AI mainet hit a bit of a detour this

week. All over the, you know, three or

four days, AI stocks were down across

the board because of this MIT study that

went viral as well as Sam Alman's

comments about a bubble and Zuck

instituting a hiring freeze in AI after

going on a complete blitzkrieg. So,

let's get into it. Act one, Monday,

Fortune dug up a generative AI study

that MIT published last week or last

month, I should say. In that study, MIT

found that 95% of Geni pilots are

failing to make it to production because

of employee resistance, poor quality

output, and the most interesting problem

seems to be resource misallocation.

According to the study, 70% of JN AI

budgets are going towards things like

building sales and marketing tools,

which have poor ROI. The highest ROI was

found in back office optimization

like automating tasks that cut back

spends you know on various departments.

Basically

these pilots aren't working. Shamath is

what the study found they evaluated 300

AI implementations and interviewed 150

leaders across 52 companies. You've been

grinding it out with your own software

company now called 8090.

Does this align with what you're seeing

on the field, Jamal?

>> I think what I would tell you is that I

think the first wave was just a lot of

boards who read the words AI somewhere

in an article and then went to a board

meeting and turned to the CEO and said,

"What's your AI strategy?"

>> Mhm.

>> And then the CEO turns around and sends

that down into their or eventually hits

the CTO's desk. And I think the first

wave is mostly people just spending

money because they had large existing

budgets. And so they were like, let's

just go and try a bunch of different

things. And I think now we're going

through the sorting function of

realizing that there's a big difference

between probabilistic software and

deterministic software. That's probably

the biggest reason why you're seeing so

many failure modes in sales and

marketing. It's very hard to codify

sales and marketing into a set of

heristics that never change. But back

office processes,

why they're so good and a great target

for AI is ultimately you have so many

people that have been hired to deal with

edge cases, right? I think like that's

what people do in most companies is

they're in charge of a process and

they're they're dealing with edge cases.

And I think that you can get extremely

high rates of accuracy if you implement

AI correctly in back office tasks.

I think the real question is like what

happens to all of this revenue that has

been generated. You're seeing companies

generating $50 million of ARR in a

matter of months and then raising huge

rounds. I think what we haven't seen is

whether there'll be any sort of either

logo churn or dollar churn as new

companies come in with even cheaper

solutions. the foundational models move

up the stack and just absorb capability

or things just don't work and they get

abandoned. All of that churn happened in

social. I remember when I was in the

middle of helping build Facebook, we

went through that whole cycle. There was

seven or 8,000 social companies

>> and within six years there was five of

us left. It happened in SAS when I was

investing in SAS. There's a couple of

very early and important successes like

Yammer which Sax started which I was

very lucky to be an investor of but then

it took many years for the handful of

winners to get really sorted out and I

suspect we're about to go through that

same cycle in AI. So I think that

article basically paints a very accurate

picture. There's been a lot of triing

and experimentation.

We now need to go through a sorting and

a cleansing and then we'll rebuild from

first principles around the

>> and not surprising

not surprising to our sultan of SAS

David Sachs because the sales and

marketing departments they they're very

promiscuous when it comes to new tools

getting a great lead closing a sale you

can directly connect it so we always see

them test stuff out doesn't surprise me

that we'd see sales and marketing go

after this first but what do you think

about the brittleleness of this revenue

the churn sachs. Are we going to see a

lot of these companies rocket up to 100

and come back down to 50? Is this

something you're seeing uh or your your

firm which I don't know your status at

the firm? Maybe you could tell us how

how that's working out in terms of your

intelligence there. But what is craft

seeing on the field there?

>> I think we're seeing a lot of

interesting AI applications being

developed, but it's still very early

days. And I think that over the past

week or so, there was a correction in

sentiment towards AI, but I think it was

a healthy correction. I don't think this

was the beginning of a bus cycle or

something like that. I I still think

that we're in a boom. I still think

we're in a investment super cycle, but I

think there was a healthy dose of

skepticism applied to some of the more

fantastical claims that have been made

about AI. And I think this is why you

saw there was like roughly what, like a

10% correction in public AI stocks. And

there was that MIT report that said that

95% of projects and companies are are

not making it to production yet and so

forth and so on. So I feel like we're

getting in the weeds a little bit here

and what we should be talking about is

just sort of where we are in this um in

this AI super cycle.

>> And where do you perceive us at? We're

in the experimentation phase. We're in

the pilot phase. But this issue around

probabilistic versus deterministic makes

it hard to trust the software. Is that

what you think the key issue is? Well,

let me tell you why I think that this

correction is actually healthy is that

after chatbt launched at the end of 2022

and then throughout 2023, the dominant

narrative in AI is that AGI was just two

to three years away and everyone kind of

had their own definition of AGI was, but

it was kind of this idea of smarter than

human super intelligence and kind of

magic AI. AI would be able to do

everything. And as a result of that, you

kind of had both utopian and dystopian

narratives really proliferated. And so,

you know, you started getting this like

job loss narrative that within a few

years, 50% of knowledge workers would be

out of jobs. You got this rapid takeoff

narrative that basically the leading AI

models would be able to turn their

intelligence towards improving

themselves towards recursive

self-improvement and therefore within a

couple of years the leading models would

basically achieve super intelligence and

leave everyone else in the dust and then

capture all the value of humanity and

then based on that narrative which again

it was the same underlying narrative

that fueled both utopian and and

dystopian or doomer takes on AI. You

got, I think, a huge backlash which has

already been forming where you have a

thousand bills running through state

legislatores right now and you have all

this AI safety legislation. You got

bills like in California the SB 1047

which would have applied tremendous

amount of of new regulation to AI. So

you saw this policy backlash happen as

well and it was all based on these

fantastical and kind of magic views of

what AI was going to do in just the next

2 to 3 years. And I think that the

reason why this recent skepticism is

healthy is because I think it's

rebutting all of that and it's showing

that, you know, AI is a powerful tool. I

mean, I I definitely think it's a new

and important form of computing and it

is going to unlock tremendous value in

the economy, but it's going to take us a

while to get there. I mean, you can't

just tell the AI, you know, be a sales

rep, be a customer service rep, and kind

of throw it over the wall and expect

that it's going to replace a human. It

takes a lot of prompting and iteration

and validation to make the AI work to

make it generate business value. And if

we were on a path towards rapid takeoff,

then what you would see is that the

leading AI models would be increasing

the distance between like the top one or

two models would be increasing the

distance between you know the rest of

the models. And instead what we're

seeing is a clustering of model

performance around the same performance

bench.

>> It's incremental, right? They're

incrementally

>> the progress to be a little bit more

incremental. It's more evolutionary

rather than revolutionary. And I I think

this really crystallized around the

launch of chat GPT5 where a lot of

people were expecting GPT5 to be this

huge breakthrough. Sam Alman was sort of

teasing this concept by posting photos

of the Death Star that the idea that

this model was going to blow everybody

else away and the reviews ended up being

very mixed and then we saw that on the

performance evaluations.

>> It's not that the model didn't represent

progress, it just fell short of these

lofty expectations that have been

created. So Freeberg, let me get you in

on this

>> just to sorry I've been kind of

longwinded here, but just let me just

sum this up, which is please I think

that what people can now see is that

we're not in like a a loop of recursive

self-improvement. We're seeing that

there are a handful of of great model

companies, but the development of this

technology is going to be a more normal

technology race. It's not like the

leading players just all of a sudden

going to achieve AGI just very quickly.

And as a result of that, I I think

because it is a more normal technology

race, I think we can apply a more normal

logic to it from both an investment and

a policy standpoint. And I think that a

lot of the narratives that were hyped up

about imminent doom or imminent utopia,

depending on what side you were on, were

just massively overhyped. And this is

why I think it's just a very healthy