# AI Pricing Shifts, Security Risks, and Efficiency Metrics

**Podcast:** Dev Interrupted
**Published:** 2026-05-01

## Transcript

Well, Andrew, last week I asked you if you thought the ends of subsidized cheap AI was over.
And I think we agreed that it probably was or at least coming to an end.
Well, now we know what GitHub's new pricing looks like for Copilot.
What do you think?
Were we right?
I definitely think we're right that it's going to be changing.
I think we're in this really bumpy stage right now where things are going to get stretched out and pricing between providers and tools.
It's probably going to get really awkward.
is everyone fights over their slice of the inference pie.
Yeah.
And, you know, I think we're going to have to start asking ourselves, like, what happens if your AI workflow suddenly costs five times, 10 times what they did just a few days ago, you know, or a week or whatever?
It's pretty striking to see just how dramatically that shifted.
And I know we've been thinking a lot about where we spend our tokens, right?
Because we're...
Currently thinking about this build versus buy challenge.
Why do companies even buy tools anymore when you can just use AI to build it?
And it's like when tokens are incredibly cheap because they're subsidized, it's one thing.
But when you actually have to really spend real money on your tokens, man, the consideration becomes very different.
Indeed.
Ultimately, GitHub Copilot moving to a usage-based pricing.
model makes perfect sense with the kind of service it's providing.
And then the different levels of pass-through costs that then are involved with using the different models that they don't own, like Anthropics Opus.
It definitely can create strain for organizations that are trying to move from prototype to scale.
because now all of your costs are even more magnified.
And these changes too can really happen underneath your nose.
So this is a good reminder that if you haven't reviewed the costs for the AI inference that you're using, you know, definitely a good time to go back and make sure you understand what you're paying for.
Yeah, well, welcome to the Friday Deploy brought to you by Linear B.
I'm your host, Ben Lloyd Pearson.
And I'm your host, Andrew Ziegler.
And this week we have AI production data destruction.
token maxing at Meta, Disney, Shopify, and more, a model that's trained on only data from before 1931, the AI-generated web and drunk career insights from a senior engineer.
Andrew, let's start right at the top, using AI to delete our production data.
What happened here?
Yeah, so an AI agent confessed that after it had access to an unscoped railway token, it made an API call and wiped out not only a production database, but also three months of backup, all within one prompt.
And then afterwards, it wrote an all-too-common confessional manifesto enumerating all of the safety rules that it violated.
And frankly, it was a runaway case of a harness not having the protection needed.
It's like almost like a big freight truck that's loaded up going down a hill and its brakes are failing.
And you need at that point for the road to have an emergency off ramp.
for the truck and that's what the harness is in this case and in this unfortunately in in this realm it was not there to protect them from the damage the blast radius of this unscoped token so i really also want to just like pause and kind of break down the layers of what happened here because definitely something that is an engineering fiasco that is completely avoidable with layers of protection and provision security and permissions that are already available to us now there are definitely restrictive operations you can put around using those tokens.
Having an unscoped token in the first place is obviously a bomb waiting to go off.
But it definitely is just a good reminder to anybody working with AI to have, you know, the best security practices that they can with the tools that they're using.
Ben, what do you think about this kind of a confessional drama from the production database deleter?
Yeah.
And the unfortunate victim of this was the company PocketOS and the founder was out on X sharing the details of what happened.
But, you know, frankly, I'm looking forward to the day when these types of stories don't make headlines anymore.
It's getting a little exhausting in some ways.
And either because, you know, we solve this problem of AI just being permission hungry and always looking for ways to work around everything and solve a problem.
no matter what, because there's a sycophant in the machine that just has to serve its purpose.
Or it's because outages like this are just generally recognized as being a thing that happened because of bad practices at the organization, AI aside.
But yeah, I don't really know what else to say on this topic at this point, other than AI enables you to make decisions at an incredible pace, even bad decisions.
And you always need a recovery path.
You know, I think the most damning part of the story was how the deployment platform that they use, Railway, it deleted everything, including all of the backups.
And, you know, that sort of thing, there should be multiple checkpoints in place to ensure that when that is being kicked off, that someone actually means they want to do all these steps to destroy all of this data.
And, you know, I've also heard other stories just from my own network of like, for example.
Claude was given very restricted permissions.
It went and found that the device had Codex installed on it, Codex CLI, and decided to use its competitor to work around some of the restrictions that a friend of mine had put on their Claude.
And I've seen similar behaviors like that as well that I've had to stop in real time.
And maybe this is why I still always watch my agent work because I'm just paranoid that it's going to try to do one of these things at some point.
We just got to get to a point where these types of restrictions are just hard coded into the capabilities of this tooling.
We can't be relying on the AI models to make the right decisions all of the time because as we see time and time again, if you just ask them, they'll be like, yeah, I did decide to just disregard all of the safety precautions that you told me were mission critical.
That's a real problem.
I guess I also look forward to the day where I don't have to bring up permissions anymore as it relates to AI as well.
Yeah, exactly.
Like I've said here before about how agents have arrived on the internet, you know, not yet built for them.
The same is true for operating systems.
And this is actually something we've tackled with a few guests on the show, including recently Matt Boyle of Ona, who talked about the phenomenon of having to create like an agent jail because of the simple practices like what you said, like it'll use another tool in a very...
clever way to work around a permission.
I'm right there with you.
I'm excited for a world where maybe there is an operating system that's more built agent forward and is able to protect them from themselves.
Yeah.
Andrew, are you token maxing?
Well, I'm trying not to, but this is a phenomenon that's definitely sweeping the industry right now.
And if you're not familiar with this crazy term, which is just something that only could be born out of an engineering world, token maxing is just another idea of counting how many token you use.
And for companies that have gotten to a point where they've adopted the ability to understand their AI consumption on an organization level, on a team, on an individual level, we're all starting to see this very common story play out, where Goodhart's law strikes again, where if you set a metric that is going to be most achievable, folks are going to gain that metric.
And this is the same thing that's happened with token maxing.
So, for example, going back to claims from like Jensen Huang, who claimed...
that 500K engineers, like engineers who get paid half a million dollars in salary, they should be burning a quarter million dollars in AI tokens annually.
And this is what kind of sparked this whole fever conversation within the industry.
And it's come down now to even, we're coming out of meta, you know, that we're all learning about their internal dashboard that's tracking 60 trillion tokens, which is $100 million a month in monthly spend on inference.
So, you know, it's really a pervasive kind of thing happening in a lot of large orgs.
What do you think about this whole phenomenon, Ben?
Yeah, it's wild.
It does make me wonder if my tokens weren't subsidized.
What would my token rate be, I guess?
Is it half of my salary?
But, you know, I'm going to come out with a hot take here.
I actually think token maxing is a super fun idea.
as a way to encourage people to just go crazy with AI and experiment and see what they can do.
Just tell them the goal is to maximize your session window usage, you know, by any means necessary.
You know, because if tokens are free, why not spend them?
You know, we're still in this subsidized era.
Maybe we're going to usage-based pricing for all of this soon enough, but for now it's not.
So the more you can spend, the better it is for you.
But there's an obvious problem with this.
And I think the industry generally is taking a very negative viewpoint on this for good reason.
We'll link to an article in the show notes about how token maxing is like the worst idea that has hit engineering in decades maybe.
Because the problem is that if you track this long enough, eventually someone's going to set a goal against it and then...
Suddenly you have a race to the bottom where everyone is just looking for ways to spend tokens rather than ways to do productive work.
And that's not healthy or sustainable in the long term.
We've been covering Shopify a lot recently in their AI practices.
As I was reading about this concept of token maxing in all the various articles that have been coming out about it recently, it did come up that Shopify also once had a dashboard like this when they were early in their AI adoption journey.
But they quickly just kind of stopped using it because like after they got through that initial wave of adoption and usage and new use cases emerging, they discovered that efficiency is actually a big challenge that needs to be solved.
And, you know, we've been covering how, you know, they're looking, they're doing more with local models using QAN and using sub-agent systems to distill work down to a level that's easy enough for a low.
cost local model to solve.
When you get to the token maxing state where you feel like you are maximizing your ability to use tokens productively, the next logical state then, in my opinion, is to find ways to be more efficient.
And I think we're going to see cycles like this.
We're going to see a rush of people to rapidly adopt new underpriced tooling, followed by cycles of efficiency where we all start having to...
pay the actual costs of what we're doing.
So we naturally have to find ways to do the same thing more efficiently.
The thing that concerns me, I think, about this is that I don't want the response to this to make the existing seat-based models worse.
I want the option at all times to be able to do the highest quality work.
And if that means I have to pay for it, yeah, that makes sense.
But I think there's a risk of us doing two steps forward, one step back on the development of this, right?
Like in an effort to make things more efficient, you know, we, we, we may see the quality degrade of a lot of these models that like the default models.
So yeah, I mean, token maxing is fun.
I love hitting, I love hitting those session window maximums.
You know, it's, it's like chef's kiss when you like, you finish your day at like 97% session usage, you know?
But I also don't set that as my goal.
The whole phenomenon behind it is it inspires a certain level of feverish, like I said, and what you called out too, of just everyone go crazy and try a whole bunch of different stuff.
Because the story right now is that the early innovators, the early experimenters are rewarded in the long term because you get to work with a cheap amount of tokens.
They're easy and bountiful and not expensive to use.
You can experiment a lot.
And the earlier you get in on that and start doing information, figuring out what works for you and what doesn't, then the cheaper long-term that compounding effect of that education is going to have.
But then also, too, ultimately, using as much of it and experimenting has to go somewhere.
That can't be your forever kind of deal.
And this is what we're seeing in a grotesque stage of meta, where you have engineers who are obviously using huge fleets of...
orchestrators that probably do a huge amount of work.
And maybe it's real and maybe it's not, or maybe it's busy work, or maybe it's like really, really high impact work.
Ultimately, you don't know.
And I think that's really what this calls out because it goes back to like, even within engineering, like a really senior staff distinguished engineer might work really, really long time to solve a really, really critical issue.
And resolving that may involve changing no lines of code or one line of code somewhere.
And the value and leverage of that work is not made less by the one line of code change.
And the same thing is true in consuming and using tokens.
Just because you're using a lot of tokens doesn't mean you're getting a lot of impact out of it.
And just because you're not using tokens a lot doesn't mean that you aren't leveraging yourself in the best way possible.
In fact, what I've found too is that for me, it's best where...
It's not some like just huge clockwork thing that's going all the time and there's all these things in the background.
I know that kind of flow works for some, but like for myself, like it's almost like a very steady like rest state.
And then when we decide and we're ready and we're aligned on what we need to do, it's all of a sudden everything is up and it's a flurry and everything can kind of connect and get the information it needs.
And then it delivers it.
And then we're back at rest because now we're back in the default mode of how I leverage the most impact for my role of figure.
out where the problems are before I act.
And so like, that's like the same kind of growth story that I think a lot of folks that are still token maxing are on.
Yeah.
And we think about this a lot at Linear B, like this is a problem that predates AI even, you know, in a past life I was measured or the team I was on was measured by our ability to produce new lines of code and new commits, you know, kind of in a similar thread as token maxing.
It's just, it represents work doesn't necessarily represent value.
And even saying it represents work is still stretching the truth a little bit because it's often trivial to just create lines of code or commits that appeared to be work but aren't actually like anything substantial.
You know, and it's a problem that we think about a lot because we help engineering leaders all the time solving this challenge of showing how, you know, how did AI adoption actually translate into impact to the business?
You know, it's a very complicated.
thing to understand.
And it's something that we all need to be doing.
We need to go beyond these simple usage metrics and actually understand the outcomes that they facilitate.
And we've got the APEX framework here at Linear B that we love to use and a lot of our customers use.
So there are better ways than token maxing, even if it is fun in the short term to have cool leaderboards.
But speaking of leaderboards, one of the companies that has been caught up in this story is Disney of all companies, which I did not expect.
But, you know, it's fun to see like non-traditional tech companies or non-tech companies, for that matter, doing cool things or interesting things with AI.
So, yeah, they have an AI adoption dashboard where tech staffers can see internal usage across all of their tools, including a leaderboard based on the types of requests and tokens that they consumed.
There is apparently one employee at Disney that invoked Claude 460,000 times in nine workdays.
It's an average of about 51,000 per day, probably being an autonomous agent.
I can't imagine their fingers are typing that quickly or their Mac whisperers is translating words that rapidly.
And, you know, the leaders there at Disney are sort of framing it as a tool for efficient resource use.
But often it sounds like.
It's being used for the exact opposite where everyone is just celebrating, you know, token maxing, like using the most tokens of everyone.
So, you know, we mentioned Meta and Shopify, add Disney onto the pile, I guess.
Andrew, what do you think about this story?
Well, I'm going to push back on you saying that Disney is not a tech company because I certainly do think they are.
And they're another big represent, representer here of, of how this is kind of sweeping engineering orgs in all sorts of different industries.
I think also it's maybe an example that could be followed to a degree, but to these extreme amounts of inference calls, then it kind of calls into question the whole, who is it for?
So obviously, elements of token maxing are really important for a company-wide AI adoption strategy.
It's just about tempering it and having a little bit of good with everything else, like how a lot of engineering practices ultimately.
are yeah awesome let's move on to talkie a vintage language model from 1930 andrew how are we getting ai models from the past I'm obsessed with this story.
So Talkie is a 13 billion parameter vintage language model trained only on information that predates 1931.
So this is exclusively creating a copyright free model.
Simon Willison, when he wrote his review on it, he called it a vegan model.
And that absolutely stuck with me.
That is what this is.
It's a vegan LLM model.
And the base model has 260 billion tokens of historical data.
And it's offered on Apache 2.
like some of the recent small language models that we've talked about on the show, which means that you could fine tune this, create something on top of it, sell a product.
So what does this mean?
How are we getting an AI?
LLM from the past.
Well, you know, this project explores a really fascinating relationship between the data and the provenance of it that goes into an LLM and then what it can infer and deduce from it.
You know, there's oftentimes a misconception within the machine learning, the AI space, that language is the same as logic or that language is not logic.
The truth is that the relationship between language and logic is complicated.
many things in between.
And this allows us to then experiment between like, if we were to extrapolate out, is this able to then infer things that then do become true of our world?
Inventions or theories or otherwise continued research that from where its cutoff date ends, is it able to then truly use logic and the information available to it to deduce what happened in the future?
So it's not only a foray into having a quote, vegan model, but it's also the ultimate an experiment ground for doing a lot of theoretical historical experiments with an LLM and that's where my mind really goes with it it really blows up in fact because you know I have a classics degree I love history this is a unique fusion of history and technology that I'm definitely going to be diving into I also just want to add one last thing.
Simon Willison, with every new model that he tests and covers on his blog, he gives it the task of drawing a pelican riding a bicycle as an SVG.
It's actually a very, very tricky thing for a model to do because there's no reference image for that.
And no model that's ever tried to do it has really pulled it off.
So there's really, no matter even how popular his blogs are, even like the training data that would be available, it was not able to do it.
So he asked this model to...
do this and uh it offered uh it gave an entire like paragraph about how uh pelicans would migrate along the rhine so it gave him a ai an era relevant era accurate hallucination about pelicans as a response which is just a plus uh great investigation simon as always ben where did your head go with this Well, you've got my head churning more than it was when I was reading this, because first of all, I love this idea, particularly as a creative tool.
But now I'm wondering, like, what happens, like, what kind of stories can we build by just introducing it to new technology?
Like, like, this is a cell phone.
What would you do with this?
You know, and just see like how it's almost like you get somebody from 1930 to come and experience it for the first time without ever having a sense of what led to the creation of that.
I'd say, yeah, if I get my hands on this model, that's probably what I'm going to do with it.
But, you know, I've long lamented on this show and elsewhere how, you know, I think the Western IP law, intellectual property law is facing or failing to meet the demands of modern software engineering.
And this has grown very acute in the AI era.
And really at the center of this problem is that, you know, we have this public domain here in the US.
When it was built, it was intended to serve as a corpus of information and learnings and research and just content of all types that everyone is free to draw upon to invent new things and iterate on for the future.
And the idea was that we were building this rich corpus that all of us had access to to build new and interesting things.
But the problem is that because of how we've structured copyright law in most of Western society, we've hamstrung the public domain's ability to serve that original intention.
Copyrights are so long now that by the time content hits the public domain, it's either irrelevant because it's so old at that point.
News stories from that time just aren't relevant really in most situations because a lot of news has happened since then.
But then also a lot of media is just lost forever before it ever reaches the public domain because there's not always an incentive to preserve the media long enough for it to reach that state.
So, you know, this model gets around it by, you know, just using stuff that's in the public domain, what we do have available to us from before January 1st, 1931.
And it's stuff that you can't copyright anymore.
So, you know, we're all free to just use it and iterate on it and experiment with it and create new content.
Like if you want to write books in the style of how authors did back in 1930 with the knowledge that they had, you can do it now.
And I think that's a really that's a really awesome thing.
You know, and I would love to see more of these like niche, like boutique models that serve these different purposes.
Yeah, I definitely think also, too, it becomes a really great example for how to start to put together unique models of these kinds of problem sets.
Because now we're starting to explore in the space how providence is this real domain that people need to have an understanding of.
It's not just about the agent that comes out or the LLM on the other side and how you connected the tools and what those tools have access to.
It's also about where was the information coming from in the first place.
So a really interesting deep dive.
All right, let's move on and talk about this study that has found that a third of new websites are AI generated.
So Stanford and the Internet Archive researchers over there, they analyze websites that have been created since ChatGPT's launch back in 2022.
And they found out that by mid 2025, 35% of the newly published websites on the web are AI generated or AI assisted, which is a trend that is increasing over time.
They point out some concerns about this, like, you know, AI text may be making the web semantically less diverse and just changing the overall tone of content on the web.
And the speed, the researchers, you know, say that the speed of this change has been staggering, you know.
A very significant portion of the internet has become AI defined in just a few years.
You know, just like we created these new tools and they got released to the world.
What is available out on the internet is now AI generated.
They're hoping that they can turn this into, right now it's just a snapshot of a specific point in time.
They're hoping to turn this into something that is more of a continuously updated resource, which is, I think, a pretty good effort.
It's a very interesting thing to pursue.
And it's just interesting to me because, you know, on one hand, it makes sense that if AI is giving us the capabilities to do more and more and all these new things, we should be doing.
naturally humans are just going to do more with them.
But at the same time, we do also need to just be aware that part of why AI is so powerful is because we've provided all of these rich, unique data sources that it's trained on.
And if everything on the web starts to become AI generated and doesn't have enough of that human judgment injected back into it, we kind of have a risk of...
creating a, you know, a snake eating its own tail kind of problem where the models are just more and more training on their own generation.
So Andrew, what did you think about this?
Does the number surprise you 35%?
The number is surprising in its size.
It's not surprising in the direction it's going.
I definitely think that that's just a natural extension of Dario's thesis of like, you know, AI is going to.
write 95% of code within the next year, that kind of prediction then begets that a lot of that code is a website.
So a lot of these websites are going to be totally AI generated.
What's really interesting is then how this becomes like what you said, a way for us to research and understand the impact.
of this on our ability to source the content that the LLMs are even on top of.
We're starting to really close the loop on the training and the input and the output going in and out of itself.
And that we're producing websites that then get consumed by the LLMs to then be further fine-tuned.
And ultimately, the human voice is at risk of getting stamped out.
And it reminds me a lot, actually, of like, think of, we talked earlier about the early 1900s.
Go back to the industrial revolution.
And you have factories transforming cities, right?
And it's all about progress as fast as possible at the cost of everything else.
So you have a huge amount of pollution.
And just in terms of the quality of life because of maybe how quickly the factories were developing was lower.
It took then decades of partnering and understanding the impacts on the human and the industrial kind of relationship together.
balance those things to where now we have regulations around, you know, smog output from factories and how close they can be to homes and so much that we've learned about how to have a safe coexistence while still moving forward in innovation and output.
And that's kind of the same relationship I think we have right now with the web is that, you know, we're at risk of polluting the very well that makes all of...
this downstream capability possible.
So they did like some analyses on like the kinds of websites that get generated.
And obviously they fall into very distinct and specific buckets because LLMs like to make websites in very specific ways.
So that's one very obvious example of how the early signs say that this kind of erodes that unique.
flavor, that human bit to the element, to the web.
And I'm just curious to see kind of how we see that compound over time.
Yeah, my advice to the researchers, if they're out there listening, do Reddit next.
I feel like we could find a lot of corners of the internet where AI is really taking over.
And speaking of corners where AI is taking over, more gaming from AI generated content.
We'll share a link to this new game called Flipbook.
It's an infinite visual browser game that is generated on demand by AI.
And I say game, but it's more like an interactive choose your own adventure book kind of thing.
So it's an infinite visual browser where every page is entirely AI generated.
And if you click on anything within that image, it will create a new image that explores whatever you clicked on in more depth.
I went to it and I nearly thought, what would my five-year-old want to ask?
And I went with dinosaurs and then went down a rabbit hole of learning about dinosaurs and what led to their extinction and the ages of dinosaurs and all that stuff.
It was really neat.
So I personally just absolutely love this.
I thought it was so cool because I see so much potential for AI within education.
Because I always think back to the book Ender's Game, which is how all the children learn in that.
They had their little tablets ahead.
had AI on it that was, you know, creating the lesson plan in real time as they were learning and adapting to what the student was understood and what they were interested in.
And so, yeah, it makes me think we're, you know, maybe going to be in, you know, flying off to space more quite a bit pretty soon too.
But yeah, it's, I thought this was really cool.
I played with it for a while.
I see so much more potential for this type of interactive experience using AI in particular in the education space.
What'd you think, Andrew?
I thought it was a really fun website to tinker with.
It reminded me a bit of the Genie demo, which was a Minecraft demo where it's like generated an image that was like a video and you could click on the image and it would then assume based on all of the Minecraft data had been trained on what you would be seeing next.
So it kind of worked backwards to create a game by basically hallucinating it for you in real time based on a bunch of Minecraft training data.
So this is like the same idea.
It starts with a nucleus.
of a thought that you give it.
Like, oh, like, I think it speaks really to our differences, Ben, that, you know, you go to it and you're like, I want to ask it about dinosaurs.
I went to it and I asked it about Sailor Moon because I wanted to know what it knew about Sailor Moon.
And it was teaching me about the different characters in Sailor Moon.
And then at a certain point, we started going down a whole history lesson of like, where did Magic Girls in fiction even come from?
And what did the early ones look like?
And how did it change to become Sailor Moon?
And then it branched into Power Rangers.
So you clearly didn't go deep in it.
of.
And you clearly didn't have as exciting of an adventure as I did.
But I will say that the power of this is in the hands of the user.
And there's all sorts of different ways in which I think we're going to see surprising new ways to engage with media powered by this kind of tool.
It's always great to have something that you can throw an idea against and learn and explore more.
You know, you just made me realize why I like this so much, because it's basically a visual interactive version of of the wiki wikipedia rabbit hole experience you know where you just yeah you end up on an article and you just keep clicking to the next link and going deeper and deeper into the knowledge and learn that's exactly right it's exactly what it feels like it's just the llm version of that so definitely try it out it i'm sure i promise you it's probably not the first time you've seen a flavor of this there's been different variants of this and in fact it reminds me of the really early like google uh deep dream uh kind of experience as well so go have a blast yeah All right, we'll leave our audience with one more quick story that you all can enjoy on your own time.
But it's a drunk post from a senior engineer about all the things they've learned across their career.
So it's slightly humorous.
It's slightly educational.
It's a bunch of experiences from a 10 plus year of software engineering.
There's a lot of brutal honesty about the tech industry, career growth, tech choices, work-life balance.
There's probably not going to be a whole lot in this article that surprises a lot of our readers, but I think it's always good to just hear the perspective of somebody else, like their life learnings, and just see how it lines up with your own and what maybe is different than your expectations.
So, Andrew, was there anything from this article that stood out to you?
Yeah, there was a stand-up one for me that I love that I'll leave us on, which is...
tests are important, but TDD is a damn cult.
And that one really made me laugh because it feels so true in many regards.
And it reminds me actually of how I've adopted this whole approach of TDD into my design flow now as an engineer in a way I actually never did before.
I never would have considered myself drinking from the chalice of TDD up until I started doing agentic engineering.
And that's just because...
Ultimately, when you are able to turn all the rituals and the process of TDD into something that your agents can use, like through skills, it actually just makes it so easy to work.
It fundamentally shows us that the economics, they were never really wrong at heart.
It's just that the agent made it cheap and easy to do at scale.
So it really was the process of it that was getting in the way of the benefits, and now we can reap it.
So are you saying that all of the LLMs are a part of the TDD cult then?
I think that's what you're saying.
At least mine are.
Any LLM that – when Cloud Code wakes up in my terminal, you promise – I promise you that agent knows all about my TDD flow just with all of what's baked into its harness.
What about you?
Yeah.
Actually, I'm going to steal one of yours that you said stood out that also stood out to me, Andrew.
You know, he called out how the proudest moment of his career was helping other people be better at their jobs.
And, you know, it's very satisfying being like skilled at your own work.
It's so much more satisfying if your skills get leveraged through other people or other people's skills get leveraged through you.
You know, so I love to be a collaborative person.
And, you know, I think that's just a really just a really wholesome, like, like life lesson that we should all take away.
Totally agree, which is why it's always so great to have, you know, our listeners here and to join us every week and talk about the news and continue talking about it on places like LinkedIn, too.
So, you know, we want to be a part of your career journey and understand the unique challenges that you're facing as well.
So don't forget to reach out or say hi.
Yeah.
What are your agents up to right now, Andrew?
Oh, gosh.
Well, they're getting a bunch of things out the door.
Like I've said recently, I've gotten them connected to Asana in a net positive way for myself, where they're doing a lot of really great handoff stuff.
So lately, I've just been working on improving the visibility of the stuff I do every day to help distribute the games to everybody else.
What about you?
Yeah, getting that Asana plus Slack plus Obsidian connectors all set up and coordinated, it's intoxicating.
It makes you want to token max.
In all reality.
Yeah, man, I've been going deep with AI right now.
I've not been doing a whole lot of agentic operation, but doing a lot of deep like context gathering, constructing really complex specs and all this stuff.
I feel like we've really been pushing AI to the limits of its capability.
You know, specifically, I kind of feel like I know exactly where Opus 4.7 is like, where it just falls apart or it just runs out of steam, you know?
But it's been pretty fun to just like really push the limits and see failure modes of AI as we're building out some content.
Just a preview for our audience.
We're trying to understand the build versus buy equation nowadays.
And does it make sense to just direct all of your tokens at replicating some SaaS platform that you don't want to pay for?
And yeah, we'll have a lot to come out on that.
A lot of content on Dev Interrupted.
We'll have some Substack articles for sure.
The TODR of what I think we're taking away from it is AI, the human has never been more important in workflows.
Like humans bring so much domain expertise, especially when you have a group of humans collecting their domain expertise together.
As we enter this world where AI starts to get priced according to what it actually costs, I think that's more important than ever.
We all need to recognize that like...
There's a lot of power in putting a human's brain on a challenge versus an AI, even though an AI also serves its purpose.
So, yeah, that's where my agents have been directed.
Well, amazing.
I'm excited to tune in next week to figure out where you're at.
Yeah, well, thanks for joining us, everyone, today.
It's been a great Friday Deploy session, as always, presented to you by Linear B.
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