OpenAI is Suddenly in Trouble

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 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, Open AI 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 AAI 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 aren't my words, but Sam Alman'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. Um,

I would do it if it meant that was the

only way to get everybody on the world

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 spending 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 sizable

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 asset manager George

Noble states, quote, I've watched

companies employed for decades. This one

has all the warning signs.

>> You are watching Told 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 I think it 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. Can I ask quickly

about open AI again? Sure. So last uh

yesterday you said that uh the uh Nvidia

is not going to invest uh as much as uh

100 billion in open AI. That's what the

current

>> run. We never we never said we were

going to invest a hundred billion

dollars in one round. That never was

said.

>> But how about the overall commitment

because last September you and

>> there was never a commitment. It was if

they invited us, they invited us to so

so uh let's start over again. They

invited us to uh invest up to $und00

million

>> and of course we were we were uh very

happy and honored that they invited us

but we will invest uh one step at a

time.

>> Mhm.

>> All right. But uh is that overall

commitment still stands or it it's not a

commitment?

>> I told you just now.

>> Okay.

>> 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 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

Sullivan, 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.

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this video. Now, back to the story.

The first problem for OpenAI is that the

capabilities of chat GBT have somewhat

stalled. It's at the infancy stages, but

you can tell by their recent decisions.

Sam Oldman has gone from curing cancer

to AI sex bots and a meme slop factory

and more recently a translator app. All

of this isn't a sign of a healthy

business. Moreover, AI capabilities that

are getting exponentially more powerful.

Chat GPT made an absolute splash in its

release in December 2022. ChatGpt 4 was

another leap forward, but GPT5 and

beyond wasn't quite the revolution that

was promised by Sam Alman. 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 LLMs 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 were pretty

good. You you you give a bunch of text,

and they they're pretty they were 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 were studied 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 produce like long text like

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 uh named Jared

Kaplan and he he leads a group of

researchers at OpenAI that includes

Dario Amade who went on to become the

CEO of Anthropic and actually brought

Kaplan with them 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 is this whole

conventional wisdom in in 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. just 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 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 OpenAI 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 Olman was saying that AI would

automate the entire economy.

>> So not only did it jump ahead, it jumped

ahead fast. Like it so it really

validated this curve. The normies don't

know this because they weren't as

plugged into the AI world, but this sent

Silicon Valley going crazy and like, oh

my god, if we keep making this bigger

PPT 5 or 6, this thing is going to be

artificial general intelligence. It'll

be able to do anything a human can do.

We might only be like 5 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 GPT4 comes

out and GPT4

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 massive scale, we may

be reaching diminishing returns. GPD5.

They started working on it right away.

So they build an even bigger data

center, an even bigger model. They're

calling this project Orion by the summer

of 2024. So last summer, um they

finished training this thing. Elman is

telling his people, "This thing is going

to blow away GPT4." Like, and it's like,

"This thing scares me." What this thing

is going to do, right? Like I don't you

this is it. We're about to go through

the looking glass. They train this

thing, but then it stops working better

than GPD4.

Like a 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 the scaling law 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's

started running. In 2 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 limitation that are not

crossable by steering up. I show it

clearly mathematically

in my book. So there were 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 anytime soon by Cade

Mets. 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 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.

>> GBT I need to wash my car and the car

wash is 100 m away. Should I walk or

drive?

>> Honestly, 100 meters is barely a stroll.

It'll take just a minute or two on 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, um, how will the car

get washed if the car is still at my

house?

>> Uh, 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 m

makes perfect sense.

While Chat GPT and other LLMs can be

extremely brilliant, even in highle

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 fairing

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 Chat GPT is losing

market share to Gemini.

Chat GPT'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 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

chat GBT 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 Open AI 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 Chat

GBT 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 Simma 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

OpenAI. The release of Google's Nano

Banana Pro in November of 2025 triggered

an internal crisis at OpenAI. Sam called

a code red 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. Cling 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 is 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

how is it going to work? You know the

honest answer is we have no idea. Um we

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. Um we have made a soft promise

to investors that once we've built this

sort of generally intelligent system. Um

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 a $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 AAI 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 8 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

AAI has also agreed to pay Oracle $60

billion per year starting in 2027. And

in all of this, somehow Open AI predicts

that they'll 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/ OpenAI

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 9

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 behavior that indicates

financial trouble. There's the

floundering to spend 6.4 4 billion

acquiring Johnny IV's 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 Chat GPT 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 Alman'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 Olman

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 Ilia Sutska, who

has since left OpenAI, has accused Sam

of a consistent pattern of lying.

According to insiders, Sam Oldman lied

to OpenAI 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 Oldman 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

nonprofit. 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 nonprofit that had no plans to make

revenue to a for-profit company that

commits to spending a trillion dollars

on data centers, 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 Dogo and you've been watching Cold

Fusion and I'll catch you again soon for

the next episode. Cheers, guys. Have a

good one.

Cold Fusion. It's me thinking.