Alphabet CEO Sundar Pichai on Leadership, AI, and Big Tech
HBR IdeaCast · 30 May 2023
Source: https://hbr.org/podcast/2023/05/alphabet-ceo-sundar-pichai-on-leadership-ai-and-big-tech
Transcript: https://podcasts.opennash.com/shows/hbr-ideacast/alphabet-ceo-sundar-pichai-on-leadership-ai-and-big-tech-cd2d11/ (via OpenNash Podcasts, machine-generated)

Welcome to the HBR IdeaCast from Harvard Business Review. I'm Adi Ignatius. If you're like me, you're spending a lot of time these days talking about AI, specifically generative AI, products like ChatGPT that have the potential to remake how we do business on almost every level. Will this technology make us more productive? Will it eliminate huge categories of jobs? Will it create new ethical challenges? Sundar Pichai is on the front lines of this evolution, and he has an immense opportunity to impact our generative AI future.

He's the CEO of Google and its parent company Alphabet, a company with a market value of more than $800 billion. He's been its CEO since 2015. Pichai is remaking the company around the technology that we're all thinking and worrying about the rapid advancement of artificial intelligence, which is changing Google's business model and which may change all of our business models. I recently spoke with Pichai for an HBR live event on leadership. We talked about generative AI, how he's thinking carefully about Google's role in the future, and the role of business leaders today.

Here's our conversation. So let's jump right in. So so generative AI dominates almost every conversation I have these days. I think that's true for a lot of people who are who are listening now. You're really in the thick of it. And you know, I'd love just to start the conversation. How should how should we be thinking about what gen AI can deliver in the workplace, you know, now and and maybe in the short-term future?

It's definitely an exciting time, and you know, uh feels like a point, you know, moment of inflection. The easiest way I would think about this is I think this notion, even in the context of your workplace, to have an AI collaborator with you. You know, software engineers often, you know, do something called pair programming. We have found that two programmers working together are better than them working separately. So you can now imagine AI being your paired programmer or paired financial analyst or name if you will.

So I think that's the direction, that's the promise, and we're seeing it happen. It's definitely happening for programming, but we have clients like Deutsche Bank is now using generative AI to give insights to their financial analysts. Uh you can imagine radiologists as they're looking at images. They have an AI collaborator which is triaging the pipeline for them, giving them suggestions in case they've missed something and so on. So, you know, that's the trend. So in general, I would say more having an AI collaborator and the use cases for that.

You know, you could be a customer service agent and you have uh a AI chat bot assisting you. And so those are the kinds of use cases and workspace we are beginning to see emerge. But I think the possibilities will keep growing over time.

All right, so let let's get specific about Google. So you've just announced the release of Gemini, you know, which which sounds like a powerful AI tool to compete against OpenAI, you know, chat chat GPT four. What will it be capable of, and how does it compare with, say, you know, Microsoft's being?

Look, we are all building what I call state-of-the-art generative AI models. The model in production, which we are using across our products and which we have launched is Palm two. And the next state-of-the-art model we are working on, you know, with our new combined unit, Google Deep Mind is called Gemini. Where these models are progressing is, you know, they they're all today you have text models, you have image models and so on. But the next generation of models will be multimodal.

Uh they're both trained on different modalities text, images, audio, maybe video, and hence can also have outputs spanning all those modalities. So what does that mean? Uh you go and say, write me an essay about a topic. It's not only going to give you an essay, but if there are visuals and pictures that need to go with you, they can generate that as well. So those are examples of it. Or if you want to bake a cake and you go and ask that question, it doesn't just give you text output, but it also shows you pictures.

And over time, this will keep progressing. So that's the state of the art, and that's what we are excited about Gemini, the notion of adding multimodality. The other progression we are all driving is uh these models can start using tools. You know, if you if you think about humans, you're using tools all the time. You you may pull out a calculator, you're using a word editor. Uh if you want to find out something, you go to Google and find it out.

So training these models to natively understand there are tools out there in the world, and if they need to help the user with something, they can also call on these tools. So that's another thing we are building into these models. So those are examples of how the state of the art is progressing. So I think it's an exciting time. I think there are a few companies which are building what what I call frontier models, you know, these AI models, which are, uh, state of the art.

And you know, this is our seventh year, you know, as an AI first company. We built a lot of the underlying technology powering these models, and so we are deeply committed, uh, in a in a responsible way to continue driving the state of the art here.

So I'd like to say that, you know, since since this became available to the the public broadly in, you know, November thereabouts, that we've sort of gone through three stages. The first stage, we all just played around with it, you know, write me, uh, you know, uh a version of Ulysses in HBR style or something, or and then and then we tried to break it, you know, do you love me? Uh, chat GPT, but but now there's a sort of application thing.

But but I'd love to ask, you know, did you have a moment where you played around with this and you were surprised and amazed and wowed by what you got back?

No, this is, uh, you know, we have had. So internally, we had built what was called Lambda. Internally, we had built a conversational dialogue based on, uh, these large language models, and I remember, you know, speaking to it. Uh, we gave it various personas. So, for example, we you could ask it to behave like planet Pluto, and you could have these long conversations with it. And you know, it's a wonderful learning tool. In fact, I had my son and both of us spent some time talking to it.

And, you know, you can learn about the solar system and you can keep asking it questions. But there was a moment talking to Pluto. At some point, I felt like, you know, it felt very, very lonely, and the conversation slightly went to a darker place. And, you know, that was my first experience, which kind of unsettled me and showed the power of what's possible, how the effect it can have on humans. By the way, it makes sense because you can imagine, uh, the model trying to think about Pluto.

Pluto is in a in a cold, faraway place in the uh universe. So no wonder that, you know, it kind of started taking some of those attributes in its personality. But that was my first experience, and you know, since then I had a few other experiences. So these are powerful models, and you know, and I think a lot of us are working on making sure we build in safety systems. Uh, we add a layer of responsibility uh before we really widely deploy it.

It's part of the reason I think as Google, we've been, uh, more conservative in our approach, given the scale at which we serve users. But, yeah, I've definitely had those experiences.

It it does seem like Google has been more conservative than than some others who have have rushed, uh, generative AI out there, but it's still pretty fast. I mean, this has all happened in in sort of days and months. And can you talk a little bit more? I mean, how do you balance the need to to be in the market? The product is out there and you need a product. How do you balance the need to be there, the need to innovate with the kind of caution that you were just mentioning?

I think it's a great question. And you know, we we know there is inherent, uh, you know, trade-offs and tension here. And, you know, and we frame it internally that way. And, you know, we want to be innovative. Uh, there is amazing opportunities to be unlocked, and so we want to take a bold approach to drive innovation. Uh, but we want to make sure we get it right, and so we want to be responsible in our approach.

And so we call, uh, you know, we we think about our approaches being bold and responsible, and and and understanding that framework and approaching it that way. And so we are we are not focused on always being first, we're gonna be focused on getting it right, but working working at it with a sense of excitement and an urgency to make progress, but slowing down when needed to make sure you get the additional safeguards in. You give early access to other people outside so that they can test it, give feedback to us and so on.

So I think all that is going to be important. And I I think, you know, it's something you have to, you know, build into the organization to embrace those trade-offs and work work at it, work at both the same time. And, you know, we just recently had our largest, uh, developer conference, and we spoke about all our I-o-pro, uh, you know, all our AI product work we are doing. We are thinking about making AI helpful for everyone across our products, and so we have incorporated generative AI in over twenty five of our products, be it Gmail or Google Docs or search or YouTube and so on.

And, uh, again, we, you know, so we want to be bold and responsible at the same time.

So when you say putting in safeguards, talk about that a little bit. What would safeguards mean in this case?

Let me give a few examples. One is what we call adversarial testing. So we ourselves try to break it, right? So, you know, we have our safety and security teams, we have red teams whose explicit goal is to break these models in various possible ways. So making sure after development, you give these teams time to stress test these models, and then and then drive an iterative cycle where you make the models, uh, much better. That's one example.

Another example is we are we are still in the process of doing this work, but we are doing work to add watermarking and metadata. So think about AI generated images. I think it's going to be a responsible way to do it is to help people understand that these images were generated by, uh, generative AI. And so, you know, adding watermarking so that other systems can detect that these images were generated using AI. And and, you know, having associated metadata so that if you want to know when this image was created, who created it, et cetera, you can get that information.

So now we are doing the underlying technology and the research work to make sure those capabilities exist as we deploy these more widely. So those are all examples of the kind of work you you can do with a lens of, uh, safety and responsibility.

So you used the term inflection point earlier in our conversation. You know, we've all seen technologies come, arri, you know, and and it it it feels like the next big thing. Some are, some aren't. This feels different, this this feels truly transformative. I is that how you see generative AI, and, you know, c project a little bit of the longer term future, then how does how does this technology remake what we do?

I mean, AI is, uh, you know, is a is a deep platform shift. Uh, you know, many years ago, I I called AI the most profound technology humanity, uh, is working on and will ever work on, uh, more profound than fire or electricity. And and that was the reason we said our company is going to be AI first. So I do think it's a it's a deep platform shift. It will touch, uh, every aspect of our lives, every aspect of society, uh, every industry sector, if you will.

But it is important to understand while we are talking about AI broadly, generative AI is a moment in time and it's one aspect of AI. It's just that these large language models are now useful enough, uh, to use them in a variety of, uh, scenarios. But I think there is more progress to be had and and I do think, you know, we will go through some moments of ups and downs. Uh uh but but the progress I think will continue.

Uh, but generative AI is just one facet of the broader progress we are making with AI overall. But I do think it's important to prepare for it. Uh, I think we should channel all this excitement to make sure other stakeholders are getting involved. I think this is an area for sure where it, you know, governments will have a role to play, nonprofits, academic institutions, international countries needing to come together and developing frameworks by which they can align for safety and responsibility.

So all those systems need to adapt and that's going to take time. So we need to embrace the excitement and channel it in a way in which, as as society, as humanity, we are building the foundational blocks to tackle, uh, what's coming our way as well.

So there there's talk in the air of regulation. Would you welcome regulation in this sphere? And and what? What's the kind of regulation that we would need, where, you know, companies like yours could still innovate, but as you say, we would we would ensure safety and and other things.

The way I think about it is it's too important an area not to regulate, and also too important an area not to regulate. Well, you know, you have to get the balance right when when a technology in its early stages and developing. Uh, you know, you have to allow for innovation to proceed, uh, but at the same time building in the capabilities and and effectively the safeguards that you would need. So I think regulation will play a strong role.

I think to me, you know, at least speaking from a US standpoint, you know, I think the most important regulation which we can pass, which will also help AI, is is a stronger privacy foundation. So privacy regulation and framework, which we still lack, uh, a national, uh, privacy bill, I think would be a foundational approach, I think, because AI can build upon that. I think there are many sectors today which are already regulated, and AI can naturally, you know, fit within that framework.

If you're in healthcare and you're deploying systems today, you go through a lot of regulation to get that done. And so I think AI can fit in that framework to start with. The main areas I would think about it is, you know, what is a framework by which, you know, governments or regulators can validate, uh, uh, the the models that are being developed and make sure they are safe for public use. And I think you can have a progression in terms of how onerous you make them.

But I think initially it's both building the capabilities, uh, amongst governments, and so thinking through the right agencies, the right regulatory bodies who can have oversight and over time both imposing requirements. And and you have to be careful because, you know, you can't make the uh regulations onerous. That means the big companies can do it, but, you know, you stifle innovation from startups or from the open source community. So it's gonna be difficult to get this right.

So I would focus in more initially on building the capabilities in terms of, you know, developing the actual talent and abilities to interact, form the right public-private partnerships, and over time c codified into better laws. But I think it's it's got to be a multi-stakeholder process to do to get there.

I I do we. We solicited some questions from our subscribers beforehand, so I wanna I wanna ask one of them. This is from Afaf from Uh, who's in North Carolina in the US. And the question is, you know, how should companies think about training and adapting their non-technology workforce to support a, you know, a a generative AI journey strategy?

I think it's a great question. I think I think, you know, in every organization, I think it's important to unlock use cases and and deploy it in the context of your workflows. I think one of the interesting things we have learned about these models is we call this fine-tuning. You can take these base models and in the context of your organization, you know, fine-tune it based on the data of the organization, and they can really start working well for for for the context you have.

So I would think about, you know, deploying it in the context of these organizations. It could be as simple as, you know, we're building this into products, like into our productivity tools, be it, uh, Google Docs or Google Slides or Google Sheets, and others are doing the same. And so you can imagine getting your workforce used to this notion of of working collaboratively with AI assisting you. And I think that mindset change is going to be important for, uh, organizations to go through, for workforces to adapt.

And and so I think that's where I would start. But I think it's important in any organization, from the senior most levels, you're thinking about what are areas which you can transform by deploying generative AI. Uh, you know, to me, I was excited, you know, uh, I mean, last week we announced this, but Wendy's has used generative AI so that people can, you know, use voice and as part of that drive-through, you know, order and and the system works that way.

But what they've learned, you know, people speak in thousands of different ways, and so to use the AI system to make that process more efficient. I think that's an example of an organization applying generative AI in a way that delights their customers, their workforces get becoming more familiar with it. And so I think the sky is the limit in terms of how you can imagine to use these things. But I would get the journey started.

Well, so so a little bit more on that. So if if somebody's watching this, they're like, okay, this sounds pretty cool. I I don't really know how to apply it in my company. I'm not sure if there is an application in my company. How do you? How do you get started? How do you get comfortable with the technology and figure out its potential?

You know, today, many of many of these companies are using a cloud provider, and so I think it's a good conversation to start with your cloud provider. Hopefully it's Google, uh, you know, to talk about. We all have, you know, generative AI's tools and solutions which we can apply in the context of your workspace. And so that's where I would ask the question and and I would get pilot programs started. I think people tend to overthink the initial approach.

I think, literally, this is about seeding your organization with four to five pilot ideas, challenging your organization from the top down and saying, you know, where all can you apply generative AI seeking ideas and then getting a few pilot proposals underway. And I think that gets the organization thinking about it. It's almost like a new muscle memory you need to develop. So there is a cultural transformation to go with it. And so, to me, it's about challenging your teams, your leaders, and and getting a few pilot ideas underway.

So let's shift gears a little bit. I, you know, the the tech sector, and and including Google, has taken some hits in recent months. You know, there've been layoffs, spending cuts, you know, the what happens in a cycle like this. What's your expectation for, you know, the severity of this downturn, and how are you trying to weather the storm and and emerge from it, you know, stronger rather than weaker?

You know, we've taken so many macro shocks as as an economy and as a global system, uh, you know, from the pandemic to the war in Ukraine and and to rising interest rates and so on, right? So, you know, there's a lot of macro shocks. And and so at this point, I think the right thing, and I think what most organizations need to assume, is that, you know, the you know, that these tough conditions are here, and you know, you have to constantly, uh, work on making sure your organization is adapting.

From a Google standpoint, I've approached it with two main, uh, ways of thinking about it. First is it's important to stay the course in terms of driving innovation for the longer term. I think that's what over time will separate the pack from, uh, uh, the the companies which will get this moment right, particularly for us, sensing this moment, the point of inflection with the AI. We are focused on investing in R and D, driving that long-term innovation that is needed with AI.

And and in, if anything, doing more of it, uh, through a moment like this, I think is, uh, is extraordinarily important. So that's that's an aspect of weathering this moment. The second part of it is to do the first part well. You know, you have to make trade-offs and and so really going to first principles, having clarity about what are the things of all the things you're doing that really make a difference for the long term, and hence sharpening your focus as a company, driving efficiencies and and making the tough decisions needed.

Uh, and doing that on a sustained, ongoing way is what will allow you to build for the long term well. And so it's doing both, which is not always easy. Uh, you know, I think you're always pulled towards doing more of the latter, but I think it's important to get both right. And at least at a Google standpoint, you know, focused hard on making sure we are investing both for the long term and doing that well at the same time.

Using this moment, it's a moment of clarity, and having all these constraints actually drives clarity. And so you dig deep and find what really matters, and then you focus organization more on those efforts.

So you're the CEO of one of the most recognizable brands, uh, in the world, and you're CEO at a time where, you know, the rules have kind of changed. The the the rise of social media and the expectations are that CEOs do more than run companies effectively, that they need to to be have a public presence and take stands on certain issues and and and address their own, uh, workforces, sometimes publicly, if they're not happy with something.

And it's very complicated. I I, you know, how do you think about this evolving role? And, you know, what, what is the what is the role of a CEO in 2023?

I mean, it's a it's a good question, something I think in in a Google context has meant a lot as well. Uh, I do think, you know, I do think the world has evolved to uh, to a place where, as a CEO today, you have a lot of stakeholders. It's not just your shareholders or your customers, it's your employees, it's the communities in which the company operates in. So it's important to keep that in mind. I think the way I've approached this is, I think you have to be clear about the few issues that really matter to the company.

And and it could matter to the company because it it matters a lot to your employees, or it matters to the company because you want to be a good citizen in the communities you you work in, et cetera. But having clarity around around the few issues, the few values you stand for, and being consistent about it, I think, is more important. And I think where you tend to drift is by, you know, by spreading yourself too thin, if you will.

So, you know, what I've tried to do is being clear about the values we care about as a company, and be it sustainability or building a a diverse workforce, and and making sure, you know, we stay committed to it. And but, you know, but committed to it in the context of the work we do and the fact that it would, uh, drive a better company in that process. So I think that's where maybe, you know, you want to have a framework with which you're working on and and.

But I do think it's important to keep all stakeholders in mind as you're running a company and doing it with empathy, I think, is more important than ever.

When I think back to the the the days when Google was was founded, you know, I feel like its ambitions were were relatively limited and relatively clear. Now the company is much bigger, the you know, there's the there's much more going on. How do you think about, you know, what is your big ambition, I guess, for the for the company now?

I mean, we set out our big ambition, uh, many years ago when we said we felt fortunate that our mission feels timeless. You know, you know, you know, having a mission to organize the world's information and making it universally accessible and useful. You know, if anything, or or with with time passing, it just felt more relevant than before. So, you know, we feel fortunate with that. But what's excited us that AI allows us to, you know, pursue the most ambitious, you know, version of that mission.

And so we think about it as, you know, how do we make AI helpful for everyone? And and we are focused on four main areas, you know, first is to improve knowledge and learning, second is boosting creativity and productivity, third, which has been important to us. It's not just for us. We want to enable others, other organizations, be it companies, be it nonprofits, be it governments, to use AI to make their organizations better. And finally, and and arguably the most important of it all, is to do it safely and responsibly.

That's our ambition. And, you know, and and doing it, doing it in a way that it benefits everyone, uh, you know, is what what I'm really focused on, uh, uh, with the company. And and you know, we couldn't be more excited about it.

So, so building on that, I wanna I wanna bring in one more question from, uh, that we solicited from our subscribers. This is from Antonio in Portugal. Question is, we've seen Google experiment with various moonshot projects. So Sundar, if you had the chance to pursue an entirely outlandish or whimsical project, what would it be and why?

You know, uh, we are working on quite a few, you know, we are trying to solve quantum computing, uh, which is as moon shoty as it gets, or, uh, you know, we have other efforts underway. Maybe I would say two things, uh, one, you know, I think, you know, if we could do more, and we are doing it today by supporting other companies, so we don't need necessarily do it ourselves, is to work hard to enable a technology like nuclear fusion to happen.

I think providing abundant, clean, renewable energy at an affordable price point, you know, is as game-changing as anything I can I can think about. And and so that's an example of a moonshot, you know, would love to be able to do. The other thing I would say is, you know, my life got transformed by getting access to computers and technology and and gaining the power of, uh, you know, products like Google in my hands. I think with AI, we have the chance.

A moonshot is, I think, over time, we can give every child in the world and every person in the world, regardless of where they are and, uh, you know, where they come from, an access to the most powerful AI tutor, which can teach them anything they want on any topic. And obviously, it needs to work in conjunction with their teachers and parents and so on. But I think a promise of something like that is real, and you know, that's an example of a moonshot, you know, I'd get super excited about.

That's Sundar Pichai, the CEO of Google, who was speaking with me at HBR Live Leaders Who Make a Difference. And we have more episodes and more podcasts to help you manage your team, your organization, and your career. Find them at hbr dot org slash podcasts or search HBR in Apple Podcasts, Spotify, or wherever you listen. This episode was produced by Mary Du. We get technical help from Rob Eckhart. Our audio product manager is Ian Fox, and Hannah Bates is our audio production assistant.

Thanks for listening to the HBR IdeaCast. We'll be back with a new episode on Tuesday. I'm Adi Ignatius.