ColdFusion - OpenAI is Suddenly in Trouble

Episode Date: July 3, 2026

Click this link https://boot.dev/?promo=COLDFUSION and use my code COLDFUSION to get 25% off your first payment for boot.dev.OpenAI is the company that lead the generative AI revolution. But that was 2022, today in 2026 things look very different. From growing competition to top talent leaving to losing 10s of billions of dollars with no way to profitably.. they're in a tight spot. In this episode we explore.Watch or listen to ColdFusion on Spotify: https://open.spotify.com/show/1YEwCKoRz8fEDqheXB6UJ1ColdFusion Music: https://www.youtube.com/@ColdFusionmusichttp://burnwater.bandcamp.com ColdFusion Socials: https://discord.gg/coldfusionhttps://facebook.com/ColdFusionTV https://twitter.com/ColdFusion_TV https://instagram.com/coldfusiontvCreated by: Dagogo AltraideProducers: Tawsif Akkas, Dagogo Altraide Learn more about your ad choices. Visit megaphone.fm/adchoices

It is a bit scary to know that the most valuable private company in the world has your address and has shown up and has questions for you. They were asking for. Every former employee that we had spoken to and what we said to them, every congressional office that we spoke to, every potential investor that we spoke to. Tyler is just one of many advocates suddenly being targeted. Hi, welcome to another episode of Cold Fusion. What you just saw there was basically open AI knocking on the doors of people who had spoken. and ill of them. Why are they so scared of what people are saying? Well this is part of the reason why. On Friday the 16th of January 2026, OpenAI dropped a bombshell. We are

starting to test ads and chat GPT free and go new $8 a month option tiers. That's right. Open AI is incorporating ads into chat GPT. Now for any other startup this is normal, even expected at this point. But for Open AI it's an admission that things aren't going so well. In fact, it's their last resort. Those are my words, but Sam Altman's words in October of 2024, he stated, I kind of think of ads as like a last resort for us for a business model. I would do it if it meant that was the only way to get everybody in the world, like access to great services. But if we can find something that doesn't do that, I'd prefer that. So after hundreds of billions of dollars in investment, increased competition, stupid side projects like the Sora app losing

$15 million per day, having trillions in special. commitments, are we witnessing the beginning of the end for Open AI? After taking 40% of all the RAM on Earth and causing a myriad of social, environmental and economic problems for everyone, there's a sizeable section of people that would love to see this company go down in flames. And if things continue just the way they are, they just may get their wish. There's talk of the whole company going bankrupt by 2027. As former Fidelity Asate Manager George Noble states, quote, I've watched companies employed for decades,

This one has all the warning signs. You are watching Tull Fusion TV. Last episode, we saw how AI failed at 96% of freelancer work, but in this episode, we're specifically looking at Open AI and the problems they're facing. From Anthropics Claude to the open source Chinese models, the consumer AI landscape has rapidly changed. Today, Open AI is no longer the clear leader it once was. Look, the way this works is we're going to tell you

it's totally hopeless to compete with us on Training Foundation models you shouldn't try, and it's your job to like try anyway. And I believe both of those things. I think it is pretty hopeless, but... They've spent too much money they don't have. The competition is catching up, and they're feeling the heat. In a nutshell, it doesn't look good. They've lost $12 billion in a single quarter.

Their traffic has been falling for one year straight. Both Salesforce and Apple have ditched them for Gemini. Top leadership is leaving and they need $143 billion to become profitable. At this rate, even Nvidia sounds less enthusiastic about investing in them. So let's quickly about Open AI again. Sure. So yesterday you said that the Nvidia is not going to invest as much as 100 billion in open AI. No, we never said we were going to invest $100 billion in one round.

That never was said. But how about the overall commitment? Because last September, you can't open that. There was never a commitment. It was if they invited us, they invited us to, so let's start over again. They invited us to invest up to $100 million. And of course we were very happy and honored that they invited us.

But we will invest one step at a time. All right, but is that overall? is that overall commitment still stands? Or it's not a commitment? I told you just now. Yeah, you keep putting words in my mouth. It's not, yeah, yeah, yeah, I know that. Yeah.

They invited us to invest up to $100 billion. And we are honored that they invited us. We will consider each round one at a time. Yeah. It appears that confidence in open AI is fading. As reported by the Financial Times, their closest partner, Microsoft, has signaled that they're distancing themselves from Open AI. Microsoft's AI chief, Mustafa Selleman, said that Microsoft is aiming to be self-sufficient

in the AI space. So, the problems for Open AI can be split into four main parts. One, the scaling problem. Two, losing market share. Three, the financial black hole. And four, the trust problem. If Open AI was the only company on earth with this technology, then maybe there'd be more of a chance to overcome these challenges.

But with so much competition, it's going to be tough. Now researching this topic made me realize one thing. Understanding software is crucial, which is why we've partnered with today's sponsor, boot. .dev to help you learn how to code. It sounds great in theory, but most people just watch a few tutorials, copy some code, and then give up. But with boot.dev, instead of passively watching videos, you learn by building things and solving problems from day one, just like how developers learn on the job. We've been using boot.

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business, moreover, AI capabilities that are getting exponentially more powerful. ChatGPT made an absolute splash in its release in December 2022. ChatGPT4 was another leap forward. But GPT5 and beyond wasn't quite the revolution that was promised by Sam Altman. It seemed like stagnation had hit and hit hard. But why is this? It's an issue called the scaling problem. The scaling problem in AI put simply is the following. Giving LLM's exponentially more compute doesn't make them proportionally smarter. Once upon a time, this was true, but that seems to be coming to an end. Here's computer scientist, Cal Newport, to explain it in more detail. It takes a second to get through the story, but it's interesting. In the beginning, we had language models. So we had

these for a long time, and they're pretty good. You give them a bunch of text, and they're pretty grammatically good. They could produce pretty fluent text. But it was kind of, they would veer off, and they couldn't really respond well to specific questions. But that was like the state of the world, right? So we had language models. These are studies. for years in academia. Then we start to get this sort of accelerating sequences of advances. So the first of these advances comes in 2017. It's a team of researchers at Google, figure out a better way to build these models.

They're called transformer architectures. The details don't matter, but it made it possible for these models to produce like long text, to produce a whole article, to produce a couple thousand words. That was exciting. Then the second breakthrough comes. They do a research study. There's a researcher at OpenAI named Jared Kass, Kaplan. And he leads a group of researchers at OpenAI that includes Dario Amadee, who went on to become the CEO of Anthropic and actually brought Kaplan with him.

And they do a pretty simple experiment. They took basically GPT2 and said, what happens if we make this bigger? It seems like an obvious thing to check, but there was this whole conventional wisdom and machine learning at this time that says, like, look, you can't make a model too big. If you make it too big, it's just going to memorize the training. And then when you give it new examples, it'll be terrible. And they said, let's check what happens if we actually just. make these things bigger and forget about that concern. And what they found in that paper was, uh, it gets much better. It like defied the conventional wisdom of decades within the field of machine learning, which was like, don't get too big. Your model's going to stop working. If it gets too big, it's going to, you know, in it. They're like, oh, it gets better. And not only does it get better, but it gets better pretty fast. And they, they drew a curve through the data points they had and they extrapolated that curve and it went up really fast. And so they said, let's try this. And the thing they

tried it on was GPT3. The GPT3 model hype encouraged Open AI to just make the model bigger, 15 times bigger. The performance was so high that it validated the scaling laws. This sparked a frenzy in Silicon Valley. Soon, Sam Altman was saying that AI would automate the entire economy. Not only did it jump ahead, it jumped ahead fast, so it really validated this curve. The Normies don't know this because they weren't as plug into the AI world, but this sent Silicon Valley going crazy.

And like, oh my God, if we keep making this bigger, GPT 5 or 6, this thing is gonna be artificial general intelligence. It'll be able to do anything a human can do. We might only be like five years away. All right, so what happens next? Well, they say we need to show this to the public. So chat GPT is GPT3 tamed for public consumption. So now the public all knows about this.

Four months later, GPT 4 comes out. And GPT 4 leaped up the curve exactly as predicted. Exactly, huge leap forward exactly as predicted by the paper. So now they're like, oh my god, we're like two iterations away. Like this is it. All the money in the world needs to come to us because whoever wins this race is going to control the economy. But despite this mess of scale, we may be reaching diminishing returns. GPD 5, they started working out right away. So they build an even bigger data center and even bigger model. They're calling this project Orion by the summer of 2024, so last

summer, they finished training this thing. Altman is telling his people, this thing is going to blow away GPD 4 and it's like this thing scares me. It scares me what this thing is going to do, right? Like I don't know this is it. We're about to go through the looking glass. They trained this thing, then it stops working. Lightly better than GPD 4. Like crap.

This leaping up the curve every time we make this much bigger, this didn't work anymore. And so there was like this realization of, oh, just making models bigger and training them on more data. It's scaling a lot broke. It broke around GPT4. There's a real risk that there may be inherent limits to current day LLMs. Just adding more data may not be feasible for an exponential increase. Think of it this way. It's like a father saying, my son was born just 10 months ago, and in that time he's gone

from crawling to walking, and recently he started running. In two years, little Timmy is definitely going to be able to fly. Obviously, that's not how it works, but it's the same concept here. There are certain limitations, mathematical limitations. limitation that I'm not crossable by staring up. I show it clearly, mathematically, in my book. So there were many different ideas of how to advance AI. One was we already actually have all the techniques that we need and we just need to scale

them. But that was considered a very extreme opinion. And then on the other extreme it was we don't even have the techniques yet. And interestingly, recently there's a New York Times story that says why we likely won't get to AGI any time soon by Kade Metz. And he cites this stat. that 75% of the longest standing, most respected AI researchers actually still think to this day we don't actually have the techniques to get to AGI if we will ever. So it's, we're kind of coming

full circle now and it is starting to become unpopular again, this idea that you can just scale your way to so-called intelligence. TBT, I need to wash my car and the car washes 100 meters away. Should I walk or drive? meters is barely a stroll, it'll take just a minute or two one foot. Unless you've got heavy gear or there's something unusual going on, walking is probably the simplest and most pleasant choice. Plus, you'll save a little gas and get a tiny bit of fresh air. All right, so you think I should walk to the car wash instead of driving my car there? That's what I'd do. It's a short jaunt, and it'll probably be less hassle than starting the car just for that. All right, so how do I wash my

car if I walked there? You'll just bring what you need. maybe a small bag with your payment method and the car wash will have everything else. Once you get there, they'll handle the washing while you wait. You'll walk back with a sparkling car and maybe a little extra pep in your step. So my question is, how will the car get washed if the car is still at my house? I see what you mean now. If the par stays at home, it's not getting any cleaner, right? So yes, to actually wash it, you'll need to bring the car along. In that case, driving those 100 meters makes perfect sense.

While ChatGBTGPT and other LLMs can be extremely brilliant, even in high-level physics and maths, they don't yet have a true model of the world. Some computer scientists believe that that's an integral part of intelligence. Now I could be wrong, a new fundamental neural network technique could be discovered, and that could move things along again. But as it stands right now, it seems that we're reaching a local limit. Now, I have to be clear, every AI company faces this problem,

But some are faring better than others, and one of those is Google. Now that Google has found their footing after the shock release of ChatGPT, new data suggests that ChatGPT is losing market share to Gemini. ChatGPT's market share dropped to 65% in January, which is approximately 20% lower than its 86% market share in January 2025. ChatGPT usage also stalled in late 2025. Average daily time spent per user dropped from 27,000, 27 minutes to 21 minutes. While both have their strengths and weaknesses, Gemini appears to be

much better in research, real-time information and multimodal tasks, while chatGBT is better at writing, coding and conversation. Real-time information and multimodal tasks, i.e. uploading a photo or pointing a phone camera at a scene and getting information about it, is arguably more useful for the everyday person, especially on mobile. So Apple pushing OpenAI aside and going for Gemini makes sense. It's amazing to think that back in late 2022, Google was caught with their pants down when ChatchipT first came out, but today they've more than caught up. And after all, it was Google researchers who laid the groundwork for the AI revolution with their 2017 breakthrough of the Transformer architecture. Open AI simply took Google's work and ran

with it. So in theory, Google researchers have the brains to come up with new theories in computer science to push AI forward. Some recent papers include nested learning. and Simmer 2, an AI that can reason and play video games generally. Open AI on the other hand has a problem with staff continuously leaving. AI Images is also another loss for Open AI. The release of Google's Nanobanana Pro in November of 2025 triggered an internal crisis at Open AI. Sam called a code read and paused all other projects to focus on image generation, but they

still ended up falling short. And then there's the flood of open source Chinese models. Kling AI and Quen are also gaining. ground. Then there's the wild cards like Google's Project Genie, an AI that builds worlds, albeit static, just from a prompt. All of this is to say that open AI has threats from all sides. Knowing this, it's possibly the worst time for Open AI to be shopping around for billions more in investment. If just in a year's time, the competition will only be stronger. But it is a business. So I'm just wondering, like, eventually is the idea to kind of like,

license technologies, will you have customers, you're going to be customizing algorithms for them, or how is it going to work? You know, the honest answer is we have no idea. We have never made any revenue. We have no current plans to make revenue. We have no idea how we may one day generate revenue. We have made a soft promise to investors that once we've built this sort of generally intelligent system, basically we will ask it to figure out a way to generate an investment return for you. The third issue for Open AI is the company's finances. The publication, the information, saw internal documents from Open AI, and the numbers don't look good. Setting aside the myriad of lawsuits, including $134 billion one from Elon Musk, there's some real financial problems.

After hundreds of billions in investment, 2026 will see a $14 billion loss. That's roughly three times worse than early 2025 estimates. Open AI expects their first profit of $14 billion in 2029, but that's after losing $44 billion first. By some estimates they'll be out of money by 2027. Open AI is committed to spending over $1 trillion in AI data center infrastructure over eight years, and that's despite only bringing in $13 billion a year in reoccurring revenue. That's 1% of what they're promising to spend.

Open AI has also agreed to pay Oracle $60 billion per year. starting in 2027. And in all of this, somehow, Open AI predicts that there will be at $100 billion revenue by 2029. That's close to what Nvidia makes. So it's possible, but unlikely. Other investors think so too. Blue Owl Capital recently pulled out of a $10 billion deal to fund an Oracle slash Open AI data center in Michigan. It could be a sign that investors are worried about Open AI's ability to pay them back. Google, on the other hand, doesn't really have to worry about cash flow. The company made $86 billion in nine months, and they can basically pour as much money as they want into AI. Open AI, on the other hand, has to scream at the top of their lungs

to attract more venture capital. There's yet more company behaviour that indicates financial trouble. There's the floundering to spend $6.4 billion acquiring Johnny Ives' design firm, and that's to build an AI hardware device. But according to reports, the development is going poorly, and it could end up like the Humane's AI pin. The AI erotica version of Chatchip E! is self-explanatory, and the SORA app's user base has collapsed. Despite not having much to show versus the competition, Sam needs to talk a big game to get the investment rolling in. Curing cancer, replacing your GP, and discovering new science is a massive promise. But can we trust him?

The final issue for Open AI lies with Sam himself. His track record, frankly, is poor. It's almost like Altman's entire career was a series of promises that didn't pan out, all starting from his first company looped that he founded in 2005. It was kind of like a strange GPS based social network. Sam Altman claimed a massive user base of 50,000, but they didn't exist. In reality, they had only 500 users, but he sold off the company for millions anyway. The next example happened in 2014 with Reddit. He scraped the whole website to feed into OpenAI's products, and then he promised to give 10% of the value back to the community, but this never happened. Next, OpenAI co-founder

Ilya Satskava, who has since left Open AI, has accused Sam of a consistent pattern of lying. According to Insiders, Sam Altman lied to Open AI board members before being fired in 2023. So with this kind of track record, is he the guy who's going to deliver trillions in value? Or is most of this just talk pumping up new investment? I'll leave that up to you. So a little personal story. Back in 2022, I believe, I was in Melbourne and I watched Sam Altman and give a talk. After the talk, he was sworn by crowds of people wanting to take a photo with him. But today, the sentiment couldn't be more different. And it's partly to do with this. In 2015, Open AI started as a non-profit. It was meant to benefit humanity. Now, the only thing the company

cares about is valuation and saying whatever they need to to attract new investment by any means necessary. So to summarize everything, Open AI went from a non-profit that had no plans to make revenue to a for-profit company that commits to spending a trillion dollars on data centres, a trillion dollars for diminishing returns due to the fundamental scaling problem with LLMs, all the while losing billions of dollars and losing out to growing competition in a sector that may just become a commodity in the end. Just in my opinion, it's not really a great financial bet as it stands. But after all that we've talked about, what do you think? Do you think OpenAI will survive? Or will the competition eat them alive? Anyway, that's about it from me.

My name is Degogo and you've been watching Cold Fusion and I'll catch you again soon for the next episode. Cheers guys. Have a good one.