# Enterprise AI Context Gap and Cybersecurity Automation

**Podcast:** a16z Podcast
**Published:** 2026-09-18

## Transcript

As a business leader, there's a tragedy of the commons.
If you want to stop, if you want to go slower, why don't you go slower?
Like, I'm competing.
I want to win.
There's almost two camps.
There's one camp which believes that this actually is an engineering problem.
And there's others which actually believe you have to slow it down.
Humans don't respond fast enough to the attacks that are happening.
You need to automate all of those.
And most organizations are actually not supposed to doing that.
Is RSI and recursive self-improvement that the labs are doing leading us there?
That's the big question.
Something Elon said, this is some elaborate 4D chess, because on the one hand, you're saying all of humanity will die.
On the other hand, you're saying, hey, what do you want for your IPO allocation?
For the first time ever, a company at scale last week said that they're moving from the frontier models to GLM.
Do you think that that's a trend or do you think that's just like a one-off anecdote?
AI may already be smart enough for the enterprise.
The problem is that it doesn't understand your company.
Databricks CEO Ali Godzi.
joins A16Z's Martin Cassado and Sarah Wei to discuss what's actually holding back AI adoption and why the answer may have less to do with building smarter models and more to do with giving them the right context.
They also debate the push to pace frontier AI, recursive self-improvement, and where the risks are real today.
Ali argues that superintelligence remains far from what we currently see, while cybersecurity is an immediate problem as agents make attacks faster and harder for human security teams to keep.
Thank you for being here, Ali.
Super excited.
So we obviously want to get to Databricks, but there is a broader conversation going on right now about AI.
And Dario's weighed in, Jakob's weighed in, Elon's weighed in.
But we want to hear what Ali Goetze thinks.
In terms of, you know, if you call the topic broadly speaking, pacing the frontier, et cetera.
What is your strongest agreement with what's being out there?
Where do you disagree?
And maybe where is there nuance that's not being captured?
Yeah, happy to cover it.
And me and Martine argue a lot, so I'm sure that's not going to take long.
I'll try and rein it in this time.
try to stay calm.
But, well, I do think first and foremost that there, maybe we agree on this, that leaders have responsibility to not freak people out unnecessarily unless there's really, really good reason.
And I think, you know, there's always different people in society that are in different places, you know, in their mind space.
So, you know, talking about these kind of existential risks and, you know, scenarios where all of humanity is going to be wiped out, I think...
It's irresponsible.
It can tip a lot of people over and it can cause a lot of mental health issues.
Unless you have something that's going to wipe people out.
Yeah, as I said, yeah.
If there is an actual reason for it, then, you know, that's a different story.
But I think that right now, the existential risk is close to zero.
So why freak everybody out?
It's not actually needed.
There are risks.
We'll get into it.
That's probably where we disagree.
But first and foremost, I think that leaders should not freak everyone out.
You know, if there's like technical nuances in how we're doing AI research and so on, well, researchers can discuss that.
You don't need to every time go on TV and or blast on Twitter to millions of people that, hey, you know, I think there's like this percentage, 10% risk that all humanity is going to be wiped out.
I don't think that's like helpful for a lot of people.
Actually, I think causes a lot of harm for a lot of folks who get stressed out and actually are not in the nuances of all of this stuff and what it means.
So that I don't think we should do.
I don't think it's fruitful.
It doesn't really help anyone.
I mean, I think this is very, very true for the general public.
Like my sister, who's great, who's a school teacher in rural Arizona, on Sunday texting me and said, Martine, should I prepare the cabin for, she's kind of a prepper anyways, but should I prepare the cabin for the AI apocalypse?
You know, I've got water set up.
Like, when are you showing up?
I'm like, hold on.
Like, we're not there.
So clearly this has kind of spilled over the populace, which I agree is unnecessary and has blowback.
I think there's a second one, which is, I don't know if you saw, like, walking in here, I was checking X, and Elizabeth Warren just talked about pausing all of AI development.
That, of course, is on the coattails of Bernie, who is also working with Bannon, like Steve Bannon.
So now, in addition to, like, you know, just scaring people...
The federal complex is now spinning up.
And I think that could be actually quite contrary to the actual goals of the message.
And so there's more than just, you know, I think public hysteria at stake here.
Yeah, there's a lot of politics going on, but I'm like in all of these groups and, you know, I see both sides.
There's heavy politics happening on both sides, we should say.
Like, right?
This is happening on both sides.
No, this is a, I think both parties.
that do not include Trump himself, agree that AI should be constrained at some level.
I'm talking about the other side of this argument as well.
Let me give you an example.
Even Greg Abbott, right?
Even Greg Abbott was like, you know, you can't have data centers in Texas.
Well, I'm not talking about politicians.
I'm talking about there's politics going on on both sides, right?
There is politics on the business side, people who want to see great IPOs and they want to get returns on their investments.
And they're like, don't mess up my IPO.
And they want to get like, hey, can't everybody just shut up so that we can get our money back?
So there's that.
And they're, you know, they have resources and they're using them.
And, you know, so there's politics on that side.
And those are like not, they're not sitting quietly and not doing anything.
And they can pull strings and they have connections.
On the other side, there's all the people that are like, okay, how do we weaponize this?
This is awesome.
This guy tweeted this.
You know, let's like, let's weaponize this one.
Let's plant this.
If I, you know, let's pump these, you know, threads.
Let's talk to the specific leverage point.
that everybody is using.
Because I actually think that this is like a classic case of a PR misstep.
And it's not just the doomy, gloomy type stuff.
So here's the PR misstep, I think, which is, like, it is not unusual for industries to try and regulate themselves.
It's just not, right?
And I think saying, like, security and safety is important.
It is with every tech epoch, and we want to have some oversight.
That was very, very sensible.
The problem is just couching this notion of pacing.
And there's a number of issues with pacing.
First off, It's orthogonal to safety and security.
Like, you can slowly build a weapon.
Like, that's not different than building a weapon.
People don't feel it's genuine because, like, these companies have been at a dead run.
They're still buying more compute to be even faster.
No, no, no.
I mean, like, they just haven't done it.
They haven't done it historically.
But also, like, it kind of feels like this kind of almost.
milk toast capitulation to the pause people.
So you're like, well, you say pause, well, I say pacing, which is almost like pause, but it's not like pause.
So like they chose this kind of like flag to follow around pacing.
But if you actually read, did you read the document?
It's a totally sensible document.
I read it, yeah.
It just has nothing to do with pacing, right?
And so I honestly- No, he doesn't mention it.
Look, I can a little bit disagree.
Look, there's a tragedy of the commons.
There's this like, hey, if you want to stop- If you want to go slower, why don't you go slower?
Why do you write articles?
There's a lot of people making that argument.
But no, I mean, as a business leader, I understand that.
There's a tragedy of the commons.
Like, I'm competing.
I want to win, you know, and you're running ahead.
There's also the market equilibrium, which suggests that pacing is probably impractical anyways.
Yeah, so I'm just saying that, you know, so it makes kind of sense for people to say, hey, if you guys don't stop this, this tragedy of the commons is going to continue.
I'm not going to stop racing because, you know.
there is ipos at stake there is a competition at stake there's also some animosity between the people so like i'm not going to stop unilaterally i'll be a sucker you know you why don't you stop first you know so then they're saying hey can you come in and stop us um but you know i think that uh you could also make the argument that if you look at the hugging face opening incident that uh but i think these companies are great and i think they are probably investing a lot of resources but it's very clear from if you read what happened is that uh there weren't monitoring every token coming out and having it, you know, they were just like running these RL experiments and then after the fact coming in and checking out what happened.
So they should have paced.
They should have been much slower in that particular incident, right?
I just don't want to just...
quibble on syntax, but like words matter with PR, right?
So let's take the hugging face incident.
When I read that, you know what my reaction was?
Was not, oh, OpenAI should paste.
It was like, dude, fucking secure your thing, right?
Like do security controls like we always have done.
It is pacing though.
It is pacing.
It's not pacing.
In the history of the internet, we had all of these things.
We're like, let's pace the growth of the internet.
It is pacing.
Let's do security.
Let's do control.
Let's do like whatever.
You should do that.
But it is pacing in the sense that, look, I face this all the time.
I have a legal department at Databricks.
I have a security department at Databricks.
And, you know, they're always like, hey, slow everything down for everything, not AI.
Like literally every little thing.
Like, oh, you're going to go on a podcast.
Well, what's the script for it?
What are you going to say?
And let's review that.
And, you know, what's illegal?
You cannot say this, you can say that, you can say that.
You know, everything you say has to be materially true.
Are these pacing people in this room with us right now?
So, you know, so you're running an RL experiment, you're training the next model.
Should the security team be there and run all their monitors and look at everything?
I mean, millions of hours of GPU hours of tokens were produced and these agents were running a mock in the sandboxes.
It would have slowed them down significantly if you had security team sit there and look at all the stuff.
I agree.
And they're saying, hey, if we do that, it'll slow us down.
And I'm not sure the other side is doing that.
So can you guys come in and slow us down?
Just tell us.
Put some guardrails around us.
We'll happily then follow the rules and do the secure thing.
Otherwise, it doesn't make sense because we'll get our butts kicked.
I just think like nuanced second order words don't work when like people are really afraid.
You're like, I'm going to pace and therefore things like these don't happen.
I literally think we should have just been like safety and security is paramount.
We're going to put in these controls.
Like that's the important thing.
And I do think that nuance actually got lost.
Kind of what Zuck said.
I thought Zuck did it.
He was like, hey, we're going to pace ourselves.
We're going to put in security.
Like, the reason we released this later is because of security.
Well, what I thought was so great about Zuck is, like, he was very focused on, like, security, safety, and self-regulation.
Dario's first five words or whatever, like, we need to pace the frontier, right?
It just puts you in a very different mindset than what he could have said is, we need to secure the frontier.
Fine.
We need safety.
I mean, like, at some level, I think they were trying to optimize both for the doomers, which caused for pause, and for politicians, and they kind of didn't satisfy either.
But don't you think people are actually freaking out inside the labs?
And there are a lot of safety people that are freaking out genuinely.
By the way, and not all of them are EA people and so on.
People are like, hey, they're surprised, right?
Right.
But here's the thing is, like, using the worst pace doesn't help either of them.
I think it's like, literally, you're like, you're trying to find this.
Pace is like the uncanny valley of making the Doomer people unhappy and the policy people unhappy.
Because the Doomer people are like, that's not a pause, this is pacing.
And, you know, everybody else is like, well, like, this is, you know, this isn't real, you're not going to do it anyways.
And you're not focused on security.
So, again, independent of what we should do, which we should talk about, I just think that the way it was presented was just bad and it just didn't work.
And that's what we're having to blow back.
These guys are, you know, they're not trained.
you know, PR people, you know, and yes, some of the stuff, I agree, I agree with, I mean, I agree with the, I agree with the core premise that we shouldn't freak the public out.
I think that existential risk right now is close to zero, you know, but let's talk about the core thing, which is the fact that, you know, anyone who's doing big reinforcement learning runs and they're giving it a reward function, unleashing, you know, saying, hey, here's like, you know, 10,000 agents and here's $100 million.
Let's put them in parallel and let them run on a gigantic cluster for a month or two.
Try to solve anything.
And it doesn't need to be a security thing.
It could be like, do anything.
You know, solve this math puzzle.
Really bad things can happen.
Really bad things, meaning things get hacked.
And it has, you know, cyber is the primary one.
Right?
That is real, right?
I think of this making it, hey, this is an existential risk and so on, which I think was a mistake.
I think it's not good.
to scare the public that way.
I think it's become something that everyone, not just your sister, everybody around the planet is like now talking about.
I've had all kinds of people that never care about this stuff and they find this extremely boring.
Ping me and say, what do you really actually think about this?
This is really important.
Now I'm starting to worry about it.
So then it becomes a political issue and we have elections here coming up, but there's elections all around the world.
So you're going to see they're not going to sit still in other parts of the world either.
But I think that's our responsibility to talk about this in a balanced way and actually expose the risks.
I think that superintelligence, that idea from that book is very, very far away.
I don't see any evidence that we're actually marching towards that or that's going to happen.
Apparently some people, yeah, apparently some people at the labs are freaked out that maybe there's progress towards that.
And I think it comes from RSI, recursive self-improvement, the models improving themselves.
I would love to understand how much, have they seen something we don't know?
There's, you know, kind of four criterias.
If those four things are happening, I would love to understand them.
One is, if we end up in a situation where the following four conditions are happening, which is the next model requires less resources, less GPUs to train.
And, you know, super linearly, not just like tiny little bit.
The next model, you know, takes less time to train as well.
So the second condition.
Third, accuracy of the model, the intelligence is increasing.
And fourth, we can do the former three again and again and again.
You know, it's not just...
All those at the same time, right?
All at the same time, not just any of them.
Yeah, all four.
If all four are happening, then you can imagine in a way where you can, you know, because any of them does not happen.
Like, for instance, if resources is constant, then that's okay because we're going to run out of hardware.
So then it will pace itself.
Like, we will not have enough hardware to do that.
Not enough GPUs, right?
Time, the same.
So it needs to be that you end up in this situation.
So if you just mean that the software is writing itself...
we're already there today.
Like 90 some percent of the software in Databricks is written by AI.
Does it matter if the last few percent is also written by AI?
No, it doesn't matter really that much.
But if you're getting these four conditions, then you might get a speed up where the next model, let's say, takes half a month of time and half the resources.
And it is more intelligent.
And you keep doing that, you know.
then you might end up in a situation where I don't know.
By the way, I don't even know if that necessarily leads you to superintelligence per se.
But it could, so then that would be more risky.
So it would be nice if they can share all that data and we can shine some light and transparency on that.
I actually think it's a great breakdown that you have.
I don't think anyone is using that as a definition, actually, right?
I think there is a little bit of people freaking out about like, oh my God, emergent behavior, now it's creating itself and so on.
But I think, like, as I said, a lot of people, their definition is just, hey, if I'm not even coding anymore and it's coding itself.
Right.
But I think they're conflating, hey, what's my value and is it scary for me?
Versus, hey, that then means we'll get that super intelligence that 2014 theoretically was hypothesized by Botstrom.
Well, your point in compute, though, is a really good one that's missed in, I think, a lot of arguments on RSI, right?
Because as far as we can tell, the minimum threshold for compute needed to train a good model just keeps going up.
Like it was 100 million, billion, now it's probably like 5 billion.
To train a model now?
To train like a frontier model, right?
Five to ten billion.
Right, right, exactly.
Million or billion?
Billion.
Billion.
Billion, yeah.
Versus your second point.
The frontier is very expensive.
Yeah, frontier is very expensive.
To replicate the frontier six months later is about one twentieth the cost.
No, I think Sarah has a great point, which is, this is a good argument against this whole thing, which is that the next model, first of all, there's only one or two such runs a year that each of these labs do.
And it's the opposite of the four criteria that I mentioned, right?
Which is, it's going to take more resources, more humans involved, and it's even more brittle.
And they have to build out the data centers.
I mean, like the labs are not necessarily doing that, but others have to build the data centers and they have to be gigantic and they have to get the GPUs and they have to get the networking right.
They have to do the engineering to make sure that they can tolerate.
Because, you know, every order of magnitude, more GPUs are crammed in there.
You have to now worry about errors that before you didn't have to worry about.
So you have to increase robustness.
So it's like a very brittle process.
And if it fails, you've squandered so much money.
So they're like very, very careful with that run.
And there'll be multiple runs that have been watched.
So it's the opposite of that, that, hey, the next model is faster, cheaper, smarter, and recursive improvement.
It's the opposite.
It's like it's taking longer, and it's more brittle, and it's more people, and it's harder to pull off.
So I do think that that is true.
With respect to RSI.
With respect to actually cyber risks and things getting hacked, we need to take it super seriously, yeah.
So I'm going to have like another mis-test here.
It's like, I actually love your four criteria.
I was like literally just waiting to argue with it, but I actually think it's very good.
So let me give you like a black box.
When you're dealing with these dynamic adaptive systems, what are you going to believe?
Are you going to believe the numbers are your lying eyes?
I think you kind of have to go to the numbers on these ones.
What are the numbers to look at?
I really think you should just basically, and maybe going public is the right way to do it.
If these companies continue to grow, reduce the number of people and the number of money that goes into them.
then I would say something is definitely happening here.
Like, I do think that, like, you can actually black box this and take a look, but none of those indicate, like, they're hiring like crazy.
But that's not fair.
That's not fair, because, you know, companies are not necessarily efficient, right?
So, like, what if you have, I mean, OpenAI itself was doing, like, a million different activities.
A very small team of, like, 10 people were doing LLMs, and the LLM stuff was useful.
Twitter, there was a lot of people.
Now it's much less people.
I agree.
Just another litmus test.
We have two litmus tests.
We have your litmus test, which I think is great, but then you would actually have to have a way to instrument it.
And then we should have the black box litmus test.
I mean, listen, if Anthropic in two weeks is, you know, 12 people and they continue to grow and they're putting out models at an increasing rate, I think we should probably take notice of that.
That's sufficient criteria, but it's not a necessary condition, right?
I'm just saying that, you know, there could be that, you know, and really the right way to do this then to look at, okay, the pre-training and the post-training that's been doing that.
really necessary because they have so much resources that they might be doing a lot of other stuff they don't need to do but they're doing it just and they can just hire the people because they have infinite money and infinite so really the people that are training the next model is that team tiny tiny and it's actually getting reduced and they're doing less and less work and just the ai is doing it and then post training and then they're all just using less dpus That's not the case.
We've had this argument many times as an industry before.
I remember when we learned how to really cluster computers.
Because the mainframe was actually kind of limited by things like memory coherence.
Remember that?
You can only make it so big.
And then we kind of went to the client server area and then we didn't have that problem.
And then we started creating supercomputers, which were basically just clustered computers.
And at some point the internet happened.
And that was kind of also roughly like when GPUs started getting good.
And do you remember that we would actually like export controlled PlayStations?
Because we were worried that Saddam Hussein would use them to do simulation.
And the arguments were very similar, which is like...
These things are getting infinitely powerful.
We're using them to simulate nuclear weapons, which we were, like I was.
Like we can't, you know, this stuff has existential risk.
Actually, they didn't use those words, but like this has the potential for like nuclear weapons or whatever, and we should stop it.
And like none of that came to path.
So I think a very reasonable discussion is, is this time different?
Yes or no?
I don't have an answer to that.
But I'm a VC, but you know, you're a...
Yeah, I mean, look, I think I was, I'm not old enough to remember.
So ignorance is bliss.
So I can take this.
I don't recall PlayStation being illegal and Saddam Hussein being, I just don't know.
Maybe I'm just ignorant.
This is like 1999.
Maybe I'm old and ignorant.
Maybe they didn't care in Sweden.
Maybe it's just amnesia from age.
But whatever it is.
Sweden doesn't care about the expert controls in the US.
Yeah, you know, whatever it is, I think it's at a different scale now, right?
With AI and, you know, with, you know, what we're doing.
uh the pace of development and so on uh they are freaking out the frontier i do think cyber is actually one of the biggest ones that we're going to see right because um it's there's just so much infrastructure on the planet by the way way way more than it was whenever whatever saddam or xbox or whatever it was you're talking about i mean like we've just interconnected way more things and they're dependent and like the planet just looks different today from uh internet tech dependency interconnection um than 30 years ago.
So I just want to make this point.
There's so much infrastructure that's insecure, right?
And if you're going to unleash these agents, they're going to find loopholes, they're going to find exploits, they're going to break in here and there.
So this is a real risk.
And by the way, this time it didn't do that, but you could imagine a scenario also where it starts hopping.
Like it takes resources and it starts executing itself elsewhere.
So it's kind of spread like a virus a little bit.
That's a real risk.
And this is pure curiosity.
I promise I'm not trying to be a foil here.
But like, why do you think we just haven't seen very much then?
Like, again, again, I'm much older than you.
I remember very well.
So when the internet came out, by this point, we had literally taken out 10%.
We disabled hospitals.
We'd taken out critical infrastructure.
tens of billions of dollars in economic damages from worms.
Like, all of that had already happened.
And to your point, we had much less build-out, you know, less of the economy was on it.
And so, you know, AI has so many people that want to find risks and threats.
We're running so fast, so much money has been poured into it, and we haven't seen anything commensurate with the early days of worms.
What does that disconnect?
Yeah.
Look, so I do remember that, those days.
At the same time.
Yeah.
So look, I would just say that I am sleeping well at night and I don't think there's existential risk right now.
I do think there's a lot of infrastructure that needs to be secured.
We have a product in the market, in the detection market, Lakewatch, that helps you do detections.
And the space just there is moving so fast.
Because, you know, you used to have the SOC team, security operations center people, that would look at...
What intrusions are happening?
How are we being attacked?
And so on.
And now the humans just can't keep up.
So this is the whole space, security cyberspace is being transitioned into fully automated, using agents for detection on the other side.
If we don't do that, I mean, now we're rushing.
We are rushing.
The industry is rushing to do that super, super fast.
If we don't do that, I do think you will start seeing those kind of things, like sites going down, you know.
whole systems that stop working for a while and there will be consequences, not existential, but economic damage and, you know, people getting hurt and so on could happen.
So we just have to race very, very fast to do all of those things.
It's just, you don't have the, the humans don't respond fast enough to the attacks that are happening.
So it just, you need to automate all of those.
And most organizations are actually not close to doing that.
The banks are doing it.
Some of the people that are super security conscious are doing it, but most of the industry today.
is running with old school security operation centers and people that are waking up every day and there's like hundreds of emails of detections that have fired.
Many of them are just false positives.
So you don't need to, you can ignore them, but some of them are not.
They just don't have time to go through those.
And you need to identify that.
You need to have threat hunting that's automated where you're actually attacking your own systems automatically with agents and so on.
It hasn't happened.
So I do think like if we just say, hey, this is just like the internet in the early days.
bad things are going to happen.
So there is a race going on.
I was actually very surprised.
Earlier this morning I was on a conversation.
I feel like you and I are pretty close.
I feel like I know a fair bit about Databricks.
I was on a call this morning where a founder was basically like, yeah, listen, we're doing all of this observability, agent threat detection, and we're using Databricks.
I didn't even know that you...
You had this offering, like, quite frankly.
So, like, I mean, this is just from an education standpoint.
Like, how extensive have you gotten in, like, the agent AI observability security safety thing?
Yeah, I mean, we gave a talk this year at RSA, actually with Ben Horowitz.
But the issue is that data and AI is blending with cyber.
These two markets are...
Collapsing.
And the reason they're collapsing is that it used to be like, okay, we have like data and AI, the kind of stuff Databricks and these kind of companies used to do, which is like, okay, you have a bunch of data and you run AI and machine learning and that just lives separately.
And then you have the cyber world.
Cyber world is, you know, we want to detect if something bad, if like bad people are trying to hack us, if bad people are doing things, we need to detect that.
Okay.
But now on the data and AI side, we have agents running internally in the company, people are having agents running.
And the agents are also like doing things with other people's agents.
And they're producing a lot of data, logs, trails, you know, fingerprints that are being left.
And so, you know, then now we have internally these agents that are doing that.
So these worlds start merging more and more, which is like, okay, well, all the data that's being produced needs to be analyzed.
And the scale at which you need to do that is just like many, many orders of magnitude more than just one or two years ago.
So things have changed dramatically.
Like 2018, 19, the time it would take from...
you know, a CVE vulnerability being sort of published until you see it actually weaponized in the industry would be like two, three years.
That went down to, you know, 2022 significantly, but it was still like eight, nine months.
So that's kind of fun.
You have eight, nine months from a vulnerability to that was 2022.
Now, if you look at this curve from 2022 until now, now it's down to like basically hours.
So it's like down to like basically no time.
Like things get immediately weaponized.
So you need to just do it in an automated with the data and AI sort of platform approach.
So these markets, I'm going to argue, are just going to collapse actually.
So it's a very specific question.
I actually think a lot of like to pull back on like the existential, ex-risk discussion, it feels like there's almost two camps.
There's one camp which believes that this actually is an engineering problem and like companies like...
Yeah.
Like Databricks can solve it and they can solve it through product and through engineering solutions and through services.
And so like we just as an industry need to solve that problem.
Yeah.
And there's others which actually believe, seems to me that there is no engineering solution.
You have to, you know, slow it down.
You know, you have to use regulation.
It's more like a nuclear weapon, et cetera.
So like, does this mean you believe it is an engineering problem?
Or are you not quite comfortable saying that yet?
Because why would you even buy Databricks, man?
Let's just put this stuff in a national lab.
Just pace it is the only solution.
I'm just kidding.
Everybody just pastes themselves a little bit, then we'll be fine.
The bad guys too.
There is no line of inquiry ever that gets to pacing, I don't think.
I think it's like you pause it or like you solve it.
What we've learned here is that Martin really hates the word pacing.
I will never use that word with you ever again.
Okay.
Clearly.
Duly noted.
Yeah.
Is it an engineering problem that can be solved by engineers or is there more to it?
I actually think which problem are we talking about?
There's two separate problems that I think are being conflated.
There is the super intelligence problem.
Yeah.
You know, and I think a lot of this comes from like Bodstrom 2014 superintelligence book.
And if you look at the definitions, like I think people don't have these clear definitions of what superintelligence, if you read his book, those definitions are kind of crazy.
So I think what he had in mind when he said superintelligence is, you know, AIs that, I don't know, I don't know what the examples were.
Something like they write a whole PhD thesis with novel, like peer-reviewed stuff in a couple seconds.
And they can do like millennia worth of thought, you know, in like instantaneously.
And, you know, so this is like the level of, you know, how fast they are, how intelligent they are.
They can learn.
Yeah, yeah.
It's just, yeah, I mean, yeah, but it's just many, many, many orders of magnitude, right?
It's like, it's just the scale of the problem is just completely different.
So if such a thing exists, do I think that's just an engineering problem to solve?
No, I think that's actually very, if such a thing would happen, that would be very existential.
Of course.
And that's what everybody agrees on.
So I think that's being mixed with, now we have agents that are nowhere near that.
It's not even like, there's nothing like that, and we don't have anything towards that path right now.
But these agents are capable, and you can do something with them that you could never do before in a history of mankind.
So I do think an inflection point has happened, something has changed, which is, we have good security researchers at Databricks, but I could never say, let's get 10,000 of them in a sandbox for a month and have them do $100 million worth of salary wage work.
We can do that now.
We just turn on a button and we can get 100,000 of them.
Or mathematics.
Like we can say, hey, you know, we want to solve a conjecture.
Okay, let's get pretty good mathematicians, but let's have 10,000 of them collaborate, you know, and then you can like make very fast progress.
So this, I think, this leads to all these cyber risks.
I think cyber is the major problem here.
This is, I think, you can solve with engineering.
And I think it's like we are working on it.
Many others are working on it.
There's still risks.
They're not existential.
I think we should do it.
There is the superintelligence thing.
That's the thing that could do, write a novel PhD thesis or like reason intuitively in 11-dimensional space physics instantaneously without writing anything down, something humans can't do.
Like that kind of superintelligence.
The question is, is RSI and recursive self-improvement that the labs are doing leading us there?
Are we going to get there?
I'm trying to do.
trying to do, is that what's going to happen?
And how fast is that going to happen?
That's the big question.
And they've suggested that, you know, hey, we should have inspectors that come in and look at what we're doing.
And that's a good idea.
Have them go in there and get the data.
I would love to, like, the question is, who are the inspectors?
Because you can, like, you can stack, right?
You can stack that.
Who do you think, Martin?
Yeah, there's a bunch of people that like, on either camp, actually, I wouldn't care.
If they're the inspectors, I would not be very impressed by what they say, because they've already made up their minds even before they would go in there.
Right, exactly.
But let's say, like, as an example, if Jan Bakun, who was one of the inventors of this, you know, deep neural network technology, right, one of the pioneers, if he said, hey, there's nothing to see here, there's no risk.
You know, I'm paraphrasing him.
This is nothing.
This is super intelligent.
This is just nonsense.
Keep on going.
Go fast, fast, fast.
None of us wouldn't believe it.
I'm putting words in his mouth.
I mean, I'm not exactly right.
Now, if he was one of the inspectors and he went in there and he had a look and he came out and he said, hey, I've looked and it's just what I said.
There's nothing to see here.
Just keep going.
I would feel very good about that.
I would say, okay, well, I would feel very good.
Or if he comes out and says, oh my God.
you know, he's wobbling and he would change his mind a little bit, that would also have a lot of interesting signals.
So I think it comes down to who we pick as inspectors.
And I think it's a good idea.
Let's have some of them and pick a diverse set of people so that we can get different nuanced points of view.
What do you think about this kind of Elon Musk view, which is like, it's less third party, it's more, it feels like there's kind of three proposals.
Like the OpenAI, Anthropic one is a third party.
The Elon Musk one, as far as I can tell, is the labs cross-check each other like peer review, like you do in science.
Yeah.
And then the Mark Zuckerberg one is police yourself, right?
What do you think about this middle one?
That they should pace each other, like, you know, evaluate each other.
I mean, I think, like, if we have boxing matches in the ring, the boxers should just be the judges of each other.
Would that work?
No, they would scream foul all the time.
Foul, foul, foul.
Like, you know, it's like, ah, you know, the moment the other guy puts out the great model.
And it's a big super intelligence risk.
Absolutely.
Like, you know, they have not been responsible.
Like, you know, when vested interests are at play and there's like IPO plans and these two companies are so competitive and they have like this history also between them.
Yeah, they'll be very fair to each other.
I'm sure.
That's why you need a third party, right?
Why do we have judges in the world at all?
Why do we have third parties at all?
Like, why can't just people like figure things out between themselves?
But I mean, they should try.
If they want to do it, they should try.
But I'm skeptical that they wouldn't just, you know, be biased, you know, in multiple ways to self, like, you know, judge each other.
So I have to ask Ali, do you think something Elon also said, I think it was on the All In, you know, summit, he was like, this is some elaborate 4D chess, because on the one hand, you're saying all of humanity will die.
On the other hand, you're saying, hey, what do you want for your IPO allocation?
Right.
And so, I mean, that is probably a more cynical view.
But like, how do you?
How do you reconcile that?
I mean, the dissidence, I think, gets a lot of people.
Like, how do you think that gets reconciled?
Look, I think all of these things get mixed.
Like, I think there are people that are freaked out.
And I do think that there are people saying, like, hey, if there was a regulation that would pace us, sorry to use the word.
That would be good for us.
Right?
That would be good for us.
But I also think that people have vested interests.
Right?
These things like, you know, usually people figure out a way to always get all of these things to align in their, you know, harmonically in their head.
So yeah, do I think that there has been a tendency in the past of, in general, using also marketing stunts by saying, you know, oh my God, this latest model is so good that I trained.
It's like unbelievable.
It's like almost scaring me.
And then the whole world kind of starts focusing on it.
Yeah, there's been that kind of marketing going on.
But at the same time also, as I said, the time from CVE to actually weaponize exploit has been going down from years down to minutes now, just in three, four years.
So it's real.
The cyber attacks are real.
But there's also a great marketing ploy to, you know, whenever you train a new model, make lots of noise around how much of a crazy risk it is to the world.
It helps you, right?
So, you know, maybe they're not in contradiction, these things.
I mean, you and I are networking folks, and there's a long history of forming third parties to help arbitrate things, right?
Like IETF or, you know, IEEE or even like ICANN.
I see where this is going.
No, no, no, no.
So my question to you is like, so I think it's actually, this is a very sensible proposal that they actually have.
I actually agree with you.
You probably want to make sure it's independent, which is not fair right now.
And there's going to be a lot of arguments on who you put there and everybody's going to disagree.
Right, right.
But you said, you know, why do we have judges?
So that's like, actually like the state stepping in is actually quite a different thing than basically industry self-policing.
So like at what point in time do you think it makes sense to actually consider?
federal involvement?
Or do you think like now is the time to actually consider actual federal involvement as opposed to like more industry self-policing?
These are very different.
They are different, but they kind of bleed into each other.
Like, you know, like, for instance, FINRA, you know, is not like a completely independent self-policing.
It is, but, you know, it's linked to the government.
So, like, I think these things will bleed over.
Do you think that they evolve into, historically, they've, like, industry self-polices and then it evolves into regulation.
Look, if they are saying there is existential risk, which they're saying.
You know, and then saying, come police us and regulate us.
I think it's very hard for regulators to say, no, we're not going to do that.
So far, I've said that, but I think that's not going to last very long.
It wasn't David Sachs.
It was like, I've never had a CEO ask us to regulate them.
And my favorite thing is like— And the CEOs are, I've never had a regulator that says no to that.
I mean, the reality is like the actual like metapolitical machinery is actually in motion already, right?
I mean, like everyone has a talking point.
Obama has came out.
It is a major issue.
Do you think that there's a reality that it's too late, this will be a major issue in the midterms, and we're actually going to, like, heavy-handed federal regulation, and this is all going to be paused, you know, Anthropocos into the DOE, and we're past that point, or do you think we can actually end up with, like, a sensible self-policing regulation?
I mean, I think we should strive towards doing the right thing.
I think there's still some degrees of freedom of how things evolve, and there's still time.
And yeah, you're right, that largely, you have these...
companies where we're pumping in so many billions of dollars.
And the way reinforcement learning works is that you, you know, give it the reward function that's verifiable.
Like we're going to solve this math problem or this kind of, you know, this narrow area of programming and so on.
And we pour in so much money into that.
You can get...
quite good results in that narrow kind of, that doesn't mean that you're getting that super intelligence.
No, but you can even trick yourself into thinking that like less inputs are giving you a better outcome just because you're running so many experiences and you've thought about it so much, right?
But it's actually very hard to do a closed.
a closed experiment this way, given how many resources are going on.
Well, the fundraisers are going up astronomically, to your point.
Yes, yes.
That's why these companies are going public, right?
I think they would otherwise...
They would stay private.
I mean, as someone who runs a private company at scale, I think they would prefer to stay private otherwise.
Why are they going public?
Because they need the capital.
And they consider the scaling laws and the capital to be a strategic advantage.
So that's why they're going public.
But I would say, let's go back to the four things that I listed.
If those are true, you know, if those four are true, would you want to know about it?
And would that be worrisome?
That could get out of hands.
Now, there's no evidence that those four are happening.
But if there was like, you know, no, that is actually where it's headed.
Yeah, I actually think understanding for any system.
Yeah.
Like any sort of self-propelling property is important.
And we've done this in the past with dynamic systems, right?
Like we've done this with like whatever, compilers.
We did this with all the research on nanotechnology.
Like it's been a common interest of ours.
Yeah.
And I don't think that that's new, that it's an interest.
We should continue to have the interest.
I just think the fear is that these particular systems...
are net economic systems that are so complex that the risk is crying that you're seeing it when you're not seeing it.
And I think a lot of that's happening right now.
But of course, if you see it, yeah, of course.
I mean, you want to know.
But it is fair to say that the labs are now focusing a lot on RSI, and that's where they're headed next.
And maybe they're just unjustifiably worried themselves, just like they were worried about GPT-2.
They were like, GPT-2 is world ending, and then it wasn't, and GPT-3 and 4 came out.
So I don't want to quibble.
A lot of the times when they say RSI, they're actually talking about autocatalytic effects.
And autocatalytic effects have been in our industry for a very, very long time.
So for example, there's no way you can create a computer chip without a computer chip.
It's like you cannot do it.
Anyone with computer science degree.
A compiler writes its own compiler.
Well, that becomes closer to RSI.
But like the steam engine was autocatalytic, right?
So listen, my full-time job is people coming out of labs and starting companies and they all say RSI because everybody says RSI.
And like maybe 1% of those are Axie RSI.
Like they're more like we use AI for data cleaning.
We use AI for making GPU panels.
Let's make this thing.
So you're saying it's basically catalyst in the sense that they're using AI to speed things up.
It's autocatalytic, yes.
So I would say, of what we hear to be RSI.
Like a model trains another model, right?
You're using a model to build a GPU kernel.
You're using a model to do data cleaning.
Just like I use a computer to design a computer.
It's autocatalytic, which is every tag.
The internet was autocatalytic because it allowed people to collaborate remotely.
So I would say, this is anecdotal, 90% of the calories are autocatalytic, which is 100% what you would expect.
And that's been going on for a while, though.
I mean, that's not even new.
But I would say, but also now there is a focus on let's move towards actually, can we get the model to train itself?
This is kind of like the auto research that Karpathy did, but now they want to do that.
There are teams that do that.
It is not nearly as many calories as you would expect.
And I just feel like I actually have a good sampling of this because they all come and talk to us.
Great.
So maybe you can be one of the inspectors.
Can we get all that data and all of us look at that data?
Maybe there's nothing to see here.
I personally don't think it's like very high probability that those four criteria are happening.
Greg Brockman went on the pot this week and said we're in AGI era.
Yeah.
So now I asked the same question after he said that and now everybody's saying we have AGI.
So, you know, people follow what they say.
But people have for a very long time, when I asked this question, said that, AI is smarter than most of the people around me most of the time.
That's been almost like since Q3, Q4 last year, they've been saying that.
Then I asked them, how many of you have, you know, hundreds or thousands of agents that you are managing that are coordinating with each other in swarms and negotiating and, you know, automating your life and everything around you?
And if so, raise your hand.
It's like almost nobody raises their hand.
Of course, Martina has done that at home.
No, no, most enterprises are on Microsoft Copilot.
Like that's the extent of their AI.
Most enterprises I talk to when I ask this question, they're like, no, we don't have any of that.
So we're like, what are you doing then?
They're using a chatbot.
Like they're asking questions from a chatbot.
That's basically very, very glorified, efficient Google search of the old day.
The results is just faster Google search.
And then coding is happening.
So people are using it for coding.
Though the ROI is, you know, we can discuss that right there.
But there's no like agentic like work that's automated the whole enterprise.
That has just like not happened.
So then why is that?
And I think that the real reason is if you actually look at it is the models are smart enough, but they just don't have the context that exists inside of any organization.
Like they have not been in every meeting.
They don't know what's in everybody's heads.
They don't know all the processes.
They don't know.
There's always like a couple of employees who know everything in every organization.
You know, you go tap on their shoulder and they're like, everybody's like, oh my God, what would happen if he or she quits?
You know, they don't have that context.
And if you just fused that and gave that context into the AI models, just the frontier today, I think there's so much productivity gains you could get for any organization on the planet.
For that, we actually don't need smarter models.
So we don't need a smarter model that can actually solve Navier Stokes or conjectures or do better on humanity's last exam.
Like we needed to just go from 60 to 70%.
None of that is needed.
So I think actually people are very upset on something like, oh, if we pace the frontier, but actually if the frontier doesn't advance, It doesn't actually matter, I think, for vast majority of organizations on the planet.
They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff.
But it would be disastrous in the labs because the price of intelligence is dropping asymptotically.
I think it's going down by one-tenth every six months or something like that.
So that would dramatically change their businesses if, like, you weren't pushing the frontier.
Yeah, but this is what we should focus on, right?
We should...
Focus on, like, you know, there's two sides.
We discuss here a lot the costs.
There's cost-benefit analysis that we should do on everything, right?
We've discussed the costs a lot here.
Like, oh, is there, like, existential threat?
Is there cyber risk?
Are there things we should be worried about?
And so on, that's, like, the cost side.
What's the benefit?
And I think now that this has become, like, a public thing and the whole public cares about AI, they're asking, hey, what's in it for me?
What am I getting out of it?
It seems nothing.
So how did they get there?
What are some of the use cases you've seen to date that have maybe surprised you to the upside?
Yeah, I mean, first of all, there's like so much worry about, you know, existential risk and so on.
So I think a lot of people just don't know what a, you know, cool use case is where people are actually doing interesting things.
We have a lot of use cases that are, I mean, just fascinating.
One that I like is Crisis Text Line.
So, you know, they actually use large language models with us to...
detects if teenagers want to do self-harm or suicide.
Oh, wow, yeah.
That's an awesome use case.
So it actually saves lives.
So that's a great company.
And that organization is doing amazing work.
Another one that's kind of interesting is the Omnipod, which is for diabetes patients that can put the Omnipod and it uses AI to really learn your insulin release and your glucose levels and actually exactly release.
I don't know if you remember, people used to stick themselves, right?
But this now happens automatically, and it's like, you know, self-learned AI for your body.
You know, that's a cool use case.
Zipline is another one.
They're doing awesome.
Oh, yeah.
You know, but when they started, it was like these drones that had, you know, they were completely automated, all AI-driven, everything from the, you know, battery optimization to the routes and everything.
And they were delivering food in, you know, areas of need.
Blood to refugees.
Blood to refugees.
Yeah, started in Africa and then elsewhere in the world.
So, yeah, that's awesome.
That's all AI.
It's an AI use case, you know.
built on Databricks.
So it's a cool one.
But there's more advanced ones also.
Like one that I kind of like, but it's harder to maybe explain, is this model that we built, a transformer-based model that we built with Merck.
It's called TEDDY.
Transformer Enhanced Drug Discovery.
Love that.
Yeah.
And they published actually the research, so you can check it out.
But basically it's a model, instead of predicting the next token in English, it predicts what the gene regulatory network, the DRN, is going to respond.
And it can really detect, you know, which cells are causal and which ones are just reactive.
So they're just reacting.
And therefore, they can start using this in drug discovery and get costs down significantly for developing drugs that are targeting specific diseases.
So that's a super cool use case.
There are lots of these.
You know, Jeannie, I mentioned, you have this ontology and you can ask any questions.
Novo Nordisk is using this.
So, you know, they built this GLP-1 drug.
But what Novodores is doing is now they're using it for all of their trials that they're running.
And it can compress the time it takes to get insights versus if you're in an obesity study or something from weeks down to minutes.
So there are a lot of amazing use cases of AI.
We should not forget these upsides also.
Like we want all of these and we do not want to paste these.
Yeah, exactly.
You're totally right.
And so...
How do they, let's say if you map out the next 12 months, how do the enterprise actually get value?
You dropped the word context, but like, how do they operationalize that?
It's actually harder than most people believe.
But, you know, first and foremost, we have to make sure that we have digitized everything that's happening in an organization.
That actually, you cannot actually just, you know, have a magic wand and make that happen.
So, you know, every meeting has to be transcribed, you know.
So you have to be able to get all the context of all the meetings and everything that's happening.
All the digital content has to be fed to the AI.
So you have to build, we call it an ontology, we build that.
But first and foremost, you have to collect that.
That itself is a problem in many organizations because legal teams will say, don't record every call, don't record every thing.
So you have to do that in a way where...
Can you define ontology for everyone?
Because I know Palantir...
says the word a lot, but it's not like they own the word.
Like, what does that mean?
And for the people listening, like, how should they think about it?
Yeah, I mean, you know, ontology just means that in an organization, the relationship between all the abstract concepts of all the goals and all the departments and all the people and all the projects that are going on, what do they exactly mean?
And what's the relationship between them, the people, the resources, and what that company does?
So it's the difference between a person who...
is a new employee in the company and just started today, and a person that has worked there five years.
You know, let's say they're equally skilled.
They have the same educational background.
They're equally smart and hardworking and all of that.
But one, it's, you know, her first day today at work.
The other one, she's been there five years.
What's the difference between these two people?
One has an ontology of how that organization works, who the people are, how you get stuff done.
Don't look at the org chart.
Don't go ask that person.
He will not get anything done.
You go ask this person, you know, he'll get it done for you.
And that's not how it works.
You don't need to file that paperwork here.
And, you know, and this is this project.
This is what's going on.
This is essential.
So there's just a lot of ingrained knowledge that's sitting in everybody's heads.
Who knows how an organization works?
That's why it's people say in startup land, they say, hey, if you lose most of your people, that company can't recover from it.
You can't just replenish and hire new people.
Like the people are so essential.
How do we get that context?
That's the ontology.
and give it to the AI.
Part of that is we just have to have, you know, the recording and all of that.
But the second part is how do you actually distill it down into a graph?
Actually a digital graph that you can then feed to the AI.
So the way a lot of the agents work today, like a cloud code or any of them, codex or pi or, you know, open code, you can go through the whole school of them.
You know, they have this loop, agentic loop, it can reason.
But then it goes and checks every resource one at a time.
So we'll go to this MCP server for your question and try to see, is the answer here?
Is there another one?
It synthesizes it and gives you an answer.
But it's kind of slow.
I liken this to if Google would have built Google Search this way 25 years ago, we would have said, okay, we're going to get 10 blue links.
We search for key terms here.
But instead of giving you 10 blue links, it would have gone to one website, summarized with an LLM what it does, found a few hyperlinks, jumped in parallel to a few of them.
read a few websites, done that for 10 minutes, and then giving you like its best 10 blue links it would find.
Well, that would be very expensive, cost a lot of money to do that every time, go on the web.
Two, it would have taken a long time, and then you've got to wait to 10 minutes.
And three, the quality would be bad because you're actually only looking at a very small subset of everything that exists out there, right?
So how do they do it?
They have an index, right?
You never leave Google servers.
You search for, it hits the index, the reverse index immediately gets you the 10 blue links.
within, you know, less than 100 milliseconds.
We need to do the same thing for the AI.
So the ontology is that we need to compute that index offline all the time.
So it's almost like the PageRank algorithm that Google had invented back in the day, but it's more complicated because Google was just looking at a web where everybody can go on the web.
Here, there are permissions.
And the links existed.
Yeah.
Here, there's permissions involved.
The data I'm allowed to access might not be the data that you're allowed to access.
So there's privacy, there's access control.
Also, there's many different types of objects here that we're dealing with, not just websites.
So the problem is a little bit harder.
But it's manageable.
You can actually do it.
So, you know, I'm convinced you can do this and you can get massive productivity gains out of it because we did it for the Airbix.
We did it for ourselves.
Yeah, and we're like, the company has just completely changed.
It's like not the way it was, I would say, a year ago because of this.
I mean, you've been dogfooding Databricks for Databricks forever, but maybe say more about the impact you've seen as an organization.
Yeah, I mean, once we got this ontology and we started working on it, and we actually have probably the largest of all of our customers, we have the largest ontology.
Our ontology is bigger on us than any of our customers when they use us to build their ontology because Databricks uses Databricks more than anyone else uses Databricks.
And so it's like millions and millions of nodes in the graph, in the ontology graph that we have.
So it's just, you know, what happens in an organization?
What happens in an organization, you have a tree structure organization and information flows up and down the tree structure.
You know, if you can't make a decision, you escalate your boss, maybe they can tie break, it escalates up.
They need to get up to speed on what's happening and they need to get all the context and then they make decisions.
Once decisions get made, you have to percolate them down in the organization.
A lot of this can now be done by AI if you have an ontology.
Why?
Because, you know, what happens in a meeting in a meeting you go through some you know someone has done the analysis you probably have a powerpoint deck with some pretty graphs in it um that person did the analysis is some smart person that used excel made some models there's some numericals so a lot of that you can now just do with ai so the ai can do the analysis for you it has all the context It can present it in a way that you want.
You can ask questions about it instead of having follow-up meetings.
You can directly ask questions directly from the AI.
So it's very similar.
It's along the lines of what Jack Dorsey has said that you can do to the organization.
It's just a concrete way of implementing it.
So it's game changer for us.
Like, you know, it's just everybody's on their phones now in the meetings on Genie.
And they're like asking Genie questions.
You can see as soon as someone says something complicated or something, it's like, you know, you see everybody go through the phone.
Can you share that finance?
Like the finance anecdote you mentioned once in a board meeting.
Yeah, sure.
Internal board meeting.
Only in kosher.
Yeah, exactly.
No, it's actually needed for one of our presentations.
I need to know how many customers do we have in Fortune 500?
What's our penetration of Fortune 500?
And I asked one of the people in sales ops because I thought she would have it.
And she texted me back and said, oh, sorry, I can't log in to Genie right now.
I'm on a flight.
That's why if you're just going to log in to Genie, I can do that myself.
I asked you because I thought you had like something alternative that I don't have access to.
So then I was kind of a little bit angry.
So I texted the CFO instead, Dave.
And so I texted Dave and I said, hey, do you know what our Fortune 500 penetration is?
And he just copy-pasted a screenshot of Genie back to me.
So he also asked that.
So I said, does anyone do anything novel here?
Everybody's just going to Genie and asking the ontology, you know, for questions.
It's like, let me Genie that for you.
That's what everybody's doing.
Now we just say, hey, can someone just Genie this?
Can you just get it from the ontology?
So I do think it's a game changer.
But it's not just you press a button, you have an ontology in an organization.
And I think Palantir actually has done a great job of going to organizations.
getting a lot of that tacit knowledge written down and getting it into the organizations, we automatically take that and build the graph.
And then we feed that graph into the agents so that we can answer the question and answer it in a way that business leaders would like to see it, which is in graphs, you know, analytical way and a way where you can interrogate that question and, you know, continue asking questions and getting answers to those so you can make decisions.
And then disseminating that information in the organization.
Yeah, it's pretty amazing.
You sort of bookmarked the developers are obviously using AI, questionable value.
I want to follow up with you on that because I feel like you guys were one of the earliest.
And I say, I don't want to use the word token maxing because it has such a negative connotation.
But I think in terms of applauding people who can use AI to become more productive, you guys were, you know, at the forefront of that, right?
And then, of course, there's this cycle of, oh, shoot, people are being wasteful.
Now we need to value max.
Like, what was your own journey on that?
And like, how do you guys think about value maxing, not token maxing?
And then I'm going to throw in Unity Gateway in this, right?
Because I think the managing of cost piece is actually getting more important and you guys are helping people do that.
But maybe tie that in to extend it.
Yeah, so around Q4 last year was when, you know, the models got really, really good.
And we started noticing that, okay, it's actually starting to give much better productivity.
So I actually started using the models myself to sort of start.
commit code into production for Databricks.
I want to take it all the way to production.
So I did that and started pushing the organization that, hey, everyone needs to do that.
I have done it.
Why are you not?
If the CEO can commit code to production on a very sensitive data platform that has all these security requirements, you should be able to do that too.
You being any manager, anyone in the organization.
So I started pushing everyone very hard and we started making leaderboards in Q4.
And at the beginning of, I say, January, February, when kicked off the year, we were already full swing.
Everybody was using the stuff and we were pushing and we were managing this.
But, you know, the whole token maximum thing was happening around, you know, February, March period, already it was happening.
So, yeah, we just had the luck of being maybe a few quarters ahead of folks to see what was happening here.
And it was getting out of hand.
So we already had a gateway.
So it's called Unity Gateway where we were already, this gateway was being used to provide token capacity.
So you can get OpenAI, Anthropic, Gemini, Grok capacity.
Like any customer can come to us and we'll just provide them that capacity because we have relationship with those.
And any open source model.
So we started putting in budget constraints in place and giving people warnings like, okay, you have this much of your budget left.
you're getting close to your kind of ceiling.
So we started doing that per person and for group.
And then we started doing great analytics so we could predict exactly where the costs were going.
And then, yeah, and then we added smart routers that could actually pick cheaper models if you're getting close to your budget or if, you know, you have simple questions.
We started doing that.
We also built a harness called Omnigent, which can multiplex between the different harnesses.
Turns out actually the harness itself matters.
Like if you use...
The same model, but different harnesses, there's almost 2x different cost difference.
Even exactly same model, you know, same version, but different harness, you get 2x difference in actual cost.
So if you can change harness, you can get a lot of leverage in the cost.
So we started using all of this.
That way we were able to actually bend the curve.
And actually our cost for AI has been basically, the tokens have continued to go up, but the costs have been sort of stagnant.
So that's been actually super, super important for us.
And there's a huge demand for this.
I think every organization is going through this now.
Yeah, for the first time, I do a lot of board meetings, some on 20-something boards.
For the first time ever, a company at scale last week said that they're moving from the Frontier Models to GLM.
This is a large engineering organization.
Do you see this?
Do you think that that's a trend or do you think that's just like a one-off anecdote?
Because I've been hearing about, I remember the first DeepSeek moment and like NVIDIA Stock Drive and then that turned out to not be real.
And then the Kimi moment and the next DeepSeek moment.
None of it seems to have actually had an appreciable impact on the market.
But now the amount of anecdotes that I have are pretty real and it seems to be happening.
Yeah.
Love your view.
I mean, I think people want both.
They want, you know, they want the latest model that's super intelligent for the difficult task where they get ROI.
But then there's a lot of mundane dumb things.
Like, you know, people literally use their harness to rename files and whatnot.
You know, like, you're paying, you know, orders of magnitude more for that.
Please type that in yourself.
Don't have the model do that.
It's going to spin for five minutes, and then it's going to rename the file for you and cost you, you know, cents.
I get some people.
I'm just wondering, do you actually see Mark's movement?
No, people are moving on it, but what they're doing is that, you know, the pattern is either use, you know, you can use this expert pattern where you have, you know.
small, cheaper open source model that uses an expert model, the big ones, or vice versa, or a way in which they can sort of ping pong them to each other, but also multiplexing harnesses and just changing harnesses so that you can control the costs is also what people are doing.
You know, people have found, for instance, you know, there's...
Pi is very efficient when it comes to as a harness.
So yeah, I think there's going to be a multitude of these.
It's easy to, the models themselves are stochastic, as you said, every time they give a different answer and they're changing so much.
So there's just a lot of experimentation happening.
So I think we're going to get to a world where you're not always using the smartest model for everything, which has kind of been the paradigm for the last couple of years.
Like a new model comes out, it's super smart, they use it for everything, even really, really.
Simple mundane tasks.
Yeah.
I'll tell you what I see.
I see people using Fable and Astra for like architecture, a cheap model for implementation, and then Fable or Astra for audit.
Yeah.
Like that seems to be like this emerging.
What are you guys seeing in the startups?
I mean, aren't they?
That's it.
That's honestly the pattern.
How much open source?
By token or by dollar?
Either.
So by dollar, open source is like 5%.
It's very little, but by token count, it's over 60%.
Yeah, I was going to say, I mean, we talked to, let's say, a Decagon or something like that.
Well, I think it's different internal use versus external for product.
On the external for product, I think they're almost up to 90% open source.
On the internal, and I don't want to say for Decagon in particular, but a lot of them are like, we don't care, we'll just use Frontier.
We're not thinking about cost control.
But as it gets bigger, right, you and I were in another board meeting where they actually did bring that down, just from a waste perspective.
So I definitely see that.
moving more toward open source on the product side.
And actually, that's related to another question, maybe around open source, but post-training specifically.
I feel like you were kind of early.
I remember talking to you in 2023.
When did you buy Mosaic?
2023.
2023, okay.
So this vision that you had in 23 kind of came true in 2026.
I don't know if you guys would agree, right?
Like that's sort of what we're hearing across, you know, of course.
Oh, just sort of like, hey, we're going to actually, you're going to own your own intelligence.
You're going to be, you know, post-training your open source models, et cetera.
And that's definitely what the startups are doing.
I don't know if that's what the enterprises are doing yet, but like, I mean, do you feel like you were early to that?
Yeah, I mean, first of all, you know, there was, when we started, it was also, hey, we're also pre-training for you.
which that doesn't make any sense.
You know, you can, there's so, they're very good pre-trained model now that you can use.
But that you can do actually post-training on the model and you can do reinforcement learning.
Yeah, we're actually doing it at scale.
And many of those startups are actually customers.
So we actually help them, you know, using RLE or reinforcement learning environments where we can make the models very, very good at the specific tasks that they are doing.
It makes a lot of sense for them to do that.
If you have a repetitive task, so if you have a startup and it's offering a product and a product does something specific, it's not just a general.
It does something specific for you.
It makes just a lot of sense to take a really good open source model and, you know, use reinforcement learning and make it really good at that specific task.
You can cut the cost down.
You can make it really fast.
You know, they control their own IP.
So in that sense, that is possible.
But large enterprises, they just need basic automation.
And it's just too much for them to do this right now.
I think one of the challenges is, you know, you need good evals.
And making good evals is hard.
So while the startups can do that and they're motivated to do that, other organizations, the easy button might be just to use a frontier model than having to create your own evals.
We actually generated even evals for the customer automatically in the product and we had it front and center.
But then people didn't want to use it.
So we said, okay, let's move it to the back end so that it's optional.
And then they would never go to it.
So I would say in general...
Why?
They just don't want to get into it?
It's too complicated?
I think you want quick...
You know, quick reinforcement of like, you know, hey, there's a new model.
I want to try this out.
I want to get this problem solved.
You don't have time to go do this scientific method of let's make an eval.
Let's have a great baseline.
And it's sort of like TDD, test-driven development.
You know, in software engineering, if people actually do test-driven development, very few did, right?
Everyone said it's the right way to do it, but nobody actually in practice did it.
So that's the same.
That's kind of a little bit of the curse.
of, you know, training your own model is the evals is the hard part.
I know you have an FDE model at Databricks.
That's a very popular word right now or acronym.
But does it, like, to get these at enterprises that large, is it a full FDE model that's required?
Or like, how do you, and how has that evolved, maybe?
Yeah.
Yeah, I mean, we've had these FDs and it's the demand for it's gone up significantly.
A lot of it is, you know, how do we build that ontology?
Like, the ontology is automatic.
But if you're not collecting any information, like you're not recording anything.
Right.
So that's one of the key things that we do.
But also things like, you know, I want to build an agent.
I want to put it on, you know, I want it to be customer facing and it's have really low latency and I want it to have guardrails to not people coming to abuse it or ask it things that we don't want it to answer and so on.
So we can build that, like, you know, like sports AI that Fox has.
You can go chat with it about sport events.
You can try to ask it actually about politics and it's very good at rejecting you and moving and talking about sports instead.
So the FDA has built that.
So, you know, we'll help the organizations actually get started with AI.
It is important because it's just many organizations do not have the in-house expertise to build this stuff.
So then just a little bit of help on the side and then they get started.
Yeah, makes sense.
So this is more related on the agent side, but I saw recently that I think a third party, neutral third party, I think, did some tests that Lakebase or Neon was actually the Postgres database of choice for agents.
And I thought that was interesting, one, because, you know, one, exciting data works, but two, I...
probably wouldn't have guessed that maybe a year ago.
Yeah, it was a surprise.
Yeah, it was a surprise.
Just because there's others out there that have, you know, great developer momentum as well.
But it was pretty clearly number one.
And so I'm curious, how did you guys crack this?
And what makes you win across the agents?
Because if you win the agents now, you win the market.
Yeah, I mean, I think a lot of credits should go to Neon and Nikita and team.
And I think what they've done is they've just been obsessive about How do you make the models pick and agents favor Lakebase or Neon as a database?
So what do they do?
The agents want to experiment.
They're going off.
They're trying to build a little bit of software.
They need the database.
So you need the database to come up quickly.
So they had this obsession that everything should take far less than a second.
So if a database comes up in far less than a second, you can clone gigantic databases.
You can clone it in less than a second.
Highly elastic, highly responsive.
And then they built this killer feature called branching.
So branching just lets you branch the database.
You can have many, many branches of the same database.
And they just made this very, very lightweight.
We saw this with other things with agents, right?
Like UV, you know, ripgrap, like basically re-implementation of a lot of the tools on Unix, making them really, really blazing fast and lightweight.
And also sort of fail-safe for agents.
They've just did this to a harder problem, which is database.
So like now you have a Postgres database and the Postgres database has all these advantages that it's really fast, it's nimble, it's fail safe.
You can, you know, go back to snapshots.
You can do those things.
So I think that's why it's just easier for the agents to use this.
They also made sure that it had a pricing model that was like, you don't want just because the agents are building some software and experiment, you don't want the cost to run out.
You're okay paying for your database if it's like production used and lots of people are using it, but just to experiment.
So I think they were just obsessed.
They were not trying to win the database war or trying to be better than some other vendor.
They were obsessed with how are we the best for the agents?
And that's a new persona because in databases, the obsession has been how do we help DBAs?
How do we help app devs?
How do we help the people that are using the database?
They changed the game and said, hey, how do we focus on agents and help agents get the best database they want?
And, you know, now over 90% of their...
the databases that are created on Neon and Lakebase are actually created by agents.
So it's not even humans.
So, you know, numbers speak for themselves.
By the way, it's remarkable.
So I've started to use Neon as like my standard database.
And it was bizarre to me because like normally when you enter a large company, things slow down.
It's actually like the products got materially better.
Yeah.
Are they totally independent?
Do they work with the rest of the...
No, it's a great team.
I mean, we work very closely together.
You know, we love databases and data, so it's, you know, we live that.
But no, the team does a great job of just making it super fast, snappy, and great for agents.
All right, Oli, what is your P-doom?
Less than 10%.
No.
Close to zero.
What about yours?
I don't know.
I say my...
My only answer is my P-Doom without AI is much higher than my P-Doom with AI.
Wow.
That's a tough one.
What do you say?
I would agree with Ali on this one.
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