OpenAI Is Not A Real Company

Summary

Podcast Title: Better Offline
Host/Author: Cool Zone Media and iHeartPodcasts
Episode: OpenAI Is Not A Real Company
Release Date: February 26, 2025

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Introduction

In the February 26, 2025 episode of Better Offline, host Ed Citron delves deep into a scathing critique of OpenAI, challenging the prevalent narrative surrounding the generative AI industry. The episode, titled "OpenAI Is Not A Real Company," scrutinizes the financial viability, business models, and overall impact of OpenAI and its peers within the tech landscape.

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Ed Citron's Critique of OpenAI

Financial Unsustainability

Ed Citron begins by questioning the financial underpinnings of the generative AI sector, particularly focusing on OpenAI's economic model. He asserts, "Generative AI lacks the basic unit economics, product, market fit or market penetration associated with any meaningful software boom" (07:00). Citron emphasizes that despite the widespread hype, the industry remains financially precarious.

He presents OpenAI's financials starkly:

• Revenue vs. Expenditure: In 2024, OpenAI reportedly generated around $4 billion in revenue but incurred $9 billion in operational costs, resulting in a $5 billion loss (13:01).
• Venture Capital Dependency: The company’s survival hinges on continuous venture capital influx and support from hyperscalers like Microsoft, which provides significant compute credits to offset costs.

Burn Rate and Operational Costs

Citron highlights the exorbitant costs associated with running generative AI models:

• Compute and Storage: OpenAI spends approximately $2 billion on running models and $3 billion on training them, covering cloud compute, GPUs, and other infrastructure necessities (12:00).
• Talent Acquisition: Salaries alone consumed over $700 million in 2024, excluding stock-based compensation, which further strained financial resources (16:08).

Revenue Streams and User Engagement

Despite OpenAI's claims of substantial user bases, Citron challenges these figures:

• User Metrics: OpenAI boasts 400 million weekly active users, a number Citron deems misleading without clear definitions (22:00). He estimates actual monthly active users to be between 500 to 600 million, revealing a dismal conversion rate of approximately 2.58% from free to paying subscribers (28:08).
• Subscription Model Flaws: With 15.5 million paying customers generating around $3 billion, each subscription still results in a loss due to high operational costs (16:08).

Product Strategy and Market Fit

Citron critiques OpenAI's product offerings, arguing that they lack meaningful differentiation and fail to achieve product-market fit:

• AI Products: OpenAI’s attempts at monetizing products like Operator and Deep Research are labeled as compute-intensive and ineffective, further inflating costs without generating significant revenue (25:00).
• Commoditization of Models: Competitors like DeepSeq and Perplexity have replicated similar models at reduced costs, eroding OpenAI’s competitive edge (28:08).

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Supporting Data and Analysis

Cost Breakdown

Citron provides a detailed breakdown of OpenAI’s expenditures:

• Training Costs: $3 billion on model training alone.
• Operational Compute: $2 billion dedicated to running the models.
• Total Spend: OpenAI’s total spending surpasses its revenue by a billion dollars, highlighting unsustainable financial practices (12:00; 30:14).

User Metrics Discrepancies

He challenges OpenAI’s user statistics by comparing them with independent estimates:

• Sensor Tower Data: Suggests that ChatGPT’s monthly active users are around 339 million on mobile platforms.
• Traffic Analysis: SimilarWeb reports 246 million unique monthly visitors to chatgpt.com in January 2025 (28:08).

Subscription Viability

Citron underscores the inefficacy of OpenAI’s subscription models:

• Revenue Loss: Each paying user contributes to continued financial losses due to high maintenance and operational costs.
• Low Conversion Rates: With a conversion rate below 5%, the subscription model fails to compensate for the overall financial deficits (28:08).

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Product Strategy Flaws

Operator and Deep Research

Ed Citron scrutinizes OpenAI’s flagship products:

• Operator: Intended as an autonomous agent, it reportedly functions only 30% of the time, leading to increased user frustration and additional compute costs (25:00).
• Deep Research: Aimed at generating comprehensive reports, this product delivers low-quality outputs, necessitating further user engagement and exacerbating financial losses (25:00).

Lack of Innovation

Citron argues that OpenAI has stagnated in its product development:

• No Killer Apps: The absence of groundbreaking applications beyond ChatGPT limits market penetration and revenue diversification.
• Stagnant Model Evolution: Despite promises of GPT 4.5 and GPT 5, the enhancements offer negligible improvements, failing to drive significant user adoption or revenue growth (16:00).

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Future Outlook and Predictions

Sustainability Concerns

Citron remains pessimistic about OpenAI's future:

• Burn Rate Projection: Forecasts for 2025 indicate a potential loss of $14.4 billion if current spending patterns continue (30:14).
• Dependency on Partnerships: Any disruption in OpenAI’s relationships with key partners like Microsoft and Oracle could irrevocably impact its financial stability (16:00).

Industry Implications

He extends his critique to the broader generative AI industry:

• Bubble Risk: The unsustainable financial models and lack of viable revenue streams suggest a looming bubble that could negatively affect tech valuations and the stock market (07:00).
• Media Complicity: The tech media’s unchecked promotion of OpenAI fosters an unrealistic perception of the industry’s success, obscuring underlying financial frailties (07:00).

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Conclusion

Ed Citron's Better Offline episode "OpenAI Is Not A Real Company" presents a formidable argument against the perceived success and sustainability of OpenAI and the generative AI industry at large. Through meticulous financial analysis, user metric scrutiny, and product strategy evaluation, Citron exposes significant vulnerabilities that question the long-term viability of leading AI entities. His warnings highlight the potential repercussions for the tech industry, urging listeners to critically reassess the hype surrounding generative AI.

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Notable Quotes

• Ed Citron on Generative AI's Economics:
  "Generative AI lacks the basic unit economics, product, market fit or market penetration associated with any meaningful software boom." (07:00)

• On OpenAI's Financials:
  "OpenAI spent $9 billion to make just under $4 billion in 2024." (12:00)

• Regarding User Metrics:
  "ChatGPT's monthly active users are somewhere in the region of 500 to 600 million. That's good, right? Its actual users are higher than officially claimed, right? No, it's bad." (28:08)

• On Product Strategy:
  "OpenAI’s models do not scale like traditional software... These platforms aren't innately compute heavy and so you don't need to have the same level of infrastructure to support the same amount of people." (22:00)

• Forecasting Future Losses:
  "OpenAI is on course to burn over $26 billion in 2025 for a loss of $14.4 billion." (30:14)

• On Industry Sustainability:
  "The generative AI industry outside of OpenAI is incredibly small, with little to no consumer adoption and pathetic amounts of revenue compared to the hundreds of billions of dollars sunk into supporting it." (31:00)

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Final Thoughts

Better Offline delivers a compelling and critical examination of OpenAI’s role within the generative AI sector. By highlighting financial deficits, questionable business practices, and flawed product strategies, Ed Citron challenges listeners to rethink the narratives surrounding AI advancements. This episode serves as a crucial reminder of the importance of scrutinizing the economic realities behind technological innovations.

Transcript

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[Damien Hirst]

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[Podcast Host]

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[Damien Hirst]

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[Gabe Lenners]

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[Podcast Host]

It actually rotated around our house, looking as if it was peering in each window of our home.

[Gabe Lenners]

I'm Gabe Lenners from Imagine I Heart Podcast and Leonard's Entertainment. Listen to Obscura Invasion of the Drones wherever you get your favorite podcasts.

[Podcast Host]

Media Soul of an Angel, Body of a Devil Chosen by God and perfected by science. This is better offline. And I'm your host, Ed Citron. Better offline now. While working on my newsletter last week, I was chatting with my friend, friend of the show Casey Kagawa about Generative AI and we kept coming back to one thought. Where's the money? Where is it? No, really, where is the money? Where is the money that this supposedly revolutionary, world changing industry is making and of course will make in the future? And the answer's simple. After hours of hours of grinding through earnings, of grinding through media articles, of grinding through all sorts of things, I just don't believe it really exists. It's real, but it's small. Generative AI lacks the basic unit economics, product, market fit or market penetration associated with any meaningful software boom. And outside of OpenAI, the industry may be pathetically, hopelessly small, all while providing few meaningful business returns and constantly losing money. I'm going to be pretty straightforward with everything I say in this two parter, because the numbers and the facts and my hypotheses are pretty fucking damning of both the generative AI industry and its associated boosters. You're going to get this episode, then there's going to be a monologue about something else or something related I really haven't got to it yet. And then a second part which I'm recording immediately after this one. Little behind the curtain there for you. Anyway, in reporting this analysis, I've done everything I can to try and push back against my own work, and I've sought evidence to counter the things that I've seen, like the revenue and the business models of these companies. Yet in doing so, I've only become more convinced of the flimsiness of Generative AI and the associated industry and the likelihood of this bubble bursting in a way that kneecaps tech valuations for a prolonged period or worse, hits the major stock market. Now, I really had originally written a far more jocular and outraged and pissy script, but while I was writing it, I realized I really had to be blunt, because what I'm describing is a systemic failure. Venture capital has propped up OpenAI and Anthropic, two companies that have burned a combined $10.5 billion in 2024, and that number is set to double or more in 2025. The tech media has allowed Sam Altman to twist them to validate completely fictional ideas as a means of propping up this unprofitable, environmentally destructive software company. And big tech has become so disconnected from reality that it is incapable of seeing how little actual returns there are in Generative AI and they're failing. By the way, as I'll walk you through in these episodes, the generative AI industry is very small, with the consumer market of the entire American Generative AI industry outside of ChatGPT barely cracking 100 million monthly active users. Users, which puts them below a lot of free to play games that you get on your iPhone. Hyperscalers have already spent hundreds of billions of dollars in capital expenditures for an AI industry that has the combined monthly active users of a free to play mobile game. I really must repeat myself. It's insane. But unlike most mobile games, generative AI doesn't really make any money. And for those of you wondering if selling access to AI models is the solution, it's important to know that OpenAI, the market leader in generative AI, made less than a billion DOL on API calls in 2024. And that's when people plug their models in. For those of you who don't understand. So it's the difference between, you know, load up the ChatGPT app or someone has an AI, a generative AI like ChatGPT plugged into it. Now Microsoft pays OpenAI a revenue share of 20% on them selling OpenAI's models, so $200 million. So this means that Microsoft likely only makes a billion dollars in revenue from API calls themselves this is a pathetic amount of money and suggests there really isn't significant demand at all or they're not charging enough. Neither of these are great. And honestly, I'm sick and tired of hearing people prop up this fucking industry in these episodes. I will explain as calmly as possible how the generative AI industry barely exists outside of OpenAI. And honestly, in writing this, I've become completely disgusted at Silicon Valley, at the waste. Why is nobody talking about the revenues? Why is nobody sharing real user numbers other than OpenAI? Well, I believe it's because there isn't that much money and there certainly aren't that many users. Nobody is making a profit from this other than consultants. And that's because this is a hype driven movement. What you see on TV and in the newspaper is not the advent of a revolutionary piece of technology. It's a cynical marketing campaign for one company, OpenAI. I need you to understand how precarious this all is. So much money has been wasted propping up an industry that only burns money that does not have mass market appeal. ChatGPT is not significant enough or useful enough or meaningful enough to justify spending $9 billion to lose $5 billion. And yes, those are the raw economics of OpenAI, now you may say, well, Uber lost a lot of money, didn't it, Edward? Guess what? Uber lost about $6.2 billion in one year, and that was in 2020 when they couldn't run their bloody service. Uber is a very different company. I will gladly, if you email me, I will explain this to you, but Uber is not a comparison. There is no comparison to what OpenAI is doing, what Anthropic is doing. I sound crazed as ever, but you're going to understand why when I'm done. I am deeply worried about this industry and I need you to know why. But in this first episode, I'm going to focus on one specific thing, and that's the capitalist illusion known as OpenAI. A company that encompasses almost all of the traffic, funding and attention in generative AI. And I believe they die without a constant flow of venture capital and hyperscale welfare. And I actually don't know why I said I believe that they will. They cannot survive without that money. But okay, let's take a second. Let's talk about OpenAI's unit economics. Putting aside the hype, the bluster, OpenAI, as with all generative AI model developers, loses money on every single prompt and output. Its products do not scale like traditional software, in that the more users it gets, the more expensive its services are to run because it's models are so compute intensive. For example, ChatGPT having 400 million weekly active users is not the same thing as a traditional app like Instagram or Facebook having that many users. Or indeed Uber, the cost of serving a regular user of an app like Instagram is significantly smaller because these are effectively websites with connecting APIs, images, videos and user interactions. These platforms aren't innately compute heavy and so you don't need to have the same level of infrastructure to to support the same amount of people. Conversely, generative AI requires expensive to buy and expensive to run and expensive to maintain. Graphics processing units, GPUs both for inference and training the models themselves. These GPUs must be run at full tilt for both inference and training, which shortens their lifespan while also consuming ungodly amounts of energy. And by the way, inference is just the thing that happens when you tell ChatGPT something. It infers the meaning of the prompt and the training is what they do when they throw all the training data to make the model smart. Not really though. And by the way, surrounding the GPU in there isn't like the GPUs just kind of hang out, there's the rest of A computer which is usually highly specced and incredibly difficult to cool and thus very, very expensive. These generative AI models also require endless amounts of training data, and supplies of that training data have been running out for a long time. While synthetic data might bridge some of the gap, there are likely diminishing returns due to the sheer amount of data necessary to make a large ligand Quidditch model even larger. As much as more than four times the size of the Internet. This is insane. There is not enough data and it already kind of sucks. And it's not getting better. These companies also must spend hundreds of millions of dollars on salaries to attract and retain AI talent. As much as $1.5 billion a year in OpenAI's case. And that's before stock based compensation. In 2016, Microsoft claimed that top AI talent could cost as much as an NFL quarterback to hire. And that sum has likely only increased since then, given the generative AI fren and the fact they're overpaying quarterbacks. As an aside, one analyst told the Wall Street Journal that companies running generative AI models could, and I quote, be utilizing half their capital expenditures because all of these things could break down. That's, that's, that's really bad. These costs are not a burden on OpenAI or anthropic, but they absolutely are on Microsoft, Google and Amazon. This shit's crazy. Anyway, as a result of the costs of running these services, a free user of ChatGPT is a cost burden on OpenAI, as is every single free customer of Google's, GE, Gemini, Anthropics, Claude Perplexity, or any other generative AI company. Said costs are also so severe that even paying customers lose these companies money. Even the most successful company in the business appears to have no way to stop burning capital. And as I'll explain, there's only really one real company in the industry. OpenAI. And OpenAI is not a real business. But let's start with a really, really important fact. If you forget everything I say, I want you to remember this. OpenAI spent $9 billion to make just under $4 billion in 2024. And the entirety of their revenue, that's about $4 billion is spent on compute. $2 billion to run models and $3 billion to train them. That is completely and utterly fucking insane. That is bonkers. That is crazy. That is completely nuts. This is not a real company. It is insane. We're allowing this. Everyone should be screaming this at everyone. We live in an alternate reality where this is acceptable. There has been no precedent for this. Not amaz, not anyone. No one has done this. And it's sickening and wasteful that we continue to. And in the past, I've repeatedly said that OpenAI lost $5 billion after revenue. Now, that is true, by the way. That is completely true. They made money and they lost money, but ended up losing 5 billion. Either. Either way. However, I really just. I can't in good conscience suggest that OpenAI only spent $5 billion. It's time to be honest about these numbers. While it's fair to say that their net losses are 5 billion, they spent $9 billion to lose $5 billion.

[Oracle Representative]

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[Gabe Lenners]

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[Oracle Representative]

We used the word drone because it was comfortable to other people.

[Podcast Host]

One minute was there and one minute it wasn't. Oh, that is beyond creepy.

[Gabe Lenners]

Do you feel like this drone was targeting you? Specifically?

[Podcast Host]

Yes, Absolutely.

[Gabe Lenners]

Listen to Obscurum Invasion of the Drones on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

[Damien Hirst]

Do you remember what you said the first night I came over here?

[Podcast Host]

Ow.

[Damien Hirst]

Go slower. From Blumhouse TV, iHeart podcast, and Ember 20 comes an all new fictional comedy podcast series Join the flighty Damien Hirst as he unravels the mystery of his vanished boyfriend and Santi was gone. I've been spending all my time looking for answers about what happened to Santi and what's the way to find a missing person? Sleep with everyone he knew, obviously. Mmm. Pillow talk, the most unwelcome window into the human psyche. Follow our out of his element hero as he engages in a series of ill conceived investigative hookups. Mama always used to say God gave me gumption in place of a gag reflex. And as I was about to learn, no amount of showering can wash your hands of a bad hookup.

[Podcast Host]

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[Damien Hirst]

Listen to the hookup on the iHeartRadio app, Apple Podcasts, or wherever you listen to your favorite show.

[Podcast Host]

Let's really get down into the nitty gritty of these numbers. So, as discussed previously, according to the reporting by the information, OpenAI's revenue was likely somewhere in the region of $4 billion in 2024. Their burn rate, according to the information, was $5 billion after revenue in 2024, excluding stock based compensation, which OpenAI, like other startups, uses as a means of compensation on top of cash. Nevertheless, the more it gives away, the less it has for capital raises. And these are technically costs though they money unless there's a liquidity event. But that's all. You don't need to worry about that. To put this in blunt terms, based on reporting by the Information, and I'm repeating myself here, but I really need you to remember this. Running OpenAI cost $9 billion in 2024. The cost of the computer to tra the compute to train models alone, $3 billion obliterates the entirety of their subscription revenue, which was about $3 billion by the way. And the compute from running models, $2 billion takes the rest and then some. They actually end up losing an extra billion on top of that. Sam Altman's net worth is a billion dollars, by the way. Casey Gagawa has now used this as the Altman Index. So it's like you've lost one Sam Altman. That's a billion dollars. But just to be clear, it doesn't just cost more to run OpenAI than they make. It cost them a billion dollars more than the entirety of their revenue to run the software they sell before any other costs. Why are we not more concerned about this company? Now? Something else to Note is that OpenAI also spends an alarming amount of money on salaries over $700 million in 2024, before you consider that compensation from stock, a number that will also have to increase because OpenAI is growing, which means hiring as many people as possible and they're paying through the nose for them. But let's talk about how OpenAI makes money. OpenAI sells access to its models via its API and selling premium subscriptions to ChatGPT. The majority of its revenue, over 70%, comes from subscriptions to premium versions of ChatGPT. The Information also reported that OpenAI now has 15.5 million paying customers, though it's unclear what level of the service they're paying for or how sticky these customers are, as in how likely they are to stick around, or the cost of acquiring these customers, or really any other metric to tell us how valuable these customers are to the bottom line. Nevertheless, OpenAI loses money on every single paying customer, just like its free users. Increasing paid subscribers to OpenAI OpenAI services somehow increases OpenAI's burn rate. This is not a real company. Now the New York Times reports that OpenAI projects it will make $11.6 billion in 2025 and assuming that OpenAI burns at the same rate it did in 2024, spending $2.25 to make $1. OpenAI is on course to burn over $26 billion in 2025 for a loss of $14.4 billion. Who knows what their actual cost will be now? You've probably heard about SoftBank coming in. SoftBank's going to feed them money and SoftBank said they' on this, that and the other. That round has not closed yet. Masayoshi Son, a complete fucking idiot who's lost 30 odd billion for SoftBank, the Japanese mega conglomerate. He's dedicating billions of dollars of revenue to buying OpenAI services. Unless this is a straight up trade where he's just sending money before the services come in. I don't know if it happens and I'm going to get into things like agents later. But the Information reported that OpenAI expects to make $3 billion in revenue from agents. By the end of this episode, you're going to realize how fucking stupid that sounds. We'll get there later. It's also important to note that OpenAI's costs are partially subsidized by its relationship with Microsoft, which provides cloud compute credits for its Azure cloud service. Not super technical, it's just when they host people's software and files and such and the compute to run these models. And they also offer this a steep, steep discount to OpenAI. Or put another way, it's like OpenAI got paid with air miles, but the airline lowered the redemption cost of booking a flight with those air miles, allowing it to take more flights than any other person with the equivalent amount of points. Until recently, OpenAI exclusively used Microsoft Azure services to train, host, and run its models. But recent changes to its deal means that OpenAI is now working with Oracle to build out further data centers to train, host, and run its models. It's unclear whether this partnership will work in the same way as the Microsoft deal with OpenAI provided credits and discounts like like before. If not, OpenAI's operating costs will only go up, per previous reporting. From the information, OpenAI pays just over 25% of the cost of Azure's GPU compute as part of their deal with Microsoft, and that's about $1.30 per GPU per hour versus the regular Azure cost of $3.40 to $4 an hour. I know that this sounds really technical, but in very short, they're getting a sweet deal from Microsoft, and if anything happens to that, they're completely fucked. They're fucked anyway. They don't have they're burning billions of dollars. It's insane. But let's talk about user numbers, because OpenAI has quite a few. They recently announced that they have 400 million weekly active users. Now, weekly active users is a wanky number, and a very strange one for a company like this. OpenAI may pretend to be a consumer company, but the majority of their revenue comes from monthly subscriptions, making them kind of a cloud software company. Classically, cloud software companies report monthly active users. That way you can, I don't know, compare one number, which is the amount of active users you have with the paid users you have, and then say, oh, that's a good business. That's a good business right there, man. Guess what? OpenAI isn't giving their monthly active users. Don't worry, I might have estimated it. When I asked OpenAI to define what a weekly active user was, it responded by pointing me to a tweet by Chief Operating Officer Brad lightcap that said ChatGPT recently crossed 400 million weekly active users. We feel very fortunate to serve 5% of the world every week. What a fucking liar. It's extremely questionable that OpenAI refuses to define this core metric, by the way, and without a definition, in my opinion, there is no way to assume anything other than OpenAI is actively gaming its numbers. Now, there's likely two reasons they focus on weekly active users. One as described, these numbers are easy to game. You can choose any seven day period. And also the majority of OpenAI's revenue comes from paid subscriptions to ChatGPT. And that latter point is crucial because it suggests OpenAI is not doing anywhere near as well as it seems based on the very basic metrics used to measure the success of a software product. The information reported on January 31 that OpenAI, like I mentioned, had 15.5 million monthly paying subscribers. And they added in this piece that this was less than a 5% conversion rate of OpenAI's weekly active users, a statement that's kind of like dividing the number 52 by the letter A. This is not an honest or reasonable way to evaluate the success of ChatGPT's still unprofitable software business, because the actual metric like I mentioned, would have been to defy paying subscribers by monthly activity users or the other way around, I guess a number that would be considerably higher than 400 million. And the reason they don't want you to do that, by the way, is because you would divide them and see that they have a piss poor conversion rate. Good conversion rate is way higher than 5% by the way, and theirs is definitely lower. But don't worry, I'm a sneaky little shit. So I went and looked some stuff up and I talked to some people. Based on data from the market intelligence firm sensor tower, OpenAI's ChatGPT app on Android and iOS is estimated to have more than 339 million monthly active users. And based on traffic data for market intelligence companies, company Similar web chatgpt.com had 246 million unique monthly visitors, and these were in January 2025. There's likely some crossover with people using both the mobile and web interfaces, though how big that group is is kind of hard to tell and remains uncertain. Though every single person that visits chatgpt.com might not become a user, it's safe to assume that ChatGPT's monthly active users are somewhere in the region of 500 to 600 million. That's good, right? Its actual users are higher than officially claimed, right? That's good. No, it's bad. First of all, each user that uses ChatGPT for free is a drain on the company, whether they're free or not, honestly. But either way, their free ones definitely are. It would also suggest that the real conversion rate is somewhere in the neighborhood of 2.583% from free to paid users on ChatGPT, which is astonishingly bad. And it's a fact that's made worse by the fact that every single user, regardless of whether they pay or not, loses the money either way. And while it's quite common for Silicon Valley companies to play fast and loose with metrics, this particular one is. Well, it's deeply concerning. And I hypothesize that OpenAI is choosing to go with weekly versus monthly active users in an intentional attempt to avoid people calculating the conversion rate of his subscription products. As I will continue to repeat, these subscription products lose the company money every single time. Now let's talk product strategy, shall we? Because I don't think OpenAI really has one. OpenAI makes most of its money from subscriptions, approximately $3 billion in 2024, and the rest on API access to its models, approximately a billion. As a result, OpenAI has chosen to monetize ChatGPT and its associated products in an all you can eat software subscription model, or otherwise make money by other people productizing it. And just to be clear, in both of these scenarios, OpenAI loses money on every transaction. OpenAI's products are not fundamentally differentiated or interesting enough to be sold separately. It has failed, as with the rest of the generative AI industry, to meaningfully productize its models due to the massive training and operational costs and a lack of any meaningful killer app use cases for large language models. The only product that OpenAI has succeeded in scaling to the mass market is the free version of ChatGPT, which loses the company money with every single prompt and output. This scale isn't a result of any kind of product market fit, by the way. It's entirely media driven, with reporters making ChatGPT synonymous with artificial intelligence, a thing they regularly write about without think. As a result, I do not believe that the generative AI industry is real. It's not a real industry, which I will define as one with multiple competitive companies with sustainable or otherwise growing revenue streams and meaningful products with actual market penetration. And I feel this way because this market is entirely subsidized by a combination of venture capital and hyperscaler cloud credits and, well, real money. I guess ChatGPT is popular because it's the only well known product, one that's mentioned in basically every article on AI. If this were a real industry, other competitors would also be mentioned all the time. They would have similar scale, especially those run by hyperscalers. But as I'll get to later, data suggests that OpenAI is the only company with any significant user base in the entire generative AI industry, and it's still wildly unprofitable and unsustainable. OpenAI's models have also been entirely commoditized. Even its Reasoning Model 01 has been commoditized by both DeepSeq's R1 model and Perplexity's agonizingly named R1176 mod, both of which have similar outcomes at a much discounted price to OpenAI's 01. Though it's unclear and unlikely, in my opinion that these models are profitable anyway, OpenAI as a company, well, they just piss poor at product. It's been two years and ChatGPT mostly does the same thing, still costs more to run than it makes, and ultimately does the same thing as every other LLM chatbot from every other company. The fact that nobody has managed to make a mass market product by connecting OpenAI's models also suggests that the use cases just aren't there. Furthermore, the fact that API access is such a small part of its revenue suggests that the market for actually implementing large language models is relatively small. If the biggest player in the space only made a billion dollars in selling access to its models unprofitably, and that amount is the minority of its revenue, there might not actually be a real industry here. And I must be clear. If there was user demand, this would be where it was in the APIs. It would be doing gangbusters because people wouldn't be able to help themselves. They'd just be all up for this generative AI. But they're not.

[Gabe Lenners]

Have you ever looked into the night sky and wondered who or what was flying around up there? We've seen planes, helicopters, hot air balloons and birds. But what if there's something else, something much more ominous that appears under the COVID of night. Silent, unseen, watching. They may be right above your car late one night as you cruise down the road, or look like mysterious lights hovering above your home. Drones. Or are they?

[Oracle Representative]

We used the word drone because it was comfortable to other people.

[Podcast Host]

One minute was there and one minute it wasn't. Oh, that is beyond creepy.

[Gabe Lenners]

Do you feel like this drone was targeting you specifically?

[Podcast Host]

Yes, absolutely.

[Gabe Lenners]

Listen to Obscurum Invasion of the Drones on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

[Damien Hirst]

Do you remember what you said the first night I came over here?

[Podcast Host]

Ow.

[Damien Hirst]

Go slower. From Blumhouse TV, iHeart podcasts and em comes an all new fictional comedy podcast series. Join the flighty Damien Hirst as he unravels the mystery of his vanished boyfriend and Santi was gone. I've been spending all my time looking for answers about what happened to Santi and what's the way to find a missing person. Sleep with everyone he knew, obviously. Mmm. Pillow talk, the most unwelcome window into the human psyche. Follow our out of his element hero as he engages in a series of of ill conceived investigative hookups. Mama always used to say, God gave me gumption in place of a gag reflex. And as I was about to learn, no amount of showering can wash your hands of a bad hookup.

[Podcast Host]

Now take a big whiff, my bruh.

[Damien Hirst]

Listen to the hookup on the iHeartRadio app, Apple Podcasts, or wherever you listen to your favorite shows.

[Podcast Host]

Now, I want to address one counterpoint. Some might argue that OpenAI has a new series of products that could open up new revenue streams, such as Operator, its Agent product, and Deep Research, their research product. And I I'm so tired of hearing about agents. Whenever you hear someone say agent, really look at what they're saying because they want you to think autonomous bit of software. What they're actually talking about is either a chatbot or well, well, the dog shit that OpenAI and Anthropic have warmed up, which we'll get to shortly. But first, let's talk costs. Both of these products are very compute intensive. Operator uses OpenAI's computer using agent, the CUA, which combines OpenAI's models with virtual machines that take distinct actions on web pages in this extremely unreliable and costly way where they take screenshots as they scroll down and it just doesn't fucking work. I had a whole thing about Casey Newton writing about this. It's just, it was just so bad. Like Casey Newton, you please go outside challenge. Just, just go outside. Casey, stop. Stop with the computer, you don't know what you're talking about. But failures with these, and remember these models, pretty much all of them are inconsistent. And the more in depth the thing you ask them to do, the more likely there's going to be a problem with it. So think about it like this. Failures from something you've asked them to do will either increase the amount of attempts you make to get the thing you want, or make users not use it at all. Not a really great idea. Now let's talk Deep Research. They use a version of OpenAI's O3 reasoning model, which is a model so expensive because it spends more time to generate a response based on the model, reconsidering and evaluating steps as it goes. That OpenAI will no longer launch O3 as a standalone model. And that's really a good thing. When you see a company, be like, yeah, you can't touch it. It's too expensive. In short, these products are extremely expensive to run, and this means that anytime their outputs aren't perfect, which is to say a lot of the time, there's a high likelihood that they'll be triggered again, which will in turn spend more compute. But let's talk about the product market fit because this is really important to use. Operator or Deep Research currently requires you to pay $200 a month for OpenAI's ChatGPT Pro, a $200 a month subscription which Sam Altman recently revealed still loses the money because people are using it more than expected. And that is a quote. Furthermore, even on ChatGPT Pro, deep research is currently limited to 100 queries per month, adding that it is very compute intensive. And though Altman has promised the ChatGPT plus and free users will eventually get access to a few Deep Research queries a month, well, that's not good for their cash burn. That's actually bad for the cash burn. I'm not sure it's gonna make them. Not really sure how that turns into money anywhere. But let's talk about Operator. Operator is this agent product where you're meant to be able to be like, hey, look, go and look something up for me. And it only works like 30% of the time and it takes. It's just very bad. And as I covered in my newsletter a few weeks ago, this product and it claims to control your computer and does not appear to be able to do so consistently. It's not even ready for the prime time and I don't think it has a market. The way they're selling this is that you'll be able to make it do distinct tasks on the computer. But even Casey Newton in his article was like, yeah, it only works sometimes. And the things it works on are like searching TripAdvisor. Imagine this, if you will. What if for the cost of boiling a lake and throwing an entire zoo into the lake and boiling the animals inside it, you could sometimes be able to search TripAdvisor in 2 minutes versus 10, like 5 seconds. The future's so cool. I love living in it. But let's talk about Deep Research for a second. It's already been commoditized. Perplexity, AI and XAI have launched their own versions immediately, and Deep Research itself is not a good product. As I covered in my newsletter last week, the quality of the writing that you receive from Deep Research is really piss poor, and it's rivaled only by the appalling quality of its Citations which include forum posts and search engine optimized content instead of actual news sources. These reports are neither deep nor well researched and cost OpenAI a great deal of money to deliver. And just to give you a primer on what Deep Research is meant to be, you're meant to be able to type something in and it does like a 3,000 word report. It's gobbledygook, it's nonsense, it's bullshit. I really. If you, you should go and look up, go to my newsletter. Where's your red at? It's the. It's the piece before the ones that's going to come out. When these episodes come out, I forget the name. Exactly. You need to go and look at how shit Deep Research is. It's incredible that this money losing juggernaut piece of shit thinks that this is a real product and it's insulting to the intelligence of readers that people like Casey Newton claimed it was good. But now we've established that both of these products are expensive, commoditized and don't work very well, let's talk about how they make money or don't both operate. And Deep Research, like I told you, currently require you to pay $200 a month to a company that loses money all the time that also loses money on the $200 a month. Neither product is sold on its own and while they may drive revenue to the ChatGPT Pro product, as said before, said product loses OpenAI money. These products are also compute intensive and have questionable outputs, making each prompt very likely to create another follow up prompt. And the problem is you're asking something that doesn't know anything that probabilistically generates answers to research something. So as a result, the research isn't going to be any good. It's not like it's going to research it and go hey, what would be a good source? It's going to say what matches the patterns? What matches all the patterns that are being trained on? Eh, that's fine. Who gives a shit? It's like having the world's worst intern, except the intern gets a concussion every 10 minutes. But in summary, both Operator and Deep Research are expensive products to maintain, are sold through an expensive $200 a month subscription that like every other service provided by OpenAI, loses the money and due to the low quality of their outputs and actions, are likely to increase user engagement to try and get the desired output, incurring further costs for OpenAI. Well, you know, like Ed. Ed, you say Ed, you're just being a hater, right? Just being a hater. Things don't look great today. But this early days. It isn't early days. But still, Ed, it's early days. Things don't look great today. What about the future prospects for OpenAI? Things can't be that bad, can they? Yeah, they can. A week or two ago, Sam Altman announced the updated roadmap for GPT 4.5 and GPT 5. Now, these are their next generation models that have been hyping up for the best part of a year. Except GPT 4.5 didn't exist before. It was always GPT 5. Now, GPT 4.5 will be OpenAI's last chain of thought model, referring to the core functionality of its reasoning models, where it checks the work as it goes and it really. It uses a model to ask another model whether the model's doing the right thing. Can they Both hallucinate? Yes. GPT5 will be, and I quote Sam Altman, a system that integrates a lot of OpenAI's technology, including O3. What the fuck are you talking about? Altman also vaguely suggests that paid subscribers will be able to run GPT5 at a higher level of intelligence, which likely refers to being able to ask the models to spend more time computing an answer. He also suggests that GPT5, and I quote, will incorporate voice, Canvas, search, deep research, and more. Fucking Bed, Bath and Beyond, motherfucker. Come on, my man. Your company spent $9 billion to lose $5 billion. Why is anyone taking this seriously? This is ridiculous. But both of these statements, all of these statements honestly vary from vague to meaningless. But I Hypothesize the following. GPT 4.5 will be an upgraded version of GPT4O OpenAI's foundation model you're probably using right now, and it's codenamed Orion. GPT5, which used to be codenamed Orion, could literally be anything. But one thing that Altman mentioned in the Tweet is that OpenAI's model offerings have got too complicated. They'd be doing away with the ability to pick what model you used. Gussying this up and he's claiming it's Unified Intelligence. This fucking guy. If I said this shit to a doctor, they'd institutionalize me. They'd say, you sound like a lunatic. But anyway, as a result of doing away with the model picker, which is literally the thing you click and you choose GPT 4.0 or GPT 4O mini or like the O1 reasoning things, I think they're going to attempt to moderate costs by picking what model will work best for a prompt, a process it will automate. And if there's one thing I've noticed with OpenAI, they're not very good at automating anything. So I expect this to be bad. And I believe that Altman announcing these things is a very bad omen for OpenAI because Orion has been in the works for more than 20 months and was meant to be released at the end of 2024, but it was delayed due to multiple training runs resulted in, to quote the Wall Street Journal, software that fell short of the results researchers were hoping for. As an aside, the Wall Street Journal refers to Orion as GPT5. This was from several months back, but based on the copy and Altman's comments, I believe Orion refers to a foundation model, OpenAI, which is one to replace the core GPT one that powers ChatGPT. OpenAI now appears to be calling a hodgepodge of different mediocre models something called GPT5. It's almost as if Altman's making this up as he goes along. Now the Journal further adds that as of December, Orion performed better than OpenAI's current offerings, but hadn't advanced enough to justify the enormous costs of keeping the new model running with each six month long training run, no matter how well it works, costing over $500 million, OpenAI also, like every generative AI company, is running out of high quality training data, the data necessary to make its models smarter based on the benchmark's specific made up to make LLM seem smart. And I should note that being smarter means completing tests, not new functionality or new things that it can do. Sam Altman deputizing Orion from GPT 5 to GPT 4.5 suggests that OpenAI has hit a wall with making its new model, requiring him to lower expectations for a model OpenAI Japan President Tagao Nagasaki had suggested would, and I quote, aim for 100 times more computational volume than GPT4, which some took to mean 100 times more powerful. When it actually means it, it will take way more computation to train or run inference on it. I guess he was right. Now, if Sam Altman, who is a man who loves to lie, is trying to reduce expectations for a product, I think we should all be really, really worried. Now, large language models which are trained by feeding them massive amounts of training data and then reinforcing their understanding through further training runs, are hitting the point of diminishing returns. In simple terms, to quote friend of the show Max Zeff of TechCrunch, everyone now seems to be admitting you can't just use more compute and more training data with pre training large language models and expect them to into some all knowing digital God. Max is a fucking legend. OpenAI's real advantage, other than the fact it's captured the entire tech media has been its relationship with Microsoft because access to large amounts of compute and capital allowed it to corner the market for making the biggest, most hugest large language model. Now that it's pretty obvious this isn't going to keep working, OpenAI is scrambling especially now Deepseeker's commoditized reasoning models and prove that you can build LLMs without the latest GPU use. It's unclear what the functionality of GPT 4.5 or GPT 5 will be. Does the market care about an even more powerful large language model if said power doesn't do anything new or lead to a new product? Does the market care if unified intelligence just means stapling together various models to produce more outputs that kind of look and sound the same? As it stands, OpenAI has effectively no moat beyond its industrial capacity to train large language models and its presence in the media. Media OpenAI can have as many users as it wants, but it doesn't matter because it loses billions of dollars and appears to be continuing to follow the money losing large language model paradigm, guaranteeing it will lose billions of dollars more if they're allowed to. This is the biggest player in the generative AI industry, both the market leader and the recipient of almost every single dollar of revenue that this industry generates. They have received more funding and more attention than any startup in the last few years and as a result their abject failure to become a sustainable company with products that TR is a terrible sign for Silicon Valley and an embarrassment to the tech media. In the next episode, I'm going to be honest, I have far darker news. Based on my reporting, I believe that the generative AI industry outside of OpenAI is incredibly small, with little to no consumer adoption and pathetic amounts of revenue compared to the hundreds of billions of dollars sunk into supporting it. This is an entire hype cycle fueled by venture capital and big tech hubris with little real adoption and little hope for a turnaround. Enjoy tomorrow's monologue and then the final part on Friday. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matosowski. You can check out more of his music and audio [e-mail] M A T T o S o W. You can email [e-mail] or visit betteroffline.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 betteroffline 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 coolzone media media.com or check us out on the iHeartRadio app, Apple Podcasts or wherever you get your podcasts In a world of economic uncertainty and workplace transformation, learn to lead by example from visionary C Suite executives like Shannon Schuyler of PwC and Will Pearson of iHeartMedia. The good teacher explains the great teacher inspires. Don't always leave your team to do the work that's been the most important.

[Damien Hirst]

Part of how to lead by example.

[Podcast Host]

Listen to leading by Executives making an impact on the iHeartRadio app, Apple Podcasts or wherever you get your podcasts.

[Damien Hirst]

Do you remember what you said the first night I came over here?

[Podcast Host]

Ow.

[Damien Hirst]

Go slower. From Blumhouse TV, iheart podcasts and ember 20 comes an all new fictional comedy podcast series. Join the flighty Damien Hirst as he unravels the mystery of his vanished boyfriend. I've been spending all my time looking for answers about what happened to Santi and what's the way to find a missing person Sleep with everyone he knew, Obviously. Listen to the hookup on the iHeartRadio app, Apple Podcasts, or wherever you listen to your favorite shows.

[Gabe Lenners]

What would you do if mysterious drones appeared over your hometown? I started asking questions. What do you remember happening on that night of December 16?

[Podcast Host]

Actually rotated around our house looking as if it was peering in each window of our home.

[Gabe Lenners]

I'm Gabe Leonors from Imagine I Heart Podcasts and Leonard's Entertainment. Listen to Obscurum Invasion of the Drones wherever you get your favorite podcast.

[Podcast Host]

Hey everybody, what's up? This is questlove, and every year we do special programming in February for Black History Month. Now especially important this year as we gear up for some new conversations. So the team and I compiled some clips from the show that are worth revisiting. In Part one of the celebration of Black History Month, we're listening back to Moments with Chris Rock, Solange Prodigy, Ben Vereen, Jennifer Lewis, Angela Rye and Gina Yashore. Listen to Questlove supreme on the iHeartRadio app, Apple Podcasts or wherever you get your podcast.