IBM CEO Arvind Krishna on the Good, Bad, and Ugly of AI
Its almost exactly five years since Arvind Krishna was elevated to CEO at IBM. Roughly 35 years since he joined the company in 1990. Hes led IBM research, IBM Cloud, and was a key force in the Red Hat acquisition. At IBMs recent annual Think conference, he spent an hour taking questions from analysts about the state of IBM and, not surprisingly, mostly focused on its plans for AI.Arvin Krishna, IBM CEOAI writ large will wrought many things. Innovation to be sure, Workforce decimation, very likely. AI overlords, not-so-much, says Krishna. In the war over model size if it is a war small will win for most organizations seeking to get into the game. Why does IBM (almost alone) persist in having a separate independent research organization?Since taking over as CEO (April 2020), Krishna has worked to refocus IBM, shedding less core functions, and betting big on a hybrid cloud and AI strategy. The companys stock has risen dramatically (~130%), though perhaps predictably, not enough to please everyone. The S&P 500 index grew roughly 105% in the same period.Presented here is Part 1 ofKrishnasanswers to those questions posed by analysts at the lengthy Q&A session. Rather than repeat the questions, well just note the topics. Part 2 will follow in the next two weeks. Altogether, Krishnas comments provide a clear view of his ideas on why AI is important, what some of its major impacts will be, and how IBM is attempting to both drive and ride the AI wave.Agree or disagree with Krishna and he has plenty of fans and critics his thoughts on AIs future role make fascinating reading.1 Whats AIs Impact on InnovationI think well give you a multi-layered answer. First, put aside AI as a market. How do you apply AI inside to improve innovation? I think this is something that everybody ought to understand. This is mostly a question of the speed and cost of innovation. I think I see AI directly providing if I look at a two-year picture anywhere from 30% to 60% improvement in the speed and cost of doing innovation. I hear people talking about 80 to 90%. I think thats a bit of a stretch, at least in technology. We see 10% already in terms of pure programmer productivity across the board. When I measured across 5000 people, I think that 10 goes to 20% once I include test cases and requirements, and [it] goes to 30% with just improvement in these capabilities over the next two years.[Also,] we should never forget the innovation that sits inside many, many different functions. How about procurement? How about accounting? I easily see those kinds of numbers in that range thats in there. The third and final, of course, is we want to be an AI provider for people. We want to help them become more and more innovative. Now, its not really a savings now. Its a cost of building those capabilities. Like some of the things we announced this morning, were going to lean in very heavily. I think AI models, especially smaller models. I think theres a lot of opportunity for those. I think agents and our people use them. Theres a lot of opportunity for those, but thats going to be an investment, not a not a savings like the for the first two; thats why, Im sorry, [I say there are] three different layers, and all three apply the first two help in giving us money that were going to pull into bucket three.2 AI Destroys Jobs, Ruins EconomyBut Fuels RebirthLets take a simple example. Lets take the mechanization of farms. In 1900, if I remember the number right, 47% of the US labor force directly worked on a farm. By 1960, that number was 3%, maybe around the same, maybe even lower. Now I didnt bother to go further. So mechanization, as in combines, harvesters, tractors, all of those things, dropped the 47% to 3%. The doom and gloom people turn around and say oh my god, 45% of all jobs were lost. [Thats] completely BS. I mean, the total number of jobs only increased in that time period. But these things didnt exist in 1900 and did exist by 1960. Restaurants and fast food together, about 15% of all jobs. The whole automobile industry and the appropriate service industry around that.Why? Because, as time got filled up, people now had the option to do leisure activities, go to restaurants. As productivity increased, they had the money to go and do all those things. And so to me, this is the same exact thing that is going to go on here. I mean, Im 100% sure when the steam engine came on, Im guessing that people who sold horses and oxen felt their livelihood would go away. So there is displacement, total employment increases, but there is displacement.So lets take IBM. Im pretty open about it. I think that there are about 20,000, which is about 8% of our (IBM workforce) total, which I think there will be displacement. Now we do offer a lot of upscaling and rescaling. Can all of those people be able to take advantage of that and emerge in a place inside us? I think thats only a third of those people. Ill be straightforward. At least 14,000, what happens to them? Well, we churn about 15,000 people a year. Thats just an average year, that number of people leave us either to retire or to go to other places or go do other things. So I think at the numbers and scale were talking about between re-skilling and just churning, you can absorb those things.I think theres a bigger societal question [that] should be asked. Jobs that require critical thinking will very much be in demand, jobs that require creativity will very much be in demand, but a lot of good jobs [will be eliminated]. But this has been, I mean, Im sorry to say it this way, but I think thats been the nature of progress for the last 300 years, that jobs which can be automated tend to get automated away. Now the question is, what are those people going to do who did those kinds of jobs? Theyre not going to have those jobs here. Lets just be straightforward. But are there other jobs for people like that? I think those are another data space. I think Ive always been of the opinion that I think that elderly care is a big topic. If a third of our population is going to be over the age of 65 it used to be five to 10 If its a third, thats a massive amount of a service in that.3 Managing Human and Digital Workers will be the NormSo Im one of the people who believe that AI becoming autonomous and taking over [like] Skynetis ludicrous. But are there going to be a lot of digital workers alongside the human workers? I think that were going to be managing populations where the number of digital workers, outnumber human workers ten to one. So the point that youre asking at a more serious level, I think, for the next three to five years, it is a deep understanding of how do these two augment each other, and how do they make the human a lot more productive?Where people often go wrong on being scared is if you make the human more productive, then the nature of the world has shown us that those people will actually command a lot higher wage and a lot better life, but they need to be able to work with their digital workers and that will be of incredible value. I mean, the same is true in our [IT] industry. I mean its going to maybe somewhat similar in journalism. People who use an AI system to very quickly scrape what may be in a companys earnings release are going to be more valuable [at first] but then, if they can put it in context with other companies and other knowledge and what the industrial trends are, thats incredibly more value than the people who are originally just scraping the release to give you the basic facts.Do I believe that were going to be managing both? Absolutely. Do I believe that the people who talk about the company of one, where its all digital workers and theres no humans, I think thats a bit far-fetched. I always tell our people in the field, people who are directly marketing inside the clients, taking the information of what we have for the client, could we put an AI agent do that? Of course. But the empathy of understanding what motivates a particular human being, what is the structure and what is the incentives inside a particular client, what are the exact gaps today I dont think AI is going to get there in the next 10 years. Thats very much the human whos in front of them understanding what appeals today versus what will appeal maybe a year from now. Thats very much, I think, still a human task.4 IBMs BIG Bet on Smaller Vertical ModelsI mostly believe there will be a ton of domain-specific models. And almost by definition, a domain-specific model will be a small one. If its a lot, then its not domain-specific. It just happens to be able to go across many different domains, and then you have to say, well, running it is the same cost and its equally power efficient. But Im sorry that breaks the laws of both physics and computer science, because the [model that] is 100 times the size is going to probably cost you 10,000 times more energy and compute cost to run it. So I think economics drives you to say, if youre doing specific domains, its much better to be small than large.Number two, I think the problem set leads itself to small, not large models. If Im worrying about corrosion in pipelines, how much data am I going to get that I draw upon. So its going to its going to lead you towards a much smaller data set, which means that it makes sense to have a smaller model. And three, if people want to augment a base model, lets say big or large, with their own data. Their own data, by definition, is small. What do you gain from going from large refined model to another large model, if a smaller model can ingest all your data, which is the value to the enterprise? So thats why I got to argue that there is a world for small.But I was very careful to say I think there will be space forI dont know whether its three, four, or a dozen very large models. I do think there will be space for that. If Im building a consumer app and I want to be able to answer questions, maybe for the current or the next generation of search, I cant predict what people are going to ask. In that case, the one who builds the wider model actually has a business and fundamental advantage. They dont mind the cost and the energy use of that because they win if they have a total larger base of people who use it.So I think there will be space for, lets call it a dozen of those. Those will cost you $1 billion, $10 billion, maybe $30 billion to go build, and they have a role to play. Then there will be space for 1000s of the domain-specific smaller models. And if you go into the enterprise, there are 6 million enterprises. Half the spend is in the top 2000 so suppose we just stop it there. These 2000 will guarantee, in the next five years, have enough models that are proprietary to that. So theres a space for 10,000-to-20,000 small models.[Q: Will IBM app store have verified models/] Well, I always like to experiment before coming to a conclusion. So the fact that we put an agent catalog in whats the next orchestrate [watsonx Orchestrate], and we put 150 agents, and we had people calling us wants to find out about it, to get their agent in there. Did I answer [your question] through action rather than words?Editors note: Look for Part 2 which will examine how IBM consulting will be affected by the AI revolution; what Krishna says are his biggest challenges; and more.This article first appeared on HPCwire.
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