Nikesh Arora: Mythos is Real, Analytical SaaS is Dead, and Google can be a $10T company

**Jason Calacanis** (0:00)
It's one of the biggest winners right now.

**David Friedberg** (0:01)
The big daddy of the cyber security space.

**Chamath Palihapitiya** (0:03)
Palo Alto Networks is now performer in the space.

**Jason Calacanis** (0:07)
CEO Nikesh Arora.

**Nikesh Arora** (0:09)
This might come as news to you, but humans have been writing bad code for a very long time.

**Chamath Palihapitiya** (0:14)
I spent 10 years at Google, and Google search was democratizing information. If you take that analogy and think about what AI is doing, AI is democratizing intelligence.
Money is a way to keep track. It's not the goal.

**Sacks** (0:29)
You've been the CEO of Palo Alto Networks for eight years?

**Nikesh Arora** (0:33)
Coming up in eight years this week.

**Sacks** (0:34)
Eight years. I think when you started, it was $17 billion market cap, if I remember correctly.
This morning, I checked, it's $238 billion, which if you listen to what we said yesterday, now that you passed 100, you're more likely to actually 10x. So the first 10x was actually much, much harder. So you're on your way to a trillion dollars.

**Nikesh Arora** (0:51)
From your mouth to God's ears.

**Sacks** (0:53)
Well, I think you are. Okay. So let's just double click into what you see, because you are in a really interesting position to see all of it.
You see the birth of AI, maybe you've seen the rise and fall of SaaS, all the models talk to you.

**Nikesh Arora** (1:10)
The rise again, right?

**Sacks** (1:11)
The rise again.
You were one of the first in the few that got access to Mythos. So let me just push the button, go Nikesh, start.

**Nikesh Arora** (1:21)
Well, first of all, thank you for having me here. I think AI is exciting. I think it's exciting to see all the stuff that's gone down the last possibly 24 months.
I think Sarah just said it, they were right in anticipating the huge amount of compute that was going to be needed. So all that stuff's going on. But you can see that this notion which we talked about briefly last time, that AI is really democratizing intelligence. What that means is, I have 250 people in marketing, they produce varied forms of output. Now you can get 90% of the output to be consistent across those 250 people. I have 5,000 people who talk to customers.
My failure mode is when 5,000 people do different things, where people say, I want to talk to Joe because he knows how to solve the problem, and Jim doesn't. So now you can get 5,000 people to act almost consistently in their interactions with people on the other side. So I think it's going to have a phenomenal impact to how we run businesses, how we operate, it's going to change the entire landscape. Now, in that context, you've touched upon Mythos and I know David has been very involved with this. Mythos has shown us that all the bad code that humans have written over the last 50 years can be assessed by AI and shown, the vulnerabilities can be shown. We tested for six weeks, and in six weeks we found what have taken us five to seven years. Wow.

**SPEAKER_6** (2:53)
Say that one more time.

**Nikesh Arora** (2:54)
In six weeks, we found vulnerabilities which would have normally taken us five to seven years to find.

**SPEAKER_6** (2:59)
So, Mythos-

**Sacks** (3:00)
But these are vulnerabilities where?

**Nikesh Arora** (3:02)
Sorry?

**Sacks** (3:02)
These are vulnerabilities in your own code base? In our own code base. In your own code base. Oh, wow.

**SPEAKER_6** (3:07)
So, Mythos was not oversold. It was legit.

**Nikesh Arora** (3:11)
The capabilities of AI in being able to assess vulnerabilities in code are real.
Not just that. If you put it on ultra mode, which is persistent thinking, so it keeps trying until it gets an answer, you can actually daisy-chain vulnerabilities, i.e. finding a new attack path into your vulnerabilities. Now, we pride ourselves as a top percentile of companies that test our code because we're in the cybersecurity business. If you take that and compound that across all the companies that exist in the world that write their own code, i.e. 10 million developers write code, this thing is going to find stuff which would have taken us 10 years to find.

**SPEAKER_6** (3:50)
How much did it cost? Did you track the token cost? Was it $100 million, $10 million?

**Nikesh Arora** (3:56)
No, it was in the low millions. But again, the cost, as Sarah said, the cost curve is going to come down. Already, OpenAI has got a model which is cheaper, more consistent. Anthropics come out with another model.