I think we have AGI. I think we have

artificial general intelligence. We

really have. You you hear these 95% of

projects fail, but like you know, like

that's that's that's actually what you

want. I I think the LLM is a commodity.

People are not saying that, but it is a

commodity. Like you can get gas from

this gas station, you can get gas from

that gas station, it doesn't matter.

Just compare price. Is AI in a bubble?

There is an AI bubble. Okay, so then

Glean is also in the bubble. Everybody's

in the bubble. No, I would I would say

there is a bubble.

I I would say those three camps. Yeah,

there is a super intelligence quest

camp.

I would be very worried there. There's a

second the researchers doing the, you

know, that's definitely not in a bubble.

They're like the sober. Yeah, they're

they're super sober and nobody cares

about them. And then there's,

>> [laughter]

>> all right, and they're probably the ones

that are right, unfortunately. And then

there's the third camp which is us

trying to make this valuable. We're not

in a bubble in a sense that we're not

spending huge amounts of capital on what

we are doing.

Uh, we're just trying to get actual

economic value instead of these

organizations.

Two legendary builders, Ali, Arvind.

I'm so thrilled to get into this with

you because both of you have seen every

super cycle I've lived through,

internet,

mobile,

cloud,

data and AI.

Not just through the super cycles, but

also through the hype,

the trough of disillusionment,

and this time it's different.

Today, we're going to chop it up on the

state of AI.

You know, let's let's start with the

20,000 ft view. Take stock of where we

are.

AI, we've seen consumer AI,

billions of users. ChatGPT said the

guns went off 3 years ago. Cloud,

Perplexity, ChatGPT.

People use it in the room.

On the SMB and developer side, we've got

hundreds of millions of users with

Cursor and Codex and Cloud Code and and

and so on.

Enterprise on the other hand,

there's a lot of divide. It's hard to

see, a lot of fog of war.

On one side, you've got models that are

earning

math benchmarks and science benchmarks

and engineering benchmarks.

But on the other side, you've got the

MIT report that's saying 95% of AI

deployments don't work.

What's the reality?

Bridge that gap for us. Lay it out as

you see it, a view from the top. So, I

think first of all, I think we should we

we should know that people use AI in

their personal and work lives both. So,

there's not so much of a divide. Like

you know, everybody in your company is

probably using ChatGPT

um and Claude and and other tools uh on

a daily basis. Um the [clears throat]

uh

the the thing that I I feel uh you know,

is happening in enterprises

is you you hear these 95% of projects

fail, but like you know, like that's

that's that's actually what you want.

Like you you like when you are actually

experimenting with new technology, if if

if all of all of your projects are

failing, that means you didn't just not

trying enough, you know, at the moment.

So, so I think when when I read the

study like it was not a surprise for me.

Um you know, we're going to actually see

hopefully like you know, similar stats

next year, too uh because you want

everybody in the industry to be to be

really eager and experiment and actually

figure out like you know, how to mix how

how how to actually make you know, get

you know, get benefits from this

technology.

This would make you guys by default the

5% of AI that is working.

>> [laughter]

>> Which is uh one in 20.

Maybe maybe you go to the 5%.

What is what is a use case that is

working? And not just working like it's

like saving me time, but like it's

working and it's transforming my

company. Something that you can take to

the bank to to the CFO while while the

CFO will not listen, but the legal won't

shut it down. All right, well, I mean,

look, we're seeing a lot of use cases

that are working. Uh

it's just that you know, you you just

have to it's not just you can just

unleash the agent and it just works.

Uh it's an engineering art. Like if

you're going to have a company that's

going to be really differentiated like

like my company or your company or

anyone's company and you want to beat

the competition,

you can't just like you know, quickly

put something together and think that

you know, the your competition is not

going to do the same thing. So, that's

going to be you know, something that

needs evaluations. It needs something

that you know, you're going to

productionize. It's going to take

effort. You need a great team around it.

But we're seeing a lot of them. Like

I'll give you some examples.

Um Royal Bank of Canada uh

built agents with us that basically take

as soon as an earnings report comes out,

so equity research analyst, their job is

to put together these, you know, reports

that say like you know, this is a buy,

this is you know, hold and so on. Um the

agent goes, gets the earnings report,

gets all the previous earnings reports,

gets all the competitors' earnings

reports, gets everything that's going on

in the market, does the full analysis,

the news, everything, puts it all

together and it can get the equity

report out in 15 minutes from the

earnings call. Industry standard is 2

hours.

Of course, it's going to get

commoditized and others are going to do

that as well, but uh that's actually

really important use case that we're

seeing in finance.

Um so, that's like finance example in

finance, right? It's like and there's

lots of examples like this. Sifting

through hundreds of thousands of

documents, SEC reports, so on. That's

finance.

Um let's switch gears. Let's go to to

health care. Health care completely

different. In health care, we have a

you know, customer Merck

uh that in the life science space

created a model called Teddy. Teddy

stands for transformer enabled drug

discovery.

And um this is a transformer model kind

of just like large language models that

can predict the next word, but it

instead can figure out which genome is

missing if you remove a genome. So, it

really understands the gene regulatory

network and can really start telling you

what's happening with gene expression

and so on. So, this is really important

for drug discovery. It's the beginnings,

but this is going to actually help us do

things that we couldn't do before. Let's

pick retail also. So, I'm picking

different. Health care is one. I gave

you finance, right? The RBC one.

Um let's go to retail, 7-Eleven.

Agents that completely automate the

marketing stack. I actually think the

marketing stack is going to get

disrupted pretty heavily.

So, um

these agents um can basically prepare,

they can segment audience like this

segment wants to hear this and it can

prepare all the marketing material

that's like directly targeting you guys

and it can put the campaigns together

and do that.

7-Eleven was doing this before as well,

but

you know, this and we're seeing this at

Databricks as well. More and more is

being done by agents and being automated

so you can just do it faster and you can

segment more fine-grained because before

you had to create the content for the

groups, that was a heavy content

creation was something that was human

manual labor. Now, you can actually do

that much much more. You can have all

your web materials completely customized

for a target group. So, these are

examples where it is working. There are

also lots of examples where it's not

working. Even with Databricks, we're not

just the 5%, you know, we have some of

that 95%, too, but some examples where

it's where it's where we're seeing

success. Ali, follow up on that off

These are great examples. Thank you.

Maybe uh if you if you were to take it a

layer up, what is common across these

use cases or these organizations or

these CIOs that's making these use cases

work? Is there something that we can

pattern match?

Yeah, look, I I think the LLM is a

commodity.

People are not saying that, but it is a

commodity. Like and you know, when I

took took econ classes, commodity was

when it's interchangeable. Like you can

get gas from this gas station, you can

get gas from that gas station, it

doesn't matter. Just compare price. LLMs

have become that way. Like it doesn't

really matter. This one is better right

now, next week that one is better. You

can't even keep up anymore, right?

What's happening? So, they're a

commodity. So, it's not about that. It's

really comes down to your company,

what data does your company have that's

special that your competitors don't

have?

Can you leverage that and can you build

AI that really understands that data?

Cuz that's not a commodity. There's not

an AI out there that understands all

your business processes in your company,

your secret sauce, and your data. That's

not a commodity. In fact, that's closer

to the 95%. It really comes down to

that. Or if you have a complicated

process that just your company has, this

is how you deliver your product and

services in your company, and it's uh

you know, it's

that portion can be disrupted with AI

somehow. If you can do that, now you can

get ahead of your competition. But it

comes back to what makes your company

special. Unfortunately, a lot of

companies are just building uh commodity

stuff. Like you should not be building

that cuz it's a thing that every company

can do. It's not special to your company

or to to your that's that's I think the

problem in a lot of the industry. Uh

another problem in the industry is that

a lot of demo wear. It's really easy to

make cool demos with an AI

and you know, therefore we're seeing a

lot of cool demos, but that's all they

are. Yeah. Well, you know, something we

say around at Altimeter quite a bit is

your AI strategy starts with your data

strategy. Yeah. So, you got to get the

data house in order first. And you know,

there's a lot of reasons for for for use

cases that are, you know, we were

trying, were not working. Maybe give us

an example of the 95% of an AI bet that

either of you had at Databricks, at

Glean, that did not work out and why it

didn't work out.

It's actually an interesting thing you

know, with engineering today is you

build systems and and never never before

have you been in this mode where you

start with a great idea and doesn't seem

like good idea anymore like within 2

weeks, you know, because we see a new

development that happened. So, there are

we have like numerous failures in in

engineering

uh on that front like you know, for

example, some of our fine-tuning work,

building building models uh for specific

use case within our product like you

know, didn't didn't really pan out for

us. And ultimately, the choice was that

you know, we can go with uh already

uh built models whether they are small

open source models uh hosted on

Databricks or or one of the large you

know, foundation models. The but in

internally like you know, from a

corporate you know, use cases

perspective, actually like we you know,

we are also like in in many ways in this

mode where a lot of our work actually

like I would not say like fail, but it

actually takes much longer than you

know, to actually generate success. You

know, there are you know, we are

actually trying to automate a lot of our

business processes internally.

Um and um like for example like you

know, one thing that I want is uh in our

company, I want everybody to actually

know exactly what their top priority for

the week is, what they want to work on.

And and maybe, you know, we want an AI

agent actually first tell them what the

priority should be and we want it all to

all to be documented and we want a

system which actually then, you know,

rolls it all rolls it all up and I get a

view every week where I can actually

quickly see, you know, what are all the

different people working on in the

company and are they aligned is that

aligned with, you know, what I want them

to work on. And and this this is a

simple thing like, you know,

companies have always

tried to actually have this, you know,

as CEOs you always wanted and it's

always hard to make happen and we

thought that AI would simply just, like,

you know, magically do all of this work

because, you know, like it has all the

context and it has all the context

inside the company to make it happen but

I still don't have it. So so so things

do take time

to actually, you know, to Ali's point

like, you know, there is

AI is just one more tool that you have

in the toolkit. It does not

suddenly make building complex

enterprise systems, you know,

you know, like it doesn't make it like

that you can, you know, build it up like

in in in one day. Doesn't it?

>> Yeah.

You know, the last time enterprises got

this excited about a tool was called

RPA. Mhm. And we know how that ended. It

unfortunately fizzled out and, you know,

somebody in the audience yesterday is

like, "Hey, how is this time

different from RPA?" It seems like

the same movie, bigger budgets, better

actors.

What's different this time? How how is

the nature of the architecture of the

technology different from the previous

automation cycle? Either of you. Yeah,

well, I mean I I first of all, RPA like

it didn't take, you know, it didn't

capture my attention at all. So I have

no I actually can't, you know,

>> [laughter]

>> So so so I think I

I think like I I would not compare these

two technologies at all. Like, you know,

you know, what we what we're seeing now

with AI is so fundamental, you know,

it's it's, you know, it's it's the

uh you know, when when we saw it first

it was basically magic.

And and we couldn't believe that this is

a machine

that is doing this work. Machines just

simply cannot do these kind of things,

you know, that we saw them do. Like

writing on their own, um having emotion,

understanding emotion.

Um so it's a um

it's you know, it's it's fundamental,

it's different and and the

Um

>> [clears throat]

>> and and that's why like, you know, I

don't think, you know, we this this

this technology is going to fizzle out.

Um and it it's not like, you know, you

don't have to be like a financial expert

or, you know, you know, like sort of a

deep thinker on business. This is this

is obvious stuff. Like, you know, all of

us know, all of us feel it. All of all

of us can see the capability of this

technology and we know it's special and

it's going to be it's going to be

around.

Yeah. You want to hear my RPA? Please.

You know, I mean it was rule-based

and the problem with it, especially if

you're, you know, you're you want

something that automates what's going on

on your desktop and automate the work

that's happening, it's just that there's

too much unexpected things that happen

and it's just hard and brittle to set it

up. Mhm. It wasn't learning ever. Mhm.

So there was like zero learning. It was

like you tell it exactly where the rules

and if you got something wrong, you you

need to go and go back and expand the

rules. Here, you have something that's

learning. Mhm. Right? So it can it can

improve and it can generalize and it can

understand the patterns and do pattern

recognition. Uh so that's the

fundamental difference between these

two.

>> 100%. Now, um there has been many

startups that have failed in the

generative AI we're going to replace RPA

with generative AI models. There's many

startups that failed actually that I

know of like pretty some high profile

ones.

It's because um the paradigm we live in

today with AI is there's still problems.

The biggest problem is that you bake a

model

and that's where it's learned everything

it needs to learn and then you freeze

it. Mhm. And then you launch it and then

maybe you give it some context but

that's it, it's frozen. So therein lies

the problem that, you know, we need we

need an AI that really can sort of

continue learning while it's using the

desktop and clicking around. So I do

think this problem is hard to to to nail

but I think Arvin is right that it's

like there's no comparison at all. It's

like rule brittle rule-based stuff

versus learning agentic system. Uh I

think it's going to nail it perfectly

but we haven't really nailed computer

use yet. Yeah. Working on it. The number

one shift is this move from

if then else statements Mhm. to a more

generative solution that figures out the

solution. Yeah. Um and so you're trading

breath for for maybe determinism.

Um that seems to be the difference and

you know, there's a lot of CIOs in the

room we've got here and they've got

budgets coming up to plan.

Uh if you were giving advice to them off

like, "Hey, based on everything I know

from my customer base,

here's one thing or two things that

you've got to figure out and and align

incentives on or it could be reliability

problem or org design. What advice would

you have for CIOs who are thinking about

their AI budgets right now?"

Well, spend more.

>> on Glean.

Spend more, yeah, put it on Glean but

the

I I think the

um

Like one thing, you know, which

which is important in AI market today is

that it's very new and there are many

players. In fact, um every every

software company is also an AI company

now. Um you can go and check their

websites.

So so the I think it's it's just hard to

actually figure out where to allocate

those budgets and

what we tell people is that I think the

winners are yet to be identified and and

so

experiment with more vendors, do short

and shorter term contracts um and and

you know, while that's easy to say, uh

it's hard to actually implement because

every product that you try has, you

know, it's a cost that you have to pay

to make it make it sort of even test it.

So so you have to also pick products

that are are easy to test. I mean, those

are the the ones that don't require you

to, you know, spend the next 6 months

trying to implement something and you

have no idea what's going to come out

after that. Like, you know, the products

of today, the products that are built

with the right AI, they should work, you

know,

very very quickly for you.

Crawl, walk, run.

We're going to take a peek into the

future. Shifting gears, you know, one of

the things that keeps investors like me

up in the nights is

a quarter trillion being spent on Nvidia

on on the semi side of things. Assuming

that is just 50% of the capex, you're

spending about half a trillion on capex

and then you've got to earn about a

trillion dollars of AI revenue for all

of this capex to be worth it.

This

and just to put this in context, the

entirety of the software industry earns

about 400 billion dollars of revenue.

This seems like a physics

problem at this point. How do you how do

you think this this plays out? You know,

you've got you've got to make about a

trillion dollars of revenue

to justify this present spend that's

already happening.

How do you think this shakes out? Uh

maybe we start with you, Arvin.

Mhm. Uh wrong person to start with but,

you know, I'm an engineer and I don't I

shouldn't really, you know, think too

much about who's spending what money.

Like, you know, we're here to build our

product and add value. So that's so so

so in some sense, you know, I'm I've not

really thought too much about this

problem. But but if you think about AI,

the you know, AI is not actually, you

know, extending software in a marginal

way.

It's a it's a it's a different product

and in fact, you know, it's actually

going to grab a lot of revenue that

actually today is in services industry

which is 25 times larger than software

industry. So there's there's a lot of

spend that is going to move. I mean,

that the spend that you see happen on AI

is actually sort of, you know, those

service dollars that are converting into

all AI or software dollars.

Um and and I think the but with with

that said, you know,

maybe maybe you have a more informed

view on this.

>> Arvin, do you think that's that's just

to build on? You said, you know, I'm an

engineer and I want to just build

something that's cool. I I do think it's

not binary, right? It's not like, "Okay,

so the physics doesn't work out so the

whole thing will collapse." No, there's

going to be things that work and so it

is a good idea to continue focusing on

the stuff that is obviously already

working. Continue expanding on that. Uh

but I think if you zoom out, I think

there's like three paradigms or three

kind of camps and I put Arvin in the

third camp. I actually put myself also

in the third camp. But let's start with

the first camp. I think the first camp

is this quest for superintelligence camp

and it's

you know, I think all the frontier lab

uh labs are doing this. Like, you know,

all three, four, or five of them,

however you want to count them.

And I think it's really still being a

lot of it comes from the scaling laws

mentality which is whoever has the most

GPUs and the most data is going to win

the quest for superintelligence which is

kind of intelligence that's like on

almost like god-like. It leads to

recursive self-improvement of the AI

which then once you have that, it can

cure cancer and solve all economical

problems and we can probably 10x GDP

over a few years period of time. So what

the hell are you talking about that

there's a physics problem? Like ill

anything any of your cost equations are

going to pale in comparison to the

economic value that this thing is going

to provide. So that's like one camp and

the way they're developing it is bigger

and bigger clusters, more and more

energy and that's how they're going

about it. Um and that's where most of

the capital is going, right?

That's not the kind of capital you're

spending or I'm spending but that's that

camp. Um and then how do they know that

they're succeeding? They're not just

like, "Oh, just trust us." They're, you

know, very smart people working on this.

So the way they're approaching it,

they're saying, "You know, we'll throw

the hardest questions we have at the

whatever AI we have now and if it nails

them and we're making really rapid

progress. So what's your problem? Like

we're like look at math Olympiad. We're

like nailing these math Olympiad

problems

physics Olympiad and programming

contest. It's like better than any human

being. So like that's what they're

they're throwing all the most

intellectually challenging

There's a second camp which are the

people that created the original

technology, the the scientists who

created the technology, got them

the computer science Nobel Prize for it,

called Turing Award and that's, uh you

know, Rich Sutton who created

reinforcement learning which, you know,

a lot of this stuff is built on. You

have Yann LeCun who's one of the three

founding fathers and many others. They

have for many years Actually, I've been

you know, I asked them for years.

They've been saying that that first camp

is not going to That's like not even the

right approach is their view.

They're like, "No, that's just like auto

regressive next token prediction. It's

just probabilistically predicting the

next token. That's not how and usually

they will say that's not how humans

learn. That's not how animals learn. You

know,

we operate in a different way. Your

brain is not that way." And one example

is that you know, even a child learns

very quickly to walk and talk and do

things with very little data compared to

you know, like certainly no child is

reading all of the internet's data four

times over before they learn to speak.

So, I don't So, that's like camp number

two. Those guys by the way, they say

it's 20 years out. Mhm. So, they're

saying, "Hey, it's a physics problem and

it's going to take 20 years to get

there."

Which to me it's like, "I don't know.

Like, leave me alone." Mhm. Uh let me do

research. Third camp, which is I think

what we are in, is I don't think we need

super intelligence. Like, you know, I

don't think we need that super

intelligence right now. Maybe they'll

get there. That's awesome if they do.

But, uh I think we have AGI. I think we

have artificial general intelligence. We

really have it. We absolutely have it.

It's like anyone who says we need to get

to AGI,

that's like it's it's a it's false

premise to start with. We already have

AGI.

Uh I came to the United States in 2009

at UC Berkeley not far away from here

and I was in a AI lab. It was called AMP

Lab. The A was for algorithms and AI,

machines and people. And these are all

AI people. And back then, the definition

of AGI we had, we already have satisfied

that. Like, Mhm.

I know the discussions we had. And I

actually went back to some of those

folks to see like, is it just me or what

was the sentiment back in 2009? And

everybody that I talked to said, "Yeah,

that's by those standards we had AGI,

but we've changed the definition now."

We have those definitions, you know,

ads. So, for 30 40 years we had a

definition of AGI. We've already hit

that.

Now, we're changing it and moving the

goalpost. But, very obviously we already

have AGI. Just use any of these LLMs and

have it do some reasoning.

And certainly it's smarter than many a

lot of friends that you have that,

right? Like, you know, let's let's not

name our coworkers or whatever, right?

Um

so, you already have AGI. Now, now we're

like haggling over exactly how smart is

it. You know, do you have a friend

that's smarter or not? Uh so, if we

already have AGI, we just need to make

it useful inside the enterprise. We need

to just expand that 5% to be 10% 20%

30%. So, that's why I think Arvind's

answer is actually a good answer. Like,

we have the AGI we need. Let us just

focus on solving the actual problems

inside the organizations. And I think we

can already that's that's enough to

automate a lot of the tasks and get huge

economic value out of it. We don't

actually need super intelligence for

that. That's good idea. If the super

intelligence guys nail it, amazing. Then

we've cured cancer. Um if they don't,

hopefully the second camp comes up with

a new thing in the next 20 years. That's

also awesome. We already have whatever

we need. So, Yeah. Yeah. Let us just do

our engineering. Right. Yeah. Yeah.

That's really good framing.

And the way this manifests in the in the

you know, in the world is there's a data

layer. There's the intelligence layer,

which is where camp one is presumably

producing a lot of great models. And

then there's the software layer where

the users engage with.

Where do you think value accrue if you

were to design 100 units of value across

these three layers? The data layer, the

intelligence layer, and the software or

the application layer.

Where do you think value accrues

in the next 5 years?

All right. This is a This is a tough

question. I mean, I [clears throat]

think the all all those three layers

actually are very fundamental. Yeah. I

thought you were going to add a few more

which are not

You didn't.

Yeah, cuz I think I I feel like the like

as as Ali was saying that the models are

going to be available to all of us. You

know, they are going to be commodity.

It's going to hard to sort of

see that it the more spend goes to them

versus you know, these layers on top. Um

the but how do you how do you say like

you know, I it's hard to sort of come up

with you know, where the most value will

be.

Um

and And I also don't know if if actually

changes from today's technology

architecture where again like you know,

you think about in a pre-AI world,

any any sort of like you know,

enterprise

you know, you know, application and data

systems, you know, you have you have

data systems. You you do have I guess

you don't have enough of that

intelligent layer today and then you

have the application layer. So, so I so

I so I I guess you know, some some

dollars will shift into it. And then we

do think that the intelligence layer is

actually going to be pretty thick one.

Maybe you know, it's it'll capture half

of the enterprise value.

Anything to add, Ali? Uh yeah, no. I I I

think that you know, uh yeah, there are

more layers in the stack depending on

how you want to do it. But, um

I think as I said, the the LLMs is a

commodity as Arvind said. You can get

them. Like, you know, but anyway, that's

not That doesn't mean those companies

are not going to be valuable. They can

be very I mean, TSMC is very valuable.

But, um I'm saying they're going to be

kind of like these fab-like companies.

But, uh but they're interchangeable and

we've never seen something like that

ever. I have not during all these People

just switch LLMs

like in one day.

That's not the case with your you know,

your iPhone versus Android or your

Windows versus your Mac or your anything

versus anything. Like, you know,

uh you know, Google Sheets versus Excel

is like huge religious battle inside our

company.

Uh but but LLMs is like you know,

because um it's a commodity as I said.

It just speaks English or any language

you like and you can and it gives you

different answers every time. Might as

well just try the cheaper one, the

cheaper commodity or the slightly

smarter commodity. You can't even really

tell the difference, can you?

Um so, then what is special is the data

that you have. Again, if your company

has data that it has actually collected

that your competitors do not have. Like,

Glean is amazing. But, if you remove all

the data from Glean, it's there's no use

to it, right? So, it's all about the

data that you have. Um and can you

secure the data also?

Uh so, if we're going to have agents

running around accessing this data,

like, "Oh, that's his HR data. Oh,

here's the provost's salary information.

Oops, I blurted out to all of you."

Like, you know, like that's you know, so

how do you lock it down? Uh how do you

make sure that um

the [clears throat] there's governance?

There's you know, there's also a lot of

worry around can What if it's using a

Chinese model? What if it's accessing

this information? What if it's sharing

this information with a competitor? What

if it's interacting? So, the governance

security layer is going to be super

super important. Uh but I do think most

of the value will accrue to the apps.

Yeah. So, it's kind of And I think

that's common sense. It just I just

don't know which apps. Yeah. Uh I do

think Glean is amazing. I don't think

it's

Do you think of it as an app or

I don't know. But,

Now, we see us as both app and a

platform. Yeah. Yes. So, I think it's a

let's call it an app platform. I do

think it's amazing because it has the

potential to automate

uh so much of the overhead inside of an

organization. Like, if you think about

why do organizations have hundreds of

thousands of employees, you know, some

organizations or 50,000 20,000. A lot of

it is the coordination overhead of like

you know, so many people have to

communicate with each other. Hey, what

happened? What did you exactly mean by

this? Let's do a meeting where you

explain to me. I ask some questions.

Let's Oh, let's invite these other guys

also and then write it down and then

just The just the coordination overhead

of organizations is massive. Right? It's

like this n squared problem that you

know, everybody needs to communicate

with everybody and they're communicating

inside their siloed org chart. But, how

do we get it across? So, this like you

know, through docs and Excel sheets and

PowerPoints and meetings is how we like

move companies and organizations

forward. So much of that can be

augmented and be made more efficient

with Glean. So, that's why I think Glean

is amazing.

Um but this is kind of like 2000. Yeah.

And you ask what are the killer apps on

the internet. By the way, back then we

thought it's like Cisco routers, you

know, Yeah. portals maybe with thousands

of links on them. Actually, I was like

in I just started college and

we knew that the future of internet

would be portals, which are these web

pages with a hundred links on it and you

just click click on the right link. This

is before Google search. But, the future

of internet actually didn't look that

way. Ended up being you know, things

like Facebook for friends and things

like Airbnb for rentals and Uber for

your cab industry

and you know,

Twitter and so on. So, those became

great companies. So, I don't know what

those are for the future. Uh they will

pop up. Yeah. And they will be extremely

valuable. But, okay. So, does that mean

that Databricks and Glean

then basically will die and there'll be

a new set of companies? No, back then

there was actually an amazon.com already

in '98. There was already a Google

actually existed already in '98 and so

know, only a $300 company or something

like that, right? So, uh so, it's not

binary. We'll see what happens. I but I

do think there's going to be really a

lot of value will go to the future Yeah.

you know, apps that will emerge.

Speaking Let's double click into that.

Yeah. The $300 companies of today at

that layer, software apps, Salesforce,

ServiceNow.

A lot of talk about software is being

dead. Um Satya calls them the crud apps.

What is the future of this layer that

today is called software that seems to

be heading towards becoming a database?

Um and what do you see the the the the

value accrue to to to those to these

this part of the layer?

Maybe start with you, Arvind.

Yeah, I I I think that's a

oversimplification.

Um like for example, even to say that

Salesforce is

you know, it's just a database.

Um

you know, it's a it's a full sort of

ecosystem of

workflows

and other applications, you know, that

are sort of built on top of that

infrastructure. So, um

So, I sort of like you know, I haven't

really understood this concept of that

you know, you have this

um like a you know, database you know,

where all your enterprise data is and

then um and then people can just go and

create dynamic UI experiences

uh on their own on top of that data. On

it like you know, every business can for

example, just create all the UI by

themselves on this. I don't I don't

think you know, it's going to be

uh happening like that because yes, you

know, AI makes it easy

uh for you to build um you can have a

database and you can build you can just

talk to AI and create a UI and

experience that that

that is you know exactly what you want

it to be.

But most times you actually won't know

what you want. Like you know, I think a

lot of like you know good thing about

software companies is that they actually

think about

how to actually take that data but then

present it in a way let you know make

people interact with it or modify it in

a way which sort of is natural and which

you know drives you know like more

productivity from a human. So so I think

ultimately like a software is an

end-to-end stack in my opinion and all

of these companies, you know, I don't

think they're going away. I don't think

you know they're going to relegated to

becoming a database. Humans you know

over the last 20 years we got addicted

to these screens. We scrunched over the

screens and we would input this

information with their with their keys

with the drop down and hey I met Arvin

today and this is what I learned. It

should really be hey chat met with Arvin

this is what I learned remind me two

days to catch up with him. That that

will happen. I mean that I think it's

going to happen. Yeah, that will happen

in the next couple years and even Glean

you'll you won't be able to want to type

you want to talk to it. But I think the

big thing is data entry. Mhm. How does

the data appear in that database?

>> Mhm.

And that's today

uh not completely automated. So you know

just just for like

uh I I think a company that would be

well positioned to do that would

actually kind of be Zoom.

You know a lot of people don't think

about it that way. But Zoom is really

should be the the the perfect data entry

uh application right? Cuz that's where

you're having all the conversations and

that's where all the information's

coming out. And if they could you know

if it could work with Glean and get

extract the most important information

Yeah. store it all not in like a

structured day table but like store that

information in system of record. Mhm. If

you had that that would be the full

disruption of the SaaS We have

That's that's actually is is is one of

the most common agents these days you

know with Glean which is you take these

meeting recordings you figure out like

you know what you talk to the customer

uh what were the action items and then

the agent goes updates the notes in

Salesforce with that like these these

kind of things are happening already

yeah. Meeting meetings is yeah is uh

you know like we in in Glean we have

this uh

um

policy [clears throat] where we record

every single meeting internal meeting

external meeting if our customers allow

uh cuz there's so much so much

information you know in there. I uh I

joined a meeting last week it was four

humans and uh six AI note takers. Yeah.

I heard about I think yesterday we were

talking about 17 note takers in one of

the you know discussions. This this it

felt like the first you know it's like

the first scene of a movie where where

the AI takes over. Clearly there's a lot

of sprawl. There's there's like almost

too many tools and and consolidation

coming

>> [snorts]

>> at some point. But uh but maybe your

guys's personal workflow you know you

guys are CEOs in the age of AI a lot of

CIOs in the room they've got more jobs

than time on their hands. How are you

using AI for both your personal self

and how are you driving your

organizations they're both large

organizations to to adopt AI and and

benefit from it?

Um maybe give us a glimpse of your

leadership in in the age of AI. Maybe

Ali we start with you this time. Yeah I

mean we have agents for all kind of

stuff that we use you know everything

from you know we have agents that are

really good at understanding our

customers. We have an agent Raffi's that

Raffi's is the name which Raffi. Yeah if

I want to understand anything about any

like you know tell me best customer

story on this. Like you know I told you

about RBC Royal Bank Canada but I can

just ask it I need a use case I'm going

to get on stage I'm going to talk about

finance sector. Give me a use case that

has these it'll like just find you all

the information collected. So it's

really really helpful for me for these

kind of things like when I get on stage

like this.

Uh but also customer if you go into a

customer meeting

uh you know I want to tell customer X

about their biggest competitor Y how

they're using Databricks. Now maybe Y is

not a competitor is not using Databricks

so then I shouldn't use I should use Z

which actually is using Databricks.

Maybe that's like the number two

competitor. How do I get this

information super quickly? All of those

are prepared at Databricks. So on the

go-to-market side a lot of this is being

completely automated and we're using

this.

The marketing stack I already mentioned

is heavily automated already like the a

lot of the tasks that happening in

marketing

so we're seeing that stack

that happening. Um then there's

engineering that's like a whole big

thing like you know that's how we sort

of

and I think there's a whole change

management and how to do it right.

You know initial attempts at automate a

lot of the software engineering at

Databricks kind of failed even there's

nothing wrong with the AI. The problem

is the humans and how we were organized

but that's you know so those are like

the two big orgs Databricks is a big you

know 6,000 person go-to-market org and

3-4,000 person R&D org and then there's

some back office stuff. Those two

already we're seeing heavy automation

using agents for all kind of the task.

Then there's back office so that's

finance and these functions. Finance is

all on Databricks and it's all all the

forecasting all the sort of it's all

moved to machine learning based. But it

took them a long time cuz they had their

Excel models and they're very proud of

them and didn't want to you know but uh

um again there's a change management

there. We actually had external data

science team build the AI models

and then eventually they became good

enough and now

finance has taken those over and like

you know

uh finance has kind of moved from Excel

to Python

largely at Databricks. But it was a

journey cuz you know most of us speak

speak Excel. Similar thing is now

happening to HR

and other departments as well but I

think they're like you know I think in

general HR departments are like

you know even like they're not the

closest to doing this kind of analytical

work with you know Excel and so on. Uh

so maybe that's not quite as far along

but yes it's we're seeing it everywhere.

Yeah.

Anything to add Arvin? Same for us

anything I can share some of my own

personal use

with with it like so I one of our agents

is daily prep agent which I really love

because you know every morning um it

tells me like you know what my day is

going to be what I need to read what I

need to prepare. Like most of the

meetings you know I will have not have

context it actually brings you know like

the plan for those meetings for me. So

that's that's one of my favorite

agents you know that helps me feel more

confident like you know for like how I'm

going to do my meetings in the day.

Um the other one

uh which I which I shared yesterday also

um

the like you know I I've changed my

instinct and I think you know changing

changing instincts you know take take

take take a long time um and you know

when when you're the CEO like you're the

boss and everybody listens to you and

you can just say [snorts] like you know

whenever you have you know a small

question curiously just go and ask

somebody and they're going to like you

know uh put 30 people on the task

actually get that get that answer for me

and this is You're going to have a prep

meeting before the prep meeting before

the meeting.

>> so so all of that so so that so that's

sort of like you know and and so but

it's sort of like for me it was easy. I

just get to ask somebody and and that

you know I changed that because I knew I

was actually causing like you know a lot

of

the the that was very expensive. So the

so today like you know my instinct is to

like whenever I have curiosity whenever

I have questions when I need to do

data analysis when I need to write

something you know my my letter to the

company every month all of those things

you know like fundamentally I use you

know AI of course you know Glean in this

case but to actually help me

do my do my tasks. Yeah.

>> More more I I think the

you have to

you you have to sort of have that

um belief. A lot of people won't do it.

You have you have to have that belief

that AI is a good collaborator. It's not

going to do the work for you but if you

use it you're going to actually produce

better output eventually. Even if you

don't save time you know for the first

you know first few months but you're

actually going to improve the quality of

your output.

Fascinating. Well

this brings me to my favorite part of

this conversation which is rapid fire.

Short answers are fine long answers are

welcome.

Um

Start with the 12 months from now.

Are the big AI companies that we know of

today

up or down? We'll start with OpenAI 12

months from now stocks up or down?

Ali and then Arvin.

Up.

And I'll say revenue will be up. I don't

really understand how stocks work.

>> [laughter]

>> Anthropic.

Ali or Arvin.

Up same. Okay.

Arvin Let me let me talk can I get Yes

[clears throat] of course. Because

ChatGPT is going to continue growing and

it's on fire and it's what everybody

uses.

Uh so is Gemini by the way. And then

Anthropic because more and more you know

coding we've only like eaten into a

small portion of that market it's just

started so.

Is AI in a bubble yes or no?

There is an AI bubble. Uh like saying

like okay so then Glean is also in the

bubble everybody's in the bubble. No I

would I would say there is a bubble. I I

would say those three camps. Yeah. There

is a super intelligence quest camp.

>> Mhm. I would be very worried there.

There's a second the researchers doing

the you know that's definitely not in a

bubble they're like They're sober. Yeah

they're they're super sober nobody cares

about them.

And then there's [laughter]

right? And they're probably the ones

that are right unfortunately. And then

there's the third camp which is us

trying to make this valuable. We're not

in a bubble in a sense that we're not

spending huge amounts of capital on what

we are doing.

We're just trying to get actual economic

value inside of this organization. So I

I don't think it's binary but there is a

bubble. I mean there are startups with

zero revenue worth you know 10 20 30

billion.

That's a bubble.

Yeah.

Same I mean I think the

uh there are quite a few companies where

there's a lot of optimism and valuations

which are well ahead of the business

that those companies have. In like I

guess you can say like you know compared

to non-AI companies like of course AI

companies do have

higher higher higher multiples and um

but I think it you know that sort of

comes from that you know that

there's a good reason for it you know

cuz you know these are AI companies are

going to grow

more than non-AI companies for sure.

Yeah.

My favorite game at Ultimately we ask

our CEOs is a long short game is if you

were to pick a company a product, an

idea that you're long, that you think is

going to be a bigger deal than it is

today, what is that? And then short,

which is, you know, there's more sizzle

than there's steak, more more hype than

reality.

Pick a long, something that you're

really optimistic on. Same order. Ali

and then Arvind.

Mhm. [snorts]

I am very long on agents.

You know, I think I'm very long on

speech. Speech as an interaction. Like I

think keyboards are kind of basically

going to disappear completely. We

haven't actually nailed speech. I know I

know it feels like we have, but we

haven't cuz you're still using your

keyboard. So as long as you're using a

keyboard, we haven't nailed speech. But

I think we're this close to completely

eliminating

keyboards. So I think that's that's a

big one.

What's What would I say? It's like, you

know, I do think coding is a little bit

over hyped. I don't know if I would

short it. It's I mean, I think it's

still the future. So I think that's

that's one of them. I think automating

like customer service and support is a

little bit over hyped. So, you know, I

basically I think the things that the

industry thinks are like amazing and

we've made great progress. We probably

haven't done as progress on it. And then

a lot of the other things that are being

ignored, you know, we're going to have

breakthroughs in those. So.

Fascinating. Yeah.

Yeah, and for for me, I think

the products that are

going to change the paradigm

where instead of you building a product

and you know, expecting people to come

to you,

if you understand

your user, your customer um

very deeply and actually bring the AI to

them. That's the category that I'm

excited about. I want I want I want to

see more proactive proactive AI products

coming coming to the market next year.

Yeah. That that's That's That is what is

going to actually take it from a

5% of the users being power users to

100%. Yeah. Yeah. Yeah.

Your favorite AI tool that you use in

your lives.

I think Glean is awesome. I mean, if

that was not clear.

Let's go.

So he uses it all the time. I actually a

lot of the questions I would ask from

the team. The The thing you said you

changed, I I I first ask Glean and then

see, you know, if it nails it or not.

Then if it doesn't, then I'll

spin up a 30-person team to go spend

[laughter] a week and have three

meetings and all that to get, you know,

the explanation of some simple concept

for me. But usually Glean nails it.

Yeah.

Well, for for me, I'm excited about note

takers.

I've I've used Grain and Otter.ai myself

and Fathom and a few others. But note

taking is actually fascinating. I mean,

I think the I I feel like, you know, if

you if you take those notes and then if

you utilize it the right way, like for

example, what Ali was saying, like, you

know, that becomes the source of what

then actually creates knowledge, saves

data in your systems. That's going to

change, you know, how companies work.

Yeah. You know, in closing, I'd love to

get your vision for your companies.

We'll start with Ali's favorite tool,

Glean.

Congrats, you just announced crossing a

big milestone, $200 million in revenue

run rate.

You've You're signing big deals, $10

million deals. You've got super users,

I'm seeing you're seeing casual users.

Paint us the vision for for Glean from

here to a billion in revenue. I think

we're still doing annual planning, which

also some, you know, AI companies are

telling me that's that's old school. But

but but we're doing it regardless. We're

doing it. That's just because they're

early startups. Like Did you Did you do

annual planning when you started Glean?

No. No. So. So, the But but I think for

us

um

the the thing that I'm most excited

about again is

So we think a lot about AI literacy and

how do you get everybody

along on this journey? And we're not

seeing it right now. Like Glean is a

heavily used product, but but still

there's there's a big variance between

the top users and and and and the ones,

you know, at the bottom.

And and that's what we want to change.

So for the future for us is we want to

be We want Glean to be this um very

personal companion for every person in

every company in the world. um This This

companion with which, you know, is is is

you know, you have a very confidential

relationship with this companion in the

sense that whatever you ask this

companion, you know, whatever

communication you have with them, um you

know, it's it's fully privileged. Nobody

else gets to see it. But this companion

knows everything about you and your work

life. It knows your day, it knows your

week. It knows who are you going to

meet.

Um

you know, in the day-to-day, it knows

your weekly goals. It knows what you

know, what things you're not good at or

what your career ambitions are. And with

all of that, you know, uh this this

personal companion is um

is sort of helping you now with your

work. Um it you know, hopefully takes

majority of your tasks automatically. Um

what you know, works on them before you

ask it to work on them. And and that's

So that's sort of the vision that we're

you know, taking our product to. We have

most of the you know, foundation for

this in place already. Um Today you have

to come to Glean to get most of that

work done. In the future, we want Glean

to actually come to you and do that

work.

Fascinating. Well, we can keep going for

a bit, but I'm being called on time.

Thank you so much for chopping it up

with us. You got a lot of alpha, a lot

of insights here. Really appreciate it.

Thank you. Thank you. Thank you.

Thank you.

All right, gentlemen. Thank you so much.