Ryan J. Salva (2:54)

Yeah, absolutely. And first, thank you so much for having me. You know, really what we see today is that a lot of developers are really caught in kind of the labor of writing if then else statements, getting caught up in little tiny logical loops. And so often developers and organizations are really just trying to deliver user requirements. They're trying to deliver real value to their customers. And so they're able to use AI and large language models to write those requirements and natural language translate that to code, and through that ultimately accelerate their space of their pace of iteration, their pace of learning, so that developers can focus more on building features rather than on the syntax of the code itself.

Mandeep Singh (3:41)

And so what kind of productivity benefits you think you've seen both internally as well as with clients? Like maybe talk us about one of the best use cases that you have come across with Gemini.

Ryan J. Salva (3:55)

Oh my gosh, there's so many, you know. So I'll maybe first talk a little bit about from a metrics standpoint, what we tend to see. So one of the teams within Google is the DORA research team. DORA effectively surveys thousands and thousands of engineers every year, follows that up with hundreds of hours of qualitative interviews. One of the things that we're seeing is that today roughly 90% of developers are integrating AI into their everyday work. They're using AI for roughly two hours a day. So you know, that, that, that tidal wave of adoption has already swept over us all, and now we're swimming in the ocean of AI. At Google, what we see is today roughly 50% of our code is being written by AI. And I want you to like, stop and maybe imagine every second, say that one more time. 5, 0, 50% of code is being written by AI. That is a tremendous amount of code. And this is in all of Google's products, From search to YouTube to cloud to, you name it. This is allowing our developers to really iterate again at a much, much faster pace to experiment, to learn, to test out new ideas, and ultimately to be just a little bit less precious about every line of code they wr. Because they're able to use the large language models to experiment, it's real easy for them to try out an idea on a Tuesday, put it in front of a couple of users on a Wednesday and get a feel for whether or not it provides real value. This is the real magic and the real value that I feel like AI unlocks.

Co-host (5:41)

I love that idea of less precious because it almost to me is akin to when we got digital cameras on our phone, right? And we used to take pictures with film and everyone. I used to think about everyone. How many more photos did I have left? Now I don't even care, right? I just take a million photos. Ryan, I do wonder though, if we're less precious, we're more efficient, we're more productive, which is what I'm kind of getting from this conversation. What does it mean for developer jobs?

Ryan J. Salva (6:08)

Oh, so I mean, let me tell you this. Right now, within my team, we are hiring more engineers, we are hiring more product managers. And I see this when I talk to so many other enterprises and organizations today. It's not so much that the developer's job is any less important, but what it does mean is that our job requirements are changing. The skills that we need are a little bit different because developers are spending a little bit less time writing syntax, they're spending more time thinking about requirements. We're really asking developers to think more like architects, to think about systems design, to think about negotiating the contract between components. And it means that ultimately, as our kind of next generation of creators and developers and builders are coming up, we're asking them to think not just about can they speak the language of programming, can they speak Java or Python or C Sharp, but rather can they do good basic problem solving and can they think about large systems level design? That's where the magic is at.

Carol Massar (7:16)

We're speaking with Ryan J. Salva, Senior Director of Product at Google. Ryan, you must remember that New York Times article From August, goodbye $165,000 tech jobs. It went through all the entry level tech shop attention. You're laughing, but the entry level tech jobs that were drying up and people work, you know, comp sci graduates essentially working at chipotle because they couldn't find those entry level jobs. When you say you're hiring engineers, are you hiring entry level engineers or is entry level just dried up because of LLMs?

Ryan J. Salva (7:46)

Yeah, and by the way, I don't mean to laugh because every, every job is really important and I want folks to be able to discover it. But, but I laugh because I do think that the MEM is sometimes the headline's a little bit easier to grab attention than the ground level reality.

Co-host (8:04)

So that headline's wrong. I'm sorry, so that headline, Ryan, is wrong.

Ryan J. Salva (8:09)

You know what? I think that I'm not saying that an individual use case or an individual company doesn't go through periods where they may let go of workers or they may make different hiring decisions, but what I am saying is that writ large across the industry, I'm still seeing a very, very healthy engineering ecosystem. And I'm still seeing companies really prize and value the developers who can come bringing skills that are more appropriate for this new AI era. And that does mean less kind of, again, just being able to speak programming, to be able to speak Java or JavaScript or TypeScript is not enough anymore. The developers really need to think about how they solve the problem.

Mandeep Singh (8:53)

So, Ryan, one of the stats from Google that has caught my attention is the increase, the exponential increase in their token count, you know, to almost 1.3 quintillion tokens. Where does Coding Assistant.

Carol Massar (9:06)

Excuse me, what Was that number?

Mandeep Singh (9:07)

1.3 quintillion.

Carol Massar (9:09)

So it's like that number to my son later. That's what he's going to ask.

Mandeep Singh (9:14)

So look, I mean, these numbers are staggering, but when it comes to use cases, I think there's a big variance between, you know, a simple chatbot Q and A versus coding agent or an AI agent running for days. How would you characterize, you know, the contribution of Coding Assistant and the products that you oversee to the overall token consumption at Google?

Ryan J. Salva (9:39)

Sure, sure. So I mean, I'll start here. We don't necessarily count if a token is used for a Google search versus a software development problem versus someone doing homework. Having said that, what I can tell you is that perhaps nowhere better than in software development have I seen product market fit better between large language models and a particular use case. There are a lot of reasons for this. I think probably the biggest one is that large language models, now you know this when you use Gemini or use ChatGPT or any other kind of large language model out there, if you're asking it to help you write an email or help you write a document of some kind, often the response, the quality of the response depends an awful lot upon your personal judgment and your personal taste. Whereas with software development we have decades of deterministic quality measures that let us know whether the software is good and safe and, and useful or not. We have unit tests and static analysis and all these other ways of validating the quality of software. And so what I see is a lot of organizations using AI, using agents, using large language models to accelerate their engineering life. Cycle because they can deterministically say, this is of good quality, this is of bad quality, this is something I want to use, this is something I don't. That's how I see it, really accelerating, particularly in the software development space.

Mandeep Singh (11:15)

So do you expect a big migration of legacy systems to the modern architecture that you mentioned as a result of, you know, coding agents being that good, or do you see limitations in terms of, you know, where the practical use cases are versus, you know, where the legacy technologies are just too hard to move?

Ryan J. Salva (11:37)

Yeah, you know, actually, migration and modernization is one of the areas where I see the most interest among large engineering teams today. There are a lot of reasons for that. In some cases, the engineers who are maintaining those legacy kind of applications are retiring or moving on, skill sets are atrophying. And there is a thing within software development called code rot. Effectively, when an application just sits around so long that it atrophies over time and becomes less performant.

Mandeep Singh (12:08)

So AI is good in that without consuming too many tokens or increasing your bill.

Ryan J. Salva (12:14)

So what I actually hear is a lot of organizations are willing to dedicate waves and waves and waves of tokens because the cost of maintaining those legacy applications is so high. Often they're having to maintain entire data centers, which means that you're paying not only the cost of the engineers to maintain them, but you're also paying for the facilities, for the hardware, for all of the extra IT that goes with maintaining those. And honestly, if you even just take the cost of maintaining them to the side, the fact that you're not able to carry those applications forward and innovate with them and do new things, often that's the real cost.

Co-host (12:56)

Ryan, come back. We'd love to continue this. Ryan J. Salva over at Director, Senior Director of Product at Google and of course our own Mandeep Singh of Bloomberg Intelligence.

Carol Massar (13:05)

And for more insights from Andeep and the Bloomberg Intelligence team, check out the Tech Disruptors podcast. You can find it on Apple, Spotify, wherever you you get your podcasts.

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