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On the day IBM announced she’d be stepping down, exiting CEO Ginni Rometty called Arvind Krishna “the right CEO for the next era” at the company. “He is a brilliant technologist who has played a significant role in developing our key technologies such as artificial intelligence, cloud, quantum computing and blockchain,” she said.
Krishna, for the most part, has worked at IBM his whole professional career. The electrical engineering doctorate, who has more than a dozen co-author credits on technology patents, started at the company’s Watson Research labs in 1990. He stayed there for nearly two decades and later served as head of IBM’s cloud and cognitive software division. He helped orchestrate one of the company’s largest acquisitions in 2018 and took over as chief executive in April 2020.
Krishna’s appointment came after a period of stagnation at IBM—especially when compared with its Big Tech peers. As an early leader in artificial intelligence, the company poured money and resources into its Watson division, following the technology’s victory over human contestants on Jeopardy! In 2011. IBM tried several approaches to make Watson a useful tool for doctors, which could help them to recommend treatment options for patients. But the technology didn’t deliver as promised.
IBM sold the assets of its Watson Health unit earlier this year, but the company’s work with AI remains in full force. IBM now puts AI to work helping companies in the climate crisis. These efforts earned IBM recognition this year as a TIME100 Company.
Krishna spoke with TIME recently about what we can learn from IBM’s early AI missteps, and what he sees as the company’s higher purpose.
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The interview has been edited to be more concise.
Arvind, I grew up in a town very close to IBM’s headquarters in Armonk, New York. And, in fact, my very first class trip in kindergarten was to IBM’s campus in the early ‘70s. IBM was a symbol of innovation and a way to dress for work. Is there any evidence of the old IBM DNA?
John, great question, and one that I’m really passionate about. IBM has some great things that have stood the test of times. Your comments included the people, culture and dressing. Then there’s the innovation. Innovation is a part of my life that I feel remains true. That part is based on the technical skills of our employees. When we look at it though, you see pictures. IBM is where I was in 1990. I think about the ‘70s, the scientists in lab coats with a suit and tie under it. This is gone. As a culture—as a nation, not just as IBM—we are much more relaxed about how we dress, and about all the inclusiveness, because that unlocks the most from people.
What is the latest? Technologies that we use are constantly evolving. Building physical computers was the only way to create value thirty years ago. Today, a large part of the value is found in software development and consulting. This means that technology can be deployed on behalf of clients. What has remained constant is technology that helps catalyze our clients’ business, the expertise of our people and a lot of the culture of speed and nimbleness.
You’re one of many large-company CEOs who were appointed during the pandemic. Analysts believe that IBM’s top management before your arrival was comprised of people who are skilled in sales and service, rather than technology products. Does your appointment feel like an attempt to change that?
My vision of what the company would become was the main reason that I was appointed. It is focused on artificial intelligence and hybrid cloud. My goal was to see the company grow again. This year, we have made a commitment to grow at 5%. That does not include the revenues from Kyndryl’s spinoff. If you consider those aspirations, they are much more influential than just a background.
What is my day like if I look at it from the perspective of my job? It is true that I did spend some time thinking about strategy, products and making decisions. My time is also spent with my clients to ensure that they get their checks. My time is also spent with my partners. So it’s always a blend in my position across those different disciplines. Same was true of my predecessors. All that said, technology is moving at a faster pace and with more acceleration. Given the speed and acceleration of technology change, it is likely that having a deeper understanding of certain technologies and their implications will be helpful for decision-making.
IBM was seen by many as an AI leader when Watson won Jeopardy 2011. However, many of Watson’s business dreams vanished over the following decade. Was there a lesson to be learned? Was there anything that IBM could learn from the period?
I am one who’s completely unapologetic for what transpired over the last decade. Artificial intelligence is the only technique that we know that can harness and harvest all of the data that’s being produced. It is clear that all this information has tremendous value. You might not be able achieve 1% without AI. It was a significant achievement to win Jeopardy. AI was truly put on the map by it. [But] it’s no longer a lab toy. It’s no longer in the domain of a couple of smart professors at MIT, or Stanford or Berkeley. This is something you could use in real life.
For investing and putting so many scientists to work, we can all take credit. You can’t help but be inspired by the potential of a technology once it is transformed from a lab into something practical. The market wasn’t quite ready in 2012, 2013, 2014, to begin to embrace and trust artificial intelligence in some of the more critical domains where we went.
We made the mistake of focusing on more important things and more items that have an effect on real economic activity. Successors began to apply. [AI] more in areas where it could be useful—you know, if you make a slightly wrong recommendation for a book, or a movie or a website, that’s not life and death. I’ll acknowledge, maybe we should have applied it to more areas that were less critical. Nevertheless, it is important to learn from your mistakes and make corrections. Perhaps we could help a fast-food restaurant automate its auto-tagging. We might be able to make enterprise apps more user-friendly. Start in areas where you are more restricted, rather than making decisions that could have a major impact on your life. Let’s get there. Let’s get people to trust it. Then you will be able to scale up like mad. And that’s what we’re sort of doing now. Watson is still alive. But I think we shouldn’t try and do moonshots. It is better to take more calculated steps.
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We’re throwing around a term that a lot of people think they know, but I want to get your very simple dinner-party definition of what AI is.
Artificial intelligence can be described as a method that uses data to learn. Think of pattern matching as the most basic example. By the way, an AI system is not nearly as accurate as a human infant who has been born 9 months ago. You show them a dog three times, the fourth time they’ll say dog. In AI, maybe if you show it a million photographs of a dog, the million and one time it would say that’s the dog. When A happens and B occurs, AI can learn all of those patterns by observing them together. That’s artificial intelligence.
AI today would not be able to sense gravity. You could show AI a million videos showing it how an apple falls off a tree and it would conclude that the apples fell in that same direction. It will still not say that the pears are falling in this direction. So things like knowledge I think are going to merge together, and it’ll take some time.
IBM’s AI/climate change efforts are a major focus of their current work. Could you speak a bit about this?
We believe climate change is one of the topics that is imperative for us—our generation—to help make better for the next. Therefore, we made promises that our net zero will be achieved without any offsets. However, this is not for 2050 and 2060. When you start to incorporate AI in all of this, it becomes a question about how reliable your data collection is. If you don’t have all the data you need, then you might underestimate or underestimate how great you actually are. Could I possibly find materials capable of carbon sequestration that is better than mine? 30 percent of energy that we produce is lost. Is it possible to optimize our waste and eliminate the negative effects that can be so beneficial for everyone? Also, how can we encourage open source development? Are there programs that encourage people to use artificial intelligence to make technology for climate change mitigation? These tools are available to encourage others and help them start their own businesses. These are the things we consider part of our civic responsibility.
Does IBM have any other options to draw young workers? Many young workers want to work for a company with a greater purpose, according to surveys. Are there higher purposes at IBM?
First, some statistics. Last time I checked, there were 3,000,000 people in our applicant pool. This makes me happy. Each year, we hire thousands. There must be a reason for the job.
Where we succeed a lot is we’re willing to take problems that are really hard and take years of persistence to get things done. An example I’ll take is quantum computing, which has gone from being science fiction to where everything is now just science. It’s been a journey of 10 years; it’s also got probably another five to 10 years to go. So if you come to us and you want to work on quantum, you know, we’ll have the staying power to get through to where it’s very, very successful. Referring to the example of winning Jeopardy, it was a seven year project. If you’re interested in solving big problems and enjoy being with smart people, then this job is for you. This is not the place for you if your goal is to build the next app or game on mobile.
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