AIコーディングエージェントの「人月の神話」問題（第315回）

Thank you for listening to Data Science at Home podcast with Francesco Gattoleta. You are about to get cutting-edge insights from the people who are reshaping the world of technology with machine learning, data science, and artificial intelligence. It's time for Data Science at Home. Welcome to the show. You know what's funny? that 50 years ago a guy named Fred Brooks wrote a book about why throwing more people at a late software project makes it later. And he called it the Mythical Man Month, a very famous book in the world of IT. It became definitely in the 80s and in the 90s. It became, in fact, one of the most quoted books and quotes, in fact, in our community. least followed books in the history of our industry for sure. And, you know, 50 years later, we have swapped more people for more tokens, essentially.

We kept the same myth, the same seduction, and just with a different currency. That's why I called the most recent book I wrote, in fact, the only one that I actually published, which I called the Mythical Token Month. And believe me, this episode is not going to be a cheap marketing move, but it's more about explaining why I wrote such a book in this period of technology. Welcome to Data Science at Home. I'm Frag, and today it's just me, this Mike, and the book that I've been sitting on for different months now, even wondering if I had to publish it, and I did, in fact. Now, let me be upfront about something here, because I think it matters a lot more than the book itself. I'm not an AI skeptic, okay? I want to say this very loudly because it's the easiest way for people to mishear me, especially for the things that I'm going to say in this episode

and many of the things that you will definitely read about in the book if you ever buy it. Now, I'm not the guy who thinks that large language models are a party trick. you know i've built with them i i i've broken things with them i still use them i run a podcast that's been tracking this field for years now and i've been a researcher myself in this field so uh you know why should i but honestly some of what i've seen come out of of these models in the last two three years uh still makes me stop and stare at the screen to be very honest with you. But of course, I'm cautious. And the difference between skepticism and caution is the entire reason this book exists, in fact. Now, a skeptic would say something like, this doesn't work, don't bother. A cautious person says, this works, now show me exactly how, when, and at what cost. Because we have been burned by just trust the process many times before,

and probably so have you, probably without even knowing, who still remember, you know, the days of Microsoft Windows and trust the experts, remember, for those who are at least 40 years old. Now, I started writing essays, not really a book, just essays, they were scattered notes, really, because I kept saying, you know, the same things about AI. And also I kept seeing the same pattern in teams, in Slack channels, in LinkedIn posts, you know, people budgeting AI coding assistance pretty much the way Fred Brooks' 1970s IBM managers budgeted headcount. You know, just add more tokens, just let the agent run longer, just buy more bigger machines, more RAM bigger GPUs just give it a bigger context window and it will figure it out

we are very familiar now with this conclusions about computation and intelligence as if intelligence artificial or otherwise scales the way a spreadsheet formula does Well unfortunately I would say it does not And I have the scar tissue to prove it. So that's why, you know, finding the time to sit down and write a book about all this It's kind of, you know, marking the stage with my opinions, and hopefully I'll be right. Time will tell. Now, of course, it's not about being right or wrong. It's more about expressing an opinion after so many years of being in the field. There is several patterns, and we should definitely be cautious about. Now, I'll be honest with you again. And some of this book came out of my own project, as many other things on this show. Now I'm building, for example, a video game in the last eight months now.

It's a pixel art RPG. We are looking for publishers and many other things. So there is people breathing down my neck, in a way. And what I did several times, especially when I was dealing with Behavior 3's enemy AI for the video game, you know, I used some AI agent. You know, sometimes it saves me hours. And sometimes it costed me hours because, you know, I trusted an answer that sounded confident and was quietly, elegantly wrong. Now, I have to say that these are concepts like behavior trees, enemy AI, that I'm very familiar with because I've used this for many, many years, especially in robotics and not just in video games, which is pretty much, you know, the same concepts. And, you know, so I could spot, in fact, I was in the right position, technical position to spot whenever the AI was, you know, being elegantly wrong.

and because I knew the topic and I knew the concepts very very well I could actually understand the generational mechanic of the AI generating stuff for that particular concept and construct now the tool has a big problem in AI agents in general especially for coding it doesn't know the difference between two outcomes, you know, sounding confident and being quietly wrong. Okay. These two, you know, being wrong and being confident are very blurred for an AI. So that's the mythical part. You know, the myth isn't that AI helps you code faster. It does, in fact, for certain things, there is no doubt. But the myth is more that it helps you code faster for free, like as if there is no tax on velocity and no debt piling up somewhere off the balance sheet of the software engineer and developer.

But there is always a tax, and the problem is that you just don't see this hypothetical invoice until three, four, five weeks later when nobody, including the AI, can explain why the crafting system in the video game does what it does. And good luck, of course, doing maintenance on that code that you never wrote and you never understood. This episode is supported by OutShift, Cisco's incubation engine for frontier technology. Multi-agent AI is in pretty much every enterprise roadmap right now, but there is a problem with that, and we are all facing it. Agents can pass messages, but they cannot think together. So these systems silently underperform through cognitive failures, things like misreadings, unverified claims, and of course, false consensus. You know, all the stuff that conventional monitoring cannot detect. OutShift by Cisco is building this fix, the Internet of Cognition,

which is an open source foundation for building multi-agent systems from design to production, with shared context shared memory and guardrails to drive the results that we actually expect from artificial intelligence So read the paper experience the demo grab the code from outshift That's O-U-T-S-H-I-F-T dot com. So why turn scattered essays into an actual book? With my name on the spine at the end. Well, there are three reasons, in fact. first the hype cycle in my opinion needed a counterweight and now of course I'm not being pretentious here like I am the counterweight I'm definitely not there are many more vocal people out there with a lot more followers and a bigger community so they definitely can be the counterweight just better than myself but I needed to do it for myself as well

you know So, hype cycles always need a counterweight that is not just reflexive doom, you know. Half the discourse out there is typically like this. AI will replace every engineer by next week, whatever. And the other half is, it's just autocomplete, ignore it, right? So, neither of these two extremes, in fact, helps a working engineer at the beginning of the week or their project trying to decide whether to let an agent touch the payment module, for example, if they work in fintech. So I want you to write for that person, not the investor deck, not the doom poster, but the person who's actually shipping code, right? The second reason is that I run a consultancy, amethix.com, A-M-E-T-H-I-X.com, that lives in regulated high-stakes environments, you know, things like defense, fintech,

healthcare. Now, in those worlds, sentences like it mostly works, you know, are not allowed you to say out loud, if you know what I mean. So, out of necessity, I have to develop a much more disciplined way to, you know, think about where AI assistance is a force multiplier and where it becomes a liability in a way. So, that discipline is, in fact, the spine of the book. And the third reason, honestly, I was tired of the framing AI skeptic versus AI evangelist. I think it's a false binary that's been forced on all of us in the last three, four years, probably by marketing experts and the big players especially. I find it intellectually lazy. So I wanted a third position in a way, you know, clearly staked out with receipts. Conscious use, deliberate use, use it like a power tool, not a vending machine.

That's pretty much my summary on how to use AI. Now, what can you find in this book? Well, the book is structured, as I said, as 12 essays. I definitely do not want a linear textbook. You know, chapter one, basics, chapter 12, mastery. You know, that's not how working developers actually experience this technology. They experience it as a series of collisions in a way, you know, with hype, with tooling, with their own instincts about what good code even means anymore. So HSA is its own collision. I hope I could transmit that feeling well to the reader. and of course some are about the mechanics you know when to let an agent operate on its own and when you need to be in the loop line by line some are about the economics and yes of course that includes actual token economics

or tokenomics you know the mythical part where I walk through why more compute, more context, more autonomy isn't really a free upgrade that it's more a trade and you're often making it without realizing it. Some essays, I have to tell you, are more uncomfortable than others. There is one about what happens to junior engineers skill formation when the easy path is always ask the model first There is one about the seduction of fluency how for example an AI pull request can read like competence without being competence, and how dangerous that gap gets in a code base that nobody full understands anymore, including the person who wrote it. And, well, wrote, it's no longer, you know, writing in the sense of a few years ago, generated or supported the generation. And there is, of course, one other chapter, probably my favorite, about conceptual integrity.

That's literally Brook's term from the original Mythical Man month, which I definitely recommend, especially to the new generations. he argued that the best software comes from a small number of minds with a unified vision so I ask the uncomfortable question does AI assisted development erode that unity? you know, 10 different people each outsourcing a different corner of the system to a model with no memory of yesterday's decisions we have seen things going pretty crazy in that space so if you want a spoiler of course uh i can tell you now so i can save you some bucks but yes sometimes yes um many times that doesn't happen and dsa is about how to prevent that not just how to diagnose it right now i want to close the loop on something because i know how easily it gets to you know to get flattened in a tweet these days

I'm not here to tell you to slow down I'm not telling you that AI coding tools are dangerous toys That you should lock in a drawer and forget about their existence Quite the opposite, in fact I'm telling you probably the same thing that Brooks told the generation of project managers back in the 75 The resource you think is infinite isn't really Back then it was people months, right? man-month as a concept, as the currency of IT projects. And today it's tokens, it's context, it's trust, and all in many possible combinations. You spend all three like you have an unlimited budget, and you will absolutely predictably ship something that works in the demo and falls apart in production. I've been there, we've been there, we've seen this a number of times. so use AI like the sharpest tool in your shop

because it is but sharpen your own judgment right alongside it so that's pretty much the whole book honestly everything else is probably 12 different rooms in the same house which is probably a good reason to get a copy if any of this resonates of course the book is out now and you'll find the details at the usual places I post and links in the show notes of this episode And if you think I'm wrong, genuinely tell me I'm always open to discuss with you On YouTube comments as well as on Discord Where we can engage in a conversation And speak about AI and all things about software I'd rather be corrected than comfortable, that's for sure This has been Data Science at Home I'm Frag and talk to you next time Hey, quick one I wrote a book, The Mythical Token Month Here's the idea AI coding agents feel like magic, but they don't remove the hard part of building software. They just clear out the busy work around it, as we all know.

What's left is indeed the actual thinking. Now, I have to say that no AI wrote a word of this book. I have to say that. It's a self-claim, but you have to believe me. It's out now as an e-book, NoDRM. The website, mythicaltokenmonth.com. Links in the show notes. Thank you. You've been listening to Data Science at Home Podcast. Be sure to subscribe on iTunes, Stitcher, or Podbean to get new fresh episodes. For more, please follow us on Instagram, Twitter, and Facebook, or visit our website at datascienceathome.com.