
For today’s young tech employees, it can feel like being a dinosaur when the asteroid hits—suddenly, the skills that once promised stability are at risk of extinction. Many young graduates ask me how to navigate this environment, and I usually share my own personal journey—and how I learned to adapt through disruption.
Learning Disruption Early
Growing up, my father owned Exxon gas stations. I worked there on weekends and summers, and up until 1994, business was good. Oil companies ensured station owners had healthy profit margins by requiring them to sell gas at a set markup. After 1994, that mandate disappeared. Selling gas became a loss leader, and you hoped to make money through candy, cigarettes, and oil changes.
I learned very early how painful market disruption can be to your personal life—and that staying ahead of change was the only way to survive.
From Mainframes to Client-Server
When I joined the workforce as an entry-level engineer at Accenture, I was coding check processing systems in COBOL on the mainframe platforms of major New York City banks. It was a high stress job as even one mistake could result in millions of dollars being misplaced, exposing security holes, etc. While working on these systems that were first built in the late 1960s, I kept one eye on “what’s next” and noticed that client-server platforms were poised to replace mainframes.
In my spare time, I studied SAP, PeopleSoft, and Oracle—often paying for courses out of my own pocket. By 1997, the demand for client-server implementations exploded. Almost overnight, I doubled my salary because I knew both PeopleSoft and SAP. Even better, I was one of the few who understood both old mainframe systems and the new client-server platforms. I quickly became the go-to engineer for migration projects at banks like JP Morgan, Chubb, Goldman Sachs, and AON Insurance.
Riding the Internet Wave
But I didn’t stop there. The internet was emerging as the next big thing, and I thought: I need to know how to build web pages.
I still remember sitting on a NJ Transit bus after a long day at JP Morgan, firing up my laptop, launching a web server, and building my first “Hello World” page. It felt like entering an entirely new universe of opportunities.
Within a year, I was helping build the backend and frontend integration of the first online trading sites for major banks. When the dot-com crash hit and many engineers were laid off, I was fortunate to be promoted—because I could bridge multiple worlds: web-based front ends, mainframe back ends, and client-server accounting systems like SAP.
Knowing My Strengths and Limits
I also understood my limitations. I was honest enough to admit I was never going to be a top 0.01% coder. While I would never be the greatest coder in the world, I was very good at designing systems, architecting platforms, and building teams. My diverse technical background set me up to be a strong CTO or CPO.
So I pursued my MBA at Wharton, which opened the door to my dream job at Microsoft. After four years there, I moved to Silicon Valley and co-founded a startup focused on machine learning and natural language processing—what we would now call early AI
Letting Go of Arrogance in Tech
One of the weaknesses of being an engineer or developer is that you can become arrogant about your framework. You convince yourself COBOL is better than client-server, or that “click-and-build” platforms aren’t real coding. I’ve met many engineers who refused to adopt new tools because they thought the new thing was beneath them. In the end, many of them were left without great options for the future.
The truth is simple: all tech is just a tool. The faster you learn to use tools to the best of your ability, the more valuable you become. Once you think of coding platforms as just another tool in your arsenal, you let go of prior biases.
That’s why I still explore every “next new thing.” Over a decade ago, I learned WordPress because so much of the web was built on it. Around that same time, I was building custom e-commerce systems that processed more than $50 million in monthly orders for the next-generation consumer brands I had invested in. Later, I became a Shopify Certified Partner as commerce began exploding on that platform. Today, I’m experimenting with half a dozen new frameworks—each one just another tool that could unlock future opportunities.
In the end, nothing replaces the human ability to create something that has never existed before. People create valuable IP. Every engineer should think of themselves as an artisan who can also build the next great thing. Once you adopt this mindset, your ability to scale your skills will grow by 100x.
Takeaway for Today’s Graduates
The lesson I share with students is this: don’t fear disruption, embrace it. Each time technology shifted, I made a conscious effort to learn what was next, even if it meant long nights, paying for my own courses, or starting from scratch.
AI is simply the latest disruption. If you resist it, you risk becoming that dinosaur when the asteroid hits. Instead, invest in learning how to work alongside AI, how to leverage it, and how to anticipate the next wave of change. That mindset will keep you relevant—no matter what era you’re entering.