AI App Crisis, OpenAI Does Math, Big Nvidia Deal

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Welcome to the podcast.

I'm your host, Jaden Schaefer.

Guys, today is my 30th birthday, but I had to record a podcast because there was some

crazy stuff happening.

Number one, there's a bunch of research and data coming that is showing AI-powered apps

are really struggling with long-term retention.

Also, chat to PT can now create interactive visuals that are going to help you understand

math and science, which Google was kind of doing something similar.

It's going to be really cool to see chat to PT do this.

And third, thinking machine labs has just created a massive compute deal within video,

which is pretty exciting for a company that has such a legendary background and has raised

so much money.

So, we're going to get all of these stories today, but before we do, I have to say a huge

shout out in the last couple of days, I've asked people for my birthday.

If you could leave a rating and review if you haven't already, I want to read the most

recent review that someone dropped.

This is from Eating Crab yesterday, he said, just wanted to say thank you for the podcast.

I don't have very much time in my day being a full-time student and working full-time,

but I have a huge passion for AI being able to keep up with your podcast, helps me keep

in the loop.

I appreciate it.

Keep it up and happy birthday.

A huge shout out to Eden Crab.

Thank you so much for the review.

Guys, today is my birthday.

I'm turning 30 before I go through a midlife crisis.

If you guys could do me a massive favor for today and please leave a rating review if you

haven't already.

That would be the greatest birthday present of all time.

I will be eternally grateful, over on Apple or Spotify or wherever you get your podcast.

I know it's usually annoying, but today is my birthday.

If you've ever appreciated that podcast in the past or today, it would be greatly appreciated

to drop review.

Let's get into the episode today.

The thing that I think is really interesting is this idea right now that all of the AI-powered

apps are really struggling to keep long-term people engaged, long-term retention on the

app.

There are problems with this as someone that has built AI-powered apps in the past and

as someone that is actively working in an AI start-up, an AI start-up AI box and a company.

I can understand where a lot of this challenge is and that is I think with AI coming out and

the power of AI being so incredible, I think we definitely had a really big wave especially

at in the last couple of years where there was a lot of concepts of what AI could do and

would be able to do and a lot of people I think overhyped or oversold their apps and

I think that's going to be the primary driver of lower retention.

In addition, I do think that right now I try probably 10 times as much software as I

have over the last five, 10 years working in the industry and so I think right now we

just try so much more and then we kind of settle on what works best.

I think if you're a developer and you're creating a tool with AI in it, you have one shot

really for someone to go try your tool and for it to wow them and for them to be impressed

and be like, okay, I will keep this as part of my long-term toolbell of the tools

I use of it.

If they try it and it flops, there's a bunch of tools from big companies that I've tried

in the past.

They flopped and I haven't gone back.

I think one of those examples would be something like runway for video.

This is a platform that I tried a lot in the early days.

It wasn't that great and I mean, you have to give them a huge kudo for being first but

I never really got back to that platform and then Soono came out and a lot of these

other video generations, you have Higgs Field which has a whole bunch of models on there

and I tend to just use more of those types of tools today than going back to some of

the OG video tools.

I think this is kind of a trend you'll see with a lot of, I know it's kind of like a random

story from my experience but I think you're going to see that a lot.

So there was a recent report that came out of Revenue Cat and they showed that the subscription

infrastructure for more than 75,000 different developers and they kind of analyze it because

they power all of that subscriptions and by the way, these are kind of my favorite reports.

Mercury, you know, a SaaS kind of bank, SaaS focused bank is an awesome one.

They do kind of a state of AI every year where they show the top AI companies that, you

know, that people are actually using and actually have subscriptions too.

So it's kind of cool to see Revenue Cat do something similar.

What they found though, these are just awesome reliable places because you know, exactly

where money is actually being spent.

But in any case, they found that while AI apps monetize really quickly, they really struggle

to keep users around.

And I'll even say for my own startup, AIbox.ai, when we first launched, there was a lot

more bugs at the beginning.

I mean, with anything and a lot of different features that we didn't have.

And I think our churn was was pretty high.

When we first launched, I'm pretty proud of being able to pull that churn rate down and

get people to stick around a lot longer.

But it was a lot of work for us to achieve that and to be able to bring that down.

I think a lot of other people struggle with that too.

According to their 2026 state of subscription apps report, AI apps experienced significantly

higher churn compared to traditional apps.

So this is interesting, right?

It's not just like, oh, people try more software and they use less today.

Well, people actually still keep a lot of their OG apps that they have and an OG subscriptions.

But it's the new AI ones that are just trying out this new buzz thing.

I'll look at you do this crazy cool thing and make an image of me looking like XYZ.

You go try it and then you kind of dump it.

So according to the report, they analyze more than one billion in app subscription transactions.

And that was about $11 billion in annual developer revenue.

I mean, based off of their scale and their ecosystem, I think this is a pretty good indicator.

They have iOS, Android, and web apps.

And what's interesting to me is that, you know, despite, like, I think a lot of the hype

around AI, most subscription apps are not built around it, only 27% of apps analyzed

according to their report were categorized as AI powered.

About 72% of those are just non-AI apps.

So a majority of apps.

And this is interesting because I think we see a lot of the bigger players like immediately

injecting AI in.

But I think a lot of the smaller apps and companies are like, well, if we don't need it,

maybe there's not a reason to just, you know, bolt something on that's not necessary.

And that's making up, you know, almost 73% of all apps.

I think what is obviously pretty clear is how fast AI adoption is accelerating.

About one in four apps now marketing themselves are marketing themselves as AI driven.

So a lot of those are not necessarily going to be, you know, chaget, prettier, Gemini.

But they might just be, you know, an app that has some AI features in their product experience.

I think a lot of people should be and could be experimenting with.

And there's a lot of things that AI can do just inside of all traditional SaaS.

I do think this is a smart idea for most any apps.

I think definitely the categories that are adopting AI faster than others would be things

like photo and video apps.

Those are kind of the top according to what they're seeing in their analytics.

They say 61% of photo and video apps are incorporating AI features.

They say gaming is on the complete opposite extreme.

Only 6.2% of gaming apps are using AI and their code offering.

There's a bunch of other, like pretty low adoption categories, including travel,

which is only 12.3%, which is hilarious because I swear every single demo we see

from chat, you can be in Gemini is like,

chuck out the new advancements we made.

Like it's going to make planning your travel itinerary like a thousand times faster.

Like just say you want to go to Greece and it's going to give you like a 15 day

itinerary with everything you need to do every hour.

Now, I don't know how often people are planning the travel itinerary.

I don't know why this is the one demo we get stuck on with everyone.

And I apologize for my voice on the demo voice there,

but it is just one of the things that drives me crazy.

And it's so wild to see that only 12% of the travel apps are actually using this.

Well, every single AI company is like basically using this category as the main demo of a use case.

And it just doesn't turn out to be that useful or I guess the demand isn't there.

Business also has a 19.1% rate of having AI inside of the apps that the business category.

I think where things get a lot more surprising is customer retention.

So across both monthly and annual subscription plans,

AI powered apps consistently were underperforming just regular non AI apps.

After 12 months, AI apps had about a 21% retention rate for their subscriber.

So if 100 people subscribe on day one, 12 months later,

only 21 of those will still be subscribed compared to over 30% 30.7% for non AI apps.

So 10% higher if you don't have AI embedded in there.

Now, I think there's going to be some things that obviously skew that,

which is that a lot of these AI apps are kind of a new interesting use case.

And people are going to be trying them out for the first time.

I also think that there's a lot of hype.

And if the AI can't do exactly what you want perfectly, you're going to move on.

And I think those capabilities will come back in the future.

So people may retry those same apps in the future.

And they'll stop being called just like an AI app.

Like there's not going to be a buzzword.

It's just going to be like an app that does XYZ does use AI.

But you know, people don't really care.

It just does it correctly.

And I think then you'll see the retention rates higher on a monthly basis.

I think there's also a pretty big gap between these two categories.

AI apps have a 6.1% retention.

Non AI apps have a 9.5% retention rate just month to month.

I think one of the other areas where AI apps are performing a little bit better

is in weekly subscriptions.

Retention is at 2.5% compared to 1.7%.

I mean, really, that's just showing you that I think a lot of people are trying these

apps.

Weekly subscriptions is not a very common thing.

And I have actually seen this with a bunch of AI apps.

I saw I see weekly subscriptions with people that have a tool that you don't really need

it for a super long period of time.

So they try to hit you multiple times in a week.

It's basically my least favorite subscription amount of time to re-subscribe.

And it's when people are like, look, it's only $2 a week.

It's like just say $10 a month or something like so annoying.

In any case, part of the churn that we're seeing right now I think is going to be obviously

just how fast AI is going.

All of the experimentation happening in the industry, I think you're going to see metrics

like AI apps have 20% higher refund rates than non AI apps.

The median refund of 4.2% is, you know, you can compare that to 5.3%.

At the high end, I think the difference is even more pronounced with AI apps seeing

refund rates as high as 15.6% compared to 12.5% for non AI apps.

And according to what revenue cat is saying, basically volatility is coming from some big

issues about value product experience, long-term utility.

And I really just think a lot of this comes down to over-promising and under-delivering

what the AI is capable of doing.

And I see this in so many areas because I'm in marketing and I'm in AI.

So clearly this is a problem.

I think we see in the industry, I think we should probably normalize, you know, being

able to under-hype your app, but being super useful and people just use it without, you

know, having to oversell all of these capabilities.

I think AI apps right now also monetize downloads significantly more effective overall.

And down monetization is at about 2.4% for AI apps versus 2% for non AI apps.

So that is interesting.

And I think AI apps also generate higher realized lifetime value.

So here we are, you know, talking about, oh look, regular apps versus AI apps, AI apps

aren't able to keep people subscribed as long.

But they're getting a lot more money out of people, right?

On a monthly basis, AI apps produce a median real lifetime value of $18 per user compared

to $13 for non AI apps.

It's actually closer to $19 and like $13.50 for non-A apps.

So I mean, that's a pretty big step up.

People are paying more for AI apps.

Obviously, the costs of those are higher on an annual basis though.

I think it gets even bigger.

So for annual AI apps are reaching $30 versus $20 of real lifetime value for their users.

I think if you look at all of this together, basically the data, like the pattern that I

see in this is that AI features are going to help apps monetize really quickly.

But sustaining that long term is going to be the challenge and making sure your product

is actually useful, delivers on all the promises is harder.

It's totally possible.

But I think there's also a lot of competition.

And even for an app like, you know, chat GBT, for example, that was like the bell of

the ball for forever for years, number one, and then all of a sudden Gemini comes out

and Claude comes out and they have kind of some new capabilities and move reasons

why you'd want them.

So I think that we're just going to see a lot of competition and it's going to be an

interesting space.

It's no one's, you know, it's not like anyone has this completely cornered.

Okay.

Switching gears for a second.

I want to talk specifically speaking of chat GBT, a new feature that is going to support

inside of chat GBT and inside of the app, obviously, we can see that we got to try

to make these things more useful for users.

And some of this is the latest thing that chat GBT is doing, obviously, to try to keep

their turn down.

So this week, chat GBT has just introduced dynamic visual explanations inside of chat

GBT.

This is basically a feature that is going to let you have make mathematical and scientific

concepts a lot more interactive.

So rather than just, you know, getting like some sort of text explanation or maybe like

a static diagram, this new feature is going to let you manipulate variables directly.

And then you can watch equations update in real time inside of chat GBT.

This is very cool.

So an example of this is like if you were exploring, you know, the Pythagorean theorem

or something, users could basically, you could go and adjust the sides of a triangle.

And then immediately you'll see how the hypotenuse is changing.

The feature right now is supported in more than 70 different math and science concepts.

It includes compound interest, exponential decay, linear equations, columns law, ohms

law, kinetic energy, hooks law.

It's, honestly, it's pretty cool if I'm not, if I'm telling the truth, I think that

right now, if you're able to kind of turn an explanation into this sort of like interactive

module, the feature is going to shift from maybe just the tool giving you these really

simple answers to actually helping users.

And I mean, hopefully students and others explore how the concept actually works and get

kind of a deeper insight and understanding of the problem.

So honestly, you know, I remember when AI came out and never said it's going to make

everybody dumber and we're going to just, you know, outsource our brains to AI.

But I actually, it's an incredible tool for education that's going to make us smarter

and we're going to be able to learn more.

Open AI says that more than 140 million people already use chat GBT every single week from

math and science help.

I think it's over 900 million people weekly just for general use.

But you know, 140 million just from math and science is a huge chunk of that.

And so I think this is obviously something that's been very tricky for a lot of people.

It's hard to get access to good tutors.

And this is an awesome opportunity, I think, for a lot of people.

Other companies are definitely experimenting with some similar approaches in 2025, kind

of at the end of last year.

Gemini introduced some interactive diagrams within their own AI system as part of kind of

an effort to get more into education.

I think I think I did a podcast on it back at the time.

I think the race right now to build the next generation of AI infrastructure is going to

be interesting.

We have all these new features, but all of these new features have to be powered by infrastructure.

They've got to be powered by more compute as these tools just get more and more intense.

And on that note, Miriam Maraudi startup, Thank You Machine Labs, has just announced

a multi-year strategic partnership with Nvidia to deploy large-scale computing systems.

And this is actually going to start in 2027.

So not this year, it's interesting.

I mean, it's kind of crazy.

Miriam Maraudi, and then we have super safe intelligence as well, kind of these spin-offs

from some of the top brass over open AI when they came out and they raised like a billion

dollars.

Their startups didn't launch something immediately, although I do believe that Miriam Maraudi

Think You Machine Labs does have Tinker, I think, as their product.

They do have a product out there.

But I think they got a lot more on the pipe.

And they're building all of these kind of compute partnerships that are starting, you know,

not this, like, they got out maybe last year.

They didn't really put out a ton last year, then they have all of this year.

They're still working.

And these compute deals aren't rolling out until next year.

So you can expect that whenever their products are scaling, it's going to be in the future.

This agreement in particular includes deploying at least one gigawatt of Nvidia's Vera Ruben

AI systems.

I mean, honestly, even just going and signing a gigawatt deal, it's a lot of confidence

that their product is going to be incredibly useful.

I think this is one of the company's newest architectures.

And Nvidia is also making a strategic investment in Think You Machine Labs, which has already

raised more than $2 billion since it was founded last year.

It's valued at over $12 billion.

Think You Machine Labs is focused on building AI models that are designed to produce more

replicable and reliable outputs.

And they released their first API product Tinker last year.

So right now, this partnership is showing, I think, basically a bigger trend in the industry.

API companies are really having to be very aggressive in how they compete for access to

this computing power.

Jensen Lang, CEO of Nvidia, he predicted that the industry would spend $3 trillion to

$4 trillion on AI infrastructure by the end of the decade.

So so much money is getting put into this industry.

I think the exact value of Think You Machine Labs and their deal that they're doing with

Nvidia here, that wasn't all disclosed.

But I think the massive compute agreements are just becoming more and more common for some

of these big companies.

Last year, for example, open AI struck a $300 billion compute partnership with Oracle.

And so yeah, obviously this is something that's not slowing down.

If you look at all of these together, I think the industry right now is experimenting rapidly.

They're just trying to put out tons of different products and we see that from kind of the

state of AI apps I was talking about earlier.

But I also think the AI is starting to reshape how apps monetize, how people learn, how

infrastructure is built, how long-term winners are likely going to be, you know, people that

are moving beyond just the novelty and they're actually delivering a really durable value

to the users, right?

And I think it's important when even a company like Think You Machine Labs and a lot

of these others has to think like when you build a product in open AI with their latest,

you know, kind of math and science feature, like when you build a tool, when you build

a product, make sure it works really good on launch.

And then you're going to be able to keep your turn up.

And then all of these long-term infrastructure deals you have are, you know, not going

to be wasted because you're going to be able to keep all of your users using the product.

So this is an interesting time in the industry, a lot is going on.

I'll definitely keep you up to date on all of it.

If you want to try all of the AI models I talked about on the show, make sure to go check

out AIbox.ai.

You can access to over 40 of the top AI models all in one place.

You can chat with them.

We have some exciting new features dropping soon.

You can check it out.

Link is in the description to AIbox.ai.

And everyone, remember, today is my birthday.

I turn 30 today, the number one present and thing in the entire world I would ask for

my birthday is if you could leave a rating review on the podcast, it would mean the world

to me.

That's so thrilled.

So if you haven't already, leave a review and I will be eternally grateful on my birthday.

All right.

Hope you guys all have a fantastic rest of your day.

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