Ex-Google Insider Reveals The Future Of AI in 2026...

You said PhD in your pocket. What did

you mean by that? [music]

>> You'll have a system in your pocket that

you won't pay a subscription for. You'll

have the smartest system ever built that

represents hundreds, thousands of years

of human experience and knowledge in

your pocket. It's never going to go

offline. It's never going to get stupid.

It's going to have a better memory of

exactly what you like or don't like.

Every person's going to own their own

intelligence. And as long as you charge

your phone, you'll have that PhD in your

pocket. for the AI models coming out of

Chat GPT, OpenAI, Anthropic, what does

that mean for them financially?

>> I think what we're going to see is a

much more diverse explosion of models

and applications that people own and run

themselves. [music] And right now, it's

really just hackers that are doing this,

but in the near future, we're going to

see everyone's going to have the [music]

ability to have something downloaded

that they own and run. You can see

Anthropic actually making like this

[music] desperate attempt to cover

everything. They do design, chat,

coding, now they're doing private equity

stuff. They're doing marketing. They're

doing legal. Can they win that all?

There's actually just so much surface

area. I find it difficult to believe.

>> Apple and Amazon seem to have just gone

into a different path. You don't really

see or at least hear of a lot of action

there. What What's your thoughts on

that?

>> We're super excited about what they're

doing and they have been a slow mover

and said, you know, we're not going to

build our own model. It's not really

what we do. But they see this wave

coming too of why shouldn't we have a

PhD in your pocket [music] for every

Apple user? Why shouldn't one live on

your laptop and live on your iPhone and

live on your Mac Mini which is already

happening right? They have really

committed to ondevice AI that their

customers will own which really fits

into their mantra of privacy and

ownership and fully vertical

infrastructure and [music] I think the

story by the end of the year will be

Apple has emerged as one of the clear AI

winners because of that. Jack, we are

approaching a world where most people no

longer need open AI level models for the

things they actually do. Would you say

that's true?

>> Yeah, absolutely. It didn't seem like we

would get there till the end of 2026,

but we are essentially already there.

>> What does that mean? Because I think for

a lot of our viewers and our listeners,

u majority being founders, investors,

engineers, but for the nontechnical

folks, what does that mean for them?

Yeah, I think if you look at Anthropic's

last few releases, Opus 46, Opus 47,

they're incredible models. They're

they're really really powerful and they

keep emphasizing these are really

effective at the most difficult

challenges. They're really great at the

most difficult challenges. They're

pushing the frontier on there's this

extremely difficult thing and we can

actually do something that was never

been done before. But what's been

catching up behind them is the open

source models that are really good at

yes less difficult problems, but that's

becoming a bigger and bigger problem

space of what they can solve. So kind of

big open- source models are already

really good at the things that most

people use chatbots for today.

Definitely holding a conversation, doing

research. They're really good at coding

now almost frontier level with Opus and

from Claude and OpenAI. Most of the

valuable work that you do using language

models on a day-to-day basis can be done

by open models already today. And then

the thing that's amazing to us, these

newest releases of smaller language

models, specifically some of the Quen

models that have come out recently are

as good as the frontier big models.

Think from from OpenAI and and Claude

and the big open source models. Now you

have tiny models that can fit on your

laptop, potentially even on your phone

that can do that kind of frontier level

work. We already have that already. They

might not be good at the most difficult

challenges, but the reality is most of

the work you do on a day-to-day basis

isn't the most difficult work.

>> For someone who's not technical, who's

not in the industry like you and I am,

and perhaps is a teacher, what would be

a example of an open source model and

one that they could have on their phone

or their laptop and what can they do

with it?

>> Yeah, absolutely. My my sister's a fifth

grade teacher, so I I've talked to her

about this a little bit. If you use chat

GBPT to let's say you are a teacher,

you're create lesson plans, you ask it

for questions, help revise or give

feedback to to students or their

parents, any of that work can be done

for much cheaper using an open source

model. And there's really two ways to

use [music] it. You know, number one,

these all have kind of competing chat

bots to OpenAI or cloud. [music] And

what's getting better and better is you

can download essentially a really big

file onto your computer that contains

the actual model that you can then run

and chat with yourself. And then instead

of paying a a monthly subscription, you

really you own your own intelligence.

It's never going to go offline. It's

never going to get stupider. It's going

to have a better memory of exactly what

you like or don't like. And it's going

to live on your laptop as long as that

laptop has power. That's incredible. You

know, you think about technology and how

it's evolved from the internet. It was

very expensive. It was dialup and then

broadband came along and now it's

getting cheaper and cheaper and almost

internet is becoming an essential asset.

You need to have cloud you know storage

was expensive with onrem then cloud

comes along and then you look at mobiles

they're getting cheaper and cheaper and

cheaper. I don't even upgrade my phone

anymore. I think I have an old iPhone.

The question is, is that what you are

basically stating here that AI is going

to play in a very similar field where

maybe free models completely open source

models will I think you mentioned this

to me in our calls. You said it the

small models can do 95% of economically

valuable AI work.

>> Yeah, I think that's right. I think

we're already there and the model is

probably the most important part of this

entire ecosystem. what's actually what

powers the intelligence at the other end

of the line when you're you're doing a

chat or you're doing some sort of work.

The small models are really smart and

they're only going to get better from

here. And the question is, okay, so as

the frontier models push out, they

continue to do things that no one else

can do. They do frontier AI research.

They do research in physical sciences or

or biology that's never been done.

That's going to be extremely valuable

for this Frontier Labs. But the open

source models are going to do what most

people care about. Think software

engineers, think teachers, doctors,

people who are, you know, working with

computers every day, doing research,

doing sales, that kind of work. The

models are already smart enough, ones

that can fit on your laptop. And uh I

think, you know, there there's basically

two directions then where the Frontier

Labs are going to continue to to push

the envelope. They're [music] going to

do that work that no one's ever done

before. What you've probably seen in the

headlines is they're really starting to

build out apps to use their models. So

they've said, "Okay, we actually

realized that this model revolution is

coming. People can own their own

intelligence. They won't need to come

through us." So what have they done?

They've built these frankly incredible

number of products that you can interact

with them. Think Claude code and claude

design and claude co-work and and claude

chat and then you have codeex and and

chatbt and those things. And the

question will be, you know, do you want

to pay a monthly subscription to use one

of those products or do you want to own

your own intelligence on your laptop or

wherever it is? Is it like AI is going

to become like electricity? We don't

really care where it comes from. It just

generates and we use it in some ways

yes, in some ways no. I think the

threshold that we've been going after is

uh like can it do a given task? And once

you reach the point of okay, it can do

this task, then you start to care about

other things. How well does it know me?

How well can I rely on it as it

completes a number of tasks in

succession? You know, what's its

personality? How quick is it? How much

does it cost? Once you pass that

accuracy threshold, those other

questions come into play a lot more.

What we really focus on specifically is

is a couple of those. We want your AI to

be really fast. We want it to be really

accurate even as it encounters a lot of

data over a really long period of time.

That's really what, you know, my company

Subconscious does specifically on top of

open models. And um, you know, I've

talked to some companies who really

think that personality is going to be

the differentiator that think that, you

know, its ability to understand certain

types of data. think a model for doctors

versus uh biioarma versus software

engineers. Um but they will there will

probably be some distinctions between

those. One of the I guess realizations

I've seen in the market now is AI has in

many ways removed this uh protective

layer for a lot of industries like

management consultancies even doctors

lawyers where they're charging extremely

high fees or maybe inadequate in their

services. AI is becoming that adequate

solution. We're taking things even

further, talking about AI becoming

effectively free with open source. What

does that mean for the professions that

have notoriously always been very

expensive? Thinking lawyers, doctors,

architects, what happens there?

>> Yeah, it's it's a difficult question to

to figure out [music] cuz in any time

frame that's reasonable over the next

couple years, as smart as the models

will be, there's not enough trust built

up to put all of your faith into these

models. Do I really want to trust a

model versus talking to an actual

doctor? If I'm building a house for the

first time, do I really want an AI

system to do that instead of actually

hiring an architect who's done that tons

of times? Uh, if I have a very

important, you know, I'm on trial or I'm

raising money for my company, do I

really want to not talk to an actual

lawyer at any point? Probably not. But

they are smart enough to do a lot of

that work. We'll probably see that

shift, but not in the immediate short

term. I think what I'm noticing at least

and I agree with you. I don't think it's

something where you're completely not

going to use a lawyer or a doctor or an

architect, but I I feel there's maybe

some elements of some industries and

sectors where sort of the cost the unit

cost of that service is so high pre AI.

Now with AI, they can really focus on

the higher level work where purpose is

worth paying for a lawyer. If that makes

sense.

>> I think so. And I I think like it it

makes it much easier on their end to do

a lot more meaningful work potentially

billing less hours and then on our end

you know we can go into those

conversations with a lawyer you know

with an architect with a doctor and use

their time better also. So I think it'll

come from both sides but we'll still

need human expertise at least in any

time scale that probably matters for our

lives.

>> I think you know the mobile and the

internet was a very transformational

piece of technology that has transcended

everywhere. I think for a lot of people

they don't quite realize what cloud does

and unless you know what cloud does

because you use it but you know if

you're in the industry but for mobile

and for internet you don't have to be in

tech to to have used it. I think with AI

I just think about how fast it's

changing on a weekly or on a bi-weekly

basis. The technology is exponential.

Yeah. Yeah. The question I have for you,

Jack, is when you look back at the last

6 months, just in the last 6 months,

maybe the last 3 months, has there been

a moment for you where you thought,

okay, I did not expect AI to be where it

is now at this rate?

>> Yeah. Yeah. Absolutely. There was a

moment when really the Quen 3.5 open-

source models came out and and Quen is a

series of open source models from

Alibaba, uh, Chinese company where I

would say a step-wise change in what

open models can actually do. And then a

couple weeks later they upgraded and

said one of these we're actually going

to upgrade to version 3.6 and that's

this version that is a 27 billion

parameter model. To put into your mind

27 billion that's a pretty big model but

it can fit on your laptop if you use the

right techniques. Something that runs in

the cloud that you use behind chatpt or

claude is trillions of parameters. So so

100 times you know bigger some somewhere

in that range. And we have a model that

is 100 times smaller and is as good as

those that live in the cloud. And I'd

been talking to my my co-founder about

this and we said that'll probably happen

sometime maybe December of this year.

That's roughly what the trend line looks

like. It happened in March and I think

we're going to keep seeing acceleration

that's as quick in that direction. It's

it's pretty impossible to not believe

that these things are going to get small

enough to to really live on your

computers to live on your laptops

potentially even your phones um in the

very near future. So we were just

surprised about how quickly intelligence

got compressed.

>> That's impressive. Jack, you used the

phrase on our previous call. You said

PhD in your pocket. What did you mean by

that?

>> Yeah. You think what is really the

benefit of [music] these models getting

so small and so fast and it really comes

down to you'll have a system in your

pocket that you won't pay a subscription

for that as long as you charge your

phone, you'll have the smartest system

ever built that represents hundreds,

thousands of years of of human

experience and knowledge in your pocket

to do whatever you need. Whether you

need to ask it questions, you know, help

plan or coordinate events, do research,

maybe you wanted to do coding, whatever

you want it to do, you'll have that

capability. You know, today it's already

in your pocket. You can download the

Chat GBT app and use it. But what I'm

talking about is something that will

really you'll own that someone else

can't turn off. That's not going to get

stupider because ChatGpt is it has too

many people using it and they're going

to kick you off for the day. You'll have

something that's that's really yours and

lives in your pocket and understands

you. [music] And as long as you charge

your phone, you'll have that, you know,

PhD in your pocket.

>> From a, I guess, evaluation perspective

for the value of these companies, even

the ones that are providing the GPUs

like Nvidia, maybe less [music] them,

but for the AI models coming out of Chat

GPT, OpenAI, Anthropic, what does that

mean for them financially?

>> I honestly think their valuations are

justified. I think that the narrative

though isn't quite right where there

won't be these two dominant forces that

that battle for Anthropic wins the

enterprise and chat GBT wins all of the

consumer usage. I think what we're going

to see is a much more diverse explosion

of of models and applications that

people own and and run themselves. And

right now it's really just hackers that

are doing this. People who are are

tinkering enough and have the the strong

enough computers to to get that frontier

intelligence on their own devices. But

in the near future, we're going to see

everyone's going to have the ability to

have something downloaded that they own

um and run. I think it's going to have a

lot of implications for what consumer AI

looks like, what an individual asking

questions, planning their life, asking

for advice, doing work, both school work

and and kind of business employed work

means. But for businesses, I think

there'll always be a need for some sort

of cloud offering. There are certain

things that you want to run for really

long periods of time are going to need

that kind of frontier power, but they

might need smaller compute footprints

than we think. But at the end of the

day, my my overall take is we're just

scratching the surface of how useful

these systems can be. They're only

getting smarter. You know, the trend

lines aren't showing any signs of

stopping. Personally, it's just made my

work life really productive. And there's

a couple things, you know, here and

there that I use in my personal life

that I think is is really fun that that

AI has brought to the world. There was a

thing that I kept asking about two years

ago. Who's going to own the application

layer? Who's going to be the big players

in AI? We've got some incumbents here.

We've got some companies here that are

doing exceptional work like Anthropic,

like OpenAI. But in your opinion, who do

you think, if any, is the jury still out

on who the winners are in AI? You know,

I think it's it's pretty clear that

Anthropic, OpenAI, probably Google also,

they'll have models that do things that

no one else can do, and they have the

comput.

they have the research scientists in

order to to train the models. They're

going to push the bounds of what we

thought was possible with AI. When it

comes to, like I said, 95% of consumer

AI use cases, there's going to be this

explosion of what what models uh can do.

And there's just a number of open source

models are out there. So, the the cat's

out of the bag and what's powering any

given app is is totally up for grabs.

Um, I think what the surface looks like,

like how does someone actually interact

with that is also pretty up for grabs.

Uh, you can see Anthropic actually

making like this desperate attempt to

cover everything. I mean, they do

literally like design, chat, coding now.

They're doing private equity stuff.

They're doing like marketing. They're

doing legal. Can they win that all?

Maybe. But there's actually just so much

surface area. I find it difficult to

believe. And then when you come down to

individuals who are using chat bots,

there's some value to having built up a

lot of context within a given app that

the first one you use for a long period

of time, you're probably going to use

for a long time. But I think we're we're

really just starting to see these things

like really penetrate into people's

lives. And it feels like the playing

field is still open.

>> Somewhat of a sticky piece of software,

but let's say you're using it personally

and it's on your phone and as you said,

you you can use it personally for a

number of different use cases. you're

teaching it constantly who you are, what

you like, what you had for dieting or

for exercising or your health or

hobbies. And it's interesting to think

how sticky these technologies are

because I know you can migrate it over

to a different model, but you kind of

become attached to it because it knows

you. And it's interesting also that you

say anthropic open AI in Google because

I I recently had uh Andrew Dy who is a

founder of a new company called Allorean

that just raised 50 million and Andrew

was in the research lab at Google Brain.

He worked with the founders of OpenAI

Anthropic and the entire topic that we

really touched on during the podcast was

all about the culture of that particular

team and how they became I think at the

end I said it was the Google mafia

similar to the Vay Palmer. It's really

fascinating to see this new shift

happening. But if you were to pick a

player and then think about this as far

as like let's use the cloud era with AWS

and GCP and and Microsoft Azure. Where

do you think Anthropic is OpenAI and

Google if you were to sort of look at

them in comparison to the cloud as we

are right now? You know, we're we're

recording this in May 26, so this will

probably go out towards the end of this

month, but as of this moment right now,

who do you say is the biggest players?

The biggest out of the three? I'll I'll

go from first of all our preferences as

a team. We use a lot of the anthropic

models ourselves. Like as our models

have gotten better, um we're trying to

use more of our own. But but we really

like anthropic and for a number of

reasons. I think number one, their

models seem to outperform the others.

Number two, they've really just

completely dominated the mind share

among developers. Um everything from

cloud code to they develop the MCP. Um

they developed all of these tools. Uh

they developed skills. um different

things that developers all over the

world use now whether you're using

Anthropic or not and then they just

seems like they continue to dominate the

news cycle with with constant releases.

So I would say they are likely number

one. Number two I would actually say

right now is Google. I'm I'm an ex

Googleler myself. I worked at Google for

a number of years um on their on their

search team. I just think they've done

they've done a really excellent job.

Their their Gemini models are really

strong. They're very friendly to to

working with developers and and new

companies. their notebook LLM product is

is pretty great and they've continued to

come across as like a very good you know

respected player in the space. Um and

then third I'd say is is JGBT and anth

and open AI. You know they obviously

came in first um but our take has been

that their their models are less

reliable for these agentic tasks uh that

we're really building our systems around

it. It was just less reliable

infrastructure for us and it seems like

they are they are dropping in their mind

share. I think we have a a big trial

ending in about two weeks that's going

to really reveal where they sit. So,

I'll leave it there.

>> We just internally just shifted away

from OpenAI over to anthropic and we use

both Anthropic and um and perplexity as

well, which we find computers quite

good. One more question I want to ask

before we dive into talking about where

you are right now, what you're building

as a founder of a company. I'd love to

tap into that. But the last question I

want to really dive into as far and

unpack as far as this topic of the big

players is there are two other big tech

companies that seem to have just lost

their way when it comes to this new

evolution, this new wave of AI. One

being, and I could be completely wrong,

I'm looking at Microsoft. I'm thinking

Microsoft made a massive investment to

open AAI. That was kind of their play.

But Apple and Amazon seem to have just

gone into a different path. you don't

really see or at least hear of a lot of

action there. What What's your thoughts

on that?

>> Oh man, I I think that's going to be an

outdated take pretty soon. Amazon, first

of all, they own a lot of the GPUs. They

have these, you know, big deals with

Anthropic and a lot of teams that we

talked to are using Anthropic through

their Amazon Bedrock accounts. They're

running them on, you know, Amazon GPUs.

I think their massive cloud footprint

isn't going anywhere.

>> Well, that's the thing. I think I think

a lot of this is around branding and

marketing and how what is known in the

market, right?

>> But but they've become, you know,

Switzerland in a way because of that and

they're just benefiting from you might

have the best model in the world, but

you got to have the GPUs and the

infrastructure to run it. And on top of

that too, as it gets cheaper and cheaper

to run a language model and do this

agentic work, the the compute

surrounding it gets more important. How

do you call tools? How do you access

data? How do you do the things that AWS

does really well? On the other side,

I'll say Apple, too. We're we're super

excited about what they're doing. Um,

and they have been a slow mover and

said, you know, we're not going to build

our own model. It's not really what we

do. But they see this wave coming too of

why shouldn't we have a PhD in your

pocket for every Apple user? Why

shouldn't one live on your laptop and

live on your iPhone and live on your Mac

Mini, which is already happening, right?

>> Interesting. Um and they have really

committed to ondevice AI that their

customers will own which really fits

into their mantra of privacy and

ownership and [music] fully uh you know

vertical infrastructure for whatever end

experience they're giving to their users

and I think the story by the end of the

year will be Apple has emerged as one of

the clear AI winners because of that.

>> Wow. Okay. Well, we'll do a second

episode in person and talk about that.

[laughter]

>> Yeah,

>> Jack, let's move on. This was really

fascinating. Let's move on to where you

are today. I'd love to get to know your

business a little bit more and

understand what is it you're building

today. What is the problem statement?

Start off there. What is the problem

statement that you're solving? We

basically ourselves heard a lot of the

hype around building uh AI agents but

really struggled to actually build

agents ourselves and we think it was

because the tooling that was built

around AI was really built for chat bots

um and not specifically for agentic

systems. So what we do as a company is

we take open source models, we retrain

them to think in terms of these longer

running workloads and then we run them

on top of our own infrastructure that's

that's very very efficient. What that

amounts to is we offer a series of

language models to customers that are

more accurate especially on longunning

complex tasks and much much cheaper to

run and then can run at really any scale

anything from data center scale all the

way down to you know the devices that

we're talking on right now. So this wave

has been really exciting for us because

what we can do is we can take a language

an open source language model that's

already really great. You know, maybe it

can live on your computer and be that

replace your chatbt subscription because

it's as good a chatbot as anything else

in the world, but we can post train it,

run it on top of our own way that we

would serve up the model to you in a way

that it can solve long context problems,

things like, [music] you know, deep

research and coding and interacting with

your browser. Things that today only the

best most frontier models can do. We

allow uh smaller language models to do

that uh at any scale. It's very

fascinating. I'm just wondering when you

thought about this being a area of

interest, what was that epiphany or

moment where you realized this is an

interesting area to go after?

>> We really started the company around how

do we build agents that can reason over

long context, not lose the thread and

solve these these longer, more

challenging tasks. And our process to do

that as a small company with limited

resources is let's actually prove this

out with small language models and then

we'll show what we've done. we'll go

raise more money and then we'll be able

to do it with big language models which

take a lot of time and resources to to

retrain and run. And through that

process, we started working on these

smaller language models and we hit some

of our stretch benchmarks for much later

uh trainings of much more bigger bigger

and powerful models. And we thought like

oh my god the tech is already here. uh

what we've just done is actually you

know proven this at a small scale but

proven it in such a way that it actually

makes these small language models act

like the biggest and baddest AI models

that are out there and so that's really

led us down this path of okay it's very

clear to us every person's going to own

their own intelligence companies

businesses around the world are going to

have the opportunity to have you know

their entire workforce have these agents

on their computers live in workstations

in their office so that data doesn't

have to leave the floor let alone the

the company. And then there's a bunch of

implications for consumers too, you

know, as can is this something that you

can reasonably download with an iPhone

app and and live on your phone. That's

what we're we're more selling to

businesses now, but we just see the the

world opening up in front of us.

>> What would you say the TAM is for your

company for a market like this?

>> There's kind of two markets that we sell

into right now. Number one is we sell

inference uh to companies via some cloud

providers and we're able to offer

Frontier Performance at a much much

lower cost. Uh we're doing that with a

number of customers now. We think that

market is pretty pretty massive. Hard to

put a number on it, but there's there's

been tens of billions of dollars spent

on AI inference in the past year.

Probably more spent in the last 5 months

than at all of 2025. That is a clear

large and and and growing market. The

other thing that we've been doing is

working directly with some hardware

companies to ship our models on device.

So if you buy a not yet disclosed uh

workstation, it will have our our

subconscious models embedded on the

system. That is a small market today,

but we think it's going to grow to be

something that's that's very meaningful.

>> Why did you call it subconscious?

>> We wanted it to represent doing work on

your behalf in the background. And we

think that it really just captures the

essence of what we do. And even as we've

refined how our system works under the

hood, it reflects it even better because

really what we're doing is over long

long reasoning chains, we are as the

model is deciding what words, what

tokens to share next, we are doing some

compression of of the previous

information that's encountered,

specifically information that's no

longer relevant. And what that

information does is it compresses it and

it still sits in its memory but in kind

of a latent space that doesn't have full

context of all of the data around it.

And so in the model's perspective, you

know, we we we stuff that in its

subconscious. So it's still aware of

everything that it's encountered, but

maybe not every exact detail, but enough

to complete whatever task over these

really long periods of time. Kind of

number one, models working in the

background. Number two, it actually

really reflects what we do under the

hood.

>> Love it. It's been a pleasure having you

on the show. You and I talked about

doing this and the thing that stuck out

for me is this whole idea as you said of

open source and AI and smaller models.

The number 95% really struck out. The

fact that you said 95% of what is

effectively we need to do could be done

by smaller models. It really is

interesting. Jake, just want to wrap up

with one final question uh which is a

book recommendation. Yeah, I'd love to

hear yours. You know what what would be

a book that you'd recommend to our

audience? God, maybe my favorite book of

all time is uh in the three body problem

series. Uh the second book which is

called uh The Dark Forest. So so good.

It's a uh kind of fate of the world. Um

I don't know how to really explain it

without explaining the book, but I would

say the first book in the series is

seven out of 10. The second book, which

I'm recommending, is a 10 out of 10, and

the third is a nine out of 10.

>> Uh my co-founder is from China. The book

was originally actually written in

Chinese, translated to English. And we'd

worked together for a year and a half

and we finally put that together a

couple weeks ago that we both love these

books.

>> Really, really incredible.

>> Definitely worth, you know.

>> Yeah.

>> Yeah. Well, nice timing for this uh for

this podcast. Thank you so much for the

recommendation. Thank you for your time.

Yeah. I wish you all the best of luck.

It's it's really interesting the area

that you're tackling. It's it's a very

important topic, which is why I wanted

to do this. And when you talk about such

large numbers in terms of the impact on

what this really looks like going

forward, AI in your pocket, I love the

phrase you used. It's like having a PhD

in your pocket. Um

>> yeah, I I think it's something that it's

percolated through the hackers and the

people really on the edge real like, oh

my god, this is amazing. And it will be,

you know, top tier news in the next

couple months. And I think there'll be

talks of, okay, what does this mean for

OpenAI anthropic like what you're

asking? I think they will still have a

business, but I think it's going to open

up a whole new world of possibilities,

and that's what we're really excited to

to play in that arena.

>> Amazing, Jack. It was a pleasure. Thank

you so much for being on the show.

>> Yeah, thanks a lot.

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