Better Offline — "CZM Rewind: The Case Against Generative AI (Part 3)"

Host: Ed Zitron
Podcast: Better Offline (Cool Zone Media / iHeartPodcasts)
Date: December 31, 2025

Overview

In the third installment of his comprehensive four-part series, Ed Zitron continues his critical exploration of the generative AI industry. This episode dissects the financial realities and failed promises fueling the current AI bubble, exposing the unsustainable economics, deceptive business strategies, and persistent myths about AI’s capability—especially in coding and enterprise settings. Zitron marshals industry data, media analysis, direct quotes from engineers, and candid commentary to argue that generative AI is not only failing to deliver on its grandest promises, but doing so at massive and accelerating cost.

Key Discussion Points and Insights

1. The Illusion of Revenue and the Harsh Economics of AI

AI’s Supposed Profitability Debunked
Zitron unpacks the “piss poor revenue” of generative AI companies, even among top players like OpenAI and Anthropic, both of which are burning billions yearly.
- Example: AnySphere (creator of Cursor) peaks at $500M annualized, but that is dwarfed by industries seen as less revolutionary, e.g., smartwatches ($32B).
- “That’s some piss poor revenue for an industry that’s meant to be the future of software.” — Ed Zitron [03:00]
Skyrocketing Costs, Negative Margins
Most AI companies are negative gross margin businesses—meaning they lose money for each additional user due to uncontrollable GPU/token costs.
- Perplexity burns 164% of revenue just on infrastructure.
- Replit’s gross margins: 23%, excluding the heavy costs of free users.
- “Every user loses you money in Generative AI because it’s impossible to do cost control in a consistent manner.” — Ed Zitron [05:30]
Unstoppable Token Burns
Attempts to cap user costs (via rate-limiting or “all-you-can-eat” subscriptions) falter; some users manage to rack up tens of thousands of dollars in generated token costs under $200/month plans (see “CC Usage” and “Vibrank” examples).

2. Broken Business Models and User Exploitation

Anthropic’s Claude Code and Burn Dynamics
Zitron details how even best-in-class AI firms hemorrhage cash serving heavy users. One “capitalist apex predator” user burned ~$50,000 in a month on a $200 Claude Code subscription.
- “Even if Anthropic’s costs are half…the margins are devoured by a few wild users. This is not a real business.” — Ed Zitron [09:50]
Replit’s Agent 3 Pricing Fiasco
- Change to opaque, “effort-based” pricing surges user costs, with tasks leaping from a few dollars to hundreds; some report $1000 weekly bills.
- “Agent 3 has been a disaster… another user complained that costs skyrocketed without any concrete results.” — [10:30]
- The company’s subsequent backpedaling (letting users select agent “autonomy”) is seen as a “tacit admission you can’t trust coding LLMs to build software.”

3. The Core Problem: Coding with LLMs Doesn’t Work at Scale

Software Economics vs. AI Economics
Traditional SaaS relies on scaling at low marginal cost. LLM apps, by contrast, have rising marginal costs with each user as token inference costs can’t be effectively capped.
- “AI does not have a profit lever because the raw costs of providing access to AI models are so, so high…” — Ed Zitron [11:35]
Opportunity Costs, Monitoring, and Inaccuracy
- LLM-powered coding multiplies errors and additional queries, increasing infrastructure costs.
- Caching and using smaller models don’t offset expenses meaningfully.

4. Myth-Busting: LLMs Are Not Killing Developer Jobs

Media Myths Corrected
- Zitron forcefully rebuts claims that LLMs are “replacing” engineers, calling them “grotesque, manipulative, abusive and offensive lies… propagated through the entire business and tech media.” — Ed Zitron [18:00]
- Invokes real software engineers, who liken LLMs to “a slightly below average computer science graduate,” not a replaceable dev.
Engineer Voices (Direct Quotes):
- Carl Brown (Internet of Bugs, guest on previous episode):
  “[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.” [17:12]
- Nick Suresh:
  “The effort of articulating so much… in plain English and hoping the LLM emits code I find acceptable is more work than just writing the code. For most problems the hardest part is the thinking, and LLMs don’t make that part any easier.” [19:31]
- Colt Vogi:
  “LLMs often function like a fresh summer intern… They lack the experience to be wholly trusted. And trust is the most important thing you need to fully delegate coding tasks.” [20:20]
LLMs can generate code but can’t actually engineer software.
- “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.” — Ed Zitron [20:45]
- “If you’re printing this in a media outlet… you are fucking up.” — Ed Zitron [23:10]

5. Coding Versus Real-World Software Engineering

Coding ≠ Engineering
Zitron analogizes to translation—true translation (like true engineering) is creative, contextual, and irreplaceable by LLMs lacking any genuine understanding.
- “Coding is not just a series of text that programs a computer, but a series of interconnected characters that refers to other software … 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.” [27:00]
No Tangible Proof of LLMs Replacing Engineers
- Coding seemed like the most direct use case, but LLMs amplify complexity-induced hallucinations, generate more errors, and make complex tasks harder.

6. Enterprise Generative AI Is Fizzling

Microsoft Copilot: Sales Disappoint, Discounts Rampant
- Despite 440M Microsoft 365 users, only ~8M “active” Copilot licenses (1.81% conversion rate, and many non-active).
  - “This would amount to… $2.88 billion annual revenue for a product category that makes $33 billion a fucking quarter… That’s piss poor.” — Ed Zitron [32:00]
- “Active” defined generously—any action in 28 days.
- Widespread discounting further erodes gross revenue.
Failure of Enterprise Adoption
- Massive sales teams, huge partner ecosystem cannot “sell AI.”
- Quotes customer saying: “It’s easy for an employee to say yes, this will help me, but 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.” [33:26]
- GPU utilization lags, and example features (like SharePoint Copilot) are largely ignored even by massive user bases.

Notable Memorable Quotes (with Timestamps)

“Every user loses you money in generative AI because it’s impossible to do cost control in a consistent manner.” — Ed Zitron [05:30]
“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.” — Ed Zitron [09:48]
“Agent 3 has been a disaster… I spent $1K this week alone.” — Quoting a Replit Register user, via Ed Zitron [10:30]
“AI does not have a profit lever because the raw costs of providing access to AI models are so, so high…” — Ed Zitron [11:35]
“Coding LLMs 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.” — Carl Brown, via Ed Zitron [17:41]
“The effort of articulating so much of the design work in plain English and hoping the LLM emits code that I find acceptable is frequently more work than just writing the code.” — Nick Suresh [19:33]
“LLMs often function like a fresh summer intern. … They lack the experience to be wholly trusted.” — Colt Vogi [20:20]
“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.” — Ed Zitron [20:45]
“You can’t replace a software engineer with them.” — Ed Zitron [21:30]
“You are doing software engineers dirty.” — Ed Zitron [23:40]
“The media has also been quick to suggest that AI writes software, which is true in the same way that ChatGPT writes novels.” — Ed Zitron [27:35]
“That’s piss poor, buddy. That’s piss poor. That’s pissy. It sucks. It’s bad. Doo doo. Well, I just said pp, I guess. Anyway, very serious, Very serious podcast.” — Ed Zitron [32:25]
“If Microsoft’s doing this badly, I don’t know how anyone else is doing well. And they’re not. They’re all failing.” — Ed Zitron [34:28]

Important Segments and Timestamps

Introduction & Series Recap: [02:15]
AI Company Revenue and Cost Analysis: [02:15–08:32]
Claude Code & Token Burn Explainer: [08:32–11:46]
Replit Agent 3 Pricing Debacle: [10:21–11:46]
AI Economics vs SaaS Reality: [11:46–15:58]
Media Lies about Replacing Software Engineers: [16:00–21:44]
Real Engineers’ Views on LLM Capabilities: [17:12–21:44]
The Software/Translation Analogy: [26:58–28:50]
Failure of Enterprise GenAI (Microsoft Copilot): [29:40–34:30]
Closing and Preview of Next Part: [34:30–35:23]

Tone and Language

Ed Zitron's style is frank, irreverent, and confrontational—calling out media, tech leaders, and industry narratives with a blend of humor and exasperation. He frequently swears for emphasis, employs colorful analogies, and relies on direct evidence and firsthand testimony.

Conclusion

This episode of Better Offline delivers a thorough takedown of generative AI’s economics and the overheated hype about its capabilities—especially the notion that AI will replace human developers or revolutionize enterprise productivity. Ed Zitron uses data, candid interviews with engineers, and sharp wit to expose the stark realities: Generative AI businesses are unsustainable, foundational coding tasks are still out of AI’s league, and adoption in enterprise is failing to materialize at meaningful scale. With a final warning that “it really only gets worse from here,” Zitron sets up the series finale as a must-listen for anyone invested in the future of tech.

Better Offline — "CZM Rewind: The Case Against Generative AI (Part 3)"

Host: Ed Zitron
Podcast: Better Offline (Cool Zone Media / iHeartPodcasts)
Date: December 31, 2025

Overview

Key Discussion Points and Insights

1. The Illusion of Revenue and the Harsh Economics of AI

AI’s Supposed Profitability Debunked
Zitron unpacks the “piss poor revenue” of generative AI companies, even among top players like OpenAI and Anthropic, both of which are burning billions yearly.
- Example: AnySphere (creator of Cursor) peaks at $500M annualized, but that is dwarfed by industries seen as less revolutionary, e.g., smartwatches ($32B).
- “That’s some piss poor revenue for an industry that’s meant to be the future of software.” — Ed Zitron [03:00]
Skyrocketing Costs, Negative Margins
Most AI companies are negative gross margin businesses—meaning they lose money for each additional user due to uncontrollable GPU/token costs.
- Perplexity burns 164% of revenue just on infrastructure.
- Replit’s gross margins: 23%, excluding the heavy costs of free users.
- “Every user loses you money in Generative AI because it’s impossible to do cost control in a consistent manner.” — Ed Zitron [05:30]
Unstoppable Token Burns
Attempts to cap user costs (via rate-limiting or “all-you-can-eat” subscriptions) falter; some users manage to rack up tens of thousands of dollars in generated token costs under $200/month plans (see “CC Usage” and “Vibrank” examples).

2. Broken Business Models and User Exploitation

Anthropic’s Claude Code and Burn Dynamics
Zitron details how even best-in-class AI firms hemorrhage cash serving heavy users. One “capitalist apex predator” user burned ~$50,000 in a month on a $200 Claude Code subscription.
- “Even if Anthropic’s costs are half…the margins are devoured by a few wild users. This is not a real business.” — Ed Zitron [09:50]
Replit’s Agent 3 Pricing Fiasco
- Change to opaque, “effort-based” pricing surges user costs, with tasks leaping from a few dollars to hundreds; some report $1000 weekly bills.
- “Agent 3 has been a disaster… another user complained that costs skyrocketed without any concrete results.” — [10:30]
- The company’s subsequent backpedaling (letting users select agent “autonomy”) is seen as a “tacit admission you can’t trust coding LLMs to build software.”

3. The Core Problem: Coding with LLMs Doesn’t Work at Scale

Software Economics vs. AI Economics
Traditional SaaS relies on scaling at low marginal cost. LLM apps, by contrast, have rising marginal costs with each user as token inference costs can’t be effectively capped.
- “AI does not have a profit lever because the raw costs of providing access to AI models are so, so high…” — Ed Zitron [11:35]
Opportunity Costs, Monitoring, and Inaccuracy
- LLM-powered coding multiplies errors and additional queries, increasing infrastructure costs.
- Caching and using smaller models don’t offset expenses meaningfully.

4. Myth-Busting: LLMs Are Not Killing Developer Jobs

Media Myths Corrected
- Zitron forcefully rebuts claims that LLMs are “replacing” engineers, calling them “grotesque, manipulative, abusive and offensive lies… propagated through the entire business and tech media.” — Ed Zitron [18:00]
- Invokes real software engineers, who liken LLMs to “a slightly below average computer science graduate,” not a replaceable dev.
Engineer Voices (Direct Quotes):
- Carl Brown (Internet of Bugs, guest on previous episode):
  “[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.” [17:12]
- Nick Suresh:
  “The effort of articulating so much… in plain English and hoping the LLM emits code I find acceptable is more work than just writing the code. For most problems the hardest part is the thinking, and LLMs don’t make that part any easier.” [19:31]
- Colt Vogi:
  “LLMs often function like a fresh summer intern… They lack the experience to be wholly trusted. And trust is the most important thing you need to fully delegate coding tasks.” [20:20]
LLMs can generate code but can’t actually engineer software.
- “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.” — Ed Zitron [20:45]
- “If you’re printing this in a media outlet… you are fucking up.” — Ed Zitron [23:10]

5. Coding Versus Real-World Software Engineering

Coding ≠ Engineering
Zitron analogizes to translation—true translation (like true engineering) is creative, contextual, and irreplaceable by LLMs lacking any genuine understanding.
- “Coding is not just a series of text that programs a computer, but a series of interconnected characters that refers to other software … 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.” [27:00]
No Tangible Proof of LLMs Replacing Engineers
- Coding seemed like the most direct use case, but LLMs amplify complexity-induced hallucinations, generate more errors, and make complex tasks harder.

6. Enterprise Generative AI Is Fizzling

Microsoft Copilot: Sales Disappoint, Discounts Rampant
- Despite 440M Microsoft 365 users, only ~8M “active” Copilot licenses (1.81% conversion rate, and many non-active).
  - “This would amount to… $2.88 billion annual revenue for a product category that makes $33 billion a fucking quarter… That’s piss poor.” — Ed Zitron [32:00]
- “Active” defined generously—any action in 28 days.
- Widespread discounting further erodes gross revenue.
Failure of Enterprise Adoption
- Massive sales teams, huge partner ecosystem cannot “sell AI.”
- Quotes customer saying: “It’s easy for an employee to say yes, this will help me, but 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.” [33:26]
- GPU utilization lags, and example features (like SharePoint Copilot) are largely ignored even by massive user bases.

Notable Memorable Quotes (with Timestamps)

“Every user loses you money in generative AI because it’s impossible to do cost control in a consistent manner.” — Ed Zitron [05:30]
“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.” — Ed Zitron [09:48]
“Agent 3 has been a disaster… I spent $1K this week alone.” — Quoting a Replit Register user, via Ed Zitron [10:30]
“AI does not have a profit lever because the raw costs of providing access to AI models are so, so high…” — Ed Zitron [11:35]
“Coding LLMs 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.” — Carl Brown, via Ed Zitron [17:41]
“The effort of articulating so much of the design work in plain English and hoping the LLM emits code that I find acceptable is frequently more work than just writing the code.” — Nick Suresh [19:33]
“LLMs often function like a fresh summer intern. … They lack the experience to be wholly trusted.” — Colt Vogi [20:20]
“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.” — Ed Zitron [20:45]
“You can’t replace a software engineer with them.” — Ed Zitron [21:30]
“You are doing software engineers dirty.” — Ed Zitron [23:40]
“The media has also been quick to suggest that AI writes software, which is true in the same way that ChatGPT writes novels.” — Ed Zitron [27:35]
“That’s piss poor, buddy. That’s piss poor. That’s pissy. It sucks. It’s bad. Doo doo. Well, I just said pp, I guess. Anyway, very serious, Very serious podcast.” — Ed Zitron [32:25]
“If Microsoft’s doing this badly, I don’t know how anyone else is doing well. And they’re not. They’re all failing.” — Ed Zitron [34:28]

Important Segments and Timestamps

Introduction & Series Recap: [02:15]
AI Company Revenue and Cost Analysis: [02:15–08:32]
Claude Code & Token Burn Explainer: [08:32–11:46]
Replit Agent 3 Pricing Debacle: [10:21–11:46]
AI Economics vs SaaS Reality: [11:46–15:58]
Media Lies about Replacing Software Engineers: [16:00–21:44]
Real Engineers’ Views on LLM Capabilities: [17:12–21:44]
The Software/Translation Analogy: [26:58–28:50]
Failure of Enterprise GenAI (Microsoft Copilot): [29:40–34:30]
Closing and Preview of Next Part: [34:30–35:23]

wavePod

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

Summary

Better Offline — "CZM Rewind: The Case Against Generative AI (Part 3)"

Overview

Key Discussion Points and Insights

1. The Illusion of Revenue and the Harsh Economics of AI

2. Broken Business Models and User Exploitation

3. The Core Problem: Coding with LLMs Doesn’t Work at Scale

4. Myth-Busting: LLMs Are Not Killing Developer Jobs

5. Coding Versus Real-World Software Engineering

6. Enterprise Generative AI Is Fizzling

Notable Memorable Quotes (with Timestamps)

Important Segments and Timestamps

Tone and Language

Conclusion

Transcript

Summary

Better Offline — "CZM Rewind: The Case Against Generative AI (Part 3)"

Overview

Key Discussion Points and Insights

1. The Illusion of Revenue and the Harsh Economics of AI

2. Broken Business Models and User Exploitation

3. The Core Problem: Coding with LLMs Doesn’t Work at Scale

4. Myth-Busting: LLMs Are Not Killing Developer Jobs

5. Coding Versus Real-World Software Engineering

6. Enterprise Generative AI Is Fizzling

Notable Memorable Quotes (with Timestamps)

Important Segments and Timestamps

Tone and Language

Conclusion