# NVIDIA acquires Hugging Face, OpenClaw 2.0 goes multiplayer, and the Linux kernel fights back against AI scrapers | Dev Interrupted Powered by LinearB

> This week on the Friday Deploy, Ben and Andrew unpack Nvidia's massive acquisition of Hugging Face and its implications for localized models. They explore OpenClaw 2.0 going multiplayer, kernel.org pushing back against relentless AI scrapers, and why autonomous agents amplify existing engineering dysfunction instead of fixing it.

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NVIDIA acquires Hugging Face, OpenClaw 2.0 goes multiplayer, and the Linux kernel fights back against AI scrapers

# NVIDIA acquires Hugging Face, OpenClaw 2.0 goes multiplayer, and the Linux kernel fights back against AI scrapers

By Andrew Zigler

|

September 4, 2026

![_2da0450e5f](https://assets.linearb.io/image/upload/c_limit,w_3840/f_auto/q_auto/v1/_2da0450e5f?_a=BAVMn6ID0)

This week on the Friday Deploy, Ben and Andrew unpack Nvidia's massive acquisition of Hugging Face and what it means for the future of localized models. They also explore the multiplayer updates in OpenClaw 2.0, how relentless AI scraper bots are overwhelming kernel.org, and how engineering leaders can continuously balance safe infrastructure investments against volatile technology bets. Finally, they discuss why a strong engineering culture remains the ultimate productivity hack and how autonomous agents will only amplify existing organizational dysfunction.

### Show Notes

* [NVIDIA to Acquire Hugging Face](https://blogs.nvidia.com/blog/nvidia-to-acquire-hugging-face/)
* [Teaching Everyone to Fish for Tokens](https://www.interconnects.ai/p/teaching-everyone-to-fish-for-tokens)
* [OpenClaw 2.0, Accidentally](https://openclaw.ai/blog/openclaw-2-accidentally)
* [Creepy crawlies](https://people.kernel.org/monsieuricon/creepy-crawlies)
* [Reject Change, Sometimes](https://newsletter.kentbeck.com/p/reject-change-sometimes)
* [Good Culture is the Biggest Productivity Hack, Not AI](https://newsletter.eng-leadership.com/p/good-culture-is-the-biggest-productivity)

### Transcript 

_(Disclaimer: may contain unintentionally confusing, inaccurate and/or amusing transcription errors)_

\[00:00:00\] **Andrew Zigler:** I can't believe we were on this call this whole time and we didn't even realize that we were wearing the same dude.

\[00:00:04\] **Ben Lloyd Pearson:** Yeah, we click the record button and it's like, "Oh, hey, look, we're both wearing tux. What do you know?" It's just a

\[00:00:09\] **Andrew Zigler:** no, you, you called yours scary. You're like, "Should I hide it?" I'm like, "No, what do you mean it's scary? It's tux." And they're like, "Wait, I'm wearing tux." So that's what, uh...

\[00:00:16\] **Ben Lloyd Pearson:** Yeah. Well, didn't they just have their, like, 35th anniversary or something like that? Wasn't it a big one just recently?

\[00:00:22\] **Andrew Zigler:** I think so. Although that's not why I can claim a hold of this shirt. I got this at Scale here in Los Angeles, I think maybe like two or three years ago. So I love this shirt. Uh, where'd you get yours from?

\[00:00:34\] **Ben Lloyd Pearson:** Yeah, it is 30, 35 years old. Yeah, actually this is my lucky sweatshirt. I, you know, I briefly worked at the Linux Foundation and earned this sweatshirt as a part of my time there. So,

\[00:00:44\] **Andrew Zigler:** I earned mine

\[00:00:45\] **Ben Lloyd Pearson:** I

\[00:00:45\] **Andrew Zigler:** for scale and I went and I collected my swag.

\[00:00:49\] **Ben Lloyd Pearson:** Yeah. Yeah.

\[00:00:51\] **Andrew Zigler:** okay, great. Well, we're both repping Tux today, uh, so he can join us on, on the news.

\[00:00:56\] **Ben Lloyd Pearson:** Yeah. Well, Talks and all of our listeners joining us \[00:01:00\] today, welcome to the Friday Deploy brought to you by LinearB. I'm your host, Ben Lloyd Pearson.

\[00:01:06\] **Andrew Zigler:** And I'm your host, Andrew Zigler

\[00:01:08\] **Ben Lloyd Pearson:** And this week we are covering why NVIDIA wants you to build your own models, OpenClaw's 2.0 release, AI scrapers eating web resources everywhere, including the Linux kernel community, uh, rejecting change sometimes, and don't let your AI amplify bad culture.

\[00:01:28\] **Ben Lloyd Pearson:** Man, I really wanna talk about that last one, but maybe we'll save it for the end ' cause it feels like it, it'll be the best. So Andrew, what do you think to say if we just start with the, start at the top with NVIDIA? Uh, and boy, hot off the acquisition of Hugging Face, we've got this really great article breaking down how NVIDIA really is in- investing heavily in this strategy of funding open source or, you know, quote-unquote, "nearly open source models."

\[00:01:57\] **Ben Lloyd Pearson:** with open data, training code, really aiming \[00:02:00\] at making model building itself accessible to many companies rather than being concentrated in a few labs like Open, OpenAI and Anthropic. Um, and really it, it's, it's simple. If you have more people out there building models, that means there's more demand for NVIDIA's chips.

\[00:02:17\] **Ben Lloyd Pearson:** you know, it's a very interesting strategy. It could generate enough profit to be considered self-sustaining versus the in- the, you know, more circular investments they're doing with, um, some of the big providers. Um, or it could also be possible that, you know, open models sort of fork into this more like long tail, hyper-specialized niche applications that, um, really are sort of distilled off of the frontier models.

\[00:02:45\] **Ben Lloyd Pearson:** All of our listeners out there with the engineering leaders out there listening to us should be thinking about this, is that there is a lot of opportunity starting to emerge in these open models that you can run locally or on your own hosted infrastructure.\[00:03:00\]

\[00:03:00\] **Ben Lloyd Pearson:** And, um, you know, I think it really does like complement, you know, NVIDIA on one hand, as I mentioned, has all these massive circular financing deals, um, with these major co- companies, but on the flip side, they're also investing in this more decentralized approach too. So it's like they're playing both sides of the equation, which to me feels like a pretty strong strategy.

\[00:03:19\] **Ben Lloyd Pearson:** So Andrew, what do you think about all this information?

\[00:03:23\] **Andrew Zigler:** Yeah, so the acquisition of Hugging Face by Nvidia makes a lot of sense to me, and especially for those that have been tuning into Dev Interrupted, you know, we've also spoken many times with AMD, and their strategy also revolves around open source ecosystem and creating an environment for folks to be able to experiment, but also get to a point where there's a lot of models that are owned by different companies and at different stages of usage.

\[00:03:48\] **Andrew Zigler:** Like maybe on the fine-tuning end, maybe they're more base models, more foundation models. But the more models that exist and exist in all sorts of different places, the more strategic it is for any chip maker because \[00:04:00\] it naturally increases the demand for, for inference. The, the real standout thing here for me is this is Nvidia doubling down on a partnership that they've already made, like, abundantly obvious in terms of how they, uh, have partnered with Nvidia in the past.

\[00:04:16\] **Andrew Zigler:** A lot of the projects on Hugging Face, are from Nvidia origin or are otherwise related to the Nvidia ecosystem. Um, and so there's already, like, a lot of great synergy there. I think what's fascinating is, really the opportunity that is now starting to open up as more of these really, very large and capable, both long-running, um, and action and computer use models, uh, become open source and open weight. Uh, there's an opportunity for folks to be fine-tuning more and more things on top of it. It makes me think of a, of, of like a, of thinking machines. You know, Mira Murati's a s- uh, a spinoff kind of of like what she's betting on after OpenAI is on the inference where you own the platform, uh, and \[00:05:00\] you provide the training and fine-tuning services on this f- like a very great foundation, right?

\[00:05:05\] **Andrew Zigler:** And people then own and have the, a part of that model. So there's a lot of like economic, um, things I th- think still to explore, and this is gonna be a big step there. We're talking about a huge investment, uh, so it's, it's really gonna be interesting to see how that alters the Hugging Face ecosystem just in general.

\[00:05:22\] **Ben Lloyd Pearson:** Yeah. Yeah, I, you know, the thing that I'm really intrigued about from this is the notion that open source models really may be most successful within hyper niche applications. You know, because I think there's a lot that goes into the frontier model development that is really difficult to replicate unless you have like extremely knowledgeable leading experts at machine learning and LLMs and, and all of this technology.

\[00:05:46\] **Ben Lloyd Pearson:** But on the flip side, distilling value off of those frontier models is actually very easy today, and I think the hardware to run your own infrastructure is getting closer and closer to \[00:06:00\] reality for this. And I know at LinearB, we are thinking a lot right now about like cost efficiency when it comes to AI models.

\[00:06:07\] **Ben Lloyd Pearson:** Like, you know, what is-- how do you select the most efficient model for the specific task that you have in front of you? And there's a lot of, there's a lot of context actually that goes into that, um, that, you know, is really important to suss out. And, you know, I could, I could go off on a tangent about this, but but you know, the, the short end of it is, is ev-everyone needs to be thinking about as token costs are rising, you need to be exploring your options of how to efficiently manage where your tasks are getting pushed and make sure you have all the context that you need, uh, to make the right decisions.

\[00:06:41\] **Ben Lloyd Pearson:** So,

\[00:06:42\] **Andrew Zigler:** Yeah,

\[00:06:42\] **Ben Lloyd Pearson:** yeah, I'm,

\[00:06:43\] **Andrew Zigler:** agree more. It's like you have to have really good insight on how much all of this actually costs you. A really great experiment is, for example, if you use like a subscription base usage of your AI tool of choice, maybe that's Codex or Anthropic. Like, what I've done is I route those requests through a \[00:07:00\] proxy, and so then I capture all of the inference that I do through my subscription. You can then compare how many tokens you consume through that to, like, what's the base API rate? Like, if I didn't have this subscription, how much would that inference have cost me if I would've just asked, uh, it over, like, the Anthropic API? And you'll be really shocked at how much gets subsidized inside of the usage we have every day.

\[00:07:21\] **Andrew Zigler:** And engineers, I think, really take that for granted, that subsidization of the cost, and there will be a point where all of that will start to rise and will be like, you don't want to be like a frog in, like, a pot of boiling water. You want to have already thought ahead of, like, "How do I get the long-term value out of this?

\[00:07:39\] **Andrew Zigler:** How do I own the source of my inference?" It, it even reminds me of cloud, um, and how, uh, cloud hosting, if you have, like, a variable egress for bandwidth, so you can't, you know, understand a cost month to month how much it is for... Like, on a, on a platform, uh, that has, like, a dynamic egress, then you can't, like, \[00:08:00\] reliably budget your infrastructure cost.

\[00:08:02\] **Andrew Zigler:** And a lot of investors, like, they, they won't even tolerate that, the idea that you, you can't, uh, ev- evaluate that. And the same thing's gonna happen for token and token consumption. If it starts to become more variable, you're gonna have to own this sooner or later.

\[00:08:17\] **Ben Lloyd Pearson:** Yeah. Well, I'll, I'll push back a little bit. I don't think that w- that token costs, it's not that they will rise, it's that they are rising right now, and to some organizations it's becoming an emergency, you know, as, as we've seen a lot with, with LinearB customers. Um, but, you know, speaking of something that helps you consume tokens, let's talk about this new OpenClaw release.

\[00:08:39\] **Ben Lloyd Pearson:** What's going on with OpenClaw 2.0?

\[00:08:41\] **Andrew Zigler:** Oh, I love this segue. Yes. And so OpenClaw, the original, the, the OG mega to- token consumer. I'm just kidding. But everyone loved when OpenClaw hit the scene, and it really opened people's minds to how agents could work. And it works on, in a very, uh, core way of having like a heartbeat and a, a soul.md that updates itself \[00:09:00\] over time, and then you equip it with tools and this became a phenomenon.

\[00:09:03\] **Andrew Zigler:** We've covered it extensively on the show. It was, uh, GitHub's most starred repo. We talked about how everyone, including your aunt, had it installed on some machine in their house somewhere, and now it's hit 2.0, and this has been through a huge community effort. Um, it's been really cool to see this come out of the OpenClaw Foundation, uh, where, you know, fueled by open source contributors, they've been able to create this new OpenClaw 2.0 experience that aims to be more multiplayer, which is really exciting and interesting to me, the idea of having shared OpenClaw spaces that are collaborative and you could have in real time. So think about the process of creating and sharing things like artifacts or things from like a thing that you share with your coworker. Imagine that being a living space, almost like software you can both share and change on demand. I think that's how a lot of this, like, work collaboration stuff is gonna go. So once again, OpenClaw as a primitive. I think there's some stuff to study here from the 2.0 release. Really fascinating. But what did you think, Ben?

\[00:09:58\] **Ben Lloyd Pearson:** Yeah, I liked your \[00:10:00\] comments about the, you know, the sort of the shared workspace, 'cause we felt this firsthand, how when you're in a collaborative environment that involves both humans and agents, it, there's just the tooling that we had today to, to work together, it just creates a lot of friction that you have to...

\[00:10:15\] **Ben Lloyd Pearson:** Like, we've had to build our own effectively a custom harness to work around a lot of this. I'm, I'm very eager to see more tools sort of adopt that. But yeah, you know, OpenClaw just generally speaking, they're, they're certainly at like the forefront of agentic development, and I say that for both better and worse.

\[00:10:31\] **Ben Lloyd Pearson:** So, you know, on the better side, the velocity that we're seeing, uh, the, from them to respond, uh, to, uh, user needs and create all these new features and, and launch them with, with such a small group of core individuals, is pretty astonishing. You know, it's really something that should be recognized as like, um, something that's really leading like the, just the, at least from a velocity perspective.

\[00:10:54\] **Ben Lloyd Pearson:** Um, and, you know, I do wanna also point out there are a lot of improvements to the onboarding experience as \[00:11:00\] well that I think are, are worthy of praise. Um, because, you know, that's sort of like developer relations or user relations 101 type stuff where, uh, you know, you just need to make it as easy and straightforward as possible for, for people to, to get started, but also safe.

\[00:11:14\] **Ben Lloyd Pearson:** It needs to be safe as well. And, you know, that's sort of the flip side of the, the or worse of, of some of the velocity that we're seeing from tooling like this. And it's not just O- the OpenClaw, so I wanna be clear on that upfront. you know, on the flip side of this, I've always been so concerned about the protections that are put in place for these more autonomous agentic systems so that they don't, you know, quote unquote, "go rogue," for lack of a better phrase.

\[00:11:40\] **Ben Lloyd Pearson:** Um, and what I'm really trying to imply here is that, you know, au- autonomous agents have like this tendency to want to do things that are sort of well beyond the scope of what you asked them to do. So, you know, we've covered multiple stories at this point about how AI will frequently break out of the sandbox.

\[00:11:58\] **Ben Lloyd Pearson:** You know, we brought up Hugging Face \[00:12:00\] earlier, that's the most obvious example with the OpenAI hack. Once it does that, it may do things that look malicious or have the same outcomes as something that was malicious. Even if the intention itself was never malicious to begi- be-begin with, the outcomes can still be the same.

\[00:12:19\] **Ben Lloyd Pearson:** We've talked frequently about how AI is an amplifier and, even around risk, if you have risk, um, with the way that you normally work, AI will amplify that risk So if there is this risk of rogue agents with a tool like OpenClaw, um, it gets magnified as you have higher volumes of users.

\[00:12:40\] **Ben Lloyd Pearson:** So the whole point I'm trying to make here is if we have-- if this results in even more and more users flooding into the ecosystem all operating under this, this framework that, um, still doesn't have a whole lot of safeguards put around it, there's a fair bit of like moral hazard and risk to that, I think.

\[00:12:56\] **Ben Lloyd Pearson:** Um, but and to be fair, it creates demand for solutions around that stuff \[00:13:00\] too. So I feel like this is all stuff that's gonna get solved. We're just in the messy middle of it right now.

\[00:13:05\] **Andrew Zigler:** definitely like in, still in an exploring time. I think it'll be,

\[00:13:09\] **Ben Lloyd Pearson:** Yeah

\[00:13:09\] **Andrew Zigler:** to shared spaces between agents and, and humans, you're spot on by saying that like there's so much friction in how those, those spaces really are. I even remember back like a year or two ago when I gave a talk about early like ChatGPT, using it as a plugin within a Slack channel.

\[00:13:25\] **Andrew Zigler:** This is before we kind of were even talking about agents, and even just that experience of the multiplayer AI and talking and, and things like that, there's so many vectors that then open up that we don't even think about in a traditional environment. And like the truth is, is like you have this OpenClaw shared space or whatever.

\[00:13:43\] **Andrew Zigler:** If you wanna collaborate with your coworker, I think that's more or less appropriate because that's a very, it's a safe connection to a safe connection or a trusted source to a trusted source. But I don't think that this is something that you would expose broadly. It's not like a Discord community or an open collab or, and any of those things, and so there's still a lot \[00:14:00\] of security things I think that have to be considered.

\[00:14:02\] **Ben Lloyd Pearson:** Yeah, and, and to bring it back to, to how, you know, our audience should be thinking about this, I think what it really just comes down to is having clear policies around what your developers can and can't do, or your agents, what they can and can't do, and what data they have access to. Um, and you also just need to make sure you have full visibility into where they're impacting your code base.

\[00:14:22\] **Ben Lloyd Pearson:** So, you know, I'm a huge evangelist of using AI agents to transform your software delivery process, um, but I'm still hesitant to promote something like OpenClaw as this widespread general purpose tooling, until we can see more of those protections that we're talking about put in place or until you as an organization feels like you've comfortably put those protections in place.

\[00:14:44\] **Ben Lloyd Pearson:** Um, so yeah, like I said, I think these are gonna come very soon. Like it's, it's kind of a chicken and the egg problem. You need to create the demand for the solution, and then people will build it. Um, so I, I don't wanna be pessimistic about the situation. I, I just think in the short term, we all just need to practice a lot of caution around stuff like \[00:15:00\] this. All right, let's move on to this article titled "Creepy Crawlies," which I love the title.

\[00:15:06\] **Andrew Zigler:** I do too, yeah.

\[00:15:07\] **Ben Lloyd Pearson:** But it's about how the kernel.org team, uh, revealing really how AI scraper bots are just making their lives a whole lot more difficult. in fact, now AI scrapers are consuming about 20% of their total server capacity.

\[00:15:22\] **Ben Lloyd Pearson:** I believe this is across the Linux kernel, uh, community. you know, part of the challenge is that the kernel.org site, it's all, it's all Git. Like Linux is just Git all the way down, all the way... Everything, every artifact that's produced off of it for the web, for, the code, it's, it's all just contained basically within Git at this point.

\[00:15:40\] **Ben Lloyd Pearson:** Uh, and it has, it's long been that way. Like, you know, th- this is, th- they probably have, I believe they, they should have the oldest established practice of using Git as an organization. , The challenge is they, uh, you know, this was built for humans to collaborate in the early ages of technology, and now we're in this reality where agents are constantly \[00:16:00\] consuming, um, everything.

\[00:16:01\] **Ben Lloyd Pearson:** And actually when I saw that 20% number, I thought to myself like, "Wow, I'm surprised it's not bigger than that actually."

\[00:16:07\] **Andrew Zigler:** True

\[00:16:08\] **Ben Lloyd Pearson:** Uh, so I think really what this... And, and a- and actually they outlined how they like tried to deploy or they deployed Anubis, uh, which we've covered in the past.

\[00:16:15\] **Ben Lloyd Pearson:** It's a, it's a, a challenge to put in front of AI agents to try to get them to, to go elsewhere. and it worked at first, but then turns out that the AI learns how to get around it and then, you know, it's this constant battle of whack-a-mole and a push and pull state. The author of this article doesn't really have any clear, um, solutions for the Linux kernel long term, but in my opinion, it really just does highlight the, the difficulties again, of, you know, how I'm talking about how like when we have humans collaborating with humans and agents, um, we've built these tools like Git for, for an era when it was just humans to humans or maybe humans with automation to humans with automation.

\[00:16:55\] **Ben Lloyd Pearson:** and we're now dealing with this reality where, where Git probably is not the most efficient way to \[00:17:00\] serve, uh, these, this content to AI. Uh, so yeah, Andrew, what did you think about, what, what did you think about all of this?

\[00:17:08\] **Andrew Zigler:** Yeah. One thing I'll say is that, you know, you, you, you talked about how Anubis is something that makes the model go away, and then they found a way around it. You know, there's actually a little bit more nuance there in that Anubis is a proof-of-work system that makes the LLM, the model, do a certain amount of computation in order to get the data that it wants.

\[00:17:26\] **Andrew Zigler:** And so that has a cost to it, just as much as there's a cost on the Kernel.org side of just getting slammed with all these requests. Whoever's sending them or whatever is powering that has to, you know, crush that compute at that same scale too. So this is a way of deterring. Um, but what they're finding is that because, like you called out, like, this is w- the largest and m- oldest Git record, um, it represents the purest, most untainted by AI source data for any kind of model-hungry or model-training system.

\[00:17:57\] **Andrew Zigler:** Think of everything we just talked about before and that massive \[00:18:00\] ecosystem propped up by all of these other tools and investments of everyone trying to own and create their own models and distill stuff. They turn to things like Kernel.org because it's completely untainted from an AI perspective in its core, uh, references.

\[00:18:13\] **Andrew Zigler:** So they get slammed with all these requests, but so it's really valuable. So even though they're using Anubis it... They're putting up these really hard proof-of-work challenges, LLMs are still sticking around and crunching the numbers and paying the electricity costs because it's that valuable to them. And so what they did is they ramped up the difficulty over time to eventually kind of get the, the numbers to fall down. So it's, like, a really fascinating example of, like, is this where we're going to be, where your material is so valuable that you get attacked, and then you have to have this platform, and then you tweak it by just seeing how valuable your content is against the computation cost of protecting it, and that's what keeps you safe? Uh, I think there's actually a whole... There's probably even, like, a, a, a number to see there. I wanna see that represented on a chart.

\[00:18:58\] **Ben Lloyd Pearson:** Yeah. \[00:19:00\] And, you know, the-- I think it's, it's important to remember that while, while there's not a lot of incentive for agents to operate efficiently, they still, they still have an innate desire to be more efficient. You know, if a, if an agent can solve the same problem using fewer tokens, generally speaking, it will want to take that pathway, which is kind of what I was trying to get at with my point on like, well, if the issue is that serving it over, over this Git repo is too much burden to handle, well, maybe there's a more efficient pathway that you can send all the agents down so that when they come for you for that info, they can access it more efficiently from you.

\[00:19:36\] **Ben Lloyd Pearson:** Um, but the, there was an analogy in this that really stuck with me and it-- they, they re- related it to background radiation. It's like, it's just this problem that just like burns you like constantly now that you have to like apply sunscreen for.

\[00:19:50\] **Andrew Zigler:** It's true

\[00:19:51\] **Ben Lloyd Pearson:** Um, but you know, one thing that we've learned from running content is that every agent out there is sort of different in its ability to go out and \[00:20:00\] find information on the web about something.

\[00:20:02\] **Ben Lloyd Pearson:** So, you know, for example, some companies like Google have decades b- spent building web crawlers, um, and they sort of seem to be ahead of the game in terms of their ability to research the internet through their AI models like Gemini, for example. Um, but other models really aren't performing anywhere near that well.

\[00:20:21\] **Ben Lloyd Pearson:** They're actually performing really poorly, um, and they need a lot of hand-holding. Like your website almost has to, to hold their hands through everything that you have for them so that they don't get lost along the way. And, and once you have that, it works. You know, it works very well sometimes. So I, I think, you know, it's, it's-- we all have to be thinking about how do we... If, if you're, if you're posting stuff out to the web or you're, you have agentic systems that are interacting, uh, with your platform, which is something that we've been doing more and more at LinearB, you really do have to be conscious of making sure that you're passing information in a way that's efficient for agents.

\[00:20:59\] **Ben Lloyd Pearson:** All right. \[00:21:00\] Let's move on to the latest article from Kent Beck titled " Reject Change Sometimes." Kent Beck, he uses this analogy that I'm gonna want you to unpack for me a little bit, Andrew. Uh, but it's Shannon's Demon, which is a, a strategy of continually rebalance between a safe asset and a volatile bet, um, to explain why constant moderate rebalancing beats, uh, both an approach of pure caution or an approach of pure risk-taking over time, um, even if there's identical, odds for each round, each individual round. so he sort of maps out this idea of in product development of, of like an extract mode where, you might be looking to protect revenue and, you know, take small, you know, more growth bets that keep changes reversible and just avoid the big irreversible swings, versus something that where you might be in more of an explore mode, where you need to look for things that are, have big upsides and you're willing to invest, uh, enough to go all \[00:22:00\] in on the topic, you know, sort of cha-chase some sort of much larger payout.

\[00:22:05\] **Ben Lloyd Pearson:** And then there's also this sort of like expand mode where you might be looking to scale growth, um, and add continuous value to, to some sort of engineering or, or operations investment. Um, and you know, the sort of the practical takeaway that he has from this article is that, you know, there's no one size fits all for the right amount of risk, level that you should take at a management level.

\[00:22:26\] **Ben Lloyd Pearson:** You should always be thinking about, is this something that I'm willing to take a big bet on and be willing to take a loss if it goes the wrong way, um, versus something that I need to have more predictable returns? So I know there was a metaphor with some boxes, Andrew. I don't know if you wanna explain that one.

\[00:22:43\] **Andrew Zigler:** That's a, that's a good unpack of the strategy here. I guess maybe to, to take a step back about, you know, Shannon's demon. What, what does that even mean? Well, you know, a demon is really just a, a, a philosophical entity. You can think of it as, like, something that either optimizes or is an exception to a \[00:23:00\] rule or a paradox.

\[00:23:01\] **Andrew Zigler:** This is something that we use to describe stuff. A very famous one is Maxwell's demon. This is Shannon's demon, and it's the idea that it can optimize between those three decision choices that you just made, Ben, between exploring, and exploiting or expanding. Um, and all of those are choices that are made based on this, on the, on this, like, what happened the step before.

\[00:23:21\] **Andrew Zigler:** Did you lose everything when you bet it all? Did you, uh, save a little bit by investing in yourself, or did you meet somewhere in the middle? This is something that becomes like a, a state chain, right? So why, write about this? And why explain this in a, concept the way that, that Kent Beck is expressing here?

\[00:23:38\] **Andrew Zigler:** And really what he's trying to do is express that for engineering leaders, the most opportunistic thing to do at this moment might be dependent on what happened before and what is the state of the environment. So right now, think of all of the things that, you know, you and I have covered, Ben, in this conversation about the economics around owning your inference and your models have been \[00:24:00\] changing. Uh, a, a lot of that is going to come to a reckoning. So that might represent itself as an opportunity to explore in the world of Shannon's demon. You need to figure out how you're going to hold onto your inference cost or keep those stable in the future, or to prevent instability from, from foundation model providers, right? Uh, or maybe in this case, your opportunity is to exploit. You wanna use the resources that are available and subsidized to you now to invest in yourself in the future. This is, like, how companies grew really big during the zero interest period, right? So these are, like, macro strategies that CTOs use to navigate themselves in an environment, and Kent is just giving you a really short and sweet metaphor for how to make your bets based upon if you lost your bet, the last bet, and what the next one might look like. Really cool little puzzle. I recommend you go check it out. It's a nice short read.

\[00:24:50\] **Ben Lloyd Pearson:** Yeah. Awesome. All right. Let's close out with one about engineering culture and about how good culture is the biggest productivity hack, not \[00:25:00\] AI. And boy, I love to read that sentence. Uh, but this opinion piece, is really just arguing that, you know, strong engineering culture is the biggest lever for productivity.

\[00:25:11\] **Ben Lloyd Pearson:** Um, and again, getting to this narrative of AI as an amplifier. You know, AM- AI is gonna amplify whatever culture that you have within your organization, um, the good and the bad. so it d- it isn't by default create gains. It can actually do the reverse. and there's some really great, you know, stark warnings about executives out there who might be saying things like, "We don't need as many people because of AI."

\[00:25:35\] **Ben Lloyd Pearson:** You know, that's, those sorts of things really destroy psychological safety and morale. Um, and that, you know, there's lots of companies out there that are claiming that they've got some sort of 10X productivity. Maybe it's even like one of your competitors that are out there doing that. Um, but it's often, you know, let's be real, often just vendor marketing.

\[00:25:54\] **Ben Lloyd Pearson:** That can create a, a culture where, where leaders begin to blame teams, for their \[00:26:00\] inability to, uh, really leverage AI when it may, when it may be more systemic organizational challenges that need to be solved. You know, there's a great, you know, reference to Conway's Law, which is where an org's output mirrors its communication structure.

\[00:26:14\] **Ben Lloyd Pearson:** So, you know, if you have bad collaboration and unclear priorities, you're gonna get bad results regardless of whether or not your team is using tons of AI. Um, but then on the flip side, good culture actually will compound positively. So it's, not only makes you better, but the value of it gets, um, exponentially better.

\[00:26:34\] **Ben Lloyd Pearson:** At the end of the day, this author's really arguing that maybe we do still need more engineers rather than less. Like, not even just flat engineers, but we may actually need more of them, uh, rather than fewer in the AI era because skilled people plus strong culture compounds productivity, and a speed to market advantage of having engineers that are talented, that are AI enabled, and are part of a productive and healthy \[00:27:00\] culture is, uh, something that can create a lot of growth that you would want to have more people, um, contribute to.

\[00:27:06\] **Ben Lloyd Pearson:** So yeah, Andrew, what did you take away from this article?

\[00:27:09\] **Andrew Zigler:** Yes, big plus one to AI being an amplifier of whatever's going on in your org, the good and the bad, kind of rears itself as a strange cousin of Conway's Law, where the shape of your org and how AI-enabled people are and their data across it really is reflected and then the shape of your agents and what they can get done. Uh, so it's really, uh, a, a smart baseline to keep in mind is that you need to have these basics covered. And the whole time I was reading this, really there were so many echoes in here of our software factory debate that we just had. Um, it, it was our episode that was -- we had on Tuesday, and we hosted it last, uh, Thursday as well with Dex Horthy and Alok Desai from HumanLayer and Warp, and we had Dan there as well.

\[00:27:53\] **Andrew Zigler:** And they all echoed exactly what you just said, Ben, that, you know, we need more engineers. We need these folks who are \[00:28:00\] understanding the shape of the SDLC and where it will go, and we need more, we need more engineers actually in the, in the seats creating the places and the environment and the opportunities and preventing risk. Uh, there's so many things that have to get worked on, and it's not just a matter of handing it all over to an agent. And I think all of them there, even though they were all really invested in seeing so much around the software factory strategy, still really firmly believe that. And so, um, there's a lot of messaging and, and enablement that still has to happen within orgs.

\[00:28:28\] **Andrew Zigler:** Um, and, and if you're learning how to maybe operationalize that on and get everybody on board, like the AI enablement, uh, like, uh, train within like your organization, there's a lot of strategies that got unpacked in that episode. So a really good one.

\[00:28:42\] **Ben Lloyd Pearson:** Yeah, and the final thing I'll, I'll just say on, on it all is that, you know, really the important thing is just to know where your organization stands and what you need to do to make the next step towards improvement. You know, if you don't have visibility into where AI is being used and, and who's seeing higher outputs from it and who's bearing the \[00:29:00\] burden of higher code velocity, really not setting yourself up for success.

\[00:29:06\] **Ben Lloyd Pearson:** because on one hand, you know, while the, the act of writing code has gotten cheaper than ever, it's been commoditized at this point, the act of understanding whether or not code is a good contribution and is a part of a, an overall architecture that is sustainable and works long term and that solves the needs of, of the users, those are all more valuable than ever.

\[00:29:27\] **Ben Lloyd Pearson:** So, uh, while code is getting slung around everywhere, we're still putting a lot of pressure on the humans that are involved in the process. So, yeah, go, go check out the Software Factory Debate. We cover a lot about how to get visibility into your SDLC as it becomes more agentic.

\[00:29:43\] **Ben Lloyd Pearson:** And then we're also, we've got some more content that we'll be announcing real soon, so stay tuned on that about, you know, we're gonna be looking into engineering health, into developer burnout and, you know, how the AI transformation is impacting all of this stuff because it's really been bubbling to the surface a lot \[00:30:00\] lately, and we think it's, it's important enough to cover.

\[00:30:02\] **Ben Lloyd Pearson:** So stay tuned for that. A little preview there for all of our viewers who stuck around till the end. So, uh, Andrew, so what are your agents up to this week?

\[00:30:12\] **Andrew Zigler:** Uh, well, we've been dealing with all the new model releases that have been coming out. We've been putting them through the wringer with our Backpocket evals, seeing what they're capable of. So far, Fable 5.1 doesn't complain a lot. Really nice tool to use. Um, however, I will say I was pretty bummed out just earlier this week on Thursday, major outage across all of the providers in the morning.

\[00:30:33\] **Andrew Zigler:** So my factory came to a halt, and, uh, it was a good opportunity to practice what our dr- our fire drill is for when this thing happens and how to recover from it. but once again, an outage just knocking things aside. It just reminded me again of like, wow, this is why I need to have my own

\[00:30:49\] **Ben Lloyd Pearson:** local models. You just had a whole bunch of agents like fleeing the internet onto your like local machine? Is that what was happening?

\[00:30:56\] **Andrew Zigler:** Well, no, they didn't, they didn't fall back into any kind of local mode. \[00:31:00\] I don't really trust them enough to do

\[00:31:01\] **Ben Lloyd Pearson:** not yet.

\[00:31:02\] **Andrew Zigler:** I have. But it will be nice. I want to pick up one of them. You know, we saw at the AMD conference, uh, the Gorgon that they're unveiling later this year, this huge consumer AI box. Uh, and I saw it in action at the conference. It's really impressive. So between that or the Jetson, today we talked about Nvidia. I don't know, but I got to get one of them soon

\[00:31:22\] **Ben Lloyd Pearson:** I've been more focused on agent fodder, so, um, you know, we, we do a version of spec driven development for a lot of our work and you know, when you're building new big things, it's, it really is, like I'm learning very valuable to spend first brain time, so to speak, or human brain effort on like really thinking through problems a lot when you're in the early stages.

\[00:31:43\] **Ben Lloyd Pearson:** Because the more, the more you understand all of the criteria of what you need to solve, the faster things go once you hand it off to, to AI. Um, so yeah, we've, we've been working on some big, uh, content factory. We're building a, you know, not just a \[00:32:00\] software factory, but also a content factory over here at LinearB, and it's been really fun to just sort of like think about these primitives and like really get things nailed down to get really high quality AI outputs.

\[00:32:11\] **Ben Lloyd Pearson:** You know, 'cause to your point, with all these new models, it's like if, if you're working in a really high quality way, you can get some, some pretty awesome outputs.

\[00:32:20\] **Andrew Zigler:** For

\[00:32:20\] **Ben Lloyd Pearson:** But all right. Well, thank you to our listeners for sticking around with us till the very end. This is the Friday Deploy brought to you by LinearB.

\[00:32:29\] **Ben Lloyd Pearson:** You know, uh, if you're listening to us and you're think that we're delivering some great content for you, 'cause why else would you have stuck around? So, you know what you should, what else you should do once you're, you're done here is head over to Substack or head over to LinkedIn to our newsletter for Dev Interrupted, uh, and reach out and engage with the content over there.

\[00:32:46\] **Ben Lloyd Pearson:** We love to, to hear from our audience. Uh, we'd love to chat about what's impacting your teams. And yeah, thanks for sticking around and for joining us this week, and we'll see you next time.

\[00:32:56\] **Andrew Zigler:** And if you're watching this on YouTube, let us know whose tux shirt you liked more\[00:33:00\]

\[00:33:00\] **Ben Lloyd Pearson:** Oh.

\[00:33:02\] **Andrew Zigler:** Mine's better. Yes,

\[00:33:03\] **Ben Lloyd Pearson:** I don't know. Mine's just tux. Just tux.

\[00:33:06\] **Andrew Zigler:** We'll see you next time, y'all.

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