Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: You're listening to Bloomberg BusinessWeek with Carol Masser and Tim
Speaker 2: Steneveek on Bloomberg Radio. Remember last week it was all
Speaker 2: the way Yes. Last week Carol Alphabet, the parent company
Speaker 2: of Google, reported a surge and demand for its cloud
Speaker 2: and AI services. It pleased investors, who sent it shares up,
Speaker 2: even as the company said capex for the year will
Speaker 2: be even higher than expected. The company's investing record amounts
Speaker 2: to try to push progress in AI and infuse answers
Speaker 2: and assistance from its Lmgemini into its popular products, including search, and.
Speaker 1: That's where Ryan J. Salva comes in. He is Senior
Speaker 1: director of Product over at Google, where he builds AI
Speaker 1: tools for developers, such as Gemini Cli. I think I'm
Speaker 1: saying it correctly. We're talking about the command line interface.
Speaker 1: It's an open source AI agent for developers, as well
Speaker 1: as Gemini code Assist, Google's AI code assistant, Tim.
Speaker 2: We've got Ryan Jay Salva with us. Also with us
Speaker 2: Mandy Saying Bloomberg Intelligence, Global Head of Technology Research. He's
Speaker 2: also those of the Tech Disruptors podcast. Ryan was featured
Speaker 2: on an episode of the Tech Disruptors podcast that was
Speaker 2: with Mandeep back in the spring. Welcome to both of you, Ryan,
Speaker 2: our audience, some who code, probably more who don't. I'm
Speaker 2: wondering though, if you can explain for everybody out there
Speaker 2: how an AI assist in, including those from Google, how
Speaker 2: they work right now with programmers, and the vision that
Speaker 2: you have in the future.
Speaker 3: Yeah. Absolutely, and first, thank you so much for having me.
Speaker 3: You know, really, what we see today is that a
Speaker 3: lot of developers are really caught in kind of the
Speaker 3: labor of writing if then l's statements, getting caught up
Speaker 3: in little tiny logical loops, and so often developers and
Speaker 3: organizations are really just trying to deliver user requirements. They're
Speaker 3: trying to deliver real value to their customers, and so
Speaker 3: they're able to use AI in large language models to
Speaker 3: write those requirements and natural language translate that code, and
Speaker 3: through that ultimately accelerate their space of their pace of iteration,
Speaker 3: their pace of learning, so that developers can focus more
Speaker 3: on building features rather than on the syntax of the
Speaker 3: code itself.
Speaker 4: And so what kind of productivity benefits you think you've
Speaker 4: seen both internally as well as with clients like maybe
Speaker 4: talk us about one of the best use cases that
Speaker 4: you've come across with Gemini.
Speaker 3: Oh my gosh, there's so many, you know, So I'll
Speaker 3: maybe first talk a little bit about from a metrics standpoint,
Speaker 3: what we tend to see. So one of the teams
Speaker 3: within Google is the Door Research Team. Dora effectively surveys
Speaker 3: thousands and thousands of engineers every year, follows that up
Speaker 3: with hundreds of hours of qualitative interviews. One of the
Speaker 3: things that we're seeing is that today roughly ninety percent
Speaker 3: of developers are integrating AI into their everyday work. They're
Speaker 3: using AI for roughly two hours of so that tidal
Speaker 3: wave of adoption has already swept over us all and
Speaker 3: now we're swimming in the ocean of AI. At Google.
Speaker 3: What we see is today roughly fifty percent of our
Speaker 3: code is being written by AI. And I want you
Speaker 3: to stop and maybe a.
Speaker 1: Match second, say that one more time.
Speaker 3: Five zero fifty percent of code is being written by AI.
Speaker 3: That is a tremendous amount of code. And this is
Speaker 3: in all of Google's products, from search to YouTube, to
Speaker 3: cloud to you name it. And so this is allowing
Speaker 3: our developers to really iterate again at a much much
Speaker 3: faster pace to experiment, to learn, to test out new ideas,
Speaker 3: and ultimately to be just a little bit less precious
Speaker 3: about every line of code they write. Because they're able
Speaker 3: to use the large language models to experiment, it's real
Speaker 3: easy for them to to try out an idea on
Speaker 3: a Tuesday, put it in front of a couple of
Speaker 3: users on a Wednesday, and get a feel for whether
Speaker 3: or not it provides real value. This is the real
Speaker 3: magic and the real value that I feel, like AI
Speaker 3: on Locks, I love that.
Speaker 1: Idea less precious because it almost to me is akin
Speaker 1: to when we got like digital cameras on our phone, right,
Speaker 1: and we used to take pictures with film and everyone
Speaker 1: Like I used to think about everyone, how many more
Speaker 1: photos did I have?
Speaker 4: Left?
Speaker 1: Now I don't even care, right, I just take a
Speaker 1: million photos Ryan. I do wonder, though, if we're less precious,
Speaker 1: we're more efficient, We're more productive, which is what I'm
Speaker 1: kind of getting from this conversation. What does it mean
Speaker 1: for developer jobs?
Speaker 3: Oh so, I mean, let me tell you this right now,
Speaker 3: within my team, we are hiring more engineers, we are
Speaker 3: hiring more product managers. And I see this when I
Speaker 3: talk to so many other enterprises and organizations today. It's
Speaker 3: not so much that the developer's job is any less important,
Speaker 3: but what it does mean is that our job requirements
Speaker 3: are changing. The skills that we need are a little
Speaker 3: bit different. Because developers are spending a little bit less
Speaker 3: time writing syntax, they're spending more time thinking about requirements.
Speaker 3: We're really asking developers to think more like architects, to
Speaker 3: think about systems design, to think about negotiating the contract
Speaker 3: between components. And it means that ultimately, as our next
Speaker 3: generation of creators and developers and builders are coming up,
Speaker 3: we're asking them to think not just about can they
Speaker 3: speak the language of programming, can they speak Java or
Speaker 3: Python or c sharp, but rather can they do good
Speaker 3: basic problem solving and can they think about large systems
Speaker 3: level design. That's where the magic is at.
Speaker 2: We're speaking with Ryan J. Salva, Senior director of Product
Speaker 2: at Google. Ryan, you must remember that New York Times
Speaker 2: article from August Goodbye one hundred and sixty five thousand
Speaker 2: dollars tech job, as it went through all the entry
Speaker 2: level tech job attentry that you're laughing, but the edgry
Speaker 2: level tech jobs that were drying up and people work,
Speaker 2: you know, compside graduates essentially working at Chipotle because they
Speaker 2: couldn't find those entry level jobs. When you say you're
Speaker 2: hiring engineers, are you hiring entry level engineers? Or is
Speaker 2: entry level just dried up because of LMS.
Speaker 3: Yeah. And by the way, I don't mean to laugh,
Speaker 3: because every job is really important and I want folks
Speaker 3: to be able to discover it. But I laughed because
Speaker 3: I do think that the mem is sometimes the headlines
Speaker 3: a little bit easier to grab attention than the ground
Speaker 3: level reality. You know, I have, So that headline's wrong,
Speaker 3: I'm sorry.
Speaker 1: Is so that headline Ryan is wrong?
Speaker 3: You know what? I think that I'm not saying that
Speaker 3: an individual use case or an individual company doesn't go
Speaker 3: through periods where they may let go of workers or
Speaker 3: they may make different hiring decisions. But what I am
Speaker 3: saying is that writ large across the industry, I'm still
Speaker 3: seeing a very, very healthy the engineering ecosystem, and I'm
Speaker 3: still seeing companies really prize and value the developers who
Speaker 3: can come bringing skills that are more appropriate for this
Speaker 3: new AI era. And that does mean less kind of again,
Speaker 3: just being able to speak programming, to be able to
Speaker 3: speak Java or JavaScript or typescript, is not enough anymore.
Speaker 3: The developers really need to think about how they solve
Speaker 3: the pro.
Speaker 4: So, Ryan, one of the stats from Google that has
Speaker 4: caught my attention is the increase, the exponential increase in
Speaker 4: their token count, you know, to almost one point three
Speaker 4: quantillion tokens. Where does coding assistance as well as that
Speaker 4: number one point three contillion? So it's like that.
Speaker 2: Is that what he's going to ask?
Speaker 4: Look, I mean, these numbers are staggering, but when it
Speaker 4: comes to use cases, I think there's a big variance
Speaker 4: between you know, a simple chat bot Q and A
Speaker 4: versus coding agent or and AI agent running for days.
Speaker 4: How would you characterize the contribution of coding assistant and
Speaker 4: the products that you oversee to the overall token consumption
Speaker 4: at Google?
Speaker 3: Sure? Sure? So, I mean I'll start here. We don't
Speaker 3: necessarily count if a token is used for a Google
Speaker 3: search versus software development problem versus someone doing their homework.
Speaker 3: Having said that, what I can tell you is that
Speaker 3: perhaps nowhere better than in software development have I seen
Speaker 3: product market fit better between large language models and a
Speaker 3: particular use case. There are a lot of reasons for this.
Speaker 3: I think probably the biggest one is that large language models.
Speaker 3: You know. You know this when you use Gemini or
Speaker 3: use chat, GPT or any other kind of large language
Speaker 3: model out there, if you're asking it to help you
Speaker 3: write an email or help you write a document of
Speaker 3: some kind. Often the response, the quality of the response
Speaker 3: depends an awful lot upon your personal judgment and your
Speaker 3: personal taste. Whereas with software development, we have decades of
Speaker 3: deterministic quality measures that let us know whether the software
Speaker 3: is good and safe and useful or not. We have
Speaker 3: unit tests and static analysis and all these other ways
Speaker 3: of validating the quality of software. And so what I
Speaker 3: see is a lot of organizations using AI, using agents,
Speaker 3: using large language models to accelerate their engineering life cycle
Speaker 3: because they can deterministically say this is of good quality,
Speaker 3: this is of bad quality, this is something I want
Speaker 3: to use, this is something I don't. That's how I
Speaker 3: see it really accelerating, particularly in the software development space.
Speaker 4: So do you expect a big migration of legacy systems
Speaker 4: to the modern architecture that you mentioned as a result
Speaker 4: of you know, coding agents being that good, or do
Speaker 4: you see limitations in terms of you know where the
Speaker 4: practical use cases are versus you know where the legacy
Speaker 4: technologies are just too hard to move.
Speaker 3: Yeah, you know. Actually, migration and modernization is one of
Speaker 3: the areas where I see the most interest among large
Speaker 3: engineering teams today. There are a lot of reasons for that.
Speaker 3: In some cases, the engineers who are maintaining those legacy
Speaker 3: kind of applications are retiring or moving on. Skill sets
Speaker 3: are atrophying, and there is a thing within software development
Speaker 3: called code rot effectively when an application just sits around
Speaker 3: so long that it atrophies over time and becomes less performed.
Speaker 4: So AI is good in that without consuming too many
Speaker 4: tokens or you know, increasing your bill.
Speaker 3: So what I actually hear is a lot of organizations
Speaker 3: are willing to dedicate waves and waves and waves of
Speaker 3: tokens because the cost of maintaining those legacy applications is
Speaker 3: so high. Often they're having to maintain entire data centers,
Speaker 3: which means that you're paying not only the cost of
Speaker 3: the engineers to maintain them, but you're also paying for
Speaker 3: the facilities, for the hardware, for all of the extra
Speaker 3: it that goes with maintaining those And honestly, if you
Speaker 3: even just take the cost of maintaining them to the side,
Speaker 3: the fact that you're not able to carry those applications
Speaker 3: forward and innovate with them and do new things at them.
Speaker 3: Often that's the real cost.
Speaker 1: Ryan come back. We'd love to continue this. Ryan J.
Speaker 1: Salva over at Director or senior director of product at Google,
Speaker 1: and of course our own man Deep seeing a Bloomberg Intelligence.
Speaker 2: Met For more insights from mand Deep in the Bloomberg
Speaker 2: Intelligence team, check out the Tech Disruptors podcast. You can
Speaker 2: find it on Apple, Spotify, or wherever you get your podcasts.