On May 5, 2012, I gave the commencement address at Penn State’s College of Information Sciences and Technology. Then Dean David Hall offered me excellent advice on what makes for a good commencement speech: make it personal, tell us about yourself and your background, share a few lessons you’ve learned over the years, and keep it short.

I followed his advice and reflected on my 60-year involvement with computers and the IT industry. Looking back today, more than a decade later, those reflections still ring true. If anything, the recent AI-driven technological changes have reinforced the lessons my career has taught me about curiosity, adaptability, and the importance of embracing unexpected opportunities.

I started my talk by discussing the serendipitous nature of how I first got involved with computers.

The key event that launched my career took place during the summer of 1962, just before I entered college at the University of Chicago (UofC). Planning to major in math and physics, I wanted a summer job in one of the university’s research labs. I couldn’t get one and had resigned myself to spending the hot Chicago summer filing books in the university library stacks.

Looking back, it still amazes me that my entire career in computing began because I happened to have a small role in an Elizabethan comedy, “The Knight of the Burning Pestle,” which was being performed during the summer season of the UofC’s Court Theater.

One evening I met a physics graduate student who had come to watch one of our rehearsals. During our conversation I mentioned that I had been trying unsuccessfully to get a job in the university’s research labs. He told me that a new computation center was being established at the university and that they were hiring staff. I should go give it a try.

The following day I met the center’s director, physics professor Clemens Roothaan, one of the pioneers in the use of computers in physics and chemistry research. Even though I knew nothing about computers — few 17-year-olds did in 1962 — I must have said the right things because I ended up getting a summer job in the university’s brand-new computation center.

I quickly discovered that I really enjoyed programming, initially in assembly language and later in Fortran and other higher-level languages. I continued working part-time at the computation center throughout my college years, helping to develop mathematical subroutines and assisting researchers in how best to use the relatively new computers in their work.

A few years later, I went on to graduate school in physics with Professor Roothaan as my sponsor and thesis advisor. My own research involved applying computers to a variety of problems in physics and chemistry.

At the time, Professor Roothaan was consulting with IBM on the design of supercomputers, and I became involved in some of this work. A few of the IBM people I met encouraged me to apply for a position in computer science at IBM’s research labs.

Switching from physics to computer science was not an easy decision. But as I was finishing my degree, I realized that I enjoyed — and was much better at — working with computers than doing physics research. So, in 1970, I joined the computer science department at IBM’s Thomas J. Watson Research Center, the beginning of my 37-year career at IBM.

The first lesson I learned from this experience is that serendipity often plays a much larger role in our lives than we realize. The challenge is not to plan every step in advance but to remain open to opportunities that unexpectedly present themselves.

I still cherish having received a master’s and Ph.D. in physics from the University of Chicago, even though I never practiced physics after earning those degrees. Our education and our degrees are extremely important and stay with us throughout our lives. But we should treat them as a kind of general education that prepares us to be flexible and to do different things over the course of our careers.

I’ve also learned that it is important to figure out what you enjoy doing, because chances are that this is what you’ll be best at. It’s not so easy to know what you enjoy and what you’re good at until you actually try different things. This often takes time. We need to experiment, take risks, and occasionally venture outside our comfort zones until something clicks because it feels right and we discover that we are good at it.

That lesson repeated itself several times throughout my career.

During my first years at IBM, I continued to do fairly academic work in computer science. My first management position at IBM Research involved leading a small group conducting research in the then-emerging field of artificial intelligence. MIT was one of the world’s major centers of AI research, and in 1976 I arranged to spend a sabbatical year with its AI group.

Even though I worked at IBM, I knew surprisingly little about IBM’s products and how they were developed. To learn more about the company, a colleague encouraged me to explore other sabbatical opportunities within IBM.

As it turned out, I ended up spending my sabbatical year in the IBM World Trade unit which was responsible for the company’s business throughout the Americas and Far East. I was persuaded do so by the manager who interviewed me, who said that if I went to MIT, I would likely continue doing work very similar to what I was already doing at IBM Research. But if I had the courage to try something entirely different and learn how computers were actually being used in the real world of business, I should come work for him.

I couldn’t resist the challenge, which turned out to be another significant and serendipitous turning point in my career.

To my amazement, I loved the practical aspects of the job: dealing with clients, understanding their business problems, and learning much more about IBM’s products and what it took to bring them to market.

The sabbatical helped me learn something new about myself. I still loved technology and technical work, but I also learned that I particularly enjoyed the challenges involved in bringing new technologies to the marketplace.

When I returned to IBM Research a year later, I became involved in a variety of technology-transfer initiatives designed to bring innovations from our research labs into IBM’s product divisions. I joined a project investigating how to build future computers twenty times faster than our then-fastest machines and worked closely with product-development teams to better understand their challenges.

I discovered that I really enjoyed these applied research and technology transfer roles and was good at them, which became the dominant theme of the rest of my career. I then held a number of management positions, and in 1984 I was named director of computer sciences across the various IBM Research labs.

By this point I had already reinvented myself several times — from physicist to computer scientist, from researcher to manager, and from academic technologist to business executive. I would eventually learn that continuous reinvention is one of the defining characteristics of long careers in technology.

In 1985 I was offered the position of strategy leader in our large systems division. Once again, I was challenged to move to the other side of the fence and help implement many of the new ideas that we had been urging the product units to embrace. I couldn’t decline the offer without appearing that I liked telling others what to do but was too afraid to do it myself.

The late 1980s turned out to be an especially challenging period for IBM’s large systems business. For more than twenty years IBM had enjoyed a commanding position in the rapidly growing IT industry thanks to its System 360 family of mainframe computers launched in 1964. By 1986 IBM was the most profitable company in the Fortune 500.

Then everything changed. The bipolar technologies that powered our mainframes were no longer economically competitive with increasingly powerful and inexpensive CMOS microprocessors. Many believed that the end of bipolar technologies would also mean the end of mainframes and perhaps even the end of IBM itself.

But IBM’s technical community had anticipated these changes years earlier. Researchers and product engineers had been designing and prototyping the technologies and architectures needed to transition mainframes to the future, along with the software migration strategies needed to protect customers’ investments.

The transition was painful, but IBM’s mainframes survived and have continued to evolve to this day, while many competitors failed to do so. This experience taught me another important lesson: technological disruptions can be survived if organizations recognize them early, prepare for them thoughtfully, and have the courage to reinvent themselves.

At about the same time, an even more profound transition was occurring in scientific supercomputing. Everything was changing  at once — architectures, operating systems, programming tools, mathematical methods, and applications. In the early 1990s I was appointed general manager of IBM’s new Scalable POWERParallel (SP) family of highly parallel supercomputers, — another opportunity to help lead a major technological transition.

Let me conclude these reflections by briefly discussing one of the most interesting jobs I ever had: general manager of IBM’s Internet Division.

By the mid-1990s, the Internet was beginning to move from research communities into the commercial world. But no one really knew where it was heading or what it would mean for the world of business.

Former IBM Chairman and CEO Lou Gerstner later wrote in his book “Who Says Elephants Can’t Dance?” that by the fall of 1995 he had decided to make network-centric computing the centerpiece of IBM’s strategic vision.

Shortly before Thanksgiving that year, Lou asked me to meet with him. He explained his plan to create an Internet Division to coordinate IBM’s efforts across the company and asked me to lead it. We announced the formation of IBM’s Internet Division shortly thereafter.

A great deal was happening around the Internet, but its business implications remained uncertain. Our job was to understand where value would emerge, what advice we should give our clients, and what products and services IBM should bring to market. Equally important, we needed to develop an internet business model that made sense for IBM.

Looking back, those early internet years remind me a great deal of today’s AI era. The technology’s potential was becoming apparent, but its business implications remained uncertain. Much of our work involved experimentation, learning from customers, and discovering where the real value lay.

Toward the end of 1996, our strategy began to crystallize around three principles:

First, we believed that strategy had to emerge from the marketplace, not the labs. The IBM e-business strategy evolved by helping customers use the internet to create business value.

Second, we believed that every business — not just startups — could benefit from embracing the internet. Established companies possessed enormous assets, including brand recognition, customer relationships, and IT infrastructures, that could become even more valuable when combined with the new capabilities of the internet.

Third, embracing the Internet required embracing its culture — open standards, collaboration, and a more outside-in orientation. This proved to be much more than a technology transition. It had a profound effect on IBM’s culture and, ultimately, on many companies around the world.

Looking back on my long career in computing, I am struck by how little of my career could have been planned in advance. Almost every major turning point resulted from a combination of curiosity, serendipity, and a willingness to embrace change.

The technologies changed dramatically — from mainframes and supercomputers, to the internet and open standards, and now to artificial intelligence. Yet the lessons have remained remarkably constant. Figure out what you enjoy doing. Be willing to try new things. Accept that your interests and skills will evolve over time. And don’t be afraid to leave your comfort zone when unexpected opportunities arise.

Long careers in technology are rarely linear. Mine certainly wasn’t. But precisely because the future is impossible to predict, curiosity, adaptability, and a willingness to keep learning may be the most enduring career skills of all.