CZM Rewind: The Case Against Generative AI (Part 3)

31 Dec 2025 · 27 min · 13 chapters

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In short

Better Offline Podcast Episode Summary

Episode Title

CZM Rewind: The Case Against Generative AI (Part 3) Original Air Date: October 2, 2025

Overview In the third part of a four-part series, Ed Zitron critiques the notion that generative AI will replace software engineers, exposing it as a myth perpetuated by media and investors. He argues that the actual performance and adoption rates of AI tools, like Microsoft 365's AI Copilot, reveal significant shortcomings in the generative AI industry.

Key Themes

  • AI Mythology:
  • The assumption that AI will replace software engineers is challenged.
  • Zitron emphasizes the gap between media narratives and the realities of AI capabilities.
  • Market Reality:
  • Microsoft reportedly has only 8 to 12 million active paying customers for its AI Copilot, which is a stark contrast to its total user base of 440 million for Microsoft 365.
  • The conversion rate is alarmingly low at approximately 1.81%.
  • Profitability Issues:
  • Companies developing AI tools are struggling with profitability.
  • High operational costs are exacerbated by the infrastructure required for AI, leading to significant losses even among major players like OpenAI and Anthropic.

Detailed Analysis

  1. Generative AI Financial Viability:
  2. Zitron notes that most companies are losing money on generative AI projects, with only a handful making significant revenue.
  3. He highlights that operational costs for running AI models are rising, making the profitability of AI ventures questionable.
  1. AI Tools and Software Engineering:
  2. Zitron elaborates on the limitations of AI in coding, equating their capabilities to those of a "below average computer science graduate."
  3. Feedback from experienced software engineers reveals that while AI tools can assist in simple tasks, they lack the necessary contextual understanding and problem-solving skills to replace skilled developers.
  1. User Experience and Costs:
  2. Zitrons shares anecdotal evidence of users facing exorbitant bills while using AI coding tools, particularly with Replit's Agent 3.
  3. Users report that the costs can spiral without clear benefits, leading to dissatisfaction with AI tools.
  1. Media Misrepresentation:
  2. Zitron calls out the media for spreading misleading narratives about the capabilities of generative AI, urging journalists to seek a more nuanced understanding of AI's limitations in practical applications.

Critical Quotes

  • "Even if Anthropic was profitable, it isn't and will burn billions of dollars this year."
  • "LLMs often function like a fresh summer intern. They're good at solving straightforward problems but are unworldly."
  • "The raw costs of providing access to AI models are so high that the basic economics of how the tech industry sells software don't make sense."

Conclusion Ed Zitron's analysis paints a grim picture for the future of generative AI in software engineering, highlighting severe operational challenges and a pervasive myth about AI's ability to replace human developers. The episode underscores the need for critical scrutiny of both the technology and the narratives surrounding it, especially from media outlets.

Additional Resources

  • Listen to the Podcast: Available on [iHeartRadio](https://www.iheartradio.com) and other platforms.
  • Follow Ed Zitron on Social Media: [Twitter](https://twitter.com/edzitron), [Instagram](https://www.instagram.com/edzitron)
  • Engage with the Community:
  • [Discord](https://discord.com/invite/QUUQUP9szv)
  • [Reddit](https://www.reddit.com/r/BetterOffline/)

Upcoming Episode Preview

  • Stay tuned for the final part of the series, where Zitron promises to delve deeper into the implications of these findings and the future of AI in the tech industry.

Written by AI. May contain mistakes. Listen to the episode to check what was said.

Chapters

Tap a time to open that second in VO

The Financial Reality of Generative AI

2:24 to 3:56

Discussion on the financial struggles of generative AI companies and the challenges they face.

“and because this is a very big subject with a lot of moving parts and even more bullshit.”

Exploring Claude Code and Subscription Models

3:56 to 5:32

Overview of Anthropic's Claude Code and its subscription pricing model.

“Both of these companies burn ridiculous amounts of money.”

Issues with Token Pricing and User Costs

5:32 to 7:24

Analysis of token pricing and how it affects users of generative AI.

“subscriptions having more generous rate limits.”

Problems with Replit's Agent 3

7:24 to 9:10

Investigation into user complaints regarding Replit's new product, Agent 3.

“even with weekly rate limits brought in at the end of August.”

AI Business Models and Cost Control

9:10 to 11:12

Discussion on the unsustainable business models of AI companies and their cost challenges.

“Users found the tasks that previously cost a few dollars were spiraling into the hundreds of dollars, with the register reporting one customer found themselves with a$1 ,000 bill after a week, and I quote them.”

The Infrastructural Burden of Large Language Models

14:36 to 16:30

Explore the complexities and costs associated with large language models and their usage.

“I'll reiterate something I wrote a few weeks ago.”

Claude Code and AI Coding Environments

16:42 to 19:16

Discuss the performance and financial aspects of Claude Code and AI coding tools.

“But let's talk about Claude Code again, Anthropik's Code Generator tool.”

The Media's Misrepresentation of AI's Impact on Coding Jobs

19:18 to 22:20

Critique the media narrative claiming AI replaces software engineers.

“Now, for the purposes of brevity, I'm going to use select quotes from what these people said, but if you want to read the whole thing, you can check out the newsletter.”

The Reality of Software Engineering vs. AI Capabilities

22:26 to 28:00

Understand the limitations of AI in software engineering and the necessity of human engineers.

“Have you ever listened to those true crime shows and found yourself with more questions than answers?”

The Limitations of Generative AI in Software Engineering

28:00 to 29:40

Explore the inherent flaws of generative AI in handling complex software engineering tasks.

“Even software engineers, who can read code and have done so for decades, will find problems they can't solve just by looking at the code.”
Show all 13 chapters

Microsoft's Struggles with AI Adoption

29:40 to 31:20

Uncover the disappointing conversion rates of Microsoft's generative AI products.

“And I buried it in the third part of a four-part episode and truly twisted.”

Critique of Microsoft 365 Copilot Performance

31:20 to 33:20

Discuss the poor performance metrics and user engagement of Microsoft 365 Copilot.

“An active user is someone who has taken one action on any Microsoft 365 app with Copilot in the space of 28 days.”

Anticipating Future Challenges in AI

33:20 to 33:55

Prepare for the potential decline of generative AI as discussed in the upcoming final part.

“into an actual money-minting industry that changes the world.”
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Transcript

Automatic transcript. May contain errors.

0:00This is an iHeart Podcast. Guaranteed Human.

0:31I'm comedian Rory Scovel, and I'm here to tell you Josh Dean and I have a new podcast that celebrates the amazing creativity of the world's dumbest criminals. It's called Crimeless, a true crime comedy podcast. Listen on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts. I'm Stephen Curry, and this is Gentleman's Cut. I think what makes Gentleman's Cut different is me being a part of developing the profile of this beautiful finished product. With every sip, you get a little something different. Visit Gentleman'sCutBourbon.com or your nearest Total Wines or BevMo. This message is intended for audiences 21 and older.

1:11Gentleman's Cut Bourbon, Boone County, Kentucky. For more on Gentleman's Cut Bourbon, please visit Gentleman'sCutBourbon.com. Please enjoy responsibly. I know he has a reputation, but it's going to catch up to him. Gabe Ortiz is a cop. His brother Larry, a mystery Gabe didn't want to solve until it was too late. He was the head of this gang. You gonna push that line for the cause. Took us under his wing and showed us the game, as they call it. When Larry's killed, Gabe must untangle a dangerous past, one that could destroy everything he thought he knew. Listen to the Brothers Ortiz on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

1:51call zone media hello and welcome to better offline i'm of course your host ed zitron

2:09we're in the third episode of our four-part series where i give you a comprehensive explanation as to the origins of the ai bubble the mythology sustaining it and why it's destined to end really, really badly. Now, if you're jumping in now, please start from the very beginning. The reason why this is a four-parter, my first ever, is because I want it to be comprehensive and because this is a very big subject with a lot of moving parts and even more bullshit. A few weeks ago, I published a premium newsletter that explained how everybody is losing money on generative AI, in part because the costs of running AI models is increasing, and in part because the software itself doesn't do enough to warrant the costs associated with running them, which are already subsidized and unprofitable for the model providers.

2:49Outside of OpenAI and, to a lesser extent, Anthropic, nobody seems to be making much revenue, with the most successful company being AnySphere, makers of AI coding tool Cursor, which hit$500 million of annualized, so$41.6 million in one month, a few months ago, just before Anthropic and OpenAI jacked up the prices for priority processing on enterprise queries, raising their operating costs as a result. in any case that's some piss poor revenue for an industry that's meant to be the future of software smartwatches are projected to make 32 billion dollars this year and as i've mentioned in the past the magnificent seven expect to make 35 billion dollars or so in revenue from ai this year and i think in total when you throw in core even all them it's barely 55 billion dollars in total even anthropic and open ai seem a little lethargic both burning billions of dollars while making by my estimates no more than two billion dollars in anthropics case this year so far and$6.26 billion in 2025 so far for OpenAI, despite projections of$5 billion and$13 billion respectively.

3:49Outside of these two, AI startups are floundering, struggling to stay alive and raising money in several hundred million dollar bursts as their negative gross margin businesses flounder. As I dug into a few months ago, I could find only 12 AI-powered companies making more than$8.3 million a month, with two of them slightly improving their revenues, specifically AI search company Perplexity, which has now hit$150 million in ARR or$12.5 million a month, and AI coding startup Replit, which has hit the same amount. Both of these companies burn ridiculous amounts of money. Perplexity burned 164 % of its revenue on Amazon Web Services' OpenAI and Anthropic last year, and while Replit hasn't leaked its costs, the information reports its gross margins in July were 23%, which doesn't include the cost of its free users, which you simply have to do with LLMs as free users are capable of costing you a shit ton of money and some of you might say that's how they do it in software well guess what software doesn't usually connect you to a model that can burn I don't know 10 cents 20 cents every time they touch it which may not seem like much but when you're making free dollars on someone and they don't convert it does problematically your paid users also cost you more than they bring in as well in fact every user loses you money in generative AI because it's impossible to do cost control in a consistent manner.

5:06A few months ago, I did a piece on Anthropic losing money on every single Claude Code subscriber, and now I'm going to walk you through the whole story in a simplified fashion because it's quite important. So Claude Code is a coding environment that people used, used, or I should really say tried to use, to build software using generative AI. It's available as part of Anthropic's$20,$100, and$200 a month Claude subscriptions, with the more expensive subscriptions having more generous rate limits. Generally, these subscriptions are all you can eat. You can use them as much as you want until you hit limits rather than paying for the actual tokens you burn.

5:41When I say burn tokens, and someone reached out saying I should specify this, I'm describing how these models are traditionally billed. In general, you're billed at a dollar per million input tokens, as in user feeding in data, and output tokens, the output created. So you wouldn't get one token billed, so every million you get charged. So for example, Anthropic charges $3 per million input tokens and 6 million output tokens to use its Claude Sonnet 4 model. And it's about, I think, well, a word before tokens? I should really look that up. It also gets more complex as you get into things like generating code.

6:18Nevertheless, Claude Code has been quite popular. And a user created a program called CC Usage, which allowed you to see your token burn. The amount of tokens you were using, you were actually burning using Anthropix models while using clawed code versus just getting charged a month and not knowing, and many were seeing that they were burning in excess of their monthly spend. To be clear, this is the token price based on Anthropic's own pricing, and thus the costs to Anthropic are likely not identical. So I got a little clever. Using Anthropic's gross profit margins, I chose 55%, and then a few weeks after my article, 60 % was leaked, I found at least 20 different accounts of people costing Anthropic anywhere from 130 % to 3 ,084 % of their subscription.

6:59There's also now a leaderboard called Viberank, where people compete to see how much they burn, with the current leader burning, and I shit you not, $51 ,291 over the course of a month. Anthropic is, to be clear, the second largest model developer, and has some of the best AI talent in the industry. It has a better handle on its infrastructure than anyone outside of big tech and open AI, and it still cannot seem to fix this problem, even with weekly rate limits brought in at the end of August. While one could assume that Anthropic is simply letting users run wild, my theory is far simpler. Even the model developers have no real way of limiting user activity, likely due to the architecture of generative AI.

7:39I know it sounds insane, but at the most advanced level, even there, modeled providers are still prompting their models, and whatever rate limits may be in place appear to at times get completely ignored, and there doesn't seem to be anything they can do to stop it. Now really, Anthropic counts amongst its capitalist apex predators one lone Chinese man who spent$50 ,000 of their compute in the space of a month fucking around with clawed code. Even if Anthropic was profitable, it isn't, and will burn billions of dollars this year, a customer paying$200 a month ran up$50 ,000 in costs, immediately devouring the margin of any user running the service that day, that week, or even that month.

8:17Even if Anthropics costs are half the published rates, they're not, by the way. One guy amounted to 125 users' worth of monthly revenue. This is not a real business! That's a bad business with out-of-control costs, and it doesn't appear anybody has these costs under control. And, faced with the grim reality ahead of them, these companies are trying nasty little tricks on their customers to juice more revenue from them. A few weeks ago, Replit, an unprofitable AI coding company, released a product called Agent 3, which promised to be ten times more autonomous and offer infinitely more possibilities, testing and fixing its code, constantly improving your application behind the scenes in a reflection loop.

8:56Sounds very real. Sounds extremely real. It's so real, but actually it isn't. In reality, this means you'd go and tell the model to build something, and it would go and do it, and you'll be shocked to hear that these models can't be relied upon to go and do anything. Please note that this was launched a few months after Replit raised their prices, shifting to obfuscated effort-based pricing that would charge the full scope of the agent's work, and if you're wondering what the fuck that means, so are their customers. Agent 3 has been a disaster. Users found the tasks that previously cost a few dollars were spiraling into the hundreds of dollars, with the register reporting one customer found themselves with a$1 ,000 bill after a week, and I quote them.

9:33I think it's just launch pricing adjustment. Some tasks on new apps ran over an hour and 45 minutes and only charged$4 to$6, but editing pre-existing apps seems to cost most overall. I spent$1k this week alone. And they told that to the register, by the way. Another user complained that costs skyrocketed without any concrete results, and I quote the register here. I typically spent between$100 and$250 a month. I blew through$70 in a night at Agent 3 launch, another editor wrote, alleging the new tool also performed some questionable actions. One prompt brute forced its way through authentication, redoing auth and hard resetting a user's password to what it wanted to perform app testing on a form, the user wrote.

10:12I realize that's a little nonsensical, but long story short, it did a bunch of shit it wasn't asked to. As I previously reported in late May, early June, both OpenAI and Anthropic cranked up the pricing on their enterprise customers, leading Replit and Cursor both shifting their prices upward. This abuse has now trickled down to the customers. Replit has now released an update that lets you choose how autonomous you want Agent 3 to be, which is a tacit admission that you can't trust coding LLMs to build software. Replit's users are still pissed off, complaining that Replit is charging them for an activity when the agent doesn't do anything, a consistent problem I've found across Redditors.

10:46While Reddit is not the full summation of all users of every company everywhere, it's a fairly good barometer of user sentiment, and man, are users pissy. And now here's where this is bad. Traditionally, Silicon Valley startups have relied upon the same model of grow really fast and burn a bunch of money, then turn the profit lever AI does not have a profit lever because the raw costs of providing access to AI models are so high, and they're only increasing That the basic economics of how the tech industry sells software don't make sense I'm I'm I'm I'm streaming radio and podcasting call 844-844-IHEART to get started that's 844-844-IHEART have you ever listened to those true crime shows and found yourself with more questions than answers and what is this how is that not a story we all know what what's this you where is that why is it wet boy do we have a show for you from smartless media campsite media and big money players comes crimeless.

12:14Join me, Josh Dean, investigative journalist. And me, Rory Scovel, comedian, as we celebrate the amazing creativity of the world's dumbest criminals. We'll look into some of the silliest ways folks have broken the laws. Honestly, it feels more like a high-level prank than a crime. Who catfishes a city? And meets some memorable antiheroes. There are thousands of angry, horny monkeys. Clap if you think she's a witch and it freaks you out. He has X-ray vision. How could I not follow him? Honestly, I gotta follow him. He can see right through me. Listen to Crimeless on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

12:57Who would you call if the unthinkable happened? I just fell and started screaming. If you lost someone you loved in the most horrific way. My sister was shot 22 times. The police, right? But what if the person you're supposed to go to for help is the one you're the most afraid of? This dude is the devil. He's a snake. He'll hurt you.

13:23I'm Nikki Richardson, and this is The Girlfriends, Untouchable. Detective Roger Golubski spent decades intimidating and sexually abusing Black women across Kansas City using his police badge to scare them into silence. This is the story of a detective who seemed above the law until we came together to take him down. I told Roger Golubski, I said, you're going to see my face till the day that you die. Listen to The Girlfriends, Untouchable on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts. I'm Stephen Curry. And this is Gentleman's Cut. I think what makes Gentleman's Cut different is me being a part of, you know, developing the profile of this beautiful finished product.

14:14With every sip, you get a little something different. Visit Gentleman'sCutBourbon.com or your nearest Total Wines or BevMo. This message is intended for audiences 21 and older. Gentleman's Cut Bourbon, Boone County, Kentucky. For more on Gentleman's Cut Bourbon, please visit Gentleman'sCutBourbon.com. Please enjoy responsibly.

14:36I'll reiterate something I wrote a few weeks ago. A large language model uses infrastructural burden varies wildly between users and use cases. While somebody asking ChatGPT to summarize an email might not be much of a burden, somebody asking ChatGPT to review hundreds of pages of documents at once, a core feature of basically any$20 a month subscription, could eat up to 8 GPUs at once. To be very clear, a user that pays$20 a month could run multiple queries like this a month, and there's not really a way to stop them. Unlike most software products, any errors in producing an output from a large language model have a significant opportunity cost.

15:11When a user doesn't like an output or the model gets something wrong, which it's guaranteed to do, or the user realizes they forgot something, the model must make a further generation or generations, and even with caching, which Anthropics added a toll to, there's a definitive cost attached to any mistake. Large language models are, for the most part, lacking in any definitive use cases, meaning that every user is, even with an idea of what they want to do, experimenting with every input and output. In doing so, they create the opportunity to burn more tokens, which in turn creates an infrastructural burn on GPUs, which cost a lot of money to run.

15:43The more specific the output, the more opportunities there are for monstrous token burn, and I'm specifically thinking about coding with LLMs. The token-heavy nature of generating code means that any mistakes, suboptimal generations, or straight-up errors will guarantee further token burn. Even efforts to reduce compute costs by, for example, pushing free users or those on cheap plans to smaller, less intensive models have dubious efficacy. As I talked about in a previous episode, OpenAI's splitter model in the GPT version of ChatGPT requires vast amounts of additional compute in order to route the user's request or the appropriate model, with simpler requests going to smaller models and more complex ones being shifted to reasoning models, and it makes it impossible to cache part of the input.

16:23As a result, it's not really clear whether it's saving OpenAI any money, and indeed, kind of suggests it might be costing them more. In simpler terms, it's very, very, very difficult to imagine what one user, free or otherwise, might cost, and thus it's hard to charge them anything on a monthly basis, or tell them what a service might actually cost them on average. And this is a huge, huge problem with AI coding environments. But let's talk about Claude Code again, Anthropik's Code Generator tool. According to the information, Claude Code was driving nearly$400 million in annualized revenue, roughly doubling from a few weeks ago on July 31st, 2025.

16:59The annualized revenue works out to about$33 million a month in revenue for a company that predicts it will make at least$416 million a month by the end of the year, and for a product that has become, for a time, the most popular coding environment in the world from the second largest and best-funded AI company in the world. Is that it? Is that fucking it? Is that all that's happening here? $33 million, all of which is unprofitable, after it felt, at least based on social media chatter and discussing with multiple different engineers, that Claude Code had become ubiquitous with anything to do with LLMs and coding.

17:34To be clear, Anthropic's Sonnet and Opus models are consistently some of the most popular for programming an open router, an aggregator of LLM usage, and Anthropic has been consistently named as the best at coding, whether or not I feel that way is irrelevant. some bright spark out there is going to say that microsoft's github copilot has 1.8 million paying subscribers and guess what that's true in fact i reported it here's another fun fact the wall street journal reported that microsoft loses on average 20 a month per user with some users costing the company as much as 80 bucks and that's for the most popular product but wait wait wait wait hold up wait i read some shit in the newspaper aren't these llm code generators replacing actual human engineers.

18:14And thus, even if they cost way more than$20,$100, or$200 a month, they're still worth it, right? They're replacing an entire engineer. Oh, my sweet summer child. If you believe the New York Times or other outlets that simply copy and paste whatever Anthropic CEO Wario Amadei says, you'd think that the reason that software engineers are having trouble finding work is because their jobs are being replaced by AI. This grotesque, manipulative, abusive, and offensive lie has been propagated through the entire business and tech media without anybody sitting down and asking whether it's true, or even getting a good understanding of what it is that LLMs can actually do with code.

18:47Members of the media, I am begging you, stop. Stop doing this. Stop publishing these fucking headlines. You're embarrassing yourself. Every arsehole is willing to give a quote saying that coding is dead and that every executive is willing to burp out some nonsense about replacing all of their engineers, but I'm fucking begging you to either use these things yourself or speak to people that do. I am not a coder. I cannot write or read code. Nevertheless, I'm capable of learning and I've spoken to numerous software engineers in the last few months, and basically I've reached a consensus of this is kind of useful sometimes.

19:18However, one time a very silly man with an increasingly squeaky voice said that I don't speak to people who use AI tools, so I went and spoke to three notable, experienced software engineers and asked them to give me the straight truth about what coding LLMs can do. Now, for the purposes of brevity, I'm going to use select quotes from what these people said, but if you want to read the whole thing, you can check out the newsletter. first i'm going to read what carl brown of the internet of bugs said and add him on the show a few months back he's fantastic so most of the advancements in programming languages technique and craft in the last four years have been designing safer and better ways of tying these blocks together to create large and larger programs with more complexity and functionality humans use these advancements to arrange these blocks in logical abstraction layers so we can fit an understanding of the layers interconnections in our heads as we work diving into blocks temporarily is needed.

20:07This is where AIs fall down. The amount of context required to hold the interconnections between these blocks quickly grows beyond the AI's effective short-term memory, in practice much smaller than its advertised context window size, and the AIs lack the ability to reason about the abstractions as we do. This leads to real-world code that's illogically layered, hard to understand, debug, and maintain. Carl also said, Code generation AIs, from an industry standpoint, are roughly the equivalent of a slightly below average computer science graduate fresh out of school without any real world experience, only ever having written programs to be printed and graded.

20:42That's bad, because as he pointed out, whereas LLMs can't get past this summer intern stage, actual humans get better, and if we're replacing the bottom rung of the labor market, there won't be any mid-level or senior developers later down the line. Next, I asked Nick Shoresh of I Will Fucking Piledrive You If You Mention AI Again what he thought. LLMs, he said, will sometimes solve a thorny problem for me in a few seconds, saving me some brainpower. But in practice, the effort of articulating so much of the design work in plain English and hoping the LLM omits code that I find acceptable is frequently more work than just writing the code.

21:15For most problems, the hardest part is the thinking, and LLMs don't make that part any easier. I also talked to Colt Volge of No, AI is not making AI engineers 10x is productive, who we also had on the show recently, and he said this. LLMs often function like a fresh summer intern. They're good at solving the straightforward problems that coders learn about in school, but they are unworldly. They do not understand how to bring lots of solutions to small, straightforward problems together into a larger whole. They lack the experience to be wholly trusted, and trust is the most important thing you need to fully delegate coding tasks.

21:46In simpler terms, LLMs are capable of writing code but can't do software engineering because software engineering is the process of understanding, maintaining, and executing code to produce functional software. And LLMs do not learn, cannot adapt, and, to paraphrase something Carl Brown said to me, break down the more of your code and variables you ask them to look at at once. So you can't replace a software engineer with them. If you are printing this in a media outlet and have heard this sentence, you are fucking up. You really are fucking up. I'm really, members of the media hearing this, you need to change.

22:18You need to change on this one. You are doing software engineers dirty.

22:56I'll see you next time. That's iHeartAdvertising.com. Have you ever listened to those true crime shows and found yourself with more questions than answers? And what is this? How is that not a story we all know? What's this? Where is that? Why is it wet? Boy, do we have a show for you. From Smartless Media, Campside Media, and Big Money Players comes Crimeless. Join me, Josh Dean, investigative journalist. And me, Rory Scovel, comedian. as we celebrate the amazing creativity of the world's dumbest criminals. We'll look into some of the silliest ways folks have broken the laws. Honestly, it feels more like a high-level prank than a crime.

23:41Who catfishes a city? And meets some memorable antiheroes. There are thousands of angry, horny monkeys. Clap if you think she's a witch and it freaks you out. He has x-ray vision. How could I not follow him? Honestly, I gotta follow him. He can see right through me. Listen to Crimeless on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

24:26well, he's a snake. He'll hurt you.

24:32I'm Nikki Richardson, and this is The Girlfriends, Untouchable. Detective Roger Golubski spent decades intimidating and sexually abusing Black women across Kansas City, using his police badge to scare them into silence. This is the story of a detective who seemed above the law until we came together to take him down. I told Roger Golubski, I said, you're going to see my face till the day that you die. Listen to The Girlfriends Untouchable on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts. I'm Stephen Curry, and this is Gentleman's Cut. I think what makes Gentleman's Cut different is me being a part of, you know, developing the profile of this beautiful finished product.

25:23but with every sip you get a little something different. Visit Gentleman'sCutBourbon.com or your nearest Total Wines or BevMo. This message is intended for audiences 21 and older. Gentleman's Cut Bourbon, Boone County, Kentucky. For more on Gentleman's Cut Bourbon, please visit Gentleman'sCutBourbon.com. Please enjoy responsibly.

Read the full transcript

25:45Look, and I understand why too. It's very easy to believe that software engineering is just writing code, but the reality is that software engineers maintain software, which includes writing and analyzing code amongst a vast array of different personalities and programs and problems. Good software engineering harkens back to Brian Merchant's interviews with translators. While some may believe the translators simply tell you what words mean, true translation is communicating the meaning of a sentence, which is cultural, contextual, regional, and personal, and often requires the exercise of creativity and novel thinking.

26:15And on top of that, while translation is the production of words, you can't just take code and look at it. You actually need to know how code works and functions and why it functions in that way. Using an LLM, you'll never know because the LLM doesn't know anything either. Now, my editor Matt Hughes gave an example of this in his newsletter, which I think I'll paraphrase. He used to live in France in the French-speaking part of Switzerland, and sometimes he'll read French translations of books to see how awkward bits of prose are translated. Doing those awkward bits requires a bit of creative thinking, and I quote, Take Harry Potter.

26:47In French, Hogwarts is Poudlard, which translates into bacon lice. Why did they go with that instead of a literal translation of Hogwarts, which would be Verus Spork? I'm sorry to anyone who can actually read languages. No idea, but I'd assume it was something to do with the fact that Poudlard sounds a lot better than Verus Spork. And both of them I can say flawlessly. Someone had to actually think about how to translate that one idea. They had to exercise creativity, which is something that an AI is inherently incapable of doing. Similarly, coding is not just a series of text that programs a computer, but a series of interconnected characters that refers to other software in other places that must also function now, and explain on some level to someone who has never ever seen the code before why it was done in this way.

27:30This is, by the way, why we're still yet to get any tangible proof that AI is replacing software engineers, because it isn't replacing software engineers. And now we need to understand why this is so existentially bad for generative AI. Of all the fields supposedly at risk from AI disruption, coding feels or felt the most tangible, if only because the answer to can you write code with LLMs wasn't an immediate unilateral no. The media has also been quick to suggest that AI writes software, which is true in the same way that ChatGPT writes novels. In reality, LLMs can generate code and do some sort of software engineering adjacent tasks, but like all large language models, break down and go totally insane, hallucinating more and more as the tasks get more complex, and software engineering is extremely complex.

28:14Even software engineers, who can read code and have done so for decades, will find problems they can't solve just by looking at the code. And as I pointed out earlier, software engineering is not just coding. It involves thinking about problems, finding solutions to novel challenges, designing stuff in a way that can be read and maintained by others, and that's ideally scalable and secure. The whole fucking point of an AI is that you hand shit off to it. That's what they've been selling it as. That's why Jensen Huang told kids to stop learning to code, as with AI there's no point. And it was all a fucking lie.

28:46Generative AI can't do the job of a software engineer, and it fails, while also costing an abominable amount of money. Coding large language models seem like magic at first because they, to quote a conversation with Carl Brown, make the easy things easier, but they also make the harder things harder. They don't even speed up engineers. There's a study that showed they make them slower. Yet coding is basically the only obvious use case for LLMs. Oh, I'm sure you're going to say, but I bet the enterprise is doing well, and you're also very, very wrong. Microsoft, if you've ever switched on a TV in the past two years, has gone all in on generative AI, and despite being arguably the biggest software company in the world, at least in terms of desktop operating systems and productivity software, has made almost no traction in popularizing generative AI.

29:29It has thousands, if not tens of thousands of salespeople and thousands of companies that literally sell Microsoft services for a living. And it can't sell AI. I've got a real fucking scoop here. I'm so excited. And I buried it in the third part of a four-part episode and truly twisted. But a source that has seen materials related to sales has confirmed that as of August, 2025, Microsoft has around 8 million active, licensed, so paying, users of Microsoft 365 Copiler, amounting to a 1.81 % conversion rate across 440 million Microsoft 365 subscribers. Must be clear that 365 is their big cash cow.

30:09This would amount to if each of these users paid annually at the full rate$30 a month to about$2.88 billion in annual revenue for a product category that makes$33 billion a fucking quarter, this productivity and business unit for Microsoft. And I must be clear, I am 100 % sure these users aren't all paying$30 a month. The information reported a few weeks ago that Microsoft has been reducing the software's price, referring to Microsoft 365, with more generous discounts on the AI features, according to customers and salespeople, heavily suggesting discounts have already been happening. Enterprise software is traditionally sold at a discount anyway, or put a different way, with bulk pricing for those who sign up a bunch of users at once.

30:47In fact, I found evidence that they've been doing this for a while, with a 15 % discount on annual Microsoft 365 co-pilot subscriptions for orders of 10 to 300 seats, mentioned by an IT consultant back in late 2024, and another that's currently running through September 30th, 2025, with another Microsoft Cloud solution provider program. Yeah, this, I found tons of other examples too. And Microsoft 365 is the enterprise version where they sell things with like Word and PowerPoint and sometimes Teams as well. This is probably their most popular product. And by the way, they even manipulate the numbers a little bit there.

31:20An active user is someone who has taken one action on any Microsoft 365 app with Copilot in the space of 28 days. Not 30, 28. That's so generous. Now I know, I know, that word active. Maybe you're thinking, Ed, this is like the gym model. There are unpaid licenses that Microsoft is getting paid for. Fine, fine, fine, fucking fine. Let's assume that Microsoft also has, based on research that suggests this can be the case for some software companies, another 50%, 4 million paying co-pilot licenses that aren't being used. That's still 12 million users, which is around 2.7 % conversion rate. That's piss poor, buddy.

31:58That's piss poor. That's pissy. It sucks. It's bad. It's doo-doo. Well, I just said pee-pee, I guess. Anyway, very serious, very serious podcast. But why aren't people paying for co-pilot? Well, let's hear from someone who talked to the information, and I quote, it's easy for an employee to say, yes, this will help me, are hard to quantify how, and if they can't quantify how it'll help them, it's not going to be a long discussion over whether the software is worth paying for. Is that good? Is that good? Is that what you want to hear? It isn't. It isn't. That's the secret. It's not. It's bad. It's really bad.

32:30It's all very bad. And Microsoft 365 Copiler has been such a disaster that Microsoft will now integrate Anthropix models to try and make them better. Oh, one other thing, too. Sources also confirm GPU utilization, so how much the GPU is set aside for Microsoft 365. Yeah, their enterprise copilot is barely scratching the 60%. I'm also hearing that SharePoint, which is an app they have with over 250 million users, has less than 300 ,000 weekly active users of their copilot features, suggesting that people just don't want to fucking use this. Those numbers are from August, by the way. And it's pathetic.

33:07And I must be clear, if Microsoft's doing this badly, I don't know how anyone else is doing well. And they're not. They're all failing. It's pathetic. But I've spent a lot of time today talking about AI coding, because this was supposed to be the saving grace, the thing that actually turned this from a bubble into an actual money-minting industry that changes the world. And I wanted to bring up Microsoft 365, because that's the place where Microsoft should be making the most money. It's their most ubiquitous software. It's their most well-known software. And they're not. Eight million people. Eight million people.

33:37I've run that by a few people and everyone's made the same oh god noise. It's quite weird. The oh god noise and the numbers. But this just isn't happening. Things are going badly and it really only gets worse from here. And I'm going to tell you more tomorrow in the final part of our four-parter. Thank you for your patience and thank you for your time.

34:04thank you for listening to better offline the editor and composer of the better offline theme song is matt osowski you can check out more of his music and audio projects at matt osowski.com m-a-t-t-o-s-o-w-s-k-i.com you can email me at easy at better offline.com or visit better offline.com to find more podcast links and of course my newsletter i also really recommend you go to chat.wheresyoured.at to visit the discord and go to r slash better offline to check out our reddit thank you so much for listening better offline is a production of cool zone media for more from cool zone media visit our website coolzonemedia.com or check us out on the iheart radio app apple podcasts or wherever you get your podcasts

35:10Have you ever listened to those true crime shows and found yourself with more questions than answers? Who catfishes a city? Is it even safe to snort human remains? Is that the plot of Footloose? I'm comedian Rory Scovel and I'm here to tell you Josh Dean and I have a new podcast that celebrates the amazing creativity of the world's dumbest criminals it's called Crimeless a true crime comedy podcast listen on the iHeartRadio app Apple Podcasts or wherever you get your podcasts I'm Stephen Curry and this is Gentleman's Cut I think what makes Gentleman's Cut different is me being a part of developing the profile of this beautiful finished product.

35:50With every sip, you get a little something different. Visit Gentleman'sCutBourbon.com or your nearest Total Wines or BevMo. This message is intended for audiences 21 and older. Gentleman's Cut Bourbon, Boone County, Kentucky. For more on Gentleman's Cut Bourbon, please visit Gentleman'sCutBourbon.com. Please enjoy responsibly. I know he has a reputation, but it's going to catch up to him. Gabe Ortiz is a cop. His brother, Larry, a mystery Gabe didn't want to solve until it was too late. He was the head of this gang. You gonna push that line for the cause. Took us under his wing and showed us the game, as they call it.

36:27When Larry's killed, Gabe must untangle a dangerous past, one that could destroy everything he thought he knew. Listen to the Brothers Ortiz on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts. Who would you call if the unthinkable happened? My sister was shot 22 times. A police officer, right? But what do you do when the monster is the man in blue? This dude is the devil. He'll hurt you. This is the story of a detective who thought he was above the law until we came together to take him down. I said, you're going to see my face till the day that you die. I got you, I got you, I got you.

37:06Listen to The Girlfriends, Untouchable, on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts. I got you. This is an iHeart Podcast. Guaranteed human.

From the publisher

In part three of this week’s four-part case against generative AI, Ed Zitron walks you through how “AI replacing software engineers” is a myth spread by the media and investors - and how Microsoft only has 8 to 12 million active paying customers for Microsoft 365’s AI Copilot out of 440 million users.

Original Air Date: 10.2.25

YOU CAN NOW BUY BETTER OFFLINE MERCH! Go to https://cottonbureau.com/people/better-offline and use code FREE99 for free shipping on orders of $99 or more.

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