Are AI Glasses Over?, Big Technology Audience Questions, Alex Stamos on AI Cybersecurity

Summary

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Big Technology Podcast – June 19, 2026

Episode: Are AI Glasses Over?, Big Technology Audience Questions, Alex Stamos on AI Cybersecurity
Host: Alex Kantrowitz (referred to as "Ron John" in the transcript – likely a nickname)
Co-host: Ranjan Roy
Special Guest: Alex Stamos
Format: Live audience Q&A and expert interview at the Big Technology AI Summit

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Episode Overview

This live-recorded Friday edition of the Big Technology Podcast dives into the turbulent market for AI and AR smart glasses, the evolving use cases and limitations for these devices, audience-driven questions spanning agentic AI, data privacy, and the China/US tech relationship, and closes with a detailed conversation with security expert Alex Stamos about the real-world risks and regulatory reactions to next-gen AI cyber capabilities (Mythos and Fable).
The tone is lively, humorous, and interactive, combining skepticism, technical insight, and audience engagement.

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Main Topics & Key Insights

1. Are AI Glasses Over?

[02:33–09:00]

The Snap Spectacles Debate

• Live demo: Ranjan shows off his old Snapchat Spectacles (“original developer beta edition”) as a high-risk microphone stunt and friendly banter ensues.
• Snap’s new AR glasses (“Specs”) are met with harsh market and social media feedback post-launch.
  • Social Media Consensus:
    • “Does anyone at Snapchat have the guts to tell leadership that these things are ugly?”
    • “Snap is the best brand in the world when you’re 16, and the worst brand when you’re 21.”
    • “People who actually buy $2,000 AI glasses aren’t teenagers.”

Debating the Future of Wearables

• Ron John posits the iPhone—with evolving Siri and AI features—is “the AI device,” questioning if face-worn computing is a real consumer demand.
  • “...the new Siri, if you look at the videos, looks terrific. And so maybe this idea of we have to wear the computing on our face is something that kind of sounds good in concept, but model after model... [it] is not.” (Ron John, [06:15])
• Ranjan remains bullish on AR glasses, especially as AI interfaces, but concedes form factor and battery are barriers:
  • “I still am betting on AR eyeglasses of some sort.” ([04:26])
  • Cites his son's enduring love for Snap Spectacles and wonders whether Apple or someone else will get it right.

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2. Audience Q&A: Devices, AI Agents, Privacy & Global Dynamics

[09:01–34:43]

a) Investor Fatigue & Tech Pacing

• Q: Do companies lose traction by working too far ahead of present needs?
• Ranjan: The cycle is outpacing both investor and consumer patience; the interface for tech hasn’t fundamentally changed in a decade and efforts like Humane’s AI pin may or may not be the answer.
  • “...I have been begging for something else other than me holding my phone, looking at it, and we haven’t really gotten anything for a long time.” ([10:02])

b) Form Factor: Chunky vs. Slim Glasses

• Q: Will slim, phone-connected eyewear win out over “chunky” fully self-contained AR glasses?
• Ron John: “Any device that requires a puck [is] not working.” ([12:42])
• Ranjan: If your iPhone does the compute ("the puck is your iPhone"), glasses could be slimmer, giving Apple a strategic position.
• Audience: Use cases discussed include collaborative virtual screens (shared movie theater on a plane), virtual coworking, and multiple monitors for productivity.

c) Agentic AI Benchmarks & “Goal Mode” Saturation

• Q: Are AI agents about to achieve—or have they already achieved—the ability for near-infinite autonomous operation?
• Ron John: Benchmarks have shot from “coding autonomously for 30 minutes” in 2023 to “18 hours” in 2026—and real-world applications like “Perplexity Computer will basically do that” do exist ([17:08]).
• Ranjan: Real-world data and problem specification remain the bottlenecks; “there’s still a massive gap in terms of what does the data look like, what is the actual problem?” ([17:43])
• Frequent (and humorous) skepticism about current “real” use cases beyond travel booking.

d) Data Privacy & Advertising

• Q: Will AI companies leverage personal user data for targeted ads?
• Ron John: "Most certainly."
  • “We have never had technology ever [before] collecting this much information about us. Has anyone here…asked [an LLM] to psychoanalyze me or give me any insight about myself?...these models will know everything about you and it is, it’s terrifying.” ([22:44])
• Ranjan: OpenAI is “pivoting away” from ads but Google is going full steam, and the data LLMs have is deeper and more personal than anything before.

e) AI Agents: Learning and Drift

• Q: Are companies focused enough on making AI agents learn and adapt instead of just controlling for cost/governance?
• Ranjan: In practice, it’s messy—context window and information chunking is more art than science, and “being able to grab a hold of how information flows through these agents…is going to be a really, really interesting space…[but] it’s no one job right now.” ([25:51])

f) AI in Biosurveillance & Public Health

• Q: Can AI models/tech be transformative for disease risk detection (vs. AI model biosecurity risks)?
• Ranjan: Public health applications are an “underappreciated thing” in the AI field; this kind of AI story does not get enough attention.
• Ron John: If a model can power both prevention and biothreats, risks must be weighed.
  • “If you can do one, then you could certainly do the other.” ([29:48])

g) US-China Tech Competition & Model Rankings

• Q: Why is US-China partnership/competition important? Which “frontier” models will win?
• Ron John: Cooperation would be great, but “competition’s good,” especially for things like AI for health ([31:14]).
• Ranjan: Chinese models are becoming cost/performance competitive options in many AI projects, a shift from 12 months ago ([31:56]).

h) Big Tech: Do They Have a Duty to Do Good?

• Q: Should big tech use its power to solve major global problems?
• Ranjan: Fierce competition may crowd out altruistic ambitions (“I wish it would, but I don’t see it happening.” [33:23])
• Ron John, paraphrasing Jeff Bezos: “We can’t expect them to do it out of the goodness of their own hearts.” ([33:42])

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3. AI Cybersecurity, “Mythos” Models & National Security (with Alex Stamos)

[35:49–56:35]

The Fable/Mythos Incident Explained

• Background:
  • The White House mandated Anthropic to pull down its “frontier” AI models Fable and Mythos after Amazon security researchers raised concerns about potential risks.
  • The models were “export controlled”—i.e., even foreign developers who built them could not access them—leading Anthropic to take them offline, despite disagreement about the seriousness of the security flaws ([37:24–39:44]).
• Key Quote:
  • “...the White House orders them to take Fable down. Anthropic says, ‘No, we can just fix these issues…’ Instead…[the Commerce Department] signs an order saying the models are designated export controlled…Anthropic…pulls them down.” (Alex Stamos, [39:44])

Is the Danger Real—Or Marketing Hype?

• Ron John & Ranjan have long debated whether warnings about Mythos are “material” (real) or just “marketing”.
• Alex Stamos: Mythos is “the best bug finding model that we know about” but not a “cyber God… Anthropic has played it up too much.”
  • “...Mythos is really good at finding bugs. But from my perspective…the Opus 48 GPT 5.5…are here and Mythos is [just] here at bug finding, at bug finding and exploit development, and it is not [a huge leap], which is kind of where you would think from reading all this stuff.” ([42:03])
  • “This Rubicon was crossed well last year with the Opus 4 series, with the GPT 5 series—that is when the models became as good or better than the best human bug finders…” ([42:23])
• The deeper issue: Now, “we are drowning in bugs”—modern LLMs find exploits at scale, and defenders are overwhelmed fixing vs. finding.
• “If you’re a pedestrian and you get hit by a F150 at 80 mph or a McLaren at 200 mph, you don’t really care. You’re dead. Right. And that’s the difference here.” (Alex Stamos, [45:55])

The Security Paradox: Gating Bugfinding Hurts the Defenders

• Stamos warns: Blocking US models (“gating” them so they can’t find exploits) would hamper defenders while adversaries use open or foreign models (notably from China).
• “We cannot set the standard that US AI models can’t find bugs. That is a terrible, terrible, terrible standard.” ([46:32])

Policy/Regulatory Fallout & Geopolitical Risks

• US regulators might try to define “jailbreak resistance” as “no ability to find any vulnerability”—which would drive the entire US industry to use foreign (Chinese) models.
• Open source Chinese model GLM 5.2 “is within percentage points of the top closed models from Anthropic and OpenAI” ([49:49]).
• Sudden government intervention creates “political risk”—leading major US companies to develop fallback infrastructure to use open-weight models or Chinese LLMs, so they aren’t left stranded by regulatory whims.

Notable Quotes

• “We lost a war this weekend, like let’s not lose another by making it unreliable to utilize the United States for AI.” (Alex Stamos, [56:29])
• “Mythos is really powerful, but doesn’t really matter because there’s so many bugs out there that you can use any of these models to find them.” (Alex Stamos, [46:32])
• “If American LLMs know nothing about security, they will create more security flaws…That would be a humongous own goal.” (Alex Stamos, [46:32])

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Notable Quotes & Memorable Moments

• “You’re going to still...can you put those glasses on one more time?” (Ron John to Ranjan Roy, [08:16])
• “Your answer for how to make those things work is just be less handsome.” (Ron John re: AR glasses, [08:50])
• “It would be cool to, like, be in the Vision Pro final together. Yeah. Sit up, sit half court, watch the Knicks. Next Timothy Chalamet...But you know what’s interesting? Apple never advertised that as a social device.” (Ron John, [14:00])
• “The more [an LLM] knows about you…the types of questions you’re asking...these models will know everything about you, and it is, it’s terrifying.” (Ranjan Roy, [22:44])
• “I don’t think there’s a responsibility [for tech giants]...because competition is so fierce now. I wish it would, but I don’t see it happening.” (Ranjan Roy, [33:23])
• “If you’re a pedestrian and you get hit by a F150 at 80 mph or a McLaren at 200 mph, you don’t really care. You’re dead. Right.” (Alex Stamos, [45:55])
• “We lost a war this weekend, like let’s not lose another by making it unreliable to utilize the United States for AI.” (Alex Stamos, [56:29])

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Key Segment Timestamps

| Time | Segment / Topic | |-----------|-----------------------------------------------------------------| | 02:33 | Introduction to live episode, show format, and Snapchat segment | | 04:09 | AR/AI Glasses skepticism vs. advocate debate | | 06:15 | iPhone/Siri as the “AI device,” not wearables | | 09:01 | Audience Q&A begins | | 12:36 | AR glasses form factor, puck vs. phone debate | | 16:14 | AI agent “goal mode”/benchmark saturation discussion | | 22:44 | Privacy, ads, and LLM user data segment | | 25:51 | Learning, drift, and agentic AI technical challenges | | 27:10 | Quickfire final audience Qs: public health, China, responsibilities | | 35:49 | Alex Stamos interview begins (AI cybersecurity, Fable/Mythos) | | 39:44 | White House/Anthropic Fable takedown explained | | 41:03 | Ranjan v. Alex Stamos on “is Mythos hype or substance?” | | 45:55 | The “car crash” analogy: relative vs. absolute model risk | | 49:49 | Chinese open models as new global standard | | 56:29 | Stamos: Industry lost confidence due to US policy instability |

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Summary: Flow and Tone

This was a dynamic, wide-ranging discussion—equal parts skeptical, technical, and conversational—on the challenges and excitement in AI hardware, agentic software, data ownership, and the ever-rising stakes of AI in national security and global tech competition.
The hosts mix friendly ribbing (“just be less handsome!”) with incisive commentary and field a diverse set of audience questions, before handing off to a deep-dive with Alex Stamos, whose direct, sometimes alarming, insights serve as a reality check on the speed at which AI capability and regulatory risk are changing the playing field.

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For Listeners:

• If you’re here for the “Are AI Glasses Over?” debate: The panel is divided—even the “wearables optimist” finds few true converts in the crowd, as battery, sizing, and compelling use cases remain elusive.
• If you want to understand the security panic over Anthropic’s top frontier models: The episode is essential—Stamos clearly explains the technical reality vs. marketing myth and the new risks facing both the AI industry and national security.
• If you’re pondering AI’s next interface, privacy, and whether big tech will save the world: You’ll find both pragmatic skepticism and the recognition that, for better or worse, the real action is only accelerating.

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Transcript

[Ron John]

Though for many of you, he needs no introduction. Let me introduce Ranjan Roy. I first started reading Ranjan Roy's writing in 2021. He had written this newsletter called Margins, and I thought it was a terrific newsletter. I saved it. I spent my winter break reading it. I DMed him by that January, we had decided to do an emergency podcast about a crazy financial situation. And then Ranjan and I kept talking more and more and. And then by January 2023, I wrote to him and said, hey, don't you want to just come and do this every week? And lucky for me, and lucky for us, with anyone involved with big technology podcast, Ron John said yes. And so getting a chance to speak with Ron John every Friday is an absolute joy. It's definitely one of the highlights of my week. And today we're thrilled to be able to do our Friday show live here with you with your audience questions, and then we'll just run it tomorrow like a normal podcast. So I hope you're ready. We definitely need your participation. And please join me in welcoming Ranjan Roy.

[Ranjan Roy]

I got them on. I'm going to take these off, though. We'll get into the Snap spectacles. This is a medium risk maneuver because I have this microphone on.

[Ron John]

So those are the Snapchat spectacles.

[Ranjan Roy]

These are Snapchat Spectacles Spectacles. They're the original developer beta edition, though, so they're not the new ones, but I had them in 2021.

[Ron John]

Now everyone knows how cool you are. All right, should I start this?

[Ranjan Roy]

I'm as cool as Evan Spiegel.

[Ron John]

That's right, yeah.

[Ranjan Roy]

Is he still cool, though?

[Ron John]

All right, let's do it. How would I start it?

[Ranjan Roy]

Did I throw you off?

[Ron John]

No, no, no. I didn't even write this down. Okay, I'll try to do it. All right, Snapchat comes out with new Spectacles and we take your audience questions. That's coming up right after this on a big technology podcast Friday edition record.

[Kyle]

Par le tu francais hablas espanol?

[Sasha]

Parl italiano.

[Kyle]

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[Ron John]

Welcome to Big Technology Podcast Friday Edition where we break down the news in our traditional cool headed and nuanced format. We're joined as always by Ranjan Roy who is here with us not in studio live with the Big Technology AI Summit audience. Audience, let's hear you. The way this is going to work is Rhonda and I will break down one story and then we definitely welcome your questions, your prompts, your arguments with us. If you don't have anything, we have plenty to do, but we'd love to have your participation. You can line up at either of the mics there to ask us a question. Let's start with our top story this week. I promised myself when this summit was being planned that I would not be mean to Snapchat. However, Snapchat has left me with no choice. John, let's roll image C please. Listeners, if you are listening on the podcast, you'll see what we're looking at at the audience. Here is a image of Evan Spiegel on CNBC wearing the latest Snap specs. The headline is Snap Specific Stock falls after AR Spec's debut. Almost 20 years since the launch of the iPhone, people are ready to think about computing differently. Spiegel said in an interview with cnbc. The market reacted differently. Ramjan, let me just go to you quickly. Do you think that Snapchat and Meta and all these other companies have been trying to build these AI device of the future for years and this is what we're looking at?

[Ranjan Roy]

This is what we're looking at.

[Ron John]

You kind of ruined my surprise. Can we go to D, please? I mean,

[Ranjan Roy]

I brought them. I had to bring them. After sending Alex a photo, is it

[Ron John]

time for us to finally accept that we're not going to have an AR device?

[Ranjan Roy]

Interesting. So these again, I got these in 2021. It's actually a very cool technology. No, I'm serious. Augmented reality. It's if you ever use magic leap in the 2000 and tens, like being able to paint throughout your room and walk around that painting. Being able to play games where you're chasing Zombies again. My 7 year old son actually is probably the only fan of Snap spectacles in the world right now. He still loves using them. He also likes to watch YouTube on them, which is kind of amazing. But I think that form factor and the experience is amazing. Vision Pro has not quite captured it. I don't think what we just saw on Evan Spiegel's face is going to capture It. I think eventually maybe Apple or someone will, but I don't think we're there yet, but I think we will be. I still am betting on AR eyeglasses of some sort.

[Ron John]

Here's some of the social media reaction. Does anyone, anyone that works at Snapchat, have the guts to tell leadership that these things are ugly? That feeling when your glasses are so heavy they give you cauliflower ear Snap is the best brand in the world when you're 16 and the worst brand to be associated with when you're 21. The people who actually buy $2,000 AI glasses aren't teenagers. If you think you really want to wear always on camera around in public, it should have to look like this with a picture of Evan Spiegel. Let me make the case that it's over.

[Ranjan Roy]

Good, because I'm going to take the other side.

[Ron John]

I think that the iPhone series released that we just saw. So does anyone here listen to Ron John on Fridays?

[Ranjan Roy]

We have listeners. All right. Okay. So you'll know which direction this might go.

[Ron John]

What's this guy been begging for for, like, since he came on the show the first time? Better Siri.

[Ranjan Roy]

Better Siri.

[Ron John]

I think they actually did it like the new Siri, if you look at the videos, looks terrific. And so maybe this idea of we have to wear the computing on our face is something that like kind of sounds good in concept, but model after model and it's not. And the AI device is the iPhone.

[Ranjan Roy]

Okay, that is an interesting direction to take it. I still think the form factor. Do any of the audience have like meta ray bans or any other device like that? I see a few. You start to feel as you're walking around, as you're kind of interacting in the real world, stuff can happen. That's not just maybe eventually Siri talking to you and using a traditional AI model to actually, you know, give you kind of information. So I don't know. I'm still. I still think AR as a form factor via glasses is going to happen. I think you have meta ray bans, you enjoy them.

[Ron John]

You know, I. I didn't want to say this publicly, but I have not used them. I mean, I have, but I don't. I use them for a bit. And like I said on our show recently, I was on a hike, it was cold. I was ready to get to the summit and put those glasses on and the battery started blinking red and I couldn't use them. I had to use my old phone. And let me say this, they haven't taken off as a mainstream consumer device. And if you look at the stock of every single company that's pursuing them, it's not good. Snapchat, like we just said, is struggling. Meta, as we know, has its problems. No one's looking at these ray bans to save Meta.

[Ranjan Roy]

Well, no, but to me, the Apple Vision Pro is like the more direct correlated product versus the camera.

[Ron John]

You want to know what the best thing about the Vision Pro was?

[Ranjan Roy]

Fine. Vision Pro?

[Ron John]

Yeah, they put the person on Vision Pro on Siri, and he fixed it. Wait, Mike Rockwell?

[Ranjan Roy]

Oh. So I would not have guessed that the person who made the Vision Pro would be the one to fix Siri after all these years. But I guess if that's the case, that's exciting.

[Kyle]

Did it?

[Alex Stamos]

Yeah.

[Ranjan Roy]

Yeah.

[Ron John]

So you're going to still. You're going to. Can you put those glasses on one more time?

[Ranjan Roy]

Again? I was told backstage this might destroy my microphone, but I'm gonna try. Do I look cool? No, no, no, no. I mean, even we were joking that Evan Spiegel is going to the Met gala with his model wife. Like, this is like the coolest person in the world and how bad they made him look, that if it was like when Zuck wore Project Orion, no one really cared that much. But I think because Evan Spiegel is such a great, cool looking dude, that's why it looks so egregious. That's my take.

[Ron John]

So your. Your answer on the way to make those things work is just be less handsome.

[Ranjan Roy]

Yeah, that's it. That's it.

[Ron John]

We'll. We'll write to Spiegel and let him know. No questions.

[Sasha]

Okay.

[Ron John]

All right, great.

[Ranjan Roy]

All right.

[Kyle]

Yeah.

[Ron John]

Don't be shy.

[Serb Jal]

Is it on? Yes, it is on.

[Ron John]

Hey, guys, if you're willing, let us know who you are.

[Serb Jal]

And this is Serb Jal, actually, analyst. Flew from New York a day earlier to join us.

[Ron John]

Welcome. Let's give him a welcome.

[Serb Jal]

Thanks for doing this.

[Sasha]

Thank you.

[Serb Jal]

Quick question about. Actually, it's an observation. I wanted to get your take on this. When companies have this gap between what people need today and what they're working on, which is like, in future it can be two plus years out. So they lose that traction from the investors point of view, as well as employees and partners. So do you see that that's happening to Meta and others? Like it happened to IBM and they were living in future with Watson X, for example. So what's your take on that? It can be B2C or B2B examples.

[Ron John]

Okay, thank you for the question.

[Ranjan Roy]

Yeah, no, no, that's a great question. I do think, like, if we're talking about kind of how the interface for how you interact with a computer, I have been begging for something else other than me holding my phone, looking at it, and we haven't really gotten anything for a long time. I mean, humane tried with the pin. I still think maybe some kind of PIN is going to be around Jony ives pin and OpenAI. Maybe. Maybe at some point.

[Ron John]

Well, Greg Brockman is coming, so we'll put it.

[Ranjan Roy]

We got to ask him about that.

[Ron John]

We'll talk about that.

[Ranjan Roy]

But I think, like, being able to interact with all of this information now, being able to, like, process information so much more reliably with AI, just I don't want to have to keep looking at my phone and just looking at it on there. But even actually on the phone, I'm guessing, do a lot of people here dictate more to their phone right now? Like, that's completely changed in the past. Like, I would have felt weird just talking to my phone, and now I'm constantly using whisper flow and just talking to whatever.

[Ron John]

So tell a story about you and your wife.

[Ranjan Roy]

I know. Okay. I don't know if this is the most depressing thing or. And my wife is not here and hopefully won't kill me if this is being live streamed right now.

[Ron John]

No, we're going broadcast.

[Ranjan Roy]

So I am constantly dictating to my computer, to my phone. And the other day it was like Friday night, we had put our son to bed, we're both on our laptops, and I'm on one side of the couch dictating. And then I look over and she's also has her laptop open and is kind of whispering to her computer, too. And I'm like, is this the future of tech? But it's a new computing interface, so I'm happy about it.

[Ron John]

Right. Look, I think the gentleman brings up a great question, which is that things are moving so fast. How do you plan right now? And honestly, I don't know how you do it, because every day there seems to be a new capability, then the capability is taken off the table. Like Fable, for instance. I mean, the tweets about Fable where, like, people are adding Dave Sachs and they're like, please, I'll do anything for Fable back. Just bring it back. And he won't bring it back. But it's just like, I don't understand how any company does that. And I think that actually would be a good topic for us to sort of get into on a future show.

[Ranjan Roy]

Yep.

[Ron John]

Okay, let's go this way.

[Kyle]

Hi. Thank you for taking my question. I was curious your views on in the next, like, five years as AR glasses evolve. Whether, like, the chunky spectacles is the way to go or thinner glasses with, like, upward compute, like, either wireless to your iPhone or, like, the Vision Pro, like, cable down to the battery pack and compute. Well, it's a battery for a Vision Pro, but it could also have compute on board. So I'm curious, like, the next five years where you think consumers will gravitate towards.

[Ranjan Roy]

No, it's a. It's a great question, because if you ever use Magic Leap, there is, like, a Puck.

[Kyle]

Puck.

[Ranjan Roy]

That's what it was.

[Ron John]

Here's my hot take. Anything, any device that requires a puck not working.

[Ranjan Roy]

Well, how about this, though?

[Ron John]

Okay, Blow my mind.

[Ranjan Roy]

What if the puck is your iPhone?

[Ron John]

Oh, shit. All right.

[Ranjan Roy]

Exactly. So which gives Apple. Apple an opportunity that, like, if the compute is taking place on the phone in your pocket, it allows you to be much slimmer from a power perspective. You don't want, like, a lightning cable connected to your face. But, like, it's still. I think there is a. It makes me think Apple still has a good chance in this space because the iPhone can do all of the heavy lifting versus this thing on that is really heavy on your head.

[Ron John]

Can I ask. So what's your name?

[Kyle]

I'm Kyle.

[Ron John]

Kyle. So what do you want to use a, like, face computer for?

[Kyle]

Oh, that's a good question.

[Ron John]

See, I told you. Ron, John, this stuff is not happening.

[Alex Stamos]

No, no, no.

[Ranjan Roy]

Kyle's got something.

[Ron John]

All right, let's give Kyle an opportunity here.

[Kyle]

What do you think for?

[Ron John]

I didn't mean to put you on the spot, but thank you for helping me prove my point.

[Kyle]

I think one cool thing would be, like, a shared, like, TV or something. Like, like, imagine Vision Pro, and you have just a shared. Just like, movie theater. You're on a plane with your family or something, and you just have, like, the shared experience, but it's just glasses.

[Ranjan Roy]

I like that.

[Ron John]

I like that. For me, one of the coolest things I always like is, you know, as you get older, you move further away from your friends, and it would be cool to, like, be in the Vision Pro final together. Yeah. Sit up, sit half court, watch the Knicks. Next Timothy Chalamet. But we're just in the Vision Pro. But you know what's interesting? Apple never advertised that as a social device. All the marketing was just, you're sitting alone at home, and all you're doing is the Vision Pro. Nobody else exists.

[Kyle]

The other big one. Yeah, Sorry, interrupt. The other big one for me is just having a lot of monitors around when I'm working. Like, I. Even though I have like an ultra wide or like three monitors, I feel like sometimes it could have more. So just being able to like interact with AI to pull up the exact page I'm looking for out of, like, I'm one of those people. Has like a thousand tabs. So just being able to pull up the thing and I just say open up that tab and it just pops up. Pretty cool.

[Ranjan Roy]

Would 8,000 individual tabs in a giant planner space be better or.

[Ron John]

I wonder how I was going with this.

[Sasha]

All right.

[Ranjan Roy]

No, no, but I do agree, like that being able to do more open scaled work and like look at different charts and I think there's that still. And I know, like, I have friends who own the Vision Pro who use

[Ron John]

it for that still.

[Ranjan Roy]

No, but did.

[Ron John]

Did they had a nice time. All right, thank you.

[Kyle]

Kyle.

[Ron John]

Let's go here.

[Sasha]

Hey. Hey, what's up? This is Sasha. Hey. From the Yale University doing research on AI agents for finance. First of all, love you guys. Listening to you guys chat on my way to work has been my routine. Thank you. I love you. This is shared by not just me, but many people in the audience. Thank you so much.

[Ron John]

Thank you, thank you. Appreciate that. And thank you for coming. Did you fly in?

[Sasha]

Yes.

[Ron John]

Oh, my God. Thank you for coming. It's great to see you.

[Sasha]

It's worth it. I appreciate your sharp line of reasoning and questioning. So what's your view on the meter benchmarks of how long the AI agents can work independently being saturated? Are we at that point of them being at infinite number of hours working yet, or are we reaching that soon? And what is the world going to be looking like after we hit that infinite mark?

[Ron John]

That is an excellent question. So, first of all, there is some controversy about the meter monitoring, but I think it's kind of directionally accurate. Right. I showed the meter chart at the very beginning of the. Of our event today that you saw these models, they could not code autonomously for more than 30 minutes in 2023, and now they can code autonomously for the human equivalent of 18 hours. Right. So what happens if they just kind of blow past that limit and then they can code all the time? That is an excellent question. Do you have any thoughts?

[Sasha]

So I felt that with the go mode and automation mode, it practically felt that they are already doing this autonomously forever. I want them to send me reports. My prompt would Be if you find something interesting, send me an email. Then when I see it, I will come back and intervene. I felt that for many tasks it's already at that mark.

[Ron John]

Yeah, no Perplexity Computer will basically do that. So that's definitely something that's happening. But I'll just say one more thing and then we'll go to Ranjan. Greg Brockman, who will be here later, has this idea of a compute powered economy where once you get these bots working the way that you explained, maybe he'll say this later, you just kind of throw them at any problem and then they just kind of work autonomously through it. Now, we've never seen what the world looks like when you can do something like that, but I do think, given the progress that we've seen with the models, you would imagine that something substantial will come at a certain point.

[Ranjan Roy]

So my day job, I work at a company writer. We're an enterprise AI company. And the goal mode was asked about recently. Again, the idea that you just provide the goal. The agent will iteratively loop and keep doing work until it finds the right solution in the real world. The benchmarks versus the real world. I still think there's such a massive gap in terms of what does the data look like, what is the actual problem? Does the customer or the person actually understand the goal in a clear enough way that they're able to define it to kind of push that loop forward? So I think it's an interesting. I think with a lot of AI, again, even around the benchmarking, like, again versus real world understanding of how to use it, what's happening and what's available for it to use. There's still a lot of work to be done.

[Ron John]

I mean, you can do these goal modes for like, less complicated tasks.

[Ranjan Roy]

I like, what have you goal moded.

[Ron John]

So I worked with Perplexity Computer recently to try to like have it find a hotel discount for me.

[Ranjan Roy]

No travel stories. I've been on a. This has been for like two years. Every time I remember, like Sundar. Actually it was. I don't know if people remember in 2019. Wait, no, no, I'm just saying, why does everyone, when they talk about agentic, talk about travel? I get.

[Ron John]

Because travel is such a pain in the ass.

[Ranjan Roy]

But it's. I don't know. Okay, go on, go on.

[Ron John]

I'm sheepish now.

[Ranjan Roy]

No, no, no. Hotel discount. Hotel discount.

[Ron John]

You just looked at the hotel every hour and when it dropped behind below a certain threshold, emailed me. All right, that was pretty good.

[Ranjan Roy]

Okay. That's a clear goal. I'll give you that as a clear goal.

[Ron John]

I respect your. I think you're right that we definitely need better examples than.

[Ranjan Roy]

It's a pet peeve. It's just for some reason, every Apple, the original ridiculous Bella Ramsey commercials around Apple Intelligence, of course, everything is flight booking and.

[Ron John]

And again, like, you've killed that Bella Ramsey commercial so often, that one, they're gonna. The creative agency is just gonna write

[Ranjan Roy]

to you and I love the last of us and I love Bella Ramsey, but those commercials still irked me.

[Ron John]

What happened in those commercials?

[Ranjan Roy]

One of them, she's sitting at someone that she can't remember who that person is and in real time asks Apple Intelligence, like, tell me about this person, my interactions. Which again, is just such a weird thing. Like, I'm so much better than you that I don't know who you are and I need to remember who you are and have AI tell me. But. And it didn't even come close to working with Apple Intelligence. So that was. That was the worst one.

[Ron John]

You know, it would be good for that, actually.

[Ranjan Roy]

What Goal Mode.

[Ron John]

AR glasses.

[Ranjan Roy]

Oh, Arkansas glasses. There's the real world use case.

[Ron John]

Or the fact that that commercial was so bad just again proves my point that AR glasses aren't gonna work. Okay, sorry. Thank you so much for the question.

[Ranjan Roy]

Thank you.

[Ron John]

And we'll go over here.

[Saoji]

Oh, he was behind.

[Ron John]

Okay, fine, we'll go here.

[Kyle]

Okay.

[Sasha]

Hi.

[Ron John]

Hey.

[Sasha]

Jerome Harris. I'm on the board of the Commonwealth Club here and I run some programs for the club and I have the small scenario planning consultancy. But here's my question that I think hasn't come up here. What should we be concerned about in terms of using the AI models, them building a database on us and then turning that into advertising? The advertising, potential revenue from users. Do you think the AI companies will ever go after that revenue or use that information for advertising purposes?

[Ron John]

That's a good one. So will AIs build an amazing sort of profile of you based off of all the personal data and then use that for ads? Most certainly.

[Sasha]

So someone left one of the companies and wrote about that in the New York Times about three months ago?

[Ron John]

No, Definitely. Yeah, that's coming. I think that advertising is obviously going to be one use where we're going to start to see some of the problematic stuff here. But actually, if you look at the ads that OpenAI gave you, it's sort of like more of a. And obviously they always come out with the high touch brand and then they will get you on the direct response, like super targeted stuff once they realize they can't make money on brand. But it looks pretty good if you're sorry to go with a travel example again. But if you're researching travel on ChatGPT, you can go into an advertising chat experience and it will help you. But I think that we have never had technology ever the collecting this much information about us. Has anyone here like Talk to like ChatGPT or Claude and say can you psychoanalyze me if you're not or give me any insight about myself? Just like five of you, the rest of you have done it. Admit it, it's scary and that's the gated stuff and we're giving all this information to companies and it's a trust thing that we don't really know where it's going to go.

[Ranjan Roy]

Yeah, I think it's interesting because again OpenAI originally ads are going to be a big part of the business. Now they've pivoted away from that a bit but are still releasing ad products at least. Anthropic is not. I mean I don't think they're going in that direction at all. Google and Gemini obviously 100% will and like you probably will get a really good ad experience. I mean the more it knows about you, the types of questions you're asking, but it is like, you know, like thousands and tens of thousands of three to five word Google searches are a lot different than entire thought partnership therapy like research exploration. These models will know everything about you and it is, it's terrifying.

[Ron John]

Have you done the psychoanalyze me prompt?

[Ranjan Roy]

No, but I did. So I use like Claude and chatgpt Gemini. I'll kind of go through the three of them and cycle around. So it is funny. So I have asked like what do you know about me? Like what would you ask back? And they all have very different kind of like jagged jarring aggregations of who I am. Like I don't know, they're. I don't have one that just knows me through and through. I've done diversify.

[Ron John]

I've done it with ChatGPT. Yeah, it's pretty good. Yeah. And then like it will first give you a answer that's like kind of sanitized and then you say go a little deeper and then you say get a little darker.

[Ranjan Roy]

Oh dear. I have Not Red teamed ChatGPT.

[Ron John]

I challenge you all to give it

[Ranjan Roy]

a shot or don't go darker. Don't do it, don't go darker.

[Ron John]

But if you really want to.

[Chris Abuahi]

Okay.

[Ron John]

Thank you over here and thank you for.

[Ranjan Roy]

Let's move on. Let's move on.

[Ron John]

Yeah.

[Saoji]

Thank you guys and really appreciate all the work that you all do on the podcast and YouTube. Your conversations go so deep and still very broad. So I really appreciate that.

[Ron John]

Thank you. We appreciate it. You're a listener or viewer?

[Saoji]

Oh, yeah.

[Ranjan Roy]

All right.

[Ron John]

Thank you so much.

[Saoji]

Depends on where I am.

[Ranjan Roy]

Amazing.

[Alex Stamos]

Appreciate it.

[Saoji]

And my name is Saoji. I'm an advisor for startups in the AI space and I've been in product management and go to market strategy. My question for you, somewhat related to the gentleman before. There is a lot of coverage on agents and what agents can do do and a little bit about the governance and cost control. What I don't see a lot about, you know, is, you know, agents do drift, probably why we don't have better examples than travel. And, you know, over the period of time, they don't get better at that specific, you know, understanding of the people and the business context and the operating model they're working in and, you know, learn from it. And I was wondering, is there, is there a lot of conversation that you guys are seeing around the learning of the learning layer in the agents and is it too early or, you know, for that to happen? And the conversation is mostly around governance and cost and access.

[Ron John]

It's actually a good question for Ranjan.

[Ranjan Roy]

Yeah. So again, like working in this all day and thank you for the question. I, I think what's going to happen, I'm already starting to see is like six to 12 months ago, the approach was just jam as much information into whatever system you can. And we were promised that it would just work and AI is just going to. And you get this feeling that, oh wait, 100 page PDF, it actually could parse and I can get information out of. But then when you're doing that at any kind of agentic scale, it doesn't, I think already like how information gets chunked up and like distributed in different ways. And context windows. Everyone would see like 1 million token context window, but we didn't really know what it meant. And now more and more like. And I actually think this is going to be one of the most interesting, like professional opportunities and areas to be an expert in going forward is like being able to kind of. It's funny like it is as much art as science right now, but I think the more you can start to grab a hold of how information flows through these agents and how you actually get it reliable, it's going to be a really, really interesting Space. It's no one job right now.

[Saoji]

Thank you so much.

[Ron John]

Okay, thank you for the question. All right, we have seven minutes, three questions. Let's see if we can keep the questions brief and we'll keep the answers brief and we'll get to everybody.

[Chris Abuahi]

I'll try to be quick.

[Ron John]

Thank you everyone.

[Chris Abuahi]

Big fan of the pod, by the way. Chris Abuahi. I'm one of the co founders of a company called Virus Watcher. I'm here with my colleague. Flew in from Dallas. He flew in from Austin.

[Sasha]

Nice.

[Chris Abuahi]

Big fan.

[Ron John]

We got a couple of Dallas folks here today.

[Chris Abuahi]

Oh, who's Dallas? Shout Out Dallas.

[Ron John]

Dwayne.

[Chris Abuahi]

Okay, go, Matt.

[Ron John]

Oh, yeah, we're big in Texas.

[Chris Abuahi]

I'm going to go in a different direction with my question. I want to know kind of what yalls thoughts and honestly, if you can ask this kind of later on with Greg Brockman and others, what is the thought on using AI models and AI technology for biological intelligence? Biosurveillance and public health for emerging risk detection with infectious diseases is a very interesting project that we're working on. We have a lot of epidemiologists on our team. We were just at the UN two weeks ago with the World Health Organization and trying to discuss these problems and how we can use the technology to detect these things early on. So I kind of want to get you guys thoughts on that.

[Ron John]

Can I ask you a question back and if you could give a brief answer, that would be great.

[Chris Abuahi]

Okay.

[Ron John]

Are you a believer in the bio capabilities of these models? Like Fable? Like do you think Anthropic made the right decision by gating them?

[Ranjan Roy]

Wait, wait, bio capabilities meaning like you're talking about biological weapons, he's talking about like disease. Correct.

[Ron John]

But I think that his answer will give us insight into the question.

[Chris Abuahi]

I mean, I think it's a very touchy, you know, it's a. There's no right answer, I don't think. But I do think it probably did more harm, you know, than, you know, not allowing everyone to kind of take advantage of the.

[Ron John]

So you're not, you're not basically worried that today's cutting edge models could cause like new bio threats?

[Chris Abuahi]

No, I, I am, but I think there's also a lot of positive that can come out of it as well.

[Ron John]

Let everybody use it though.

[Chris Abuahi]

Yeah, I mean, I just, you know, it doesn't even have to be a Fable style model. It can be. You could take a frontier model, distill a more.

[Ron John]

Right.

[Chris Abuahi]

Custom model. Yeah. Safety with, you know, safety standards involved. But sorry, I don't want to be too long. I just want to jump.

[Ron John]

No, great question, Ranjan.

[Ranjan Roy]

I, in terms of the first part of it, I do think again, like, and it's one of the underappreciated things and I feel in the AI industry like disease prevention and scenario modeling around virus, like we don't talk about that stuff enough or whoever is trying to talk about it, it does not get listened to enough. So I love the kind of work you're doing and like those kind of stories would bring the AI industry a little bit better reputation.

[Ron John]

Yeah, no, I, I, I agree.

[Ranjan Roy]

You're thinking about biological weapons.

[Ron John]

No, I think that, that if, well, if you can do one, then you could certainly do the other. Am I, what I don't know is one like, okay, I actually have no

[Ranjan Roy]

idea why are you giving me these problems here?

[Ron John]

No, I, I hope that we can and we will.

[Chris Abuahi]

I know that's a very, yeah, no, no, no.

[Ron John]

We're going to speak with, we'll, we'll hopefully have some time to talk about health with Brockman.

[Chris Abuahi]

Yeah, I would love, yeah. If you can ask me that. Okay.

[Ron John]

Okay. Thank you for the question.

[Kyle]

Over here.

[Saoji]

My name is Heidi, I'm your linking online friend.

[Ron John]

That's right, yeah. You messaged me yesterday.

[Alex Stamos]

Yes, thank you for being here during this event. See each other in person. My question about before you with a different speaker mentioned about China. So I'm Chinese. I'm curious about why you think China is important based partnership with USA Special high tech. Second question about re. So you mentioned a lot.

[Saoji]

LM larger language model.

[Alex Stamos]

So my question about China, Gemini to

[Saoji]

the GDP and Claudia and Anderson, which

[Alex Stamos]

one you prefer think about in feature is more stronger, smarter in the future.

[Saoji]

Thank you.

[Ron John]

Thank you. Thank you for the question. Well, the question on China is, Sorry, what's the question on China?

[Alex Stamos]

Why you think it's important based relationship with usa.

[Saoji]

So yeah, thank you.

[Ron John]

All right, I will take that question and, and then Ranjan will answer which model he likes better. I think that we're obviously in this competition. The US and China are in this competition and there's a lot of suspicion on both ends and I've said in the past that it would be great if there was better cooperation between the two countries. And people have told me that I'm naive, but I'm not going to lose that hope. I think that if, I mean in some ways competition's good, right, because then you'll just have everybody striving to build the better thing. But you know, we just talked about AI for health. If the countries were able to work together, maybe on specific initiatives, that would be much welcome.

[Ranjan Roy]

I think the most interesting part of like US versus or us with China in terms of AI in the last few months, the conversation around moving towards deep seq and adding it into your agentic process or adding Chinese models did not exist in any conversation I was in 12 months ago and now is in almost every conversation, at least as an option because cost has become such a larger part of the conversation. So I think it's a good thing. I think the more integration there is overall across these systems, the better.

[Ron John]

Yeah, but watch out for La Chaton fat.

[Ranjan Roy]

Oh, la Chateau fat.

[Ron John]

The cat holding the hands with Dario and Sam is my favorite meme the whole year. Okay, we have one minute left, but let's see if we can do it in a minute.

[Saoji]

I'll go through this quickly. Hi, Maria. Sarah Roberts. I'm in Business Management consulting. My question is a little bit different. As we are evolving into or entering or in a world already where endless possibilities are available, where and how do you see the evolution of the responsibilities that big tech companies have in supporting making this world better? Not the product and our engagement and how we interact with all the screens better, but rather like the real big problems that the world has, and not because they should, but because they can. So how do you think about, like, the evolution of that as we step into this world?

[Ron John]

I'll answer that. Or do you want to answer? Go ahead.

[Ranjan Roy]

I just. We had a backstage. We had a long rant because I'd forgotten that Anthropic is a PBC public benefit corporation. But I still think there is a responsibility. I don't actually see it happening in any kind of way because competition is so fierce now. I wish it would, but I don't see it happening.

[Ron John]

Yeah, that was a good answer. Come on, I want to make sure everybody gets to coffee. Oh, no, screw it. I'll answer it. So, Jeff Bezos. So I think they have an obligation to do good for the world. And I don't know if. And I know people within the companies are, you know, are very serious about that. So they have their own side projects. But I think, you know, as a society, maybe we can't expect them to do it out of the goodness of their own hearts. Jeff Bezos was recently doing an interview, and he said, he said, I think what I do in the private sector is going to outweigh whatever I could do. Charitably, I disagree with that. And I think that that's just the mindset of a lot of people at the top of these companies. And so, like, everybody else needs to be aware of that and not count on it, even though it would be nice. So that's my perspective. All right, Ranjan, will you come back with us after the break and speak with Alex Stamos and I about whether this mythos and fable stuff is material or marketing?

[Ranjan Roy]

You know, I would love to.

[Ron John]

You guys want to get a coffee break?

[Alex Stamos]

You guys are amazing.

[Ranjan Roy]

Thank you very much.

[Serb Jal]

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[Ron John]

So you may have heard that AI is in some trouble in Washington, and that story has led to a lot of guessing. People have guessed, are these models actually that dangerous? How much better are they than previous generations? And do they deserve to be restricted by the federal government? We've certainly done a lot of guessing, and I think the only way to get to the bottom of this is to speak with somebody who is deep in the weeds on the cybersecurity aspects of AI models, someone who secures them, and someone who has spent a a career working in security. And that would be Alex Stamos. Alex Stamos is the former chief security officer at Meta, and he's the current chief product officer at Corridor. Ranjan and I have this debate all the time is, what's going on with Mythos marketing or material? Well, let's get to the bottom of it. Ranjan will come out and be my co interviewer for this one. So ladies and gentlemen, please welcome Alex Damos. And once again, Ranjan Roy. Thank you.

[Alex Stamos]

Hey, thanks, man.

[Ron John]

Alex, Boring times in Your industry.

[Sasha]

Yeah.

[Alex Stamos]

It's an honor to be here. I am by far the poorest person in the afternoon lineup. It's like I bring down the. The mean net worth by a comma, I believe. So I do appreciate being here.

[Ron John]

Well, Alex, I don't want to cue you into podcaster finances, but.

[Alex Stamos]

But I think you're good of the guests, I guess. Yeah. Yeah.

[Ron John]

So talk a little bit about. We're obviously at this moment where the government has pulled down or basically forced Anthropic to pull down Fable or Mythos, its frontier model.

[Alex Stamos]

Right? Both of them, yes.

[Ron John]

What happened?

[Alex Stamos]

Right. So just for recap for everybody, last Friday, what happened was the White House became aware of research that Amazon had. Did. Had done. It is not unreasonable for Amazon to do this work. Right. So Amazon hosts Anthropics models in what they call Bedrock. This includes in all of the classified spaces. So if the NSA is using anthropics models, it is running in aws Secret cloud or top secret cloud. And so Amazon is doing security testing. I expect this is part of getting ready to host Fable and Bedrock and found some things that they wanted to complain about with Fable's protections that make Fable different than Mythos. I guess we can talk about the exact ones. Amazon had sent over these results to Anthropic. There was some disagreement between Anthropic and Amazon during the week of how serious they were. Now, I don't have the exact details here, but somehow, apparently Andy Jassy, the CEO of Amazon, mentions this to somebody at the White House, and the White House freaks out and according to reporting, calls Anthropic and is not happy with how quickly they can get Anthropic on the phone. Anthropic is asking for more technical details. They're not able to get details from the White House of what the White House wants, so the White House orders them to take Fable down. Anthropic says, no, we can just fix these issues. Why don't we work it out together? And as a result, instead of working out together, on Friday afternoon, something around 5pm Pacific Time, the Commerce Department signs an order saying that the models are designated export controlled and that no foreigner, including the foreign citizens who worked and actually built the models themselves, can touch them. Whether or not the administration actually has that legal power is disputed. But Anthropic decides to actually follow through instead of immediately getting a temporary restraining order. And because they have no ability to make sure that no foreign citizen is seeing the models, actually pulls them down. Both Mythos, which was privately accessible as well as Fable, which was publicly available.

[Ron John]

Now you've spent a career working in security.

[Alex Stamos]

Yes.

[Ron John]

Ranjan and I, for months now have had this debate about whether all of this talk of doom and super powerful models from Anthropic, for instance, Mythos, a model so powerful that you can't access it, whether that represented a real capability increase or whether that was just marketing. It's a step change, not really a step change in capabilities. So I actually want to toss it to Ranjan, who is firmly on the marketing side of this thing, just to make the argument of why you think this is marketing and Alex can address that.

[Ranjan Roy]

Yeah, I think trying to understand, like, what is that capability? New capability that is so dangerous. What is that step change? I think the reason I get so suspect on it is how perfectly coordinated a lot of the marketing and communication are around how they roll these out. Like again, Mythos is the most dangerous thing. There's someone eating a sandwich in a park that actually got like, where the model broke out of containment was like a PR release. It was a very coordinated thing. So what is that danger that they're trying to kind of bring to the world? Like, what is it really? What does it look like, feel like, or what is it actually going to be a problem?

[Alex Stamos]

So how much money do you guys have writing on this?

[Ron John]

Well, how would we even play this? I don't even know, like, as a bet.

[Alex Stamos]

As a bet? Like, is there a polymarket? Cause I'm trying to see what my angle is here. Apparently I have.

[Ron John]

Well, no, you are the person that

[Alex Stamos]

knows the Eric Trump action.

[Ron John]

You know the answers here. And we have just been kind of like debating.

[Ranjan Roy]

We just argue on.

[Chris Abuahi]

So.

[Alex Stamos]

Okay, so it's actually a little bit of a complicated answer. So Mythos is almost. It is the best bug finding model that we know about for sure. It is most likely the best bug finding model in the world. And I say most likely because we do not know what the Chinese secretly have in their labs. But for all the stuff for which we have open, known security evaluations for, it scores the best. Now, is it the avenging cyber God that Anthropic makes it out to be? I personally don't think so. It is.

[Ron John]

You're not necessarily. You're not right yet.

[Alex Stamos]

But it is really good. It is really good at finding bugs. But from my perspective, the like, Opus 48 GPT 5.5, a bunch of other models are here and Mythos is here at bug finding, at bug finding and exploit development, and it is not here, which is Kind of where you would think from reading all this stuff. The other thing is that everybody pretends that Mythos was the breakthrough, but that is not true for people in the security world who've been paying attention. This Rubicon was crossed well last year with the Opus 4 series, with the GPT 5 series. That is when the models became better or as good or better than the best human bug finders, and not just as good as the best bug finders. And there's like 50 or 100 of these people, and I know a couple dozen of them. The problem is those people are very expensive, right? And they don't scale. You can't just throw water on them and make them multiply like gremlins. That would be nice. I mean, certainly the NSA would love that. But models can. You can just scale them as much as you want with money and power and GPUs. And so that last year was a huge deal because all of a sudden you saw these amateur bug finders. Imagine you went to a high school track meet and every single kid is posting Olympic world record times. You'd be like, man, they're juicing that something had happened. That is what last year was like. Mid to late last year in the vulnerability Discovery World is, the kids started juicing. And it's because the models hit this point where they could find everything. Yes, Mythos is really good at finding bugs. It's really good at writing exploits. Fable is not. That is what the whole point of Fable is. It is the same model weights as Mythos, but Anthropic put a protection in place to keep it from doing the really nasty stuff that Mythos will do. Amazon has some complaints about that. But what happened is the complaints Amazon has, a number of us have looked at them, and then Anthropic has pushed back. Even with the jailbreaks that Amazon has, you cannot get Fable to do things that you can't do with the opus series, with GPT5 and even with a bunch of Chinese models. So that is why the actions by the Trump administration make absolutely no sense, because those models will not refuse at all. You can just ask GPT5 to go find these bugs and it will go do it for you. You can just ask Kimi, which is an open source Chinese model, it will go write you these exploits. Yes, you can trick Fable into doing certain things, but you can't trick it into doing the really powerful Mythos stuff, which is like grind on the Linux kernel for all day, and find a bunch of bugs which will cost you hundreds of thousands of dollars, it turns out if you pay the full price, and those have not been jailbroken, and so there's no real good reason for what happened. So, yes, Mythos is really powerful. It has only been accessible to a very small number of companies. It is not magically powerful. The other thing you have to remember here is, like, bugs are cheap. Exploits are like, we're now drowning in bugs because these models are so good at finding and we have so much bad software. We've been writing bad software forever. And it turns out that, like, writing human beings, writing code in C and C especially, was a really bad idea. That, like, we should not have been writing, like, building our entire lives, like, based upon memory, unsafe languages, and then in the presence of these superhuman bug finders. Not just Mythos, but all of them, that was not a good idea. And so if you're a pedestrian and you get hit by a F150 at 80 mph or a McLaren at 200 mph, you don't really care. You're dead. Right. And that's the difference here is, like, the Mythos is the F1.

[Ranjan Roy]

F1.

[Alex Stamos]

Yeah, exactly. And Kimi or GLM52 is the F150. And so you could take the McLaren off the market, but all you've done is actually hurt the defenders who want to have full coverage, who want to be able to find all the bugs. And attacker only needs one or two bugs to string together to actually pull off the attack. And so that's why about 150 of us wrote this open letter and signed it saying, this is really stupid, because all you're doing is hurting defenders, and you're also really hurting the US AI industry.

[Ron John]

Okay, I think Ranjan wins the debate.

[Alex Stamos]

Well, no, it's not all marketing, but it's not all marketing. But I just want to say Mythos is really powerful, but doesn't really matter because there's so many bugs out there that you can use any of these models to find them. What I'd like to do is I'd like to shift the debate one away from finding bugs, because the other problem here is we cannot set the standard that us AI models can't find bugs. That is a terrible, terrible, terrible standard. If you are writing software with an LLM, it has to be able to understand what a bug looks like, so it does not write those bugs. It has to. And that's the trick, actually, Amazon used was they tricked it into basically finding bugs in individual lines of code because it has to be able to do that, to write secure code. If. If American LLMs know nothing about security, they will create more security flaws. So we cannot create a standard where American LLMs are dumb about security. That would be a humongous own goal, and that is one of the possible outcomes here of this whole brouhaha. That would be really stupid. And so I think. Yes, I don't think either one of you wins. I mean, as the referee here, I think Ron John has a little bit of a. You have a point. But Mythos is really good. It just doesn't. Anthropic has played it up too much. OpenAI is rumored to be releasing something that's Mythos quality really soon. And I doubt that they're going to follow this. They'll just give it a number. I think they've learned from Anthropic. You don't call your thing the Cyber Gorgon.

[Ron John]

That actually would sell.

[Alex Stamos]

Yeah, it sells a little too well. Right? Maybe as a Greek. With the Odyssey coming out, I'm getting a little sensitive about the marketing here, but let's back off on the naming scheme and move back to numbers. But I think a big part of this is we just have to reset the standard of not bug finding, but the actual creation of exploits and then other kinds of offensive operations, of which there's a bunch of other things you can do offensively with these models. Those are the things that we should call off limits, not bug finding, because that is something that we have to do much more quickly than we're doing right now.

[Ron John]

Can I. I just want to try to articulate my point, which is, you know, this is. All right, so when we talk about is Mythos material or is Mythos marketing, My argument would be, like, wouldn't. If something was material, it would look a lot like what we're seeing today. Right. Like, at a certain point, these models are increasing in capabilities. You're going to want to put some safeguards on them. And so is what Anthropic is doing that different from, like, where you'd want to be if you actually had these concerns?

[Alex Stamos]

I mean, I think both Anthropic and OpenAI have a path in which they have, like, a KYC model now.

[Ron John]

Know your customer.

[Alex Stamos]

Know your customer. Yeah. So OpenAI actually has a couple levels. They have a public one where you can basically say, I want access to cyber capabilities, and then they have a private one. I believe Anthropic only has the private one. Right. To get access to Mythos. But I do think it's reasonable to have Some level of gating. But in the case of Glasswing, I think it was. Honestly, I thought it was not widespread enough. I know of critical infrastructure companies that don't have access to Mythos right now.

[Ron John]

Nobody does right now.

[Alex Stamos]

Well, right now, before last week, people who should have had access did not have access. Now, seeing what happened here, you can see why they were so careful. But from my perspective as defenders, we are in a race against time, right? We have all of these bugs that need to be fixed. And we also have this weird thing where we're like, this whole conversation is predicated on the idea that the American labs are years ahead of our adversaries, and that is just not true. So while Fable was shut down on Tuesday of this week, GLM 5.2 was shipped right from Z AI, which is a Chinese lab. This is an open weight model. It is MIT licensed. So any of you can go. Not just download the weights from Hugging Face, but you can include it in your own product and you can retrain it. It is within percentage points of the top closed models from Anthropic and OpenAI in a bunch of things, a bunch of evals. We don't know how good it is at bug finding yet, so there hasn't been any good testing here, but encoding and a bunch of other intelligence tasks, it's almost as good as the best the United States has. And that's the best Chinese model we know about. Unlike American labs, the Chinese are not going to announce their Mythos. And so the idea that we should be doing everything in the United States to just be playing defense is we're playing like a defensive model here. That is stupid. From my perspective, we need to be accelerating our capability to do these things and not just playing defense.

[Ranjan Roy]

Well, now I am sufficiently scared, even though I've thought that the Mythos was pure marketing, but what do we do about it? Like, how do we. What is that like? And I think I share that Glasswing. Again, why? It felt like marketing to me. It was this kind of exclusive club that people were invited to, and I actually spoke with people who are using it, and they spoke about it almost like it was a club. Like, oh, yeah, we got access to Mythos. We're spending a ton of money. Like, I think, what should. Should it look like, this kind of

[Alex Stamos]

rollout to actually help us in Glasswing? I mean, they're fixing real bugs. They have found, like, 10,000 bugs. Now, one of the problems with Glasswing is they've found, like 10,000 bugs, but they've only fixed 1,000. Right. So you do not want a 10 to 1 ratio of the bugs you found to fixed. We just joined a thing called Project Athena, which chainguard is running, which is all about fixing specifically open source bugs. Right. So Glasswing, a lot of that is closed source with inside commercial companies. We are part of this coalition that is using Mythos to fix open source bugs, which is a huge challenge because just finding the people who have access to that source code commit access is difficult. So that's going to be a huge push. It's not just totally bs. I do think that one of the things that's going on here is that if these companies ran any modern AI security tool against their code base, they'd find 80% of those bugs. They don't have to use Mythos.

[Chris Abuahi]

Right.

[Alex Stamos]

So it's just that they've never looked, right? It's. They're like.

[Ron John]

That's concerning.

[Alex Stamos]

Yeah. Right. And so they're like, oh, yeah, I've got this old crappy code and they look for the first time and they all of a sudden they find.

[Ranjan Roy]

And they could have done it a year ago or six months ago and found some number or some percentage.

[Alex Stamos]

I had this conversation with the CEO this morning who's like, how do I get into Mythos? Alex, who can call? And I said, don't wait. Here you go. Here's an API key for Opus right now. It works today because do not wait for the Trump administration to figure out what they're doing with mythos. Use opus4.8 right now, and I guarantee you'll get 70% of the bugs you care about.

[Ron John]

Alex, you're a security researcher. One of the stipulations about getting Fable back. So, by the way, Anthropic has said they expect Fable to come back on soon, but one of the stipulations that's been talked about to do that is that they have to prevent it from being able to be jailbroken. Is that possible?

[Ranjan Roy]

Yeah. I saw you did jailbreak in air quotes.

[Alex Stamos]

Yeah. No.

[Ron John]

So we have two minutes left. So talk a little bit about why that's not possible.

[Alex Stamos]

No. There's actually a paper from nist. It's like a kind of a godal completeness theorem kind of paper that basically says it's impossible to make a model that is completely not jailbreakable. Like I said, Fable needs to be able to understand what security flaws are to be able to do its job. I think what Anthropic might. What? What? Anthropic. The game they might be playing here. Is redefine what jailbreak is back to what their system card says. Their system card says we define different levels of cyber capability and finding a couple of bugs and knowing what a bug is is okay, but we won't let you build exploit chains and do lots of long term stuff. If they can define back to their system card what a jailbreak is, then they're fine. What would be terrible for the entire American AI and cybersecurity industries is if the Trump administration defines a jailbreak as having any ability to find any vulnerability in code. Because then all of us have to go to Chinese models.

[Kyle]

Really?

[Alex Stamos]

That's it? Yes. Like that would be a terrible, terrible outcome. And that might happen because unfortunately the news that broke just in the last couple of hours is Politico is reporting that Anthropic and the Trump administration are negotiating what the AI safety standards should be. So I don't know, it's like Howard Lutnick writing an eval right now. I find that a little terrifying. I am hoping that Anthropic obviously there's very smart people anthropic working on this. I am hoping that they are able to come up with something reasonable that moves stuff towards exploitation, not bug finding. We have to have the ability as defenders to find and fix flaws. The other thing I just want because we don't have a lot of time. The other real own goal here was that this injected a type of political instability and political risk into the USAI industry that did not exist. There are a bunch of companies that are now going to have to use open weight models because at any time on a Friday afternoon it's a good thing that Fable is only out for a week because people didn't have time to work into their critical path. But if Fable had been out for a month and at 5pm on a Friday all of a sudden it got yanked, then pagers across the country would have gone off because every systems would have fallen over. And so now this week CIOs and CTOs are signing contracts to have open weight models on different hosts on US hosts. They're not using Chinese hosts, but they're using Chinese models as backups through LLM routers because political risk is now a risk of using AI companies. That is a massive own goal. The United States of America does not need to do that. We should not do that. Again, that is incredibly stupid At a moment of maximum pressure from the People's Republic of China. And so I can't stress enough like we, we lost a war this weekend, like let's not lose another war by making it unreliable to utilize the United States for AI.

[Ron John]

Alex, will you come on the show for a longer conversation?

[Alex Stamos]

Absolutely. I'd love to. Yeah.

[Ron John]

Alex Stamos and Ron Jr.

[Alex Stamos]

Thank you.

[Ron John]

Thank you.

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