Google DeepMind's AI playbook for engineering at hyperspeed | Philipp Schmid

0:05Welcome to Dev Interrupted. I'm your host Andrew Ziegler. And I'm your host, Ben Lloyd Pearson. This week, we're looking at how Cloudflare is using Agentic AI, an interesting new partnership between AI and consumer culture, how developers really feel about their code assistance, and a special anniversary that only nerds can celebrate. Ben, what catches your eye? Well, I think I'll save the celebration for last. So maybe let's start with the new partnership with AI. Yeah, so there's been some interesting news in the last week of OpenAI partnering with, of all companies, Mattel to create and design new products without necessarily licensing the toy maker's intellectual property.

0:52Key detail. This isn't a partnership of Mattel somehow giving training data to OpenAI, although I'm not sure how useful that would really be. But instead, this is a partnership to create an end consumer product, the creation of digital assistance based on Mattel characters or games that are more interactive within the Mattel universe. We're all pretty familiar with Mattel's recently branched out into things like mobile games and even making movies. You might remember the Barbie movie that came out in recent years. And OpenAI has also been courting companies to help them enhance their products.

1:24So it's an interesting collaboration between two companies that you wouldn't necessarily expect to work together. And their first collaborative release is expected to come out later this year. Yeah. You know, recently I've been thinking a lot about why is it software engineering that's getting all of the focus with AI disruption? We have like all these companies, like the company that built Cursor, like they're rumored to be generating half a billion dollars in revenue, which is like pretty, pretty crazy. But at the same time, like there are so many other fields out there that have way more potential for disruption, potentially even faster than software development.

1:58And, you know, Andrew, just think about the amount of content that we produce here at Dev Interrupted. Like that has been completely transformed by AI already. Yet there aren't a whole lot of products that are coming out. And there's not a whole lot of focus on that market for some reason. So, you know, this story is a little bit outside of the typical beat here at Dev Interrupted. It's not about engineering or anything like that. And it's, you know, certainly not about producing like higher volumes of code with AI. But I think what's really happening here is that we're seeing all these stories about how AI companies are fighting in courts over intellectual property ownership.

2:34And, you know, I think the future is really going to be built, at least in AI, by companies that have good training data. Like the better your training data is, the better your models are going to be. If you have poor training data, your models are going to reflect that. So I can easily imagine a future that we may be moving towards where there's new regulations, new laws around the use of intellectual property within generative AI. I think what's happening is OpenAI is trying to use IP to establish a moat. Like if they build these partnerships with third parties and get access to their commercial data, that actually starts to build a bit of a moat for them where they have high quality training data that they potentially are the only ones that are able to use.

3:17So, you know, in the meantime, it might take a whirl to see if I can, you know, maybe work with ChatGPT to come up with some really cool car designs with my four-year-old because that does seem like a really fun application of this. Were you a Hot Wheels kid, Ben? You know, I was the gigantic box of cars that were collected through many generations of children. I was also that kind of kid, but I had a lot of Hot Wheels. And in fact, this is full circle for me because my very first computer was the Windows 98 Hot Wheels computer, the very iconic one. I had all of the accessories. My dad got it for me for Christmas.

3:51It probably honestly sparked all of my downstream interest in technology. and probably without having that Hot Wheels computer, I don't know if I'd be on this podcast. So it's really fun to see it all come full circle. Hot Wheels and Barbie computers and their users unite. I never would have thought that eight-year-old Andrew Ziegler could make me-year-old Ben jealous, but here we are. It was truly a formative experience having that computer. Wow. Awesome. All right. So next up, let's see. Let's talk about an interesting thing I saw out of Cloudflare in the last week. There was an article from Max Mitchell that dove into the Cloudflare's claw-generated commits around using agentic AI to work on parts of their code base.

4:40And it was really interesting on two levels. You have Cloudflare engaging in this really thoughtful practice of encouraging their engineers to directly commit the prompts they use to generate code along with the code into source control. This is a smart way of reducing the ambiguity of code ownership and also being able to communicate about what the tool is supposed to do or what the commit is achieving. It also allows a team to build best practices because you're saving all of this great record stuff right into your source control. Obviously, your mileage is going to vary. There's going to be some cases where it doesn't make sense to put that kind of thing in the metadata.

5:18But this was a really interesting analysis at how Cloudflare used this tool. And there were things that stood out to me and also to Max, who wrote this article, about how there's some things like where humans are still intervening, like moving certain files around or doing some cleanup or doing some renaming. There were some cases where the tool they were using was cursor or otherwise was trying to use like a bash command to just move a file. Like there's sometimes times where it's helpful to step in. And you see this in the commits because that thought process of, oh, I would have been faster if I'd done this myself is right in the commit.

5:55So pretty, pretty cool exploration. I really loved on top of it. Max's actual like really thoughtful personal journey, looking through all of those commits himself and reflecting on what that meant about their development process. A really great article. all around. I really love how the engineering culture as a whole, like software engineering culture is really great at just being transparent and sharing ideas and like trying to help each other build best practices. So, and you know, I've been saying for a little while that like now is the time to be experimenting and learning with AI. There's not a whole lot of like revolutionary productivity improvements happening yet, but there are a lot of people learning very interesting things about how to apply AI to software engineering.

6:39And the reality is that like the tooling in this space is still extremely immature. The people who are building AI workflows right now are mostly still doing it through bespoke custom operations. That's just the fact of the reality today. And I love seeing these like firsthand or even like a secondhand experience of AI, because there's still just so much to learn about applying AI to existing workflows and getting the most out of it. But, you know, Max made his points better than I ever could. So, you know, our audience really just needs to go to the show notes, go check out the article, go learn something about AI today from somebody who's in the trenches with it.

7:20Yeah. So what's our next story, Andrew? I love this article that came out from Lead Dev last week. This one's written by Chantal Kapani and it actually features a quote from myself in it as well as many folks in the developer productivity developer experience field but what I liked about it the most is that it built on a narrative that we've all been experiencing and talking about that you know just unchecked and goalless AI adoption doesn't help anybody and it doesn't ultimately deliver on the promises that y 'all can't even seem to agree on in the first place like I really what I'm saying in all of this is that AI coding assistants and their adoption across different organizations is not just like a one and done, zero to one process.

8:02There's a lot of communication that has to happen to make that successful. And part of that is getting your engineers on board. So this is an article that looked at how devs don't necessarily feel more productive when AI assistants are just put in their hands. You know, it needs to be coupled with things like effective onboarding strategies, education, examples. This Cloudflare example we just talked about, what a great vehicle for making your devs feel more productive with assistant AI because you can point to this fantastic resource to show them how it works for your own organization. But there's a real disconnect in those kinds of examples at most organizations in software engineering, which is what this article looked at.

8:44And this kind of is something we've been talking about a lot on Dev DevInterrupted, which is why I really resonated with the final piece. And it's about that, like, you know, velocity isn't everything. Just because you're going fast doesn't mean you're going to the right place. And we had an article that we dropped last Thursday on DevInterrupted. We've been doing more Thursday sends recently. You should go check them out. And literally, the article is called Faster Coding Isn't Enough. It talks about exactly this. So check out this article in the show notes from Chantal about AI coding assistance and how devs feel.

9:16really feel about them. Yeah. And that article was based on some research that we conducted and as well as a workshop that we held recently. We brought in experts from AWS, Atlassian, ThoughtWorks, people who really know a lot about the forefront of AI, came in and talked about the struggles that engineering teams are facing today. And as a part of this work, we conducted a survey and we asked participants about their AI usage and found that the vast majority of AI adoption has been centered around the coding process. So we all know that software engineering is way more than coding. And you have to do things like planning, designing, architecting, testing, deploying, like all of that is a critical component of the software delivery lifecycle.

10:02And AI has hardly scratched the surface outside of the IDE. You know, think of the theory of constraints. Like if all you do is generate more code, you're creating new bottlenecks. you're not necessarily producing more impact. And that's why you need to have complete visibility into where AI is being deployed, what it's impacting, and where your team is being slowed down. And this is actually my favorite part of what we're doing here at Dev Interrupted right now. Everyone is early on in their journey to AI-driven development. And we get to be here to learn from these amazing, amazing experts within our community and share their knowledge with our listeners and our readers.

10:42So there's a lot of pressure right now to adopt AI to move faster. Don't put all that pressure on your developers. It's our responsibility as leaders to be strategic about how we deploy AI and to demonstrate the positive impact that it's having on the organization without overburdening developers with new challenges. Yeah, well said. So we've got one last fun story. So what do we have here, Andrew? Yeah, we're ending this on a fun note. So congratulations on creating the one billionth repository on GitHub. Maybe we can edit in some confetti or some fanfare here. This is a fun article, or rather this is like a fun news tidbit, really, that made its way onto Hacker News.

11:29Someone created what ended up being the one billionth repository on the GitHub repository service, And the result was people swarmed the repository to congratulate the owner. And obviously, probably whatever they actually had in mind for that repo got derailed. But it did bring to mind a bunch of fun discussions. You should really go check them out in this comment thread about engineers at places with IT help desks or huge data lakes and places where they have a gazillion of certain kind of ids and people trying to game certain systems to snag you know very recognizable or huge rounded numbers a billion uh so ben have you ever gotten like the zillionth id on something yeah well this is a funny story because the user who created this repo chose a very common swear word to name it and it all took off when a github employee just showed up and left a comment like hey congratulations on being the billionth repo on GitHub.

12:29So, you know, first of all, rest in peace to that poor developer's GitHub profile who is now, I imagine he's in the mountains of Nepal somewhere trying to get away from it all with all the attention that has suddenly come down. But fortunately, like we've covered some stories in the past where like some developer goes viral and the developer masses descend on them and have all sorts of negativity. This has not appeared to be that case. There's been a lot of just lighthearted and positive humor around it. So a lot of typical developer memes, like it's a pretty funny thing. I would wager a bet that this developer, this particular developer, figured out that this moment was coming and had some sort of script that was just waiting to capture that billionth spot.

13:11And if that's the case, like kudos, he did it. Like, good for you. I wouldn't be surprised if there was a whole bunch of other developers out there who are disappointed because their script did not capture the billionth. I love to say, I think this might have created some villains for sure. I can imagine there were a whole bunch of folks who were like, that number was supposed to be mine. Imagine being the billionth in one repo. That's what I'm saying. Someone is that. Imagine being the one before it. That's a lot of nines. Well, I think it was the same developer, too. If you look at the history, I think they tried to get it to them.

13:42Oh, okay. So now we're getting down the logs. All right, ladies and gentlemen. Well, we're going to get to the bottom of it and let you know. Yeah, developing news. We'll follow up if anything else comes out. So, Andrew, tell us about our guest today. Yeah, in just a moment, I'm chatting with Philip Schmid from Google DeepMind. He's an AI developer relations expert, even has expertise from Hugging Face as well. We're going to get a closer look at Vio and Gemma and learn about how startup culture works inside of a large org like Google. If you're leading an engineering team in 2025, AI isn't optional.

14:17It's your next competitive edge. Dev Interrupted and Linear B are out with a new guide. The Six Trends Shaping the Future of AI-Driven Development. It breaks down how teams are evolving, moving from scattered tools to orchestrated AI systems. Learn how teams are modernizing infrastructure and building trust in agentic workflows. It's packed with insights from our community of engineering leaders. This is your roadmap for building the AI-driven team of tomorrow. Head to the show notes to find a link to the guide or visit linearb.io to learn more. Today we're chatting with Philip Schmid. He's a senior AI developer relations engineer at Google DeepMind.

14:57And his background also includes being a technical lead at Hugging Face. So Philip's career path is a preview of what modern AI native teams are starting to look like as product-focused engineers with AI-driven learning loops and a whole new way to think about scaling knowledge and productivity within your org. And today we're diving into how AI is fueling the need for these generalist engineers that maybe wear multiple hats, but they flex the best muscles that they have because of the skills they have available. And AI-assisted self-education is reshaping that whole process, including onboarding and skill growth.

15:32And we're going to dive into what some of that means for a small startup-like environment like DeepMind within a big tech company like Google. It's going to be really interesting. So, Philip, thanks so much for joining us on the show. Thanks for having me. So at the beginning here, I want to kind of set the scene for Google DeepMind. You know, it's a smaller kind of startup-like organization within Google. And your team is building and advocating and educating, doing really whatever it takes to move fast and scale adoption. And it's a new kind of engineering culture where the contributors are expanding what they work on, and it's changing what success looks like.

16:07You're always redefining it. So, Philip, help us have a glimpse at what it's like working in an environment like Google DeepMind within Google. Yeah, I think it's, I would say, still hard to say that's like a startup. I mean, Google DeepMind grew a lot in the recent years. There was like this merge with like Google Brain a couple of years ago. And I think now it's like a few thousand people. So it's like not like 10 to 20 anymore. It's huge. But like inside the scope of like Google and like Alphabet, it is really still much different and like much more like action driven. And I mean, you probably have seen like that the last few months with like Gemini and Gemma has been like completely crazy from like shipping on a weekly basis to like new models, new features everywhere.

16:54and since I joined in early February, we started with like a DevRel team and really like trying to push towards helping developers build a community and really help everyone kind of to adopt the models as if you only have models available and nobody knows how to use them or have like not great documentation, you will not win at the end. That's right. And so, you know, you talked about it being small and then now it's huge, several thousand folks. That's a good call out. Like Google DeepMind is big, right? And how fast did that growth happen? And what was it structured like? So I joined in February.

17:28So I'm like also still super new. I know that there was like a refocus from like Google as an org itself to really push, hey, AI is really something important. We need to work all together. That's where this kind of merged with Google Brain and DeepMind back then happened. And now like everything AI related is basically under Google DeepMind. It's from Google Gemini, Google Gemma. There's a lot of research going on, which might not be publicly visible with generative AI. There's a lot of work with AlphaFold is coming out of DeepMind. There's a lot of work with weather and machine learning there.

18:03There's a lot of research happening. And of course, with generative AI becoming mainstream, we need to move towards more like product-based approaches. And since then, we have like a more product team. We have AI Studio as like a real developer environment and also now like a DevRel team where I'm part of where we try to really help everyone build with the available DeepMind models. So that seems like a next level of it's almost like a multidisciplinary approach to AI and applying it to use cases and figuring out how to productize it, but also figuring out what's going to be the long term impact.

18:40You touched on some things like proteins and such, like places where you could use AI to create new medicine or solve life-changing problems, right? And as you experiment and try out these new use cases, it requires a certain kind of mentality as an engineer. Not the traditional mentality of like, you know, you write the code, you ship it. But this new mentality has a lot of experimentation and the lines blur between roles. What does that look like as someone who works as a developer relations engineer? Do you find yourself doing a lot of different multidisciplinary tasks? Yeah, I think especially developer relations have a much different job than they might used to have a few years ago.

19:24And then if we look back even further, there was no developer relations engineer at all. And especially with all of the AI hype and AI evolvement, software engineering is becoming much different than it used to be. We have much more supportive tools from special code editors like Cursor or Windsurf, which help us a lot to be much faster. We have now completely full autonomous agents with tools, with OpenAI codecs. There's Cloud Code. So there's a lot of more tooling for developers, which wasn't there a few years ago. So they need to adjust how you develop software and then also how fast you can ship software changed a lot.

20:04I mean, like cloud is still like getting popular, but like, I think now like a solo small team individual can like ship and like produce and create so much more than it used to be a few years ago. And like what we see and like what I see, especially when like talking to developers and to people is like the team size gets much, much smaller and much, much more people can develop things which they might not have been able before. five years ago when you look into an enterprise company you have had very clear roles in software development you had back-end engineers front-end engineers architects designer quality engineers like many many different roles and all of them kind of become now like just okay you are a software engineer and you need to do front-end you need to do back-end you need to define okay how am i or where am i going to deploy my application and it's like more that it's not that we have AI software development.

21:02It's more like that there's a new kind of software development. I think we used to call it full stack, but now everyone is kind of a full stack engineer with AI because you get so much support. You can ship some task or hand over some task to the AI, which you might not be experienced enough to do or where you think it might be easier for the AI to do some very simple UI applications or just designs can now be done completely autonomous. and yeah like you have way more different like tasks and roles and maybe in a few years or even a few months from now we like look back and think yeah software development kind of evolved even further to just being a manager where you like need to look for your different like small agents or just tasks what are they doing and like where do i need to intersect where do i need to help them and then things become much more autonomous yeah that's what we're seeing when we talk with folks on this podcast is that exact anecdote about engineers that are, you know, the definition of full stack has now extended into everything.

22:05It's like beyond full stack, right? And what you get is an engineer who's responsible for the decisions behind the actions taken more so than the output directly, which requires a different skill set, a different mindset going into the work that you do. So what do you see from like successful people who adopt this mentality? Is there something that they all have in common or something that makes them stand out as being able to work in this kind of flexible way? I think like what really stands out is the people who are very open to adjust how they work, very open for a change and really like trying to use modern technologies.

22:41Because I mean, it's the same with like when the internet came, it will not go away, like AI will not go away. And yes, maybe now it's like not good enough or I can code it myself faster or I really like to go like coding, but like this will change and you need to really be able to use AI and to know how you can like benefit from it because otherwise someone else will come in and like do the same work as you do, but maybe more productive or more efficient or can do more of it because they use AI. and what also like really helped me or helps me is like not only like focusing on the technical aspects right really thinking about okay what am I trying to solve here what am I trying to build and like not think too much about okay am I going to use react or view for like the implementation implementation because at the end if I use some kind of code editors and they help me to build it I can build a react application and view application if I know like how javascript works and how like all of the things work and then I just focus on like building and like solving the problems rather than on like the typical like software engineering mind okay I will use Postgres and Java backend with Spring Boot and all of those things and I think those become more and more shallow because we really want to solve something and like build something and I guess it will not matter that much if it's going to be written in Java or like in Angular or like whatever tool at the end.

24:10Right. And also depending on like, you know, picking a framework that has a lot of examples is also very strong because then you have a lot of it in the training data. That's why you see things like React just going faster and faster. We talked with Lee Robinson at Vercel and he was really calling out how people, you're not really seeing as much specialists as you are generalist engineers who can spread across all of those weak spots that they might have had before. It doesn't matter if they pick Vue versus React because they understand the principles that underline it and they understand what the end goal needs to do.

24:41And that's actually all they need to connect the dots. And so there's like two different work styles. You have using the agents to do the work and move faster, but then also using the agents to learn and build your own skills. That way you can then be a better manager to your AI agents. And like personally, I'm really drawn to how folks use tools like Gemini to like get up to speed really fast and almost have like a personal tutor. And recently, Dev Interrupted was actually a guest at Atlassian Team. And there, I listened to Ben Gomez, the SVP of Learning and Sustainability at Google. And he talked about using LLMs this way in a Socratic approach of asking questions, going for deeper analysis with an LLM and using it to teach yourself, not just by giving you the answers, but by asking you those critical thinking questions that you need to level up how you're looking at the problem.

25:31I'm wondering, you know, how do you see LLM driven self-education changing the ways that engineers are learning new tools, especially like yourself in your role where you are creating like a large surface area that developers are trying out? No, I think it's super important. What we see is the one side thinking that Cursor and all of the other AI tools will replace software engineer. And I'm more on the other side that, yes, we will have a lot of autonomous stuff, but Cursor will not solve some weird Stripe problems or might not do proper Git stuff. So we really need software developers for a very long time, at least for the next few years from my point of view.

26:12And if you now get started with learning a language, trying to become a software developer, the easy path is like use cursor and like copy paste whatever code it does. And if it doesn't work, like try fix me five times. Like it is a bit like how it was like 10, 15 years ago where you just copy stuff from Stack Overflow. And if it didn't work, you copied the second answers and like until you kind of like had it solved. And I think like the better approach to it is like really use cursor and all of those AI tools, but more also to help you teach. Like, I mean, it never has been easier to learn how Git works and like the Git commands with having LLMs explain you the concept, having LLMs create you some kind of like visual like charts, graph.

26:53you can create super simple like your own kind of tests and like check okay how good am i and then like go into details if you really have no idea what's better like should i use like rebasing or should i like directly merge into like main and like this hasn't been possible before except you pay a lot of money for like those different boot camps and now you can like really do it on your own, with your own pace, in your own like depth or dance into something. And it's like, it's really cool. I think it might sound scary now to become a software developer, but I think there never has been a better or easier time to get started with software development.

27:35And especially if you learn not only how to use those tools, also like how the concepts work behind the scenes, then you are one of those software engineers or developers who doesn't care if he uses Vue or React or Next.js. you know all of the principle you know how like state management works how like the different updates happens on like inside the the html and then you can like just use it and i think like something which might not be talked too much about it is also that it feels like it's much easier to ask something where you feel not like secure with an llm then like to a real person right if you are knew it you had a job you are a junior engineer and you might not understand a certain concept might not understand a certain library or how like some code works like asking your colleague or a senior engineer could feel like could you make feel insecure or you might be worried that they think something bad about you and now with like other lens like it's not a person you are talking to and it's really like more of a like a protected space where you can learn on your own tempo and like with your own style and i think that's like the best way on on how people learn because like everyone is very individual like some people prefer like practical examples or listening to it or like seeing something or reading long text having like bullet points like everyone is like super individual yeah that's so spot on i recently onboarded for a role you know in the last year since ai has been on the scene and the thing that was different this time is i I joined and suddenly all of the tools I used had an LLM or a chatbot or some kind of like tooling within it where I could ask those silly questions without feeling like I'm bothering a senior person or like I'm embarrassing myself because I don't know this obscure thing.