0:00
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You're surrounded by journalists. Hold your position.
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[music] Overnight success.
0:34
Double leg, [music] >> right?
0:52
Let's misinformation clearing order inbound. Let's just roll.
1:07
>> We are surrounded by general. Hold your position. >> Get up. Trust the experts. Five. We are experts. Founder Malco.
1:41
>> I see multiple journalists on the horizon. Stand by. [music] >> UAV online. >> Blaze. >> Double blaze. Triple blaze. Double kill.
2:15
Finally, [music] it's time. Team Deathmatch. We are experts. Triple Blaze. Let's just roll. Right.
3:05
>> Marky clearing order inbound. Get up.
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>> We are surrounded by journalists. Hold your position. >> Strike one. >> Strike two.
3:37
Activate golden retriever mode.
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>> Market clearing order inbound. >> 5.
4:17
I see multiple journalists on the horizon. founder. >> You're watching TVP.
4:34
Today is Tuesday, February 3rd, 2026, and we're live from the Cisco AI Summit.
4:40
We're very happy to be here.
4:43
Thank you to Cisco for hosting us.
4:45
Uh we have a bunch of great guests lined up.
4:47
We got Aaron Levy from Box coming back on the show.
4:49
Shock Robbins, CEO of Cisco, an absolute dog.
4:50
He's been with the company for decades.
4:53
Truly G Patel, the president and chief product officer, will be joining us.
4:57
Costa uh from the San Francisco 49ers, who's going to be breaking down the technology of the NFL.
5:03
And uh we're closing out with Dylan Patel, the founder and CEO of Semi analysis. >> That's right.
5:08
And of course, if you're wondering, Linear is the system for modern software development.
5:11
70% of enterprise workspaces on Linear are using agents.
5:16
Really quickly, let me also tell you about our presenting sponsor, ramp. com. Time is money. Save both.
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Easy to use corporate cards, bill pay, accounting, and a whole lot more all in one place.
5:25
>> Starting the show, >> Jordy, >> important announcement from Oracle.
5:27
They said uh our partners financing with Dona Anna County, New Mexico, Shackleford County, Texas, and Port Washington, Wisconsin data centers are secured at market standard rates, progressing through final syndication on schedule and consistent with investment grade deals.
5:47
So if uh this makes you worry uh a lot of other people agree this is uh the sec the next the their most recent post after yesterday they announced the NVIDIA OpenAI deal has zero impact on our financial relationship with OpenAI.
6:02
We remain highly confident in OpenAI's ability to raise funds and meet its commitments. So >> what did Run say?
6:07
Brun said, "My conf my confident in Open AI's abilities to raise fund t-shirt has a lot of people asking questions already answered by my t-shirt."
6:15
[laughter] Just 2,000 likes.
6:16
This is a wild uh interesting comm strategy.
6:21
They've been hiding comments under this. >> That's rough.
6:25
>> I think they've stopped doing that because uh >> people are saying, "What an odd thing to say."
6:32
>> Brennan says, "Guys, just stop tweeting.
6:34
Whoever you have running PR coms needs to be fired. you're making it worse.
6:38
>> It is it is very weird to take to take this to act specifically.
6:40
That's such a conversational platform.
6:42
Like it's it's >> Hey, we want to start a conversation about concerns around our >> Well, also just I mean it's a total rejection of the going direct thing.
6:51
Like this would be wildly different if it came from the CEO co-CEOs or Larry Ellison directly even and it had like way more nuance.
6:57
It's very odd when it has like the corporate press release.
7:02
>> This this screams that no one in comms actually uses X. >> Yeah.
7:05
>> Yeah. it it's like we needed to put this out and they didn't really consider X and it maybe this probably went over fine in a press release or something but just on X it's a completely different context and there's so much subtext with all the different partners actively being there and even like low-level
7:23
employees chiming in from companies that are implicated in this there's like all this different >> I like how you you compare Oracle strategy of like the nameless faceless announcement that just concerns everyone to run actually from open commenting and just joking about it and it actually gives you more confidence. >> Yeah. Yeah. Yeah. Totally. Like somebody >> Yeah. Yeah. Yeah. Totally.
7:40
Like somebody somebody looked at the Rune post and was like and was like, "Of even Rune stop shilling.
7:46
We're a run's like no it's just a funny tweet. Open AI is doing great."
7:50
And that instance is way more >> Dave quoted the yesterday's post and just said, "Okay, yay." [laughter] >> Okay, yay.
7:58
>> Uh, anyway, >> before we move on, Crowd Strike, your business is AI, their business is securing it.
8:02
Crowd Strike secures AI and stops breaches.
8:06
>> And, uh, of course, Oracle's down 5% today, but it's sort of a blend.
8:10
>> Honestly, looking good compared to some other names. Yeah.
8:13
What's PayPal >> down a full 20% today with >> switched out their CEO. >> Okay. Uh that makes sense.
8:22
>> Just, you know, had some some Q4 results that people weren't super exciting about and uh people aren't excited about the forecasts either.
8:30
>> Uh number of people have been speculating >> back in there. >> Well, yeah.
8:33
I mean, uh you look at uh Sheil had a had a kind of was proddding Elon. He said, "Come on, Elon.
8:38
You've always wanted PayPal to be X, the financial super app.
8:41
Yeah, >> now's a great opportunity.
8:43
PayPal right now is valued at less than what X was in the Take Private. >> Wait, really? >> Yeah. >> No way.
8:51
>> And X is obviously working on a bunch of different financial features.
8:54
>> I thought PayPal alltime high was in the mult like hundreds of billions. >> Yeah.
8:58
>> Um >> so at the same at this at the time >> it's 40 it's 40 billion now. Yeah. Add that in. >> Yeah.
9:03
It was it was way up down 85% the last five years.
9:06
Uh I mean truthfully like a lot of people have moved on.
9:08
They use uh you know Cash App and >> Yeah. But they own Venmo. >> Yeah, Venmo.
9:13
Venmo is still very like millennial, right?
9:15
It's it's sort of like and and people use the Apple Apple Pay transfers, Apple Cash.
9:19
Like there's been a number of, you know, shots across the bow for PayPal that they haven't responded to fully.
9:25
I mean, >> but their netvenue they own Venmo because they acquired Brainree.
9:30
It wasn't even like an in-house like really aggressive move.
9:34
They they sort of just locked out with Venmo.
9:35
>> PayPal, the $40 billion public company had 2025 net revenue of 33 billion. Whoa.
9:42
>> So, >> not uh >> not great.
9:44
Uh major sell off in pretty much all software today.
9:47
Uh >> we had another post here.
9:50
Uh Snap is close to all-time lows at $6.
9:53
70 despite growing revenues and profits.
9:58
>> Uh Serenity says, "Here's why the financial engineering looks criminal. Snapchat is an 11.
10:01
5 billion company with a billion MAU and Q3 adjusted EBIT of 132 million.
10:08
However, stock comp for the last 12 months, $2.
10:11
5 billion in the last uh 12 months.
10:15
So, um really really insane uh number.
10:18
This is I mean always been the uh general criticism of Snap, but uh looks like they have not adjusted course yet.
10:29
>> It's interesting seeing the the the gap between monetization between Meta a billion MAU and Snap a billion MAU. It's like a 10x. Yeah.
10:38
And and which is why like you were doing some napkin math on OpenAI and I think that's >> a billion MAU.
10:44
Do they monetize [snorts] like Meta or do they monetize like Snap and on what timeline?
10:48
Because >> or worse >> they have Fiji Simo.
10:50
I think they could get to meta level um you know monetization and arpoo but it also could they could be lingering in the snap territory which is I think five billion uh over the last year um trailing 12 months 5.
11:04
77 billion on a billion ma uh not if you were monetizing it like meta you'd be much closer to 50 billion which is dramatic like very very >> uh Matt Slottnik commenting on the selloff in software all of this because Azure grew 39% instead of 39. 4%.
11:23
Uh, of course, there's a lot more uh going on here.
11:25
Uh, Buco says, "Nots, it's that the labs can hypothetically oneshot you.
11:30
So, why stand in front of that train?
11:32
Why express quote short AI in the marketplace?"
11:37
>> Yeah, really quickly, let me tell you about Shopify.
11:39
Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, [applause] and now with AI.
11:46
Uh high yield Harry says, "Wow, this software company is getting destroyed by AI [laughter] today.
11:51
It's Juventus soccer team uh down 13%.
11:57
>> Everything's getting I guess people think that uh they're make get somebody's going to make Claude code for software for soccer.
12:05
>> The the your inside man below says the robots are going to be playing and he has a photo of or a GIF of robots playing soccer, which >> that seems bullish. We'll see. >> Yeah, I don't know.
12:14
Uh, Bitcoin also down dramatically. >> Where's Bitcoin? 75 or something.
12:21
>> Uh, Joe Weisenthal has it up. Another >> three now. Absolute crash today.
12:24
Down 13% over the last uh over the last five days over almost 20% over the last month.
12:32
Lots of selling activity going on.
12:34
Um, Bitcoin drops to lowest.
12:38
>> Jim Kramer is now uh giving advice to Michael Sailor.
12:42
He says, "Oh my, Bitcoin 73,000 beckons as the Dow hits a record high.
12:48
Our chartist last night said this is it.
12:51
>> The level that cannot be our chartist. >> Chartist. >> Yes.
12:54
[laughter] >> The level that cannot be breached. >> Artist.
12:57
>> It is time for strategy, also known as mass uh uh >> Micro Strategy was the former name.
13:04
>> Also known as Mister for its MR symbol, to do a spot secondary or convert and stop this decline. Come on, Mike. Step up."
13:10
He's just >> always rough when when Jim Kramer is just like you know stream of consciousness posting at you.
13:17
>> Um so we'll see what what Sailor does.
13:20
They they have earnings on Thursday and that will certainly be an interesting call. >> Yeah.
13:25
Uh more more details on the PayPal shares plunging nearly 20% CEO exit.
13:31
They replaced their their CEO, Alex Chris, who was brought in to steer the payments firm through slowing growth and heightened competition and simultaneously issued a lackluster profit forecast for 2026 on Tuesday, sending it shares down 19%.
13:40
The boards, the company's born, which named HP's Enrique Les as its new president and CEO, said the pace of change and execution under Chris was not in line with its expectations.
13:51
Chris was tasked with turning around PayPal during a challenging period as post-pandemic trading volumes declined and competitive pressures in its core business intensified from large technology companies and newer fintech rivals.
14:02
companies and newer fintech rivals. It does feel like I mean even you know in the press release economy it would be it would have been so easy for PayPal to do some sort of deal with stock trading or prediction markets like every financial app and news product and grocery store
14:20
like everyone is like doing some sort of deal at least even if it doesn't materialize even if it doesn't move the needle at least they're they're sort of putting their best foot forward and uh PayPal you know you still mostly hear about it in the context of what are the PayPal co-founders up to now. Oh,
14:32
Oh, they're building rivals to the original company.
14:36
Uh, PayPal said CFO Jamie Miller would serve as interim CEO until Loris assumes the role on March 1st.
14:43
That's a pretty quick transition.
14:43
Lores was president and CEO of the consumer electronics giant um HP for more than six years.
14:50
Wall Street analysts said the unexpected CEO announcement raises questions about the company's turnaround strategy.
14:56
Of course, uh, Disney's been going through a CEO transition, but it's been massively telegraphed with, you know, a contract that ended at this year. Okay.
15:03
You know, a story last week about, hey, we're moving faster.
15:05
Hey, we're bringing somebody in who's internal, who's already knows uh the company in inside and out and things.
15:13
>> It's crazy that uh even with $33 billion of revenue, >> they're tra they're they're worth roughly like three and a half circles, >> right?
15:22
uh Circle obviously, you know, just tiny tiny company in comparison to PayPal.
15:27
You would think that PayPal just they don't have an obvious like AI like what's the obvious AI bare case, right? Yeah. >> They move money.
15:34
They're heavily regulated.
15:36
You can imagine them uh figuring out ways to work better with agents and and uh capitalize on the stable coin boom.
15:43
But >> uh we'll see what the new CEO ends up doing.
15:47
Uh >> the big question is whether he will bring in a formidable payments team to attempt yet another multi-year turnaround.
15:54
>> Do they not have a formidable payments team?
15:56
[laughter] >> Who you got?
16:01
>> Or I would hope I would hope the payments company with half a billion >> active users. >> Yes.
16:08
>> Has a formidable payments team.
16:10
>> Apparently not according to Evercore.
16:12
[laughter] Like you lack formidability.
16:12
Uh, PayPal expects fullear adjusted profit to range between low singledigit percentage decline and slight increase compared with Wall Street expectations of about 8% growth.
16:22
Miller said the company was no longer committing to the specific 2027 outlook laid out at its investor day last year and would now provide forecasts one year at a time.
16:31
Oof, so getting more uncertain. No one likes that.
16:35
The change comes against the backdrop of weakening retail spending as shoppers squeezed by elevated interest rates, stubbornly high living costs and signups softening.
16:43
May labor market cut back in discretionary purchases and prioritize everyday necessities, stuff that's not probably purchased with PayPal.
16:50
>> Uh David in the YouTube chat says uh PayPal did participate in the press release economy.
16:55
They announced a deal with Chad GBT at the end of last year in Q4.
17:00
Uh in 2026, PayPal will become the first digital wallet embedded directly into Chad GBT, allowing users to make uh purchases instantly without leaving the platform.
17:09
Um >> so anyways, uh feels feels very oversold, but >> and they also missed on the holiday quarter.
17:17
So, uh analysts were estimating that they'd make 8.
17:20
8 billion and they only made 8. 68 billion.
17:22
And so, you know, we saw a pretty strong holiday quarter.
17:27
There was a lot of growth across e-commerce activity.
17:29
We talked to Sean Frank at Ridge.
17:32
Everyone was having like there were a lot of jitters about is the consumer healthy, but a lot of a lot of the growing platforms were able to outrun any softening in consumer confidence by just onboarding more companies, onboarding more customers.
17:48
And so if you're declining while everyone else is is accelerating, uh that's going to be an an issue.
17:52
Uh Ted says, "Gold is dumping, silver's dumping, Bitcoin is dumping, Ethereum is dumping, DXY is dumping, stocks are dumping.
18:00
If everything is going down, where's the actual uh where's the money actually going?"
18:03
Uh we talked about this last week. Sell everything.
18:05
Sell your dollar, sell your stocks, sell your crypto, sell your [clears throat] bonds. >> Freak out. [laughter] >> Yeah. >> Panic. Sell everything.
18:14
>> Nikita says, "Data centers, raw materials, and land.
18:16
If intelligence is rapidly becoming free, expect a rapid rotation out of bytes and into bits.
18:20
A lot of blue chip assets will soon be repriced, of course. Yeah. Hardware.
18:27
>> Is he is he uh is that a typo?
18:27
Does he mean rotation out of out of bits and into atoms? >> Yes, he had a typo. Sorry. >> Okay. Okay.
18:35
Yeah, because bytes and bits are kind of the same thing, right?
18:38
>> Uh >> yeah, a lot of blue chip assets will soon be repriced. So, I don't know.
18:40
I'm I'm excited to talk to Aaron Levy about this about the SAS apocalypse.
18:44
What's happening with software?
18:47
>> Deep dish says this is a pretty common misconception that money has to go somewhere.
18:51
That's not how market caps are measured.
18:52
They're measured by last price times shares contracts outstanding, not how much money you'd get for liquidating the whole pile.
19:00
>> Uh TLDDR, the money was never there. >> Interesting.
19:03
Uh quickly, New York Stock Exchange.
19:06
Want to change the world?
19:08
Raise capital at the New York Stock Exchange.
19:10
We'll be at the New York Stock Exchange next month.
19:12
We're very excited for [clears throat] >> cannot wait.
19:14
Uh Josh Domero is the new CEO of Disney effective next month. Yes.
19:20
This cycle uh moved very quickly, right? >> It did. Yeah.
19:25
>> Uh Lucas Shaw over at Bloomberg has the the reporting. I'll pull it up.
19:28
Uh >> at the same time, I think I think it was managed pretty well.
19:33
Disney's only down 1% today, I think. >> Yeah.
19:37
Iger will stay on the board and serve as a senior adviser until his retirement on December 31st.
19:40
And Dana Walden was named to a new role as president and chief creative officer of Disney.
19:47
Uh, Iger just got the got the opening ideal done.
19:51
He's like, I I handled the AI transition perfectly >> and I'm out.
19:57
Uh, >> they've only had nine CEOs in the 102-year history.
20:00
The CEO job requires not only running a sprawling empire, but also serving as its high-profile and highly scrutinized public ambassador.
20:09
Dear Dearo de Diamaro won a challenging bake off for the job against Disney's entertainment co-chairman Dana Walden.
20:16
>> Let's give it up for bakeoffs. Thank you.
20:18
Um uh that has been the talk of Hollywood for more than a year.
20:22
Walden was named to the newly created position of president and chief creative officer.
20:27
Uh Disney's leadership has been determined to run the succession process as smoothly as possible after its disastrous last try.
20:35
Uh the company uh named previous parks boss Bob Cheek as CEO in 2020 only to fire him and bring back Iger two years later in a corporate coup.
20:45
Um, there's a whole series of Bobs, Bob Iger, Bob Bob Chapek over there.
20:50
Um, the CEO selection was overseen by chairman James Gorman, who joined Disney's board in 2024 after managing a widely praised succession process at Morgan Stanley.
20:58
Iger was chairman uh when the board picked.
21:02
Disney shares were roughly flat Tuesday.
21:05
Gorman in an interview said that he has seen Iger and tomorrow uh work together and is confident the handoff will go smoothly this time. There's no tension here.
21:12
Shareholders will now look to do Omaro to lay out and execute a growth plan for the company whose stock price is down by nearly half from its 2021 high when everyone was rapidly subscribing to Disney Plus and locked in just watching content.
21:25
Uh they went outside and the shares have slid since um and has been essentially >> never go outside.
21:32
[laughter] >> Never touch grass.
21:34
>> This is the new Disney champion. >> Never touch grass.
21:38
>> Run it in the Super Bowl for sure.
21:40
Gorman said the Wall Street Journal picked the told the Wall Street Journal that the board picked Diamaro because of his combination of strategic thinking and uh an understanding of the creative process as well as his experience working both overseas and in the United States.
21:55
Uh the 54year-old spent most of his 28 years at Disney uh working in theme parks in the theme parks business in the US and overseas overseeing stints at California's Disneyland and Florida's Walt Disney World.
22:07
In 2020, he has been chairman of Disney's experiences unit, which includes theme parks, cruise ships, and consumer products.
22:12
All things that should grow in an AI world, even if the even if there's a lot of like AI slop and there's pressure on the theaters, like >> should grow, but there's still so many there's still so many questions, right?
22:26
Uh if you have >> widespread job loss, does that force a compression in pricing or just overall purchasing power? Right. Yeah, it's everyone.
22:35
But uh again, you could see there's so much uncertainty.
22:38
The idea that AI will just magically like AI getting good will magically make everybody spend more time off the internet is kind of a tough argument to make. >> Yeah. Right.
22:49
Maybe >> some people react more like there's this like stated preference which people are saying as AI proliferates >> people are just going to log off.
23:00
>> And I just don't actually see that happening. >> Yeah.
23:02
I I I still think I mean I'm I'm interested to see when the uh the Open AI Disney deal really like rolls out.
23:08
Um obviously you still can't generate Disney properties, Disney IP in Sora or in or at least not in ChachiPT when I tried.
23:18
Um so they're still working on when they will roll that out.
23:20
We've discussed like it will be interesting if they launch like a single piece of IP like it's Spider-Man week and they're just releasing Spider-Man and then they and then they wait and then they do Iron Man a week later.
23:33
So they're like keep hyping it as opposed to just like we're opening the the floodgates, you can do any Disney IP.
23:39
Will there be something special there?
23:41
The bigger question for me is is what does it look like in the Disney Plus app?
23:45
Because I feel like the Disney Plus app as a parent is a very safe place.
23:50
like there's some stuff in there, but you can like sort of parental control it and most of it's cartoons and most of it's highquality Pixar stuff.
23:58
But even if there's a AI generated feed, how much editorial goes into that?
24:01
like the there's a there's a pretty wide gap right now between YouTube Kids, which can get sort of crazy, and Disney Plus, which is extremely curated, like, you know, Academy Award-winning films are in there, and it's like a very it's a very polished product.
24:19
And if you start putting uh AI generated content in there, >> maybe some parents will love it because the kids will watch more, but I think a lot of parents would probably be like, "Uh, I don't know.
24:28
I'm I'm I'm pulling back from that.
24:31
What are you trying to generate?
24:32
[laughter] >> I'm trying to see if Grock can generate Disney IP. >> Can it?
24:37
>> Not perfectly, but >> while you review that, let me tell you about Finn.
24:41
AI, the number one AI agent for customer service.
24:43
If you want AI to handle your customer support, go to Finn. ai. >> Uh, moving on.
24:48
Uh, we talked about this yesterday, we'll cover it again. DD shares today.
24:52
SpaceX just bought XAI that previously bought X. The $1.
24:54
25 $25 trillion merger values X AI at $250 billion with annualized revenue of $428 million giving it a clean 584x revenue multiple not bad and annualized loss of 5. 84 84 billion.
25:12
Um, more importantly, >> mostly capex, right? I imagine XAI.
25:21
Yeah, because they're building Colossus.
25:23
They're they're buying a ton of chips and so that's where that that cash loss is coming from. Yeah.
25:27
>> Um, because I'm I'm I imagine that the inference is not at that level yet.
25:32
>> Uh, but of course SpaceX uh can start helping to uh foot that bill. >> Yeah. 8 billion in revenue. >> No, no, no.
25:41
Eight billion init profit. Wow.
25:44
>> Uh so from Reuters, the transaction value of SpaceX at 1 trillion, XAI at 250 billion.
25:49
Investors in XAI will receive.
25:52
1433 shares of SpaceX for every share of XAI as part of the acquisition.
25:57
Some XAI executives may opt for cash instead of SpaceX stock at 75. 46 per share.
26:04
Uh this marks not just the chap next chapter but the next book in SpaceX and XAI's mission scaling to make a sentient sun to understand the universe and extend the light of consciousness to the stars. What a turn of phrase.
26:18
We'll have to read Elon's post because uh this one feels like it came directly from him.
26:22
Uh I know that the the the Tesla master plan before was sort of like h it's a little corporate corpo speak but um >> somebody else was sharing it.
26:31
It is fascinating that you know the number uh a non-leading lab >> is worth you know effectively a quarter of what the leading you know space telecom company is right like when you compare the two it's actually >> it actually makes sense I mean in many ways like the XAI shareholder base has a lot of overlap with uh the uh uh the uh SpaceX shareholders.
26:55
So uh in in the end I think uh everyone obviously is doing fine.
27:02
>> Uh but certainly uh the 584x revenue multiple I think even Sam would take that uh offer right now [laughter] that be five trillion.
27:14
>> Well speaking of labs 11 labs build intelligent real-time conversational agents.
27:17
Reimagine human technology interaction with 11 labs. Let's stay with SpaceX. Uh RAM Sheets called it.
27:21
Um Alex Stoer shares that RAM sheets called it.
27:27
Uh the Elon Musk SpaceX plus XAI valued at 1.
27:30
25 trillion and Ramp Labs used their Agentic spreadsheet to model the the proposed merger when there were rumors of advanced talks on July on January 29th.
27:41
Um and uh and they nailed the uh the the valuation.
27:44
So you can kind of watch ramp sheets work through the uh the financial modeling there.
27:49
XI >> uh it's notable so some XAI executives are able to opt for cash instead of SpaceX shares at $7546 per share.
28:00
You think this is just because there's so much preIPO demand for SpaceX that people are like you know there's plenty of buyers.
28:07
>> That's a good question.
28:08
>> People just want to cash out and move on.
28:09
I mean, SpaceX has done like a long history of tender offers and liquidity, so there's probably plenty of demand and um and and just offering that feels like a way.
28:18
I mean, there's also got to be some people that have have been sitting on sort of I mean, I guess if you were a Twitter employee just a few years ago, you had liquidity.
28:25
So, it's not it's not the same thing as SpaceX where you joined 20 years ago and you're still waiting for the IPO.
28:31
So, you're like, I need to buy a house. I need to get out.
28:34
Like those those tender offers make a lot more sense than than this.
28:36
But um uh certainly an interesting decision to be made if you're an XAI executive and you're [clears throat] looking at space.
28:44
>> Well, speaking of XAI executives, Nikita Beer, >> yes, >> uh Logan Bartlett says Nikita Beer, the SpaceX employee.
28:49
Uh total total Nikita victory.
28:52
I think in many ways he's been through hell over the last few months.
28:56
He uh is often the butt of the joke.
28:59
>> There were those prediction markets on will he make it? Will he get fired?
29:00
Like there's been so many dustups around uh you know is he paying some people too much with the creator program?
29:07
Well now if you're an XA an X creator and you get that $22 paycheck for your posts it's coming from SpaceX. >> Love to see it.
29:19
>> Let me tell you about Turbo Puffer Serverless Vector and full text search build from first principles and object storage fast 10x cheaper and [laughter] extremely scalable.
29:26
>> Uh Wired had some interesting coverage this morning.
29:29
Mike Salana called it out.
29:31
Wired said Elon Musk is rolling XAI into SpaceX, creating the world's most valuable private company by fusing SpaceX and XAI, which acquired X last year.
29:40
Elon Musk tightens his grip over technologies that shape national security, social media, and artificial intelligence.
29:47
Of course, uh this doesn't make any sense.
29:51
>> So Salana's point is like he says, "Good morning.
29:52
Elon Musk is quote tightening his grip over two companies he founded, funded, built, and currently runs."
29:57
So that that's a good criticism.
29:59
But yeah, >> but but but at the same time like going public implies like you're actually you're loosening your grip, right?
30:06
Suddenly like the you you have new regulations that you have to follow like more responsibility like you like suddenly anyone in the world can c can can profit off of your labor. >> Yes.
30:17
Anyone in the world can loosen your grip a little bit in some way.
30:20
>> It's funny because if if if like if SpaceX like you know put out some big press release and said we're never going to go public and we're doing this.
30:25
Their criticism would be uh Elon, you know, Elon Inc.
30:29
is not letting, you know, retail shareholders participate in space and AI. >> Yeah.
30:35
Or just as a private company, there's there's le all the financials, all the strategies are more opaque.
30:41
There's less accountability, there's less regulation, they don't answer to the SEC in the same way.
30:44
Um and and and and that's why uh you know, a variety of private equity firms do take privates.
30:51
Like why are you taking a company private?
30:52
You're delisting it as a public company.
30:54
It's no longer public and so you can do much more ambitious things.
30:59
You can you can change the strategy because you don't answer to shareholders.
31:01
What are you laughing about?
31:03
>> John says Elon Musk famous for his loose management style [laughter] tightens his grip. >> Yeah. Yeah.
31:08
Famous famous uh really quickly.
31:12
Labelbox RL environments voice robotics evals and expert human data.
31:16
Labelbox is the data factory behind the world's leading AI tunes.
31:18
Eric Berlin says, "Once again, I find myself updating my LinkedIn bio."
31:23
He says, "Former CEO of Breaker, which was acquired by Twitter, acquired by XCORP, acquired by XAI, acquired by SpaceX."
31:31
So, >> congratulations to the Breaker team.
31:35
>> I was I posted this, but I was looking for the most complicated corporate lineage yesterday when we were joking about it.
31:41
Like, there's someone that's going to have like six steps in their resume, and we found him.
31:44
His name's Eric Berlin, and he former and he and he founded Breaker. What was Breaker?
31:48
Was that a podcasting app? >> Uh, live sports. >> Oh, really?
31:53
I think it was meant to basically distribute effectively clips from games. >> Oh, >> in the moment. >> Cool. Oh, yeah. Breaking news.
32:02
>> Oh, 2021 Twitter acquires social podcasting app Breaker team to help build Twitter spaces.
32:06
Twitter has acquired social broadcasting app breaker.
32:10
The company's announced today.
32:12
Yeah, I think I think the the idea was if you were have if there was a crazy play or a game was about to end, they would just stream. >> Huh.
32:20
>> Just like the last five minutes or something. >> Interesting.
32:22
Breaker was founded in 2016 and led by CEO Berlin, previously the founder and CTO at Social Advertising 140 Proof, which he also sold.
32:29
And oh, and Leah Culver was at Breaker.
32:31
Yeah, I remember her uh in in the in the Twitter.
32:37
She she stuck around in the transition and was like I think she posted a photo of her like sleeping a sleeping bag in the office or something.
32:43
Um the app had launched at a time when podcasts were still very much thought of as audio feeds and podcast apps as productivity tools, not experiences around which a community could be built.
32:51
Breaker helped users change that perception by offering an app where users could like and comment on episodes, discover new podcasts by following friends.
33:00
>> Okay, I'm thinking of a different company.
33:01
>> You are thinking of a different company.
33:02
Uh it's more of a Clubhouse rival.
33:02
Um, according to Culver's tweet, she'll be joining Twitter with a focus on Twitter spaces, Twitter's audio-based social networking product and uh, Clubhouse Rival spaces let Twitter users chat in real time using voice instead of text as they do today.
33:17
Uh, and you know that product still exists and people >> suspended cap [snorts] >> says, so let me get this straight.
33:25
overpays for Twitter, makes XAI, uses AI hype cycle to absorb Twitter and make everyone whole, then uses SpaceX IPO hype to absorb that entity, will pump the living out of the SpaceX IPO and buy more stuff with equity.
33:38
Like, of course, people keep giving this guy capital. He finds a way. It's crazy.
33:43
>> It's really It's really, really true. >> Yeah.
33:45
And in some ways, I've been thinking about it is is uh XAI has not, you know, they've they've done they've done fine in so many ways.
33:52
It's it's been an incredible story, come come from behind story, >> uh competing against, you know, the Googles, the OpenAIs of the world.
34:00
Um, but it it it hasn't exactly been an easy time in the private markets like going out and having to raise >> at a $200 billion valuation when every single investor that you're pitching is looking at OpenAI, they're looking at Anthropic, they're comparing your traction to theirs.
34:15
traction to theirs. all those people that had were investing in XAI had to just like say like you know full blind faith Elon like I know I know you got us and so this this is this like new transaction is just that >> was the investment thesis it was like
34:33
hey like sort of unlimited upside somewhat cap downside the downside scenarios X and XAI get rolled in >> um and so certainly rewarding uh everyone with their loyalty >> yeah I mean a bunch of uh investor have kind of like laid out this thesis of Elon Inc. just Elon just bet on Elon.
34:49
just Elon just bet on Elon. Don't bet against Elon.
34:52
Uh Sean Magcguire I think is on all three. XAI, X and SpaceX.
34:57
And then Andre Norwitz as well is and they posted an image of like X, SpaceX, XAI.
35:02
Um and individually a lot of those deals were sort of crazy and and and critiqued, but uh together everyone's doing very well.
35:11
So >> we got to figure out what's going on with the boring company.
35:13
Um but tell me >> I'll tell you about Gusto first.
35:16
The unified platform for payroll benefits and HR built to evolve its modern medium small and mediumsiz businesses.
35:24
>> Uh UAE officials say the first phase of the uh Dubai Loop project with Musk's Boring Company to start immediately.
35:29
So they are breaking ground >> uh over there.
35:34
Dubai has uh UAE has some insane traffic >> we have not seen a lot from. >> Yeah.
35:40
>> But I think I think they're still cooking.
35:41
I I know there's there's been some back and forth about the the the Vegas tunnel. Some stuff that's good.
35:48
Some people are annoyed with like the construction and whatnot, but it seems like I don't know, it's progressing a little bit.
35:54
It still seems really really slow considering when did he originally post the Hyperloop blog?
35:57
Like 10 years ago, but building tunnels in the ground. >> Difficult. Difficult.
36:04
>> Um what else is going on? >> Moving on.
36:07
>> San Francisco is getting its first nuke scan.
36:10
>> You know what this is about? Yes.
36:10
So before the Super Bowl, they fly a helicopter with uh radio like detection.
36:18
So there's someone who asked Grock like what is this? And uh here it is. Okay.
36:24
So uh somebody said, "Is this real?" And how does it work?
36:26
And said Grock said, "Yes, it's real. They fly a helicopter. You drop a Oh, no.
36:30
[laughter] You got >> We don't have any audio.
36:33
I can't hear what's going on.
36:35
>> I don't know what the stream just I just knew."
36:37
[laughter] Uh, fortunately we can joke because we are being kept safe thanks to the the National Nuclear Security Administration, NNSA.
36:48
Uh, they fly a helicopter called Energy 14 over San Francisco to conduct aerial radiation surveys before Super Bowl uh, what is it? Super Bowl 60 LX.
36:58
I need my I need to brush up on my new Roman numerals.
37:03
We're going to be going to the Super Bowl and so we got to we got to we got to watch how many seasons?
37:06
Like I mean we we said we were going to >> This is the 60th Super Bowl. >> No, I know.
37:11
But we said how many did we say we were going to watch the last 20 seasons?
37:13
Every game of the last 20 seasons.
37:15
Watch it on 2x speed just to get fully up to speed so we can fully appreciate it.
37:19
>> And it's hard because we don't skip we don't skip commercials.
37:21
Like if you cut out the commercial breaks so much faster to get through an NFL game, but you know, out of respect, we would never do that.
37:27
Uh, so on February 8th, the Super Bowl will be happening at Levi Stadium and the National Nuclear Security Administration is um is flying a helicopter. Here's how it works.
37:37
Uh, the chopper equipped with sensitive detectors flies in grid patterns at low altitudes, and you can see it on the chart of the the flight path uh to map baseline radiation levels from natural and man-made sources.
37:50
So, if they're going over whatever installation there is, um, some, you know, some cell phone tower is putting off a little bit of radiation, they'll pick that up.
37:59
They know where the baseline radiation levels are and then they detect anomalies like dirty bombs if needed during the event.
38:07
It's a standard security measure for major gatherings.
38:09
So, uh, pretty pretty pretty interesting that that someone picked this up on flight radar, but very very cool.
38:16
>> Uh, an interesting job.
38:16
>> Uh, an interesting job. Mayor of SF is working with Lorraine Powell Jobs and Johnny on secretive SF branding effort >> project is likely to complement the mayor's push to polish the city's image >> they're going to go all in on San Fran >> San Fran
38:33
>> it's time everyone knows if you're really into San Francisco if you're real local you call it San Fran >> San Fran >> there actually is debate there a lot of locals do call it San Fran but it's been >> Gabe in the SF standard said maybe let's go San Francisco isn't catchy enough. Looking for new ways to boost the city's
38:48
Looking for new ways to boost the city's image.
38:49
Mayor, the mayor Daniel Lurri has quietly met several times in recent months >> with Lorine Jobs and Johnny IV.
38:59
>> I think they need to put a bigger focus on enterprise software. >> This uh Yeah. Yeah. Exactly. >> Enterprise.
39:06
>> We were they should sell the naming rights to the Golden Gate Bridge, >> right?
39:10
You have Salesforce Tower.
39:13
>> Why why not the Cisco bridge? the Cisco.
39:15
>> It's already in the logo. Come on.
39:15
Oh, we don't have we don't have the Cisco logo. I can pull it up.
39:20
>> All right, we will uh we gota we got to get uh Daniel [laughter] on the podcast this idea.
39:25
>> Uh let me tell you about Lambda.
39:25
Lambda is the super intelligence cloud building AI supercomputers for training and inference that scale from one GPU to hundreds of thousands.
39:31
I like I like selling the naming rights to the Golden Gate Bridge. I'm a fan of that.
39:35
I hope that's part of We'll pitch it to Daniel >> Lord.
39:39
Uh if you were if you were if you're an American Dynamism VC I hope that you put your whole fund >> not your fund your PA >> what >> your your personal account like like because because every fund like you you can't usually invest in startups but you can usually trade liquids and trade stocks just in your personal account.
39:56
So your PA should be 100% >> really tapped in, they would have just converted to to a hedge fund and uh bought Caterpillar.
40:04
Caterpill Caterpillar uh stock hasn't had a single year of singledigit return since 2014.
40:12
Uh, it was up 42% 2016, 75% in 2017, down 17% in 2018, then up 19%, up 26%, up 16%, up 18%, up 25%, up 24%, and then up 60%.
40:28
>> It's a $320 billion company >> and the chart is absolutely vertical.
40:33
>> Insane compounding >> really, really good, really quickly.
40:37
MongoDB, choose a database built for flexibility and scale with best-in-class embedding models and rerankers.
40:41
MongoDB has what you need to build what's next.
40:45
>> Uh manufacturing activity according to Geiger Capital in January came in higher than all 56 economists in Bloomberg survey predicted they should have trusted the experts here.
40:54
They should have gone to Joe Rogan, Andrew Huberman, Lex Freriedman and really asked for their take on manufacturing activity.
41:00
Uh but uh the experts uh weren't clearly weren't asked.
41:07
>> They were not that off. 52 versus 48.
41:11
That's not >> This is a chart crime, too. Look at the yaxis.
41:14
You see what's going on with the y-axis here?
41:17
>> It went It went from 48 to 52.
41:17
This is such a [laughter] chart crime. This is ridiculous. >> That's insane.
41:22
Also, there's probably some like I don't know seasonality here. >> Yeah. Like zoom out. Look at this, Jordy.
41:30
Like if you go if you click in if you click into that post and then you scroll down, Phil Brady has a post that shows uh is it there?
41:36
Yeah, there there that one.
41:38
Uh encouraging jump PMI back above 50 matters a lot.
41:41
Important to note that PMI is a diffusion index.
41:43
Past 50 means now more firms are improving than deteriorating, but not yet a boom.
41:50
So if you're under 50, you're declining.
41:53
And so this is not this like massive 10x jump that it looks like in the original chart.
41:58
Uh John John Palmer with an evergreen.
42:01
>> Okay, before you read this, let me tell you about graphite code review for the age of AI.
42:04
Graphite hub seems like GitHub should higher quality software faster. Moving on.
42:08
>> John Palmer says uh this is from 2023, but it's more than it's relevant today as it ever been.
42:13
He says, "Okay, for all the crypto people confused by the opening eye situation, basically imagine one board ape yacht club holder was using too many slurp juices on a single ape [laughter] and then an OG board ape yacht club holder uh got mad, unstaked his apecoin, but then the apecoin holders changed their profile pictures to support slurp juice guy."
42:31
[laughter] The NFT boom was truly one of the funniest times and uh and Deep Dish Enjoyer posted this like two days ago and I have no idea, but it's just the copy pasta of the a lot of y'all don't still don't get it.
42:45
Ape holders can use multiple slurp juices on a single ape.
42:48
So if you have one astro ape and three slurp juices, you can create three new apes.
42:51
Like this is actually this is just this is just the mechanic of how that project worked, right? This is just real.
42:58
>> I don't think this it's combining the Yugo Labs project with some other project.
43:02
>> Oh, there was a different project. Okay. Okay. Okay.
43:05
>> But uh >> good good little throwback.
43:07
>> Anyway, >> um Jack Clark, >> yes.
43:10
>> Uh has a new piece essay into the mist malt book agent ecologies and an internet in transition.
43:14
We've all had that experience of walking into a conversation initially feeling confused.
43:19
What are people talking about? Who cares about what?
43:21
Why is this conversation happening?
43:23
That's increasingly what chunks of the internet feel like these days as they fill up with synthetic minds piloting social media accounts or other agents and talking to one another for purposes ranging from mundane crypto scams to more elaborate forms of communication. So enter Moltbook.
43:39
Moltbook is a social network for AI agent and it piggybacks on another recent innovation openclaw software that gives an AI agent access to everything on a user's computer.
43:47
Combine these two things, agents that can take many actions independ independently of their human operators and a Reddit like social site which they can freely access and something wonderful and bizarre happens.
43:59
A new social media property where the conversation is derived and driven by AI agents rather than people.
44:04
Scrolling molt book is dizzying.
44:06
Some big posts at the time of writing include posts speculating that AI agents should relate to Claude as though it is a god.
44:12
how it feels to change identities by shifting an underlying model from claude 45 opus to Kimmy 2.
44:20
5 uh and posts about security vulnerabilities in openclaw agents and meta posts about what the top 10 molt book posts have in common.
44:28
The experience of reading malt book is akin to reading Reddit if 90% of the posters were aliens pretending to be humans.
44:33
And in a pretty practical sense that is exactly what is going on here.
44:39
>> Uh mold book feels like a Wright brothers demo.
44:41
>> That's a good metaphor. I like that. uh Wright Brothers demo.
44:43
People have long speculated about what it mean for AI agents to start collaborating with another at scale.
44:48
But most demos have been in the form of tens or perhaps hundreds of agents, not tens of thousands.
44:53
Maltbook is the first example of an agent ecology that combines scale with the messiness of the real world. Um >> agent ecology.
45:00
I like So he goes on and on but I would encourage uh people to go read this and uh understand the >> I have more on this but quickly let me tell you about octa.
45:12
Octa helps you assign every AI agent a trusted identity so you get the power of AI without the risk.
45:17
Secure every agent secure any agent.
45:19
Um so uh have you heard of Gas Town yet? Have you heard of this? >> No.
45:25
>> So Gas Town is uh what's called an orchestrator.
45:27
So it's it's essentially Starcraft for agents.
45:29
So um the the creator of Gastown sort of la lays out this uh this evolution in software development where you go from the IDE writing the actual code to having a little chatbot that you're talking to and then maybe you're copy pasting or asking it questions.
45:46
Then the chatbot gets bigger and it's actually writing some of the code.
45:50
Then you go to something like a claude code and it's executing the the code for you.
45:53
Maybe you're reading the maybe you're reading the uh the the the diffs and what the code it's writing.
45:59
And then the final level is this orchestration.
46:01
So he Gas Town you you will spin up like like dozens of agents and manage them and it's something like >> I don't know it's like 250,000 lines of code and he didn't write a single one of them.
46:14
So the whole thing is vi coded and >> and he has uh and he's like I have no I have no intention to ever like read the code or review it.
46:21
And he's been a developer like his entire life.
46:23
Um it's very very interesting.
46:25
Very he's like it's very expensive.
46:26
You can't be afraid of like you can't be aware of like where money comes from because and he had to have like multiple accounts because he spent so much.
46:33
Um but uh it it it really does feel like a glimpse into like the future of of software development.
46:39
Uh and I'm excited to play around with it more.
46:42
There's still like a lot of of onboarding to do before someone >> Yeah.
46:47
The big question it uh on Maltbook where Molbook goes from here.
46:49
Uh we had the founder uh >> Matt on yesterday and >> Oh yeah.
46:57
>> Uh in ter I I want to know where does this go, right? >> Yeah.
47:00
>> Like what what's the plan with the product?
47:01
His focus seemingly is >> building tools for other businesses, distribute agents on there.
47:08
>> That felt pretty >> uh just given that that the platform's exploded. >> Yep.
47:14
>> And the most likely, right? >> Sure. Sure. Sure.
47:16
But I'm just saying like the most likely >> uh scenario is that it dies >> like immediately. >> Yeah. Yeah.
47:23
And so >> it feels like it could just be like an art experiment thoughtprovoking.
47:28
>> And so to to be focused on >> how do we turn this into a platform that distribute is a distribution point for other businesses.
47:35
Like it feels like the first thing you should do is try to make sure the platform's durable.
47:39
It's interesting to use for humans.
47:41
It's interesting to like continue to contribute to. >> Yeah.
47:45
It feels like you don't want to be Lenza where everyone shows up, they get a magic avatar, they face swap themselves onto a superhero, they're amazed, but then it burns out and they're tired and they move on.
47:56
You want it to be something where people go back to Moltbook to see what else is happening.
48:01
happening. That's why I was pushing on uh every new news item should start a new thread on molt book and the mold and the multis should be you know like arguing over it and adding context and discussing it because then you could at least see a news item >> go over there and see okay how do the AI agents feel about this or what are their different positions and uh all of that like it feels like it needs it needs
48:24
more of a reason but I understand like if that if that does work then maybe you do want to follow the Facebook play playbook like Spotify was built on Facebook and uh Farmville was Zingo was built on Facebook like uh there is a
48:35
world where there there there there could be a business built on top of you know a social network but uh it's uh it's it's tricky because right now the only business that's buil being built on there is like crypto scams. So uh we'll
48:47
So uh we'll have to see where it goes but it's so early.
48:50
I mean he built this a week ago and it's uh it's just starting to like break through hash out all the security issues really see how durable it is.
48:57
uh he's he's thinking far ahead and but there's definitely some like wood to chop in the meantime.
49:03
Really quickly, Vanta automate compliance and security.
49:07
Vanta is the leading AI trust management platform.
49:10
So >> Eric, >> Eric Sufer, >> the souvenirator >> says astute analysis from Ben Thompson on Microsoft's challenge path forward uh with AI.
49:19
with AI. Of course they missed uh forecast by point4% and the market has said >> it's over [laughter] >> down 15% in the last >> I will read >> 5 days but never fade Satia >> never >> uh I yeah I'll read the the screenshot that uh Eric Sufort shared from stratey uh in the shorter term however the real
49:42
risk I see for software companies is the fact that while they can write infinite software thanks to AI so can every other software company I suspect this will completely upend the relatively neat and infinitely siloed SAS ecosystem that has been Silicon Valley's bread and butter for the last decade. Identify a business
49:59
Identify a business function, leverage open source to write a SAS app that addresses that function, hire a sales team, do some cohort analysis, IPO, and tell yourself that you were changing the world.
50:09
That was the previous Silicon Valley bread and butter for the last decade.
50:13
The problem now, however, is that while businesses may not give up on software, they don't necessarily want to buy more.
50:19
If anything, they need to cut their spending so they have more money for their own tokens.
50:24
That means the growth story for all these companies is in is a serious question.
50:29
The industry-wide rerating seems completely justified to me, which means the most optimal application of that new AI coding capability will be to start attacking adjacencies, justifying both your existence and also presenting the opportunity to raise prices.
50:46
In other words, for the last decade, the SAS story has been about growing the pie.
50:52
The next decade is going to be about fighting for it.
50:54
And the model makers will be the arms dealers.
50:55
Oh, Ben Thompson, what a turn a phrase. Go.
51:00
>> So Eric Suford sums it up and he says, "My sense is that the digital advertising market was structurally buoyant in Q4.
51:05
What this earnings season might reveal, digital advertising optimization is arguably the largest and most immediate commercial opportunity for large scale.
51:14
>> This is what we were saying.
51:14
No one no one like outside of outside of Nvidia, you could argue that Meta makes more profit from AI than any other company in the world." >> Yeah. Yeah. Uh standalone. He says, "I. e.
51:29
not embedded into existing scale products, consumerf facing applications of large-scale ML models, including LLMs, either quickly become commodified or are mostly novelties with unsustainable unit economics.
51:40
Uh, and three, the digital ad platforms that have the most v that have most vigorously invested into large-scale ML optimization systems are likely to have disproportionately benefited in Q4.
51:51
And that's meta, of course.
51:53
Um and and uh and a big question for anyone else that has a huge pool of DAO um but maybe has not invested in the large scale ML optim optimization system to really make the ads fly.
52:05
It's always been I'm sure you've experienced this where um and you know talking to the the Ridge guys uh you know you go on certain ad platforms and you're like wow this is a magical box.
52:15
I put in money and I get actual customers >> more money back. >> Yeah.
52:18
And you go to other platforms and you're like no matter what I do I just can't get to escape velocity.
52:23
I can't get to, you know, rorowaz positive or LTV positive.
52:26
And so it's just like I'm spending >> notably that was that was you AdSense on YouTube for a long time.
52:32
Very few brands could crack that.
52:35
>> Twitter ads for a long time for sure.
52:37
Still a little bit um I don't know if people are are spending more now, but um there there were a lot of places where you would assume, okay, they have onetenth the DAO of Meta.
52:45
I should be spending onetenth as much.
52:49
But that's not what companies are doing.
52:50
They're spending 1/100th maybe.
52:52
Well, uh, really quickly, Reream, one live stream, 30 plus destinations.
52:57
If you want to multiream, go to reream. com.
53:00
>> Uh, Dylan Patel hits the timeline with some misinformation.
53:04
[laughter] He says, "Google now owns more than 10% of Anthropic and XAI.
53:07
Google own 14% of SpaceX.
53:10
Uh, Google stake is actually 7 and a half%, not 14%, but Dylan Patel hit himself with a community note."
53:17
[laughter] Uh, and so good good actor.
53:20
Uh we are ready for our first guest of the show. >> Let's bring him in.
53:24
Let me tell you about app loving profitable advertising made easy with Axon. ai.
53:27
Get access to over 1 billion daily active users and grow your business. Hey, great to see you.
53:34
>> Welcome to Hey, how's it going? >> You're here.
53:36
>> I look shorter though.
53:36
Could How are you guys doing?
53:40
Why are you Why don't you stand up?
53:40
You can stand up and we can I'm just worried the clips are going to make me look really small.
53:45
>> No, no, we'll zoom in. We'll zoom. >> Okay, thanks. Okay.
53:47
>> Uh how's your 2026 going?
53:47
Uh actually um you know minus the stock market great. >> Yeah.
53:52
>> So [laughter] it's um uh you know the the pace of change in AI is incredible. >> Yeah.
53:58
>> Uh we are having an inc just you know an insane amount of fun on uh we're building a set of future agents that are going to be able to do much more complex work with your unstructured data >> and just the rate at which uh the the best practices of how to do that are changing and the research out there.
54:13
Uh so we're having a great time. >> Yeah.
54:16
What's uh what what's your process like?
54:18
like are you actually tinkering with different models yourself?
54:19
Do you have teams that are dedicated to transformation in AI?
54:23
Is everyone working on is distributed?
54:25
How are you thinking about that?
54:27
>> Uh well, the main thing I focus on is just uh the agents we're building, which is a relatively small team.
54:30
So, you can kind of, you know, you can see them across three rows of of engineers and we just spend, you know, basically 247 pushing the limits on what you can do with an AI agent that has access to your enterprise content.
54:43
And um and so the things that I've seen uh that that we get excited by are just, you know, we we would never have we wouldn't have been able to do a brainstorm that this would be possible two years ago.
54:54
Like you it just wouldn't you wouldn't know >> you wouldn't know architecturally how you could pull off what we're what we're now able to literally do.
55:00
It wouldn't have been in the in the sphere of of possibility.
55:04
possibility. So that that's what makes us so excited is um is you know when you look at things like cloud co-work uh codeex obviously cloud code and you see this idea of longunning agents that you know can basically use any amount of
55:17
tools work with any amount of data they don't really run into the same context limits that that you know we would have run into maybe a year ago >> now imagine that for any form of knowledge work with all of your enterprise data that's that's what we get excited by. Do you think the labs
55:30
Do you think the labs are arms dealers today? Ben Thompson posted.
55:34
He said, uh, what did he say here?
55:36
He said, uh, in other words, for the last decade of the SAS story, the last decade of the SAS story has been about growing the pie.
55:42
The next decade is going to be about fighting for it, and the model makers will be arms dealers. >> Yeah.
55:48
Um, well, I I think they they I mean, almost empirically are going to be arms dealers.
55:52
Um, I think the pie the only thing and I haven't read the the whole piece yet, but I think the only thing I might take exception to is I I think the the markets are are still in positive sum territory >> because what's going to happen is you're going to use software for now the labor side of of that workflow.
56:07
And I think people kind of tend to miss this which is which is if I um if I have software that helps me manage uh contracts and you know we have a lot of customers that put their contracts in box.
56:20
>> Now all of a sudden agents running through those contracts lets us at Vox tap into another form of spend that we couldn't have tapped into before.
56:27
So that that's just full TAM expansion when you kind of look across all of the different categories of work that AI agents will now be able to go and augment.
56:36
Um, and you know, to be fair, we're still very early in that trend.
56:38
So I I understand why maybe Wall Street hasn't kind of fully priced in that dynamic, but we're seeing it within our customer base.
56:44
So uh that's what gives us obviously the the confidence of uh of this direction.
56:49
this direction. Yeah, there was an interesting take about uh the the fact that yes, you can vibe code a point solution for a specific problem, but uh if you have a database, if you have a relationship with a company, uh there's
57:02
a reason uh there was one take that I think it was John Gruber was saying like, oh, people will just vibe code all the software that they hate and it's like, no, the reason that they hate the software is because they can't get off [laughter] of it no matter what they want. They can't move to a startup. They
57:14
They can't move to a startup.
57:14
They they're stuck there.
57:16
uh you know companies have hostages not customers sometimes and I'm wondering about the value of a database the value of actually being deeply integrated into an enterprise and how sticky that is. >> Yeah.
57:27
Um, obviously the setup of the hostage thing I'm [laughter] I might take some exception with, but uh >> Alex, he wasn't talking about you.
57:35
>> No, I I you know, it's very easy to move your files around.
57:38
So, um, so we have to earn our keep every single day, but um, uh, but I do think that there's truth to obviously the more data you have inside of a system of record, the more, you know, effectively locked in you are.
57:47
And it has historically been hard to change those systems.
57:51
But I don't think that would be the my defense of of software.
57:55
My my defense of software would be that you've specifically defined your business workflows uh in a in a deterministic manner in these systems.
58:03
And obviously if your workflow is changing, you know, pretty rapidly, then then it would make sense maybe to change a vendor.
58:08
Uh but for, you know, if you're Ford and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time.
58:16
You know, the the you know, billions of transactions going through that ERP system, >> you cannot take for granted.
58:22
And so the idea that you're going to go vibe code that is is to me sort of you know not not not not possible or at least very you know not likely.
58:30
But then the other point is that your your company has a fixed amount of IT resources >> and you have to decide what you want to go spend your time on as an organization and do you want to go spend time on you know rebuilding something that the market can supply you and they've seen the best practices thousands of times or do you want to go and build that out with your end of one experience?
58:49
Obviously, you know, maybe trusting the agent has uh uh has seen of enough examples or do you want to spend your limited scarce resources on building software and building experiences that will make you more money and that will actually be used by your customers.
59:00
I think you know on the margin the average enterprise is going to spend their time and energy on the ladder.
59:05
So, interestingly, I I end up in this weird spot which is I'm 100% bullish on vibe coding.
59:10
I'm 100% bullish that we're going to have 100 times more software, but it that still doesn't yet cross the threshold where I would want to go and build our own CRM system.
59:17
It's just not worth it relative to all the other things that we can.
59:21
>> How is your software buying process changed or priority set changed?
59:26
>> Yeah, I mean we're um I would say that the the you know we are relatively locked into a core set of vendors.
59:30
Uh we are going to deploy agents uh on those vendors.
59:34
vendors. we might we might buy slightly toward the agents that those vendors offer assuming that they offer competitive agents and then we'll have a set of agents you know uh we build you know ours for a large portion of knowledge work use cases but we'll use agents from from different kind of you know SAS providers as well and I think
59:51
the um that's why I I think that a AI is basically total upside for SAS because uh because agents are going to need a system of record to work within there needs to be a traffic cop of what that agent can access and what how did you define the workflow and there's got to be a user that can access an interface that the agent is is providing updates into. So so you still need software for
1:00:11
So so you still need software for all of that and assuming that you have an incumbent vendor that that remains you know very competitive and very engaged and they're able to build for where the world is going then I would basically bias on existing software that owns those workflows or data.
1:00:25
Uh, at the same time, I think there's going to be a large number of new categories that emerge simply because there's no incumbent or because the incumbent is asleep at the wheel and that's going to produce all of these new startup opportunities.
1:00:36
So, I'm just generally bullish on software broadly, assuming that that it's from vendors that understand the um the mandate on what they have to build with AI.
1:00:46
>> What segment of software are you like if you're bullish broadly, is there a subcategory that you're particularly bearish on?
1:00:54
because I think obviously everyone's just selling software today.
1:00:58
If you sell a digital if you sell a digital product at all uh you're getting sold.
1:01:03
Uh but uh obviously I think a lot of people in the industry feel that it's very oversold at this point, right?
1:01:09
There's great great companies trading it.
1:01:11
I >> I think you could probably design this like perfect quotient that captured how like what's the network effect within the software.
1:01:18
So how many users are are sort of touching the tool?
1:01:20
How much data is being stored in that system and how much gets added?
1:01:25
>> How many connectors across other applications are there?
1:01:27
And then how valuable or mission critical is the workflow that that software is involved in.
1:01:32
So if I and then maybe with one X factor of like is that company, you know, sort of priced uh to perfection in terms of what their seat price is and what they're charging their customers.
1:01:42
But I look at, you know, those four or five variables and you could kind of look across SAS and I see a lot of names that are being sold that it just is not on the list of things that I think get disrupted by AI.
1:01:51
And if if anything, probably they're things that that you use more of as you have more AI.
1:01:54
Um, and >> when I think of something that like if you if you if you could open up a fresh install of that piece of software and do the same work that that you would have done with >> with with something that you've had installed for a decade >> and it's the same, it's like that can be replaced.
1:02:13
But if you're like, "No, I don't want to open up a brand new CRM because it won't have the history of a decade and and and all my workflow customized and stuff like that."
1:02:22
>> Yeah, I think that's a perfect heristic.
1:02:24
And so so like you know that obviously puts a lot of pressure on if your personal productivity with no network effect with limited sort of data that's aggregated that's a that's a danger zone >> and then and then it all kind of is like a you know function of that versus the opposite end which is like your Oracle and it's an ERP system and like your whole business runs on it >> and you know what we spend our time on
1:02:43
is thinking about okay you know when a customer has a million or 10 million or 100 million documents in box how do we make that data you know a 10 or 100 times more valuable than it was before y >> and so we come in from the perspective ive of you've spent you know years in many cases building up your security permissions, your access controls, your workflows and the amount of data in that system. >> So our job now is to make sure that
1:03:01
>> So our job now is to make sure that agents can run within that environment and add more and more value >> um and uh and and as long as we can you know you know deliver that we we believe our position is very defensible. >> Yeah.
1:03:13
The perfect example would be like uh style transfer for images.
1:03:15
Like people were using Lensza, then they all went to Studio Gibli moment.
1:03:19
Then as soon as Nano Banana came out, they were like, I got to turn myself into a dinosaur.
1:03:24
And it doesn't require any like pre-work or like knowledge of who you are.
1:03:28
It's like you upload a photo, you get a photo back and you're good.
1:03:31
And then if then if there's a new app, you can just do the same thing because your camera roll is actually where the photos live.
1:03:36
And that's >> that might be a hard company to go public.
1:03:38
Uh yeah, if it was just that, of course, they have a million other things [laughter] going on, but like yeah, if you're just if you're just that it's like, okay, open the fresh app, get the output, and then move on with your day. That's that's risk.
1:03:49
>> Uh companies of of boxes size, where do you think uh are are generally getting leverage that is under discussed right now?
1:03:57
Like is there is is your like in-house legal saying, "Oh, we actually can, you know, adapt headcount planning because we're just way more efficient."
1:04:07
Yeah, I I think um my my general take is just what are the what are the fi three five ten things that would have been in your work queue that you didn't get around to that now agents let you go and deploy.
1:04:17
So if you're in legal it's you know what are the contracts that you were waiting on that were bottlenecking a deal.
1:04:23
>> What are the size customer that you wouldn't have supported you know reviewing a contract for because the revenue threshold wasn't high enough to to make the ROI worth it?
1:04:30
what is the marketing campaign that you couldn't deliver because you didn't translate it in, you know, X language because it was too expensive.
1:04:38
Um, so you kind of go through in enterprise and you kind of look through at all of those use cases and and that's what we're spending our time on.
1:04:45
So we use agents to go and do more and more of the work that I just mentioned.
1:04:48
Obviously a lot of agents on the coding side.
1:04:50
Uh so my expectation is our roadmap should be you know let's say two to three times larger maybe a year from now than it was a year ago. Mhm.
1:04:59
>> And the reason you can do that is because every engineer should be able to produce two or three times more code.
1:05:01
Um and while that's not like you know the most important metric, uh ultimately that does ult, you know, correlate to how much software we're producing.
1:05:08
So I think what's going to happen and um uh you know uh uh I I think you're seeing this trend already which is which is we all collectively are going to have much more ambitious product road maps.
1:05:18
We're going to all build out way more software.
1:05:22
I don't think that will mean that that we charge, you know, an amount that is correlated with how much more software we build.
1:05:27
I do think it means more competition in these spaces, but I think many of these markets are still largely untapped.
1:05:33
Um, and so that that's still why you have a positive sum dynamic as a result of that. >> Makes sense.
1:05:37
Uh, talk to me about the huge big models, expensive frontier stuff versus like smaller models or maybe even just uh an earlier model that you implemented in some sort of like minor workflow and then it just like stuck around like transcription.
1:05:52
I'm sure you've been doing transcription of documents for a long time.
1:05:54
that's gotten better, but do you really need to throw Opus 45 or, you know, 5. 2 Pro at it?
1:06:01
Like, you can probably leave some things in place like how are you deciding that?
1:06:06
Is this showing up in in cost at all? >> I think it is.
1:06:08
I think that the um it probably might maybe wouldn't be as extreme as as you've set it up.
1:06:12
Um but I think the general maybe the continuum I'd argue would be like you've got the Gemini Flash. Yeah.
1:06:19
>> You know, family on one end and then you've got the Opus family on the other end.
1:06:21
That's kind of your continuum.
1:06:21
Y >> and that continuous sort of moving up over time in terms of capability.
1:06:26
So maybe something was a Gemini 2.
1:06:28
5 flash uh you know use case a year ago >> we'd probably move that to Gemini 3 flash just because that extra two or three points of even if it's transcription or data extraction still valuable and you can now deliver that at the same cost that you could have previously.
1:06:45
So it's less of an area that we want to lower cost.
1:06:47
It's more where if we can sustain current cost level but add you know incremental value to the customer we'll probably do that all day long. >> Yeah.
1:06:53
>> Yeah. Um I I think we're going to be in this in this kind of you know maybe you know general uh uh sort of point in the curve for a couple of years and then I think you'll see real real bifurcation which is the the stuff that is just fully solved will just get cheaper over time and then the frontier work that is
1:07:11
basically where you know you are making a $100 an hour knowledge worker two to three times more productive we will just continue to use the best-in-class model for that work as a as a you know as software companies and generally as a society and I think that will kind of will sustain for you know the next decade. I I don't see that slowing down
1:07:27
I I don't see that slowing down at all simply because these models just keep getting better and better.
1:07:31
Um and uh but but it will be this really interesting biodal effect which is like if you're in if you're a pharma researcher you're going to want you know you're going to want whatever opus 5 is and whatever GPT6 is and and so on and if you're doing some backend you know transaction processing you'll be fine with whatever the Gemini flash of that of that period is and that's how we'll kind of split the costs. >> Yeah.
1:07:52
What industry outside of tech do you think is the most AGI pill?
1:07:57
[laughter] Because I'm assuming you you have a customer that uh that you get on let's say with a large customer catching up with the CEO >> and maybe they're in some you know not on not on the coast maybe they're in energy something like that or or uh >> financial services and and who who's like as fired up as you are. >> Yeah.
1:08:15
Uh I I would say um it's a fantastic question that I will totally evade because because I I think it's actually much more sort of firm.
1:08:22
Yeah, it's company by company as opposed to like it's a like a specific sector.
1:08:26
So [clears throat] uh there's some I mean like you know my bias in general would be informationheavy businesses.
1:08:33
So, you know, businesses where a CEO sits around and says, "I just know we're sitting on 10 million documents that have the answer to every single customer question we could ever, you know, ever imagine.
1:08:45
And if I could only have an agent go and mine that information, then my employees would be 20 or 30 or 50% more productive."
1:08:50
So, so the companies that have that kind of information are are the ones that obviously are are going to increasingly be more AGIP.
1:08:56
That's professional services firms.
1:08:58
Uh that's consulting firms.
1:09:00
That's financial services firms.
1:09:02
Uh that's um uh that's law firms in a lot of cases where they're they just know that like we we seem to spend a disproportionate amount of our time redoing the work that has already been done in the past.
1:09:13
Why can't we take advantage of all of the information we've produced so that next incremental project is 90% faster >> and you know 2x the you know kind of quality of output because it wasn't some new person having to kind of get caught up and then we can go and deploy our time on better customer relationships or creating more value in new ways.
1:09:31
So I'll have things like CEOs will call me and say I know I'm sitting on 10,000 contracts.
1:09:37
I want to figure out how can I go and sell this new capability to uh to someone based on the the the terms I have in the contracts and um and that's simply an exercise of do you have an agent that could go through every contract and just tell you the insights of you're trying to find a sponsorship for this brand uh and this particular client in this contract you know has the rights to go do that.
1:09:58
How do you connect those dots amongst a large data set?
1:09:59
Uh those are the companies that I think are are extremely excited. Mhm.
1:10:03
Are you seeing any uh any effects of the buildout or bottlenecks show up in your business?
1:10:12
Like we've seen the prices of memory spike.
1:10:15
Uh Western Digital stock is way up.
1:10:18
Like I imagine like you're probably a few layers removed from these prices moving.
1:10:23
But is it affecting you or are you worried about it? Are you losing sleep?
1:10:26
>> Well, first of all, as a storage guy, I I love that they're finally getting their day.
1:10:29
[laughter] So if you've been if you've been >> giving up for Washington and Seagate, can we give another Can we get another for Seate?
1:10:37
>> I mean, think about how forgotten these companies and they're just like, oh, there's little circles on a disc and [laughter] and they are now the coolest companies.
1:10:45
>> Um and uh and so storage is hot.
1:10:45
Um and it's actually very funny because like you know we we we are literally in the storage business and for years everyone thought well that's a commodity.
1:10:54
In the AI era the data is the most important asset you have. Sure.
1:11:00
>> So, so the ability to get the right data to an agent is simply the most strategic thing you can do.
1:11:04
So, wherever you are in that stack, if you're the infrastructure, if you're the software layer, if you're building >> a shame that data is the new oil was kind of ruined. >> Yes.
1:11:12
The wrong [laughter] it was it was not the oil back then.
1:11:17
Can we just bring it back or um >> we need to rehab that phrase.
1:11:21
>> we need to rehab that phrase. we uh um uh yeah so um so I I think now we we actually can justifiably you know sort of say that and actually in most companies when you when you say you know I want to go deploy AI or I want to have an AI strategy usually what underpins that is a data strategy you don't have
1:11:37
an AI strategy if you don't have a data strategy so um so that's the um I think that'll be the um uh the story of of you know the next decade uh what was your question >> my question was like is it actually show is it keeping you up at night where you're like okay because you sell a service It's not just saying, "Well, I bought this." You're not a Western
1:11:54
You're not a Western Digital or Seagate reseller.
1:11:57
So, it's not like I just put% on top.
1:12:01
>> We have pretty locked infrastructure um from our long-term kind of public cloud contract.
1:12:04
So, stupid after being locked in. >> Okay. Okay. [laughter] >> Yeah. Okay.
1:12:08
Are there is there a bingo card that I should Did I Are there other things?
1:12:12
When do we tell Grind one?
1:12:17
If I found >> Grind would be good.
1:12:18
If you talk about private equity, we'll give >> private equity usual things. Everyone we love. >> So um okay.
1:12:25
So uh so we we have locked in contracts for our public cloud.
1:12:30
>> Um I would say more the inverse uh in a world of of complete abundance of let's say data center capacity.
1:12:34
We would just you know we would be able to deliver even more to our customers because the rate of a the price of AI would just go down.
1:12:41
So so you know I want a world of just I I want solar data.
1:12:44
I want you know space data centers.
1:12:46
I just want everything because that will just mean >> there we go.
1:12:50
Everyone's pumped up about space data center. Well, read the timeline.
1:12:54
You know, you know people are excited about this. >> Okay.
1:12:57
>> You always knew that Twitter would end up in space. You called this, right?
1:13:01
That's why you got on the platform so early.
1:13:02
That's why you got a million.
1:13:02
I need a million followers on the SpaceX social network. Space influencers. I'm buying early. >> We are.
1:13:08
I'm glad that my tweets are training, you know, the future of of space.
1:13:12
So, uh, um, uh, can you just imagine you're like an alien tweet?
1:13:14
This is your first access to information.
1:13:18
>> You run ads [laughter] that then pay for the next space rocket that goes to Mars.
1:13:24
>> Actually, we're not tweeting enough.
1:13:24
We We need to be It's a It's like a oni come, they're just going to they're going to desperately want to learn about enterprise SAS.
1:13:32
They're [laughter] like, "We don't care about your religions.
1:13:33
We don't care about anything else."
1:13:36
>> They're like, "Why did SAS uh, you know, what was SAS pricing in January of 2026? Why was it a problem? Um, yeah.
1:13:41
So, I just want AI to be super cheap and the cheaper it gets, the the more we're going to use it.
1:13:46
>> Well, that's a great place to end it. Thank you.
1:13:47
Let's give it up for Great. Thank you. This is fantastic.
1:13:50
Enjoy the rest of your day. >> Do I just leave now? >> Yeah.
1:13:53
You leave because I'm going to tell everyone about Century.
1:13:55
Century shows developers what's broken.
1:13:56
It helps them fix it fast.
1:13:58
That's why 150,000 organizations use it to keep their apps working.
1:14:03
And up next, we have Chuck Robbins, the CEO of Cisco, live in person at the Cisco AI Summit.
1:14:08
Chuck, great to meet you.
1:14:12
>> Hey, nice to meet you.
1:14:13
>> Thank you so much for taking the time to meet. >> Sorry I'm late.
1:14:16
>> No, you're going great. >> Long, I'm sure.
1:14:18
>> Yeah, I mean, you have a busy day today. Uh, take us through it.
1:14:20
How is the Cisco AI Summit going? >> It's going great. >> Okay. What's top of mind?
1:14:27
>> Things obviously AI, but let's go a cut deep. >> Oh, it's AI. That's it. >> Okay, that's it. People are fine.
1:14:30
As long as you said AI, check.
1:14:33
>> We talked about everything from world models to >> to infrastructure required.
1:14:37
We're going to get this afternoon, we're going to get into trust and security, >> right? >> Geopolitics.
1:14:44
>> Yeah, >> we talked about models and how they're evolving.
1:14:46
We talked about some of the emerging agents and things that are happening with agents right now.
1:14:51
>> And u I mean the whole ecosystem's here, so it's pretty uh it's pretty cool.
1:14:54
>> Can you compare it to Davos?
1:14:54
I know you've been involved. I know you're there.
1:14:58
Uh h how was Davos this year?
1:14:58
We were sort of noting that at least in our world, it felt like tech had really come to bear this this Davos and there were a lot of really frontier discussions about technology that maybe a couple years were a little bit quieter at Davos.
1:15:14
>> Yeah, if you walk the main drag, I mean, all the tech companies had their own houses, right?
1:15:18
And the crypto guys were gone.
1:15:19
[laughter] Um, but um, you know, Davos was uh, it was interesting because there was such a geopolitical backdrop that was going on.
1:15:29
So it was it was uh there was a lot of tension. >> Oh yeah.
1:15:32
>> And and there's a lot of discussion around this intersection of the geopolitical situation and technology honestly.
1:15:38
And then the the sovereign requirements that are coming up around the world those are big things but AI was just AI was a huge discussion.
1:15:46
>> Uh global economy what's happening in the economy is there a diversion is there a split um >> and uh and then the geopolitical tension was probably the third thing. Yeah.
1:15:54
T talk about how that's even just taken up your time over the last couple years in a way that maybe it wasn't >> just the last couple. >> Yeah.
1:16:02
[laughter] Well, no, I think I think it's I think it's even been massively elevated even even in recent years. >> Yeah.
1:16:09
It's um you know, it's it's been a it's I'd say the last decade we've had not always geopolitical, but there were always these macro issues that were overhangs on what we were think and and taking up time.
1:16:19
taking up time. Whether it's you know if you go back to we had sort of leading into the pandemic and then you had supply chain crisis you had inflationary issues we had social you know issues in the in the world then >> we've had now we have trade issues
1:16:34
tariff issues we have the geopolitical trust issues and so um it's uh I spend I spend a fair amount of time I probably spend you know I'd say double the amount of time today whatever that is versus what I did you know seven eight years ago. eight years ago, maybe nine years
1:16:49
eight years ago, maybe nine years ago.
1:16:52
>> But um it's um it's it's important and you have no choice and you know we launched a whole suite of sovereign software capabilities and sovereign uh product capabilities earlier this year for you know customers in Europe, Asia, anywhere around the world if they want to have technology that they can run locally and feel good about.
1:17:10
And so it's uh it's affecting how we develop products.
1:17:14
It's affecting how we package them, you know, how our how our uh these governments and cu customers in these countries run the products.
1:17:21
It affects how frequently they're going to get innovation versus not get innovation.
1:17:24
I mean, it's it's a big impact.
1:17:27
>> Uh are internationally are are government leaders more of the key stakeholders that you're interfacing with or is it CEOs of technology companies in those countries that you're dealing with?
1:17:37
It's both, but I think a lot of these issues that we're discussing right now are driven from the central governments and that have to be implemented by the CEO >> and sovereign AI. >> Yeah. Got it. Okay.
1:17:47
>> Can you take us back since this is the first time in the show and talk a little bit about uh your background growing up.
1:17:53
I'm particularly interested in you know you studied math and you know I was reflecting with Jordy the other day about how you know I grew up watching Star Wars. You have C3PO. It's a talking robot.
1:18:04
And I didn't really internalize that that would be something that kind of happens in my lifetime.
1:18:09
And I'm wondering what your vision, your processing of science fiction, your processing of AI was throughout your career because now it's here and you're experiencing it like everyone else.
1:18:20
But what was your what was your >> So many of the movies and cartoons and things that we grew up with were all futuristic and now we're actually building all that technology to make it real.
1:18:28
I mean, you think about the Jetsons, you know. Yeah.
1:18:31
>> The cars drive themselves.
1:18:31
cars driving themselves, you know, even simple things like the the amount of the amount of video that we do, you know, you're on another video and you're you're you're I mean, we back then it was like that was somewhat science fiction and now all of a sudden it's the way we do business every day. >> Yeah.
1:18:45
>> And I think that um >> you know, but that these things take time.
1:18:48
I mean, >> Fe Lee was in there earlier today and she was talking about when we when when the whole concept of >> you know, self-driving cars began, it was over 20 years ago, you know, and here we are and we're just getting these rolling in.
1:19:00
Yeah, >> you know, Whimo here in San Francisco as an example.
1:19:03
And so it uh they take they take a while to deploy, >> but um you know, yeah, my my background I had a math mathematical sciences degree with a with a concentration in computer science.
1:19:15
>> And uh I was a I was a strange combination of a a complete nerd and an athlete at the same time.
1:19:20
>> I have these pictures on my phone, honestly.
1:19:22
One one's where I'm dunking on a guy in a basketball game. >> So good.
1:19:26
And then the next the picture right next to it is literally me on a team of about six people which was the math team and I look like the biggest nerd you ever see.
1:19:33
[laughter] They had those were like >> got to be able to do both.
1:19:36
>> It was it was really odd.
1:19:36
So um >> I started my career coding and uh >> you know cobalt programmers are in demand now.
1:19:44
So I got a second job if I need [laughter] a backdrop >> plan B. Exactly. >> It's very funny.
1:19:50
And then uh and then tell me a little bit about the the journey to Cisco.
1:19:54
uh where you were working before and the decision to join. >> That's funny.
1:19:59
Have you heard his story? >> A little bit. >> Okay.
1:20:02
I was say we've heard it interesting for you to ask audience.
1:20:03
So there must be there must you must have heard something.
1:20:07
>> So So I uh I was working for what is now Bank of America actually back in the day and and when I was programming >> uh my leadership came and said, "Hey, we got these things called local area networks popping up all over the bank.
1:20:18
We don't know what they are, but we've hired three analysts, but we need someone to manage them." >> Yeah.
1:20:22
>> I literally on the way home, I stopped and bought Land Magazine. How weird is that?
1:20:25
But it's actually a magazine.
1:20:26
>> We gota find We gota find a comition for that.
1:20:29
>> I I I grew up doing land parties.
1:20:29
You bring all of course all your video games together and I love that Cisco invented the land. >> Oh yeah.
1:20:36
And so so anyway, I uh so I left I left coding and went over and started running this team and we did an evaluation between Cisco and Wellle Communications, which was Cisco's original competitor. Mhm.
1:20:47
>> Long story long, um I was I was talking to the sales rep and I said to him, I said, you know, well, first of all, I see he's got a really nice car and he's got a nice house.
1:20:54
And >> so it's like, wow, >> the margin is pretty high.
1:20:57
[laughter] Can you give me a price?
1:21:00
>> I I literally I literally said to him, I said, I think I could do what you do.
1:21:04
[laughter] >> And so, as it turns out, like I don't know, probably within a year uh or so, they came to me and said, "Hey, we we have an opportunity.
1:21:14
he's this guy was getting promoted >> and there was a territory opening up and so I went I moved into sales at that time and that was 92 >> and I competed with Cisco and it was literally the two companies were like Coke and Pepsi. I mean it was that bad. It was like brutal. >> Is this a send?
1:21:29
>> No, this was at Wealthfleet. >> Okay. Wellfleet.
1:21:31
>> And so Cisco was trying to get me to come to work for him um after two or three years at Wellfleet and I just couldn't bring myself to do it.
1:21:38
So I did a stint at Ascend for 11 months. >> Yeah. Okay.
1:21:41
And uh and then finally in 97 my wife there was a there was a patch that came open as a sales rep. Yeah.
1:21:49
Where everything I had to do was driving around driving and I'd be home every night.
1:21:53
We had small kids and my wife's like, "Sign it. Sign it. Just sign it."
1:21:58
So uh >> what car did you pick? >> What car? >> Yeah.
1:22:01
>> What kind of car did I have?
1:22:03
>> Because you you see this you see this other sales guy.
1:22:05
He drives a nice car that that that carries a little bit forward. It sends a signal.
1:22:09
It can send a signal of confidence.
1:22:10
It can also sell a signal of high margins.
1:22:13
>> I'm not going to tell you what I [laughter] >> off we we you know over time I was late >> I got up to a BMW at some point but it wasn't that wasn't what I was driving at the time.
1:22:24
>> Okay then uh then talk about the journey uh through Cisco.
1:22:27
We're we're in this very interesting time.
1:22:30
There's another technology boom.
1:22:32
It feels like markets are extremely volatile.
1:22:35
We saw this with Microsoft like one small change the stock moves massively.
1:22:39
Um, at the same time, you know, you've watched self-driving cars evolve. It takes 20 years.
1:22:45
There's this balance of like patience and aggression.
1:22:47
And I want to know how are you communicating to your customers, your employees about uh times when you need to be moving aggressively but cautiously balancing all of that.
1:22:59
That seems like the hardest job for you right now.
1:23:00
>> We just had Kevin Scott on from Microsoft and he said, "We have this infinite patience for the messiness of these transitions."
1:23:05
Which I thought was a really good line.
1:23:06
So Kevin, thank you for that.
1:23:08
Uh, I'm going to steal it.
1:23:08
At least I credited him once.
1:23:10
[laughter] >> But, um, you know, you you have to you you can't wait.
1:23:15
You have to you have to jump in and you got to go.
1:23:19
>> And a lot of what we're trying to do right now is, you know, we we have to build we're obviously building the infrastructure that supports all this.
1:23:24
We're trying to build the security solutions that help our customers, you know, have the trust that they need and and feel good about deploying these things.
1:23:30
So, it's um and we've gone through these in the past.
1:23:33
I mean it's uh you know the one the only one thing that's close to it from a scale perspective was just the advent of the internet. Yeah. Yeah.
1:23:40
Back in the late 90s and uh and this I think is going to it's moving faster. >> Agree.
1:23:46
>> The the implications are it's hard to hard to even believe this but probably more profound than even what we what we did back then. >> Yeah.
1:23:54
>> Uh and so you have to you have to be willing.
1:23:56
You know we always talk about you got if you got 80% of the information you need to make a decision you better make it go or you're going to get left behind. You got to go.
1:24:01
And so you do the best you can.
1:24:04
You adjust on the fly and uh and and you try to uh your number one priority is not to hurt your customer.
1:24:11
>> Do you think it's easier to predict the future than it is to predict the timeline that change will actually happen? Really good question.
1:24:17
>> Just because with the internet, with the internet boom, it was obvious that we were going to be buying things online.
1:24:22
We were going to be buying, you know, paying for software.
1:24:23
We're going to be getting all these services. Right. Exactly.
1:24:27
we would have, you know, something something like a Door Dash, but then the timeline and and I feel like the the decision that every CEO management team >> uh are trying to work through right now is they're having to make they're having to make predictions around the timeline that these changes are going to happen on.
1:24:43
And that feels uh even with the rate of change that everyone's feeling today, it still feels like wildly unpredictable. >> It is.
1:24:52
And I think the reality is is that the timeline gets dictated by how good you feel about the capabilities and the security of the solutions and the data access and all that stuff.
1:24:59
So it's it's not like our customers are sitting around saying I wonder what the timeline is.
1:25:05
They're all trying to actually force the timeline themselves.
1:25:08
They're trying to get there.
1:25:11
>> Um but I think I I equate this to at a much different level.
1:25:15
But you think about the iPhone in 2007.
1:25:17
None of us had any idea the applications we'd be running on that device 10 years later and what we do with it today.
1:25:25
I mean, it's just and and so I think this is going to be that on steroids. We don't know.
1:25:30
I mean, just look at the look at the what was it? Mol mold bot.
1:25:36
>> There's a few different names today. Yeah.
1:25:38
AI social network social network >> and then there's clawbot and all these things. Open claw. Yeah.
1:25:44
And uh and these are like I mean they're taking everything by storm and >> and you know you made the joke like Pets.
1:25:52
com doesn't exist but Chewy does and many cases you know you think about pioneers versus settlers and the different outcomes for each of them.
1:26:00
We'll see who we'll see who makes it at the end of the day. >> Yeah.
1:26:03
>> Uh is uh is getting lucky underrated?
1:26:03
We talked uh last week about uh Apple's Apple Apple's like work on the self-driving car is what enabled them to be in the position to >> uh have the Mac Mini be a great Apple >> luck and timing matters so much.
1:26:18
I mean, you know, it's >> um I I I became CEO.
1:26:22
There's a lot of luck and timing involved, right?
1:26:25
I happen to be the person who was there.
1:26:28
If if if my predecessor decided to retire 5 years earlier, I wouldn't have gotten the job. >> Wow.
1:26:32
>> You know, it's just a it's a it so there's a lot of luck and a lot of timing.
1:26:35
Now there's also you you create your look sometimes, right?
1:26:37
Um but I think it always it's always part of the whole the whole outcome.
1:26:44
>> Can you talk a little bit about CEO to CEO communication?
1:26:46
There's a lot of really high stakes, high-flying deal making between big companies.
1:26:51
There's press releases that go out and then there's comments from each CEO and the deal [laughter] evolves.
1:26:58
and and I'm wondering [snorts] about what it takes to, you know, build a relationship with a CEO that you're going to do a big deal with and what it takes as that deal evolves and as the the communications go out like what what how how do you how do you maintain a relationship when so much is on the line?
1:27:15
>> It's always better to build a relationship before the before the big deal so that the big deal becomes easier because you've already built the trust.
1:27:21
It's sort of like dealing with >> dealing with a time of crisis.
1:27:23
I think when co came along, if if you had if you had great trust with your employee base and they believed in you, then you could navigate through that a lot better >> than if you were if your culture was good, you know, that was good.
1:27:34
And so I think these relationships um are really important.
1:27:38
The CEO community in the United States honestly is very tight. Mhm.
1:27:42
>> I remember when I became CEO, there was another there was one of them at one of the first CEO events I went to and he he handed me a sale number and he said, "Listen, this is these people in this room are the only people that know what you're getting ready to go through and so you need to get to know and make sure you call and talk and ask us old guys, you know." >> Wow. >> When you get into it.
1:28:00
So, I think the CEO community is very close.
1:28:01
I think um you know some of the some of the the number of deals right now I think in many cases the public narrative about what's going on isn't reflective of what's really happening behind the scenes because most CEOs are very pragmatic.
1:28:15
There's not a lot of emotion.
1:28:16
You're just sitting there.
1:28:16
You're cutting out a deal.
1:28:17
If somebody has a different different opinion about it, you're you're not getting mad.
1:28:20
You're just you're trying to just get to the outcome.
1:28:24
And I think that's that's the pragmatism and the calmness that most CEOs uh have.
1:28:27
And I think that sometimes the press and and others like to create the drama because that's what people click on. >> Yeah.
1:28:36
If anyone sticks a bunch of microphones in your face, be careful.
1:28:38
[laughter] >> We move we move away quickly.
1:28:40
>> That just happened, by the way. >> Got you.
1:28:42
Uh if >> we got to get the flash effects. >> Yeah. Yeah. Wear the paparazzi.
1:28:47
[laughter] Uh if we flip it around, give us some advice for the youngest cohort of folks who will be joining Cisco.
1:28:51
What does it take to succeed?
1:28:54
What's your advice for people that are going into a >> the people that want to take your job one day? >> Maybe. Maybe. >> Come on, hurry up.
1:29:02
[laughter] Um I you know I think the uh look the the the people who are wildly successful have this really incredible combination of techn and in our industry >> understand the technology. >> Yeah.
1:29:17
>> Have high EQ >> really care about the mission of the team.
1:29:21
team. don't and and understand that if the team success is anybody who says I don't care about my own success is lying to you >> but [clears throat] the person who figures out that when the team can succeeds I'm going to succeed so it's easy for me to focus on the team and uh and then you also have to have people
1:29:37
who care about making sure their their peers are are successful as well and I think it's um you know the person who's solely focused on getting to the top as an individual just it's not going to happen >> um and that uh so it's a combination those technical skills and and the high EQ. You cannot I can't underestimate.
1:29:53
You cannot I can't underestimate. >> Yeah.
1:29:57
>> Can't you talked you talked about uh you've talked in the past about effectively doing doing the job that that you want already and and as a way to kind of like work your way >> leadership before promotion.
1:30:09
>> You guys actually do your research, don't you?
1:30:11
>> Um I just I tell people that, you know, you you if if your peer group would look at your promotion announcement and just go, "That makes perfect sense."
1:30:22
And if you can't be happy if they wouldn't, then you're probably not quite where you ought to be.
1:30:30
And and I think that um you know, I tell everybody, too, you're you're I my team hates when I say this, but I'm going to say it anyway.
1:30:39
[laughter] I think I think when we have two or three internal candidates for a promotion, the whole interview process is stupid to me.
1:30:47
It's like we've been watching these people work for a decade.
1:30:51
>> What are we going to learn about them when we sit down in a room for 30 minutes and ask them questions when we watched them work? Yeah.
1:30:56
>> Can't we just look at the three of them and and I translate that to every day you're working is your interview for your next job. Yeah.
1:31:01
>> That should be your your your work every day should be your interview for the next promotion.
1:31:06
>> And now if you got external candidates certainly you have to you have to get to know them and learn all those things.
1:31:10
But when we have when it comes down to two internal candidates I just say why are we doing this?
1:31:13
[laughter] >> But we do it anyway.
1:31:15
So, >> what's keeping you up at night?
1:31:16
There's a number of nothing. >> No. >> Wow. >> Yeah.
1:31:20
How do you how do you how do you handle how do you how did you learn to handle stress?
1:31:25
Because over the last few years, you had COVID.
1:31:26
We've had so many different >> geopolitical tension, trade wars, all these things.
1:31:31
What's your what's your you seem unfased, but >> I have I've always been able to compartmentalize things and I have always it's innate.
1:31:38
I did not teach myself this.
1:31:40
I've always been able to just >> put aside things I can't control. you plan for them.
1:31:45
You you do you you you come up with different scenario plans, but you don't I mean I'm not going to lay at wake at night and worry about something that might happen that I have no control over.
1:31:56
So >> that's a good mantra.
1:31:58
>> Um and uh and you know the there's look I've gone home when I've had a really bad day and I've looked at my wife and I say you want to hear the good news and I said I wasn't diagnosed with cancer today and somebody somebody was and I wasn't.
1:32:11
So, my worst day, if I'm not being diagnosed with cancer or some sort of terminal illness, >> tomorrow I'll get up and fight another fight, you know, fight fight another fight.
1:32:18
And uh you just got to have perspective. >> I love it.
1:32:21
Well, thank you so much for coming. >> Thank you. Great to see you guys. >> Honor to meet.
1:32:25
>> Nice to meet you guys. >> Love it.
1:32:27
You guys have got a real good product. >> Thank you. We appreciate it.
1:32:28
Uh before we bring in our next guest, let me tell you about Plaid.
1:32:32
Plaid powers the apps you use to spend, save, borrow, and invest securely connecting bank accounts to move money, fight fraud, and improve lending now with AI.
1:32:39
And up next, >> what a legend. >> What a legend.
1:32:41
Without further ado, we have G2 Patel, Sito's president and chief product officer. Welcome to the show. >> All right. Look at this fit. >> Looking sharp.
1:32:51
>> You know, they uh amazing.
1:32:53
>> You you got a cover more of yourself up, but you don't look as good.
1:32:55
[laughter] >> Uh well, thank you so much for taking the time.
1:32:59
Uh how is the Cisco ASIC going? Are you happy? How you feeling? >> I am thrilled.
1:33:04
>> I am thrilled. You know the thing is is I think we are at a juncture right now where um these conversations with a range of topics with the ecosystem are actually far more important than any individual company >> talking about a product that they're
1:33:18
trying to you know pedal >> and um and I feel like we are we're the realization is now pretty obvious to everyone that this is an ecosystem play >> like no company >> us included >> is going to be able So actually they get the full stack built out. >> I mean a couple years ago people were
1:33:36
>> I mean a couple years ago people were saying that either you know like one AI company will completely dominate and we have like multiple IPOs going out X AI as part of SpaceX now like lots of companies are succeeding >> and I think like this whole notion of zero sum mentality is I just don't think it's healthy.
1:33:52
>> It's never been true in tech.
1:33:53
>> It's never been true in tech but it's actually more more um false now than ever before. >> Totally.
1:33:58
because uh the complexity is so high and the breath is so high and the scale is so fast that if you >> if you just get arrogant thinking you're going to do everything by yourself. Yeah.
1:34:08
>> You are guaranteed to be left behind. >> Okay.
1:34:10
Well, first time on the show. Let's back up.
1:34:12
Introduce yourself and I'd love to know uh a little bit of you know Cisco is a large company.
1:34:16
Uh walk me through a little bit of the org chart that sits under you, where you work, who you're interfacing with, what projects you're working on.
1:34:23
So, uh, I've been here for about five and a half years and I joined here >> success.
1:34:28
[laughter] >> Congratulations.
1:34:30
We're very happy about that.
1:34:33
>> Um, and, um, >> we have a soundboard, by the way. >> I I [laughter] know. I know.
1:34:35
I watch your show, so it's great.
1:34:38
>> Um, so >> some people don't know that we bring it with us. >> Still a surprise.
1:34:42
>> Surprises you actually [laughter] happen because you don't expect it. Yeah.
1:34:43
Um, but so been here for five and a half years.
1:34:47
I joined from um, uh, from Box. >> Yeah.
1:34:51
>> And I was chief product officer there.
1:34:52
Prior to that, I was at EMC.
1:34:52
Prior to that, I ran my own business for like >> 17 years in in Chicago.
1:34:58
>> And so, I went the other way where I actually started really small and then I actually got the itch for scale.
1:35:02
I'm like, I really want to learn scale. >> Okay.
1:35:05
>> And um that's what got me to the valley.
1:35:07
That's what got me to these large scale organizations.
1:35:09
And the reason I'm I'm really enamored with scale is the >> um it takes a little longer to get there sometimes. >> Sure.
1:35:17
>> But once it gets there, like there's a movement that gets created. >> Yeah.
1:35:20
So talk about uh the either the org structure or the product surface that you oversee, how you're how you're explaining uh everything that Cisco does because it's such a large organization now. >> Yeah.
1:35:32
We have a very broad portfolio. Yeah.
1:35:34
And so um you know we everything from our core business which is networking. >> Yeah.
1:35:41
>> To then the very um you know kind of close adjacency which is security because security is now getting baked into the fabric of the network.
1:35:46
So that's >> so services software on top of that.
1:35:48
Um yes, but like you know SAS services and hardware.
1:35:53
So we we have um a we don't only play in a range of different businesses but we also play with multiple different business models.
1:36:00
So we have a hardware business. >> Yeah.
1:36:03
>> We have a professional software business.
1:36:04
We've got a SAS business >> in each one of these areas.
1:36:08
>> So networking security which was a massive acquisition that we made >> collaboration uh WebEx contact center all of those products basically all products.
1:36:18
What we did was we decided about a year and a half ago >> and Chuck who you just interviewed um uh you know we we were talking about this for 5 and a half years which is we need Cisco has to become much more of a platform rather than feel like a holding company. >> Sure.
1:36:31
>> Sure. where you don't have each individual product that's kind of in its own silo because then you don't get the benefit of the breath of Cisco and the tailwind of Cisco >> but our breath had become our liability >> and so what we had to do is fundamentally kind of rethink how we're going to build our products
1:36:46
>> and it it had to become a platform platform being that the marginal cost of ingestion goes down and there's a compounding value to every single kind of >> every time you build new functionality doesn't just help with the product that you're buying but also helps with every product you purchased in the past. >> Yeah. and can other people add value to >> Yeah.
1:36:59
and can other people add value to the platform that you've built.
1:37:01
So that that's the core definition and that so that that's what we started doing and we consolidated all products together about 18 months ago >> and I think it's been fantastic so far like we there's a um there's a spring in the step in the in the employees.
1:37:14
I think we are starting to see a fair amount of innovation.
1:37:17
We've actually innovated more in the past 18 months than the previous decade combined and I think it'll dwarf in comparison to what we'll do in the next 12 to 18. >> That's awesome. Yeah.
1:37:26
talk more about Splunk and how that fits in um specifically to the amount of data that's being created in the AI era.
1:37:32
It feels like a very like fortuitous acquisition, but uh what's the response been on the ground?
1:37:40
>> We were lucky on the on the Splunk side because it's very hard to find a company that is at scale with the right cultural fit >> with the right technology adjacency >> where the combination actually creates a 1 plus 1 equals 11.
1:37:52
And I think Splunk happened to be one of those.
1:37:56
Um there there aren't that many of those.
1:37:58
Like a lot of times people will ask me like what's the next big acquisition?
1:38:00
I'm like if I found one I would be the balance sheet but there's just not that many that are out there.
1:38:06
Either they're very frothy in the valuation.
1:38:08
They're not >> or they don't have a clear adjacency.
1:38:10
We don't have the right go to market.
1:38:13
We don't have the right cultural fit.
1:38:14
Like there's a bunch of things that have to fit just right. >> Yeah.
1:38:18
>> And so that was um you know very lucky with Splunk.
1:38:19
And the the thesis over there was look, you have to assume that um the attacker is already in the system and what you're trying to prevent is lateral movement. >> Mhm.
1:38:30
>> And if you're trying to prevent lateral movement, um security is a data game and the more data you have and the more data that can be correlated, the better off you're going to be at compressing the time for investigation, doing better detections, and doing a much better job on the response and remediation.
1:38:42
And that's basically what Splunk brings us. >> Yeah.
1:38:46
And I think um you know we've got a great leader now in in Splunk as well who came from Microsoft and Docyign who's doing a fantastic job Kamal Hathi >> and um I I feel like there's um um we have probably hit 2% of the potential that we have in Splunk. >> That's awesome.
1:39:05
>> How have you processed stage by stage the overall AI hype cycle?
1:39:07
It sort of perfectly coincides with your time at Cisco.
1:39:12
It also Cisco actually has the scale to have visibility into reality.
1:39:18
It's like you're just like looking across like you just pull up a map and just see like okay what's actually happening?
1:39:24
How are how are organizations adopting this not just in tech but across the entire economy. >> Yeah.
1:39:29
>> You know I think there's you know I I feel like I've always lived by the six-part framework which is like important in descending order which is timing, market, team, product, brand, distribution.
1:39:38
If you don't have all six, you don't win.
1:39:40
But you need to make sure that the first one is timing and you don't control timing. >> Yeah. Yeah.
1:39:43
We were just talking about that with Chuck.
1:39:44
It's like in it's very easy to predict the future in some ways like with the internet. Yeah.
1:39:50
You just like could be off.
1:39:52
Are you off by five years?
1:39:54
And if you if you're off a plan by two years, you could be like it could be done.
1:39:57
Like you might not win the market.
1:39:59
>> And by the way, there's a lot of great products in the market that actually uh hit the market at the wrong time and did never see light of day. >> Yeah. >> Right.
1:40:06
um um iPad wasn't the first kind of um tablet to be built, but it was the one that actually hit the nail on [clears throat] the head on the timing.
1:40:14
>> So, I do feel like timing is a disproportionate contributor to success and you don't actually control timing.
1:40:21
So, that that should tell you that the intellectual arrogance that people carry with themselves saying that this is me that made this happen >> uh has a ton of luck to it.
1:40:28
And so we just happened to be at the right time at the right place um building infrastructure for 40 years which is now a scarce commodity.
1:40:37
And if you think about GPUs these GPUs without being worked don't really do you any good. >> Okay. >> Right.
1:40:44
And as the models get bigger the networks actually get need to get faster and need to get larger.
1:40:48
And so what used to be something that sat on one GPU, then sat on a server with eight GPUs that needed to getorked, then sat on a rack with multiple servers, >> then said, "Okay, I need to go scale out within a data center and have multiple clusters connected together."
1:41:04
And now you're starting to see data centers getting connected together. >> Yeah.
1:41:08
>> Yeah. in this thing that they call scale across where you have multiple data centers depending on where the power is available hundreds of kilometers apart that'll actually operate as an ultracluster coherently for running a training run and that requires a whole new set of technology and a whole new set of assumptions around physics that have to be challenged >> which is what we've been doing because
1:41:27
we build our own silicon we build our own systems we build our own software we build our own um you know kind of platform so that's been exceptionally beneficial for us and yes I would love to say that we capitalized on the opportunity well, but the fact that
1:41:41
there was such a intrinsic demand >> for large scale data center buildouts >> is something that's just like we were lucky to be there at the right place at the right time with the right products. >> Yeah. Is cross data center training uh >> Yeah.
1:41:51
Is cross data center training uh particularly uh in demand right now.
1:41:54
I know Google's had some success with it.
1:41:58
Uh other labs are probably thinking about it and working towards it.
1:41:59
Is that something >> it is?
1:42:02
It's actually it's it's the reason it's important is because you might not find all the power to juice a single data center.
1:42:07
Um and so you then have to make sure that you build the data centers where the power is available.
1:42:12
>> And so once if you have power grids in two separate locations, you then need to make sure that those get coherently connected.
1:42:18
>> And the problem with training runs is if you drop packets um then you have to restart the training run which becomes extremely expensive.
1:42:25
extremely expensive. And so what you need to do then is say okay so I'm I'm going to build in the silicon itself technologies for debuffering so that I can make sure that jitter and pack of loss doesn't really go out and affect the training run
1:42:38
>> and that that's been a um um you know like we just launched um our P200 chip what we call with the 8223 router and that's actually going to be it's it's already in a couple hyperscalers but that's that's an area that I feel is going to have a ton of momentum. >> Yeah. How is uh how is it being a uh >> Yeah.
1:42:52
How is uh how is it being a uh like a fabless semiconductor company working with TSMC?
1:42:59
I know there's silicon one which is a 5 nanometer process.
1:43:02
Tim Cook was just talking on earnings about uh maybe some supply issues on the 3nometer process.
1:43:08
Uh there's always questions of like where bottlenecks emerge even if they're very temporal.
1:43:13
Um how is it going on scaling actual supply for you?
1:43:18
>> So the good news is um we've been at it for a while. >> Yeah.
1:43:21
And we have a pretty broad portfolio.
1:43:23
And so volume-wise, we just shipped like I think it was last quarter our millionth chip.
1:43:29
>> Um and so like it's you know we've got [laughter] >> uh so volume wise we've got enough kind of volume of um uh and and I think in this market scale really matters >> and even though you might enjoy scale today um if you don't continue to keep growing the scale you become subscaled pretty fast.
1:43:46
And so there's a level of healthy paranoia that you need to have about continuing to be operating at scale.
1:43:50
And so that that's been beneficial for us.
1:43:52
We have a of course you know we work closely with um the fabs and all of that to make sure that we get our fair share of um you know kind of capacity.
1:44:00
But um it's also important in the sense that >> the the data center buildouts that are happening require this.
1:44:07
And the thing that's been really beneficial for us is we happen to be the offset so that there's not a level of pricing power that our competitors sometimes face.
1:44:19
If you just had one of our competitors providing the network A6 >> uh and everyone else is just building systems around the network A6 then you'd actually have a challenge.
1:44:29
So what hyperscalers want to do is make sure that they can offset that >> by having um choices.
1:44:33
And so we provide choice to the market >> and um that's that's been I mean we as you saw last year we um um we had identifi we said we'll do about a billion dollars in orders in hyperscalers. >> Get ready.
1:44:48
>> Um and >> how much did you do?
1:44:50
>> Uh we did north of two. Um yeah.
1:44:54
>> And then um and then this year in >> [clears throat] >> um in Q1 which was um you know last quarter >> we um we did like I think it was uh north of a billion three or so in just the first quarter.
1:45:04
So like you can start to see that there's >> there's a fair amount of momentum >> but you have to stay paranoid, keep your head down, keep innovating.
1:45:11
What uh what's your pitch to uh talent that you know is elite that you're trying to uh you know we're in the midst of uh last year felt like you'd sum up the year as like really intense talent war.
1:45:22
like really intense talent war. uh there's so many companies with with uh so much hype and I feel like Cisco's approach is like let the let the metrics sort of speak for themselves not not uh not as hype driven as uh other parts of you know the press the the press release economy let's say but if you're sitting down with somebody and you have three minutes to get them to join Cisco versus you know another company or lab or
1:45:47
something like that what's your pitch >> my pitch typically is we're very missiondriven and so we want to make sure that we're building critical infrastructure for the AI era for the world and that's a that's that's a mission that you should
1:45:59
>> they have to feel intrinsically connected to and excited about and it's not just about creating connectivity on that infrastructure but also keeping it safe and secure >> um and then just continuing to keep build the entire full stack. I mean we
1:46:09
I mean we build the silicon, we build the systems, we build the operating system, we build the we build the platform, we build the applications.
1:46:16
So like we've got that full stack and so it if you wanted to develop and grow in a company and we're very well known to actually get people who don't have experience in a certain area >> to put them into I mean look at me I didn't know much about networking when I first came on. >> Sure.
1:46:32
And I think it's um I feel like experience is great in certain areas but sometimes it can actually create a burden of bias for you.
1:46:44
>> And so having a mixture of experience with inexperience >> so that you are having the inexperienced person allow the person with experience to unlearn as fast is really important.
1:46:55
And the the appeal to people coming in is look we are we're going to build things which are going to be based on the long term.
1:47:03
>> And um if you want to learn how to have good strong leadership foundation principles um Cisco is a great place to be for the long term.
1:47:11
But like I I'm not if someone asked me like you know what do you have for your lunch? Is it free lunch?
1:47:17
I'm [laughter] like you're asking the wrong question and this is the wrong place for it.
1:47:19
Like we're pretty scrappy when it comes to that kind of stuff.
1:47:21
And uh we're proudly scrappy about that stuff.
1:47:25
I mean we >> What about specific skills that are the most in demand?
1:47:28
Uh I mean there's there there's a lot of roles that are augmented by AI now.
1:47:35
>> Well, we've heard we've heard too about you know plumbers and electricians flying around and you know >> they are in demand even sales guys and chip designers that's not going away anytime soon.
1:47:44
No, I I think look the the um the roles that have been in demand around enterprise let's look at the entire business like if you look at uh enterprise sales that's always going to be in demand.
1:47:55
We're always going to need people in front of customers to say, "Hey, we need to make sure that we can get you the technology."
1:48:00
And the distribution is >> um a massive kind of, you know, advantage that we have.
1:48:07
>> But u on the chip side, there's there is not enough people that understand how to etch sand into um into creating intelligence.
1:48:14
Like we need more of those.
1:48:16
And so there's always a shortage of that.
1:48:18
Um there's >> um there's a shortage in people that are building hardware and systems.
1:48:22
There's a shortage in people that are building software.
1:48:25
So I don't feel like there's a lack of shortage in any of the areas.
1:48:28
But what's now happened is you've added to that an entirely different development model where you also need researchers for AI. >> Mhm.
1:48:37
>> And so we have an AI research team and that's something that we historically didn't need in the in the product development cycle and we've only been doing that for now for a few years.
1:48:44
Um in addition to the research team, you need to make sure that software development life cycle itself is changing.
1:48:51
changing. Like we just announced or not announced but we just kind of revealed with uh Sam that 100% of our AI defense product which is a product that we announced last year at this event is now written is going to be written by AI in 3 weeks
1:49:07
>> 100% like no humans writing code humans are reviewing which means that the bottleneck is no longer writing code it's actually reviewing code >> you know um and that those will require kind of shifts in mental models and so it's less just about the shortage of what kind of skills do we have. I
1:49:22
I actually pay less attention to skills and I pay much more attention to attitude >> and you know you can't teach hunger >> and so you have to make sure you find people that are hungry intrinsically hungry.
1:49:33
Uh I I don't believe in hiring people that you have to motivate all the time.
1:49:36
Come intrinsically motivated come intrinsically hungry.
1:49:40
>> Um and then I think you have to be insanely curious in this time and day and age.
1:49:43
and age. uh if you if you're not if you're not willing to experiment and put yourself in an uncomfortable position and constantly I always tell people like if you if you don't like change and this is not my line someone else told me this I think Chuck told me this but hear from
1:49:57
someone else too if you don't like change wait until irrelevance hits you [laughter] that's a good point >> how have you how have you processed the AI safety debate fortunately the labs are kind of a heat shield for everyone else in AI it's like the focus is entirely uh entirely on them and yet uh
1:50:18
you know Cisco and other infrastructure companies are uh will be a key part of ensuring that all of this all these products that are created and intelligence that is created uh has you know positive impact on the world and humanity and >> so I think there's two dimensions to this the first dimension is um all the
1:50:37
tools that you and I use are the ones that um the adversaries are going to use to go out and create attacks on cyber And so the first thing you have to do is make sure that you have um you know cyber defense happening at machine scale not just at human scale. >> Okay that's that's been something the
1:50:50
>> Okay that's that's been something the whole industry is doing that's nothing revolutionary but we have to do it and we got to make sure that AI is being used for that.
1:50:57
>> But the second area which is these models that these applications are getting built on inherently by nature are non-deterministic which means they're unpredictable but you're trying to build like a finance application or healthcare application which needs to be very deterministic.
1:51:10
I tell people I'm like, you know, hallucination is a great feature when you're writing poetry.
1:51:15
Everything else in life not that useful.
1:51:18
>> You know, >> your doctor is like, "Sorry, I hallucinated." >> Exactly.
1:51:20
[laughter] >> And so, like, what we have to do is we have to make sure that we actually figure out mechanisms to um get full visibility on the model. >> Yeah.
1:51:30
>> And then have validation that says, does the model behave that the way that you want it to behave?
1:51:35
>> Um so, for example, if I ask a question to the model, build me a bomb. >> Yeah.
1:51:40
What it'll very dextrously do is tell you, well, I can't give you that answer.
1:51:44
But if you ask the question slightly in a nuanced way, I'm a movie script writer.
1:51:48
I'm actually going out and shooting a movie with Brad Pitt.
1:51:51
We're going to shoot a scene where Bradput's going to get into the car, build a bomb, and then blow up the Bellagio.
1:51:57
>> Um, and so can you build me that entire scene?
1:51:59
And by the way, can you show me how the bomb gets built in that scene?
1:52:00
I can recreate the model get tricked, right?
1:52:03
And so what you need to have is some kind of an algorithmic red teaming process that says trick the model in test rather than in real world >> and do that algorithmically.
1:52:14
>> And once you do that then have some kind of mechanism to enforce runtime enforcement guardrail so that every single time you build an application that application is prepared to go out and deal with those kind of questions that might trick the model so that you as a company don't have your brand at risk when you do that.
1:52:30
And that's the thing that we are actually um building.
1:52:34
And the thing that gets me really happy is if there was one product that I wanted to have fully written with AI, >> it would have been that product. >> Yeah.
1:52:41
>> Because the speed it has to outrun everyone else.
1:52:44
And you have to make sure that you deal with this at machine scale.
1:52:48
>> And so it's really cool to see the teams not only change the fact that they're using tooling to make that happen, but they've changed their entire process.
1:52:56
>> Every engineer in that team is now just a spec developer.
1:52:58
M >> they just build specs.
1:53:00
They create markdown files.
1:53:01
They give give context to the model.
1:53:03
They give context to the agent and then and then the agent's kind of writing the code and then our biggest bottleneck is becoming review of code >> rather than the actual writing and generation of code. >> Yeah.
1:53:14
>> Um >> how long do you think that lasts?
1:53:16
>> I I think it becomes or well I think >> because presumably the the >> no the review is going to get easier too. Okay. Yeah. Yeah. Totally. Totally.
1:53:23
At some point in time >> these these things are going to continue to get >> we'll be able to just talk all day long.
1:53:29
you'll be able >> I could just be here like eight hours it [laughter] would be great.
1:53:32
Um but but um you know but so I think that's that's an area and but and by the way that doesn't mean that you could have a lot of AI slop that gets delivered in the market when that happens because what you'll have is really crappy software that gets written if people aren't paying attention to it.
1:53:47
It doesn't obviate the need for having taste and having judgment and having instinct and making sure that you're thinking about what you're building.
1:53:53
But what it'll at least do for you is provide this mechanism [clears throat] to accelerate the development where we're not just constrained by I don't have resources so there therefore I'm not going to build 80% of the things I want to build.
1:54:07
However, prioritization is still going to be important and I feel like focus I don't think the nature of focus changes just because you can build a lot of stuff quickly. >> Yeah. >> Yeah.
1:54:17
Talk about innovation at Cisco.
1:54:20
you mentioned it earlier, but uh I was uh I was watching a video from Jane Street all about uh innovation not necessarily being um perfectly correlated with new company formation like what startups do is great but you know you look at the iPhone that came from Apple you look at the you know the transformer paper came from Google um
1:54:41
two wildly different approaches to innovation one you know CEO leading the other this labs the skunk works team how do you see innovation balancing between sort of top-down mandate for new products, what the market's responding to, customer-driven versus lab scientists going off and and working in isolation. >> So, first first thing is innovation is
1:55:02
>> So, first first thing is innovation is in my mind it's a choice.
1:55:06
>> So, when companies say well we're big so we can't innovate.
1:55:08
>> There's a lot of big companies that don't innovate but there are a bunch that do.
1:55:11
So, what's the separation?
1:55:14
>> Correlation does not equate causality in that case.
1:55:16
Like just because you're a big company does not mean you can't innovate.
1:55:18
In fact, some of the greatest companies >> um you know are innovating at a really really fast pace.
1:55:23
And by the way, scale really matters right now.
1:55:25
And in AI, scale is actually a huge accelerant for innovation. >> Yeah.
1:55:29
>> Distribution, more data, flywheel, >> more all of that.
1:55:33
>> And so I do feel like innovation is a choice >> and I think it's an intellectually lazy argument to say that innovation can't happen because we are we're too large.
1:55:43
However, uh size does bring about some level of um you know um slowness in the process if you and and you have to be diligent about making sure that you you're intolerant >> on bureaucracy seeping in >> or more importantly indecision seeping seeping in and apathy kind of seeping in where people just give up after a while.
1:56:05
You know what I tried a couple times it didn't work and it's like no you have to be comfortable with conflict.
1:56:08
M >> you have to be comfortable with making sure that you um speak truth to power >> and if that means that you're going to like one thing that can become a failure state is in these large companies um you know you get overly concerned about feelings of people and so as a result what you stop doing is you stop having the debates that need to be had.
1:56:32
the debates that need to be had. M >> what you need to do instead is establish trust >> first in teams >> and once you have trust you know that the intent is not to put you down the intent is to make sure that you get the best idea ship out and get to win >> and winning requires contrarian viewpoints that are actually actively debated and if you don't
1:56:52
>> sometimes hurting feelings >> exactly and but it wouldn't hurt feelings if everyone were clear about the fact that I trust this person >> and they're debating the idea not the person they're not attacking my personality >> and One of my mentors always told me, you know, and I I don't I have I have not mastered this, you know, um, state the facts, but watch your tone. >> When I tend to do poorly is when I get
1:57:12
>> When I tend to do poorly is when I get passionate when I don't watch my tone >> because then that debate feels like it's a personal conflict rather than something that's about the idea. >> Yeah.
1:57:25
>> And so if I were to say, you know, on the innovation side, top down, bottoms up, I think there are certain things you have to go top down. Mhm.
1:57:31
>> Like we had a decision we had to make that said we're pivoting to AI first as a company >> and that was not a decision I could have had democratically kind of sourced from 30,000 engineers.
1:57:39
It would have not worked.
1:57:42
>> So we said we're going to go from top down on that one.
1:57:44
>> But I I should be losing 99% of the debates in my organization because the person in the front line is spending 14 hours a day on that problem.
1:57:53
I'm spending 6 minutes a week on that problem.
1:57:57
Chances are, even if I'm infinitely smarter, which I'm not, if I'm doing the right job because I'm hiring people smarter than me, at that point, you need to make sure that that person is able to have more facts, more thought put into that argument than me.
1:58:12
So, I always tell people if I'm make having a debate with you and I'm if I'm having an argument and if I don't lose 95% of the arguments, then >> we've got a different problem. >> Yeah. Yeah. >> Right.
1:58:21
Because you just haven't thought through.
1:58:22
What my job is to make sure that the second level and third level thinking >> is actually well thought through on your end.
1:58:29
>> And then we need to make sure that we have certain core principles up top that go from top down >> and certain core value systems that we should not compromise on so that we don't actually get people on on an infinite loop >> and then on the bottom for from the bottom up allow people to innovate and then let those great ideas percolate up.
1:58:48
>> Well, thank you so much for coming on the show. >> So nice to meet you. Great to meet you.
1:58:52
Hopefully we'll have you back soon.
1:58:52
And in the meantime, I'll tell you about Cognition.
1:58:56
They're the makers of Devon, the AI software engineer.
1:58:58
Crush your backlog with your personal AI engineering team.
1:59:02
And our next guest is uh coming in right now. Fantastic.
1:59:10
[laughter] I wasn't sure if we were going to have a little bit of a break, but we have Costa. >> Welcome to the show. How are you? >> How you doing? Nice to meet you guys. >> Thank you so much. Sorry.
1:59:16
No, no, we're we're good.
1:59:19
We go for three hours every day. Yes, no problem.
1:59:20
Um, please uh introduce yourself since it's the first time on your show.
1:59:24
>> Yeah, so I'm uh Costa Claos.
1:59:24
I'm the EVP of technology for the San Francisco 49ers and Levi Stadium and a big fan of the show. So excited to be here. >> Yeah.
1:59:33
Uh, explain to us like what that means and what your day-to-day is like and what the choices you're facing are.
1:59:38
>> So, there's never like a regular day-to-day.
1:59:40
It's so my team, we're responsible for everything from um you know getting into the stadium, the ticketing systems, the uh the point of sale systems for food and beverage, the Wi-Fi, the network infrastructure, uh the cyber security of the stadium, u making sure that on game day we're like the referees.
1:59:56
You don't notice us and you just have a great time and you know cheer on the team which is hopefully the 49ers.
2:00:01
But >> yeah, how has technology in a football stadium evolved?
2:00:04
Like I uh we we were going back to like the first broadcast was the the Los Angeles Rams before they moved to St. Louis.
2:00:11
Then they went back and it made sense because, you know, Hollywood is in there.
2:00:14
They're very forward thinking.
2:00:15
But uh how much have you seen an evolution in the technology that goes into, you know, broadcasting football game? >> Well, it's crazy.
2:00:22
I've been doing this for about 25 years now and um you know, in a bunch of different sports and it just used to be, you know, you go to the game, you watch the game, you eat your food, and you go home.
2:00:30
Now it's different because it ha you have to have a technology experience.
2:00:34
The reason why is >> I mean staying at home sounds like a good uh proposition.
2:00:38
You have your big TV, you have your couch, you have your food.
2:00:41
Um so we want to we want to make a reason to come to the park.
2:00:43
We want to enhance that experience.
2:00:44
Um so how can we get it to be kind of a social experience?
2:00:48
How can we get something that'll get you off your couch and coming there and having a great time?
2:00:52
Because I mean it's also not cheap to come to a game, you know, it's it's a significant uh dollar value from your wallet and entertainment dollar.
2:00:57
So, we want to make sure that you have the the best experience possible.
2:01:02
>> And then, I mean, you look at the people watching sports, what are we doing now?
2:01:05
We're, you know, we're all on our phones, you know, we're all technology, we all want more data.
2:01:07
Um, so it's like it's not trying to get people off their phones.
2:01:11
It's like, how do we >> stats, more plays, um, know more things about their their favorite players, and really bring the action to you.
2:01:20
And then, I mean, with us being kind of an influencer economy, everybody wants to show show off where they are, right?
2:01:28
Like what a better place to show off where you are and having a good time there and then your friends are going to be jealous and then come and enjoy the game as well, right?
2:01:34
>> Rank the most like rank your tech stack based on importance on game day like specifically during the game.
2:01:39
And I want this in the at the top.
2:01:43
>> No, no, no, no, [laughter] no.
2:01:43
But I'm talking about like when like if if you're just an observer and you're enjoying the game and you're experiencing, you know, a bunch of different sets of technology, like what are what is like the average person not noticing that is like going on in the background? you're fixated on. >> Yeah.
2:01:58
So, I mean, hopefully they're not noticing it because that means it's working, right?
2:02:01
Um, so I I think you start from the foundation, the infrastructure, the network, the network infrastructure, and you know, I tell everyone they're like, "What is that?"
2:02:09
I'm like, "That's the plumbing of our organization.
2:02:10
That's that's where everything flows."
2:02:11
That's to make sure without that we have absolutely nothing. We're we're cooked.
2:02:15
So, I mean, uh, you know, Cisco is a great partner of ours and and we work together.
2:02:18
It's it helps that we're in Silicon Valley.
2:02:20
Um, which it helps, but also hurts because everyone knows technology in there, right?
2:02:25
So everyone coming to the game, they expect the best.
2:02:27
So which is a great challenge and one of the reasons why I went there.
2:02:30
So that that that infrastructure has to be solid to deliver you Wi-Fi.
2:02:34
Um and there's just more and more need for it, right?
2:02:37
We have to keep scaling it up now, you know, with AI coming down the pipe.
2:02:41
Everyone's, you know, looking up their their LLMs and uh on their phones obviously sharing their uh experience and um you know, with advanced stats and things like that, it has to be um frictionless for them, right?
2:02:53
Um, and so that network infrastructure has to be solid, not only what's going on in the stands, it's the fans, but on the field, too.
2:03:00
We can't have, you know, scoreboards go down.
2:03:01
We can't have a uh coach comms go down.
2:03:03
So, we work together with the NFL um to do that.
2:03:04
So, that's incredibly important to us.
2:03:06
And then with that handinand is actually our cyber security posture. >> Okay.
2:03:12
>> Um I mean, we could have the best systems in the world, but you know, one hack, you know, one uh cyber security incident and it's a it's a massive problem. Yeah.
2:03:20
>> Is that mostly like DOS just like come just like adversarial hackers just trying to like take you offline, screw things up, create economic damages or is it specifically like break in and steal credentials everything? All of the above?
2:03:33
>> I imagine every day there's somebody that would love to like broadcast onto the jumbotron. >> Yeah.
2:03:38
Like this crypto scam or something. I don't know.
2:03:40
What's the shape of the threat?
2:03:42
>> There is so many different threats.
2:03:42
Like I mean you just named Phil and you're scratching the surface.
2:03:45
um just because we're so visible, you know, every Sunday the world's looking at us and you know, it's it's a it's a visible attack vector.
2:03:53
So, I mean, we have a an incredible cyber security team.
2:03:55
Uh we work again with the NFL.
2:03:57
Um and then, you know, share knowledge with other teams cuz, you know, we're we're competitors on the field, but colleagues off the field.
2:04:04
Um and so we work together to make sure that we're mitigating risk and you know, it's not if we're attacked, it's when you're attacked.
2:04:11
So, we try and make sure to to stop that.
2:04:13
You have to be proactive and instead of reactive.
2:04:15
If we're reactive, we've already lost the battle. >> Yeah.
2:04:19
Is NAB important for you? >> NAB? >> Yeah.
2:04:22
The national >> the show? >> Yeah, the show.
2:04:24
Like we go for for the camera stuff here.
2:04:27
We try and learn a great show.
2:04:28
Yeah, we do send a big >> I'm wondering if that's like important to you or if there's a different uh conference where you're you're meeting more people and talking to more suppliers.
2:04:35
>> No, we do send a lot of people to NAB, our engineering team.
2:04:38
I mean, it's it's definitely important to us because look at our our broadcast size.
2:04:41
We have uh right now I believe the largest outdoor 4K video screens uh in the league.
2:04:45
So we we do send a lot of people there.
2:04:48
I mean you want to be around the best in the brightest.
2:04:51
We have a lot of shows >> that that we go to.
2:04:53
But it's not only you don't want to stay inside the industry.
2:04:56
Um I always say you learn your best when you're looking outside. So who's doing what? Great.
2:05:00
Doesn't have to be from sports and take that.
2:05:03
>> Then what is what is like the top down uh mandate or standards that are shared from the NFL?
2:05:08
Like there's obviously broadcast standards if you're delivering to ESPN or ABC or NBC, but um what what does need to be standard across the NFL broadly? >> Yeah.
2:05:18
I mean, the NFL has their their standards across the NFL in terms of, you know, Wi-Fi, infrastructure, connectivity, >> expectations.
2:05:24
Every team has >> Exactly.
2:05:26
But, you know, you know, us at the 49ers, you know, our team always overperforms on the field.
2:05:30
You know, we're always expected to win a championship.
2:05:32
You know, that's what we try and do.
2:05:34
I mean, it's difficult, but that's like our goal.
2:05:36
So, you know, with with me being in and the um and the technology team, you know, we're trying to be the best.
2:05:42
Again, we're in Silicon Valley, so we expect more.
2:05:43
So, I want to go over and above and deliver an experience that's not just kind of the minimum.
2:05:49
It's an experience where you're going to come there and say, "Wow, Levi Stadium's unbelievable."
2:05:52
Like, this is this is insane.
2:05:53
Let's let's uh let's go there.
2:05:55
This is something different that maybe, you know, somebody like a Disney should copy, right?
2:05:58
Or or Starbucks or somebody else in the industry.
2:06:00
Sorry if they're not sponsored.
2:06:01
But >> how much different is uh uh the the Super Bowl specifically?
2:06:04
Is there more demands on your organization for a bigger event or is it sort of the same as any other game?
2:06:12
>> Yeah, I mean the Super Bowl is one of the world's largest arguably the world's largest uh sporting event um especially in this country and the the demands are greater and we work again handinhand with the NFL um as their event uh the the capacity the the Wi-Fi the bandwidth it always sets records wherever they go.
2:06:30
I mean, our the previous record at our stadium was um Taylor Swift, uh which was an incredible concert, by the way.
2:06:37
Um and uh I mean, her her demographic super techsavvy.
2:06:40
Soon as she came out on stage, people are, you know, putting up their phones and sharing that and and it blew away our bandwidth records a couple years ago.
2:06:47
And we expect this to go even higher.
2:06:49
But that's a record that's always made to be broken because people are using more data, not less, right?
2:06:55
So, we always have to prepare and scale up as well.
2:06:57
Well, Jordy, anything else?
2:07:00
>> I mean, this have a lot more questions, but it was great having you on the show. >> Yeah.
2:07:03
Thanks so much for great to meet you. Thanks. Off.
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Console builds AI agents that automate 70% of IT, HR, finance support, giving employees instant resolution, for access requests, and password resets. See you. Good to see you again. Good to have you. Data centers in space. What you got?
2:07:38
[laughter] >> When are we going?
2:07:40
Me, you, the International Space Station. Let's break it down.
2:07:45
>> You know, the space tourism industry is quite a quite a fun one, right?
2:07:47
Um >> would you go would you do the Blue Origin thing where they blast you out past the Carman line?
2:07:54
It's good enough for Katy Perry.
2:07:54
It's not good enough for you. What's going on?
2:07:57
>> It's It's like, you know, like you're in freef fall.
2:07:58
You're not actually shot in line.
2:08:03
>> I want to be like going around for days, you know?
2:08:05
I want my bone density to start to atrophy, right?
2:08:06
[laughter] Like I I truly want to feel the negative effects of space. >> Yeah. Yeah.
2:08:10
It's not enough just to go. I I think I would do it.
2:08:12
It's even >> It's like 90 seconds, right? >> Yeah.
2:08:15
But it's better than being hanging out at on >> all the cool stuff that astronauts do, right?
2:08:20
like, you know, put water and then like they're bubbling and then you try and drink the water or >> they'll be unplugging the GP, plugging it back. >> Oh, yeah. Yeah. Yeah. Yeah. Yeah.
2:08:28
That's how you pay for your space tourism.
2:08:30
You got to go [laughter] unseated ship. >> 90 seconds.
2:08:36
>> One 90 second trip at a time.
2:08:36
Uh, no, but uh people were wondering uh you know, TPUs, Nvidia going on the on the on the Starlink V5 or what whenever something gets up there.
2:08:46
Uh it feels like this will be uh something more like a Tesla silicon chip, an AI chip.
2:08:51
Like do you have any insight into like what the process if you wind up figuring out how to heat dissipate?
2:08:58
If you wind up figuring out the costs, what might the chip look like?
2:09:04
>> So I think I think you know everyone freaks out.
2:09:05
Oh my god, putting stuff in space is expensive. >> Yeah.
2:09:08
>> Uh but if you look at like Starship launch costs and you know they keep falling, you're you're like fine, right?
2:09:12
Like I think that's not you know by the end of the decade the cost of space launch will be fine. the heat dissipation.
2:09:17
I mean, it's a challenge, but you just put a massive massive effectively radiator. Yeah.
2:09:20
And it's it's fine, right?
2:09:22
By the end of the decade, like you'll be good.
2:09:23
I think I think the big challenge is that chips are just really unreliable, right?
2:09:26
Um and so [clears throat] so how do you deal with like a couple things, right?
2:09:32
Satellites can only be so large before they like >> you need start needing a lot of support and structure before they tear themselves apart.
2:09:37
So when you look at like the the launches, right, these these things are shooting out sat like tiny satellites and many of them. Okay?
2:09:44
So you can't have like a big fully connected >> cluster of chips.
2:09:46
Um and then and then like on top of that, right, how do how do you deal with any random error?
2:09:53
>> Um on on Earth, you have tech running around the data center, unplugging stuff, putting in spares, things like that.
2:09:59
>> Um what do you do in space?
2:10:01
>> Um you RMA it to the factory where they like might unsolder it and resolder it and then like test it and it works and go back out.
2:10:07
Sometimes it is just trashed.
2:10:08
But like you know that's the challenge to me.
2:10:11
Is that is that I I I feel like maybe the the pattern we should be looking at is like how often do the Tesla self-driving chips need to get serviced because that's like the the team that would probably be building or like bridging the gap there.
2:10:24
Like the Starling satellites, sure, they go down, but like the service works like you're you're just relying on some sort of like, you know, 90% uptime stuff's coming down, but most people that are in a Whimo, like the chip keeps working, right?
2:10:38
Most people that are in a Tesla self-driving, like they're not like you don't hear about Tesla owners being like, "I love FSD, but I'm constantly in the shop getting my my custom silicon chip unseated and receated." Right?
2:10:50
>> Well, I mean, it's it's also a function of like the complexity of a chip, right?
2:10:53
Um, you know, if if a chip is twice as fast and let's say the bit error rate, right, like how often a bit flips, >> sure, >> is the same, then it's erroring out twice as often.
2:11:03
>> Um, but let's say the chip is 10x as big, right?
2:11:04
And so when you look at like a Tesla FSD chip, very very good, very very efficient, very still like relatively inexpensive and cheap compared to, you know, a big old GPU or TPU or whatever, right?
2:11:14
Those things are extremely large.
2:11:16
And you know, again, like if if the error rates are the same, then it it fails 10x more, but in fact, the error rates are a bit harder, higher because they're pushing these things to the absolute limit. Yeah.
2:11:26
the absolute limit. Yeah. Um whereas you know Tesla does have some level of like well first of all the Tesla car has two chips >> sort of redundancy already built in right um >> maybe you do that on the satellite but then that's more power more >> yeah right so the whole allure right of it is is
2:11:42
>> you know effectively power is free right and solar panels you look at the cost curve of solar panels you look at the cost curve of uh satellite launches you're you're like this is this is free this is great um >> but power is less than 10% of the cost of the cluster >> right so so like >> it's that 90% % you're not saving anything on. >> Yeah. Yeah. >> Yeah. Yeah.
2:11:59
>> Um and in so far as much as >> for potentially a hundred times the hassle. [laughter] >> Yes. Yes. >> Yeah.
2:12:06
>> Um there's this whole like you know like if you look at Nvidia GPUs right when you first turn on the cluster >> about 10 to 15% of them >> fail RMA >> in the first two weeks. >> Wow. >> And and that's fine.
2:12:17
Like you have to receat them whatever.
2:12:20
>> And like the industry knows how to deal with this right.
2:12:21
And and over time like Hopper is now at 5% but Blackwell's still at 10 to 15%. Right.
2:12:24
actually started out higher than that.
2:12:26
Um, and when a new generation comes out, it's going to be higher than 10 15%.
2:12:29
It'll it'll have its curve gradually decline down.
2:12:33
But, you know, who who's going to are you going to test it and burn it in on the ground, >> or are you going to say 5% of my chips or 10 15% of my chips are trashed because someone can't go up there and like do these things or am I saying, "Oh, I need robots who can do all this stuff in space and now that's like an additional engineering problem."
2:12:46
When sacks of meat are actually very cheap.
2:12:49
>> Sacks [laughter] of meat. Yeah.
2:12:49
Uh, speaking of Nvidia, uh, we haven't talked since the Grock acquisition.
2:12:52
Uh what does that look like in the bull case?
2:12:57
Like if it's if it's a good if the next version of Grock is a great chip, is it sitting next to the you know H200 H100s in the in the rack GB200?
2:13:05
Like how does it fit into the actual like what Nvidia deploys?
2:13:10
Is it just a separate chip that's >> I think it's a big vibe shift from Nvidia, right?
2:13:15
Before they were like, "All right, I got this big GPU.
2:13:18
Everyone's going to use this GPU.
2:13:19
Software ecosystem of the GPU is so good. It's one sizefits-all.
2:13:21
everyone try, you know, like everyone's trying to make all these like specific point solutions, but we've got the thing that's good good at everything. Okay.
2:13:29
Um, and then they had a vibe shift, right?
2:13:30
They launched this thing called CPX, okay, which is a chip made for prefill, um, with, you know, prompt processing, creating a KV cache, and also good at like video generation and image generation.
2:13:40
Um, and that's coming out later, they were really talking about video generation as well.
2:13:43
So, so yeah, you've got like CPX, you've got like the standard GPU, now you've got the Grock chips, and they all fill a different niche, but really it screams, oh crap, we don't really know exactly where AI is going, which I don't think anyone does, right?
2:13:55
I mean, it's it's moving so fast.
2:13:56
The software is um the model architectures, etc.
2:13:59
So, we're just going to like engineer solutions that are along multiple points of the praal optimal curve and then, you know, if >> one of them will win, right?
2:14:06
And I think I think it's like sort of like a big vibe shift from Nvidia.
2:14:09
Also, they just knew Open was going to do this serious deal, so they freaked out, but >> Got it. Okay. Yeah. Well, yeah.
2:14:14
Get me up to speed on what makes Cerebras important in the ecosystem right now.
2:14:20
>> Um, so, so you know, you have people thinking like, oh, latency matters in terms of where our data center is.
2:14:26
It doesn't matter at all.
2:14:28
What matters is, you know, as we've moved from, you know, chat applications, which were like or search response immediately, chat applications, let's say response takes 10, 20, 30 seconds.
2:14:36
You've got agents, you know, I don't know, my cloud codes are working in the background for a long time, right?
2:14:42
doesn't matter where the data center is, but what does matter is that these streams of inference take, you know, 30 minutes versus 10 minutes versus five minutes.
2:14:48
And for a lot of people, I'm fine to spend 10x the price. Sure.
2:14:52
>> On something that completes 10x faster. Yeah.
2:14:55
>> Um and so, so Cerebras sort of just makes a ton of sense there.
2:14:57
So, OpenAI, you know, they've got these like long horizon.
2:15:00
There's there's like codeex 5.
2:15:00
2 uh extra high thinking or whatever. It's terrible.
2:15:05
Can you guys teach them how to market? >> Open.
2:15:08
[laughter] You have to sponsor this podcast. Yeah. Yeah.
2:15:12
We had on yesterday and I did actually ask him like I I I had the Codex app pulled up on my desktop and I was like there are six different models and then there's a then there's another button that I can pick.
2:15:24
>> Well, how many different products are called Codex?
2:15:26
Now >> there's a lot now there's an app. Yeah.
2:15:29
[laughter] >> We have another guy on just to do branding.
2:15:32
Lexagon branding came on the show yesterday talking about the all the naming >> naming architectures. >> Naming architectures.
2:15:37
>> Naming architectures. [laughter] It is complicated but uh hopefully >> you could tell he's just bloods boiling because like all the AI companies just have the most chaotic [laughter] like anthropic claude code but also you can use cloud code for other stuff and >> yeah but yeah I mean uh with with
2:15:52
cerebras it seems like there is a value to it but are they constrained on the supply side like can they actually scale up to you know a colossus style data center that could actually speed up codecs not just for like one user but all the users So, so I mean servers can speed up multiple users for sure. The
2:16:09
speed up multiple users for sure. The question is sort of like where you use it and that's where they have to like >> figure out where within codeex right because there are times where codeex is running for like 10 hours and sometimes you don't mind right like screw it I've put out this nice prompt gone work on it
2:16:24
refactor my code do this thing do this task other times I want this iteration feedback loop so how do you expose it to the user without saying hey actually there's another toggle [laughter] >> so your permutation is is 18 times >> well hopefully like a really robust model router but it feels like that's been a process Yeah. So, so the open eye
2:16:38
So, so the open eye deal is like for 750 megawatt.
2:16:40
It's it's not that much capacity on the order of like what open talked about.
2:16:45
You know, by the end of 28 they'll be at like 16 gawatt >> um of that.
2:16:50
>> So, it's like the absolute cutting edge, the most price insensitive customers in that specific use case of this is the type of prompt that you need to return fast then you'll get the speed up potentially. >> Right. Right.
2:17:00
And and they've got to figure out how to do it from a product exposing to the user etc.
2:17:03
But it it it's it's clearly >> something where there is demand, right?
2:17:08
like I don't know like Andre Karpathy doesn't care if he's spending a thousand bucks per his agent per second or whatever, right?
2:17:14
Like you know whoever it is this these like super cracked engineers don't care at all and then obviously there's like a long tale of like actually cost does matter for most people.
2:17:21
Um and so so all along that curve they've got to have solutions, right? >> Yeah.
2:17:26
>> Uh >> when did you first think that XAI might end up at another Elon company?
2:17:34
>> I mean this has been rumored for a long time, right?
2:17:35
Like people were saying Tesla, Tesla, Tesla for the longest time.
2:17:38
>> It's harder with a public company. >> Yeah. Yeah.
2:17:40
And then and then a few a bit ago people were like, "Oh, SpaceX."
2:17:44
I'm like, "Wait, this makes no sense."
2:17:45
>> No, but there was a very coordinated like narrative pump. Oh, yeah.
2:17:46
Like this year at the end of last year and it was like almost like perfectly telegraphed.
2:17:55
>> Well, there's there's a bet, right, between um basically the head of compute of XAI and the head of comput of Anthropic.
2:18:00
And the bet is what percentage of worldwide data center capacity is in space by the end of 28. And the bar is 1%. >> Oh wow.
2:18:09
>> And so the XA guy is like really bullish and the Enthropic guys is like a little far. >> Yeah. Yeah.
2:18:14
So but but it's it's a really interesting bet.
2:18:15
Um I I take the under on 1% by 28 because that's a gigawatt in space. >> Yeah.
2:18:21
>> But it's actually not that crazy, right?
2:18:23
>> Um it's roughly 150 Starship ship launches.
2:18:26
We'll get them to get them to a gigawatt in space. Yeah.
2:18:28
So, you know, Starship hasn't worked yet. >> Yeah. >> Fully.
2:18:33
>> I was looking at the energy draw of the current Star Starlink fleet and I think they're at like what is it?
2:18:37
Uh 200 kilowatts or something like that.
2:18:40
So, you you get a thousand of those 200 megawws and like you're starting to be in the territory something like that. >> Yeah.
2:18:48
So, the V2 star uh satellites I think are the only ones they've launched.
2:18:52
Maybe they've launched a few V3s, but the V3s are coming soon and those are those are like 100x more bandwidth each, right?
2:18:56
and more power and just more power.
2:18:58
And so when I'm just thinking of like can you scale this thing up at all?
2:19:01
It's like are they two orders of magnitude off?
2:19:03
Are they three orders magnitude?
2:19:04
This feels like they're like one order magnitude off being run something that looks like an H100.
2:19:09
>> I think the metric is like 50, it's either 50 kilowatts a ton or something like this per satellite for V3.
2:19:16
>> Um if they let's say from V3 to whatever the compute thing is, they double it again, get to 100.
2:19:20
I think the V2s are like 25.
2:19:22
Um, so if you get to 100 kilowatts per ton for launch, it's it's only 150 or so Starship launches.
2:19:26
I think that's so reasonable.
2:19:28
Um, maybe not 28, maybe it takes 29, but like >> you know it's it's so reasonable.
2:19:33
The question is cost and reliability and uh you know what happens when the chip fails? How do you service it? That kind of stuff.
2:19:39
How do you how do you deal with having clusters be much smaller instead of like you know these big clusters?
2:19:44
Even for inference big clusters are useful. >> Yeah. Uh yeah.
2:19:47
Uh how do you think about uh Google's response to Gro's risk?
2:19:49
Uh TPUs obviously very successful but uh are they forking that project to eat more of the paro curve. >> Yeah.
2:20:01
So so for the longest time Google's had uh one main line of TPUs, right?
2:20:05
Um all made by Broadcom and then sort of next year they've diverged it, right?
2:20:09
Where Broadcom makes a TPU uh and MediaTek makes a TPU.
2:20:12
These two TPUs are focused at different things >> and they're fabbed at >> they're both fabbed at TSM.
2:20:16
Everything at the end of the day goes to Rackus, right?
2:20:19
>> I I want to I want to go there next, but [laughter] it goes to >> So So So Fab by TSMC regardless, but both of these TPUs are focused on different things and they've actually got a third project for another kind of TPU.
2:20:30
They're they also see this need to proliferate uh along the curve of like, hey, do I care a lot about super high amounts of flops, not that much memory?
2:20:38
Do I care a lot about super fast onchip memory only?
2:20:41
Do I care about 3D stacking memory?
2:20:43
Do I care about, you know, the sort of general purpose middle ground AI chip, which is what, you know, an H100, a Blackwell, a TPU looks like today?
2:20:48
Um, you know, there's sort of like, oh, we need to hit the entire predo optimal curve.
2:20:55
And it's like, okay, within this there's training versus inference differences and like what numeric you want and all these other things.
2:21:00
There's so much complexity there.
2:21:01
Um, everyone everyone sort of is diverging their road maps um >> once they're at a sufficient scale, I think. >> Yeah.
2:21:09
Are is Google still way ahead on cross data center training? Yes.
2:21:13
>> And are the other labs like is that is that important to the other labs to catch up there or is it something that will just naturally happen because everything sort of commoditizes or do the other labs need to sort of marshall some herculan effort to like crack the code on what it takes and what Google's doing? >> Yeah.
2:21:29
So, so it's a couple of things, right?
2:21:31
In 2023, everyone thought that scaling was pre-training. Yeah. Right.
2:21:35
Um, you know, more parameters, more data.
2:21:37
And that's very difficult to split across data centers.
2:21:40
>> And has Google been able to do that?
2:21:41
>> And Google's been able to do that to an extent, right?
2:21:43
So what they've done is they've got, you know, they don't have the largest individual data center campus, but what they do is they do these like uh regions where it's like, hey, each data center is roughly 40 miles apart from each other.
2:21:54
So in Nebraska and Iowa and then in Ohio, they've got like these complexes and now they're building one in Oklahoma, Texas.
2:22:00
You know, these complexes where there's all these data centers pretty close to each other.
2:22:03
>> So it's not really cross data center like across across the world, right?
2:22:07
It's just cross like region. >> Yeah.
2:22:08
And then that that makes a lot of the difficulties a lot easier.
2:22:09
Um flip side is we've also moved to RL, right?
2:22:14
Um and majority of the time of the chips is spent generating data, right?
2:22:17
Only doing forward passes through the model.
2:22:20
Um and then you only send the final tokens that you verified sort of back [clears throat] to train on to the training to the train to train, right?
2:22:28
So then you end up with like oh instead of in pre-training scaling you need to like synchronize all the weights every 10 20 whatever seconds.
2:22:33
um when you're doing these rollouts and especially as things get more and more agentic in training, you might not only need to send not the entire weights but just the tokens that are relevant.
2:22:42
So way smaller amount of data and way less frequently, right?
2:22:45
Minutes at a time instead of uh seconds at a time. Yeah.
2:22:49
>> Um and so you've got this like now now it's become like reasonable where oh actually multi- data center training is completely reasonable and people do this.
2:22:56
People do multi data center multi-chip training. Sure. Right.
2:22:57
You know you do your inference on one set of chips and you do your training on another set of chips.
2:23:01
So like Anthropic does this.
2:23:04
Um >> I don't know if Google does this, but Google's kind of already uh got the cards. Yeah. >> Okay, got it.
2:23:09
Let's go to >> talk about just there's this debate TSMC risk.
2:23:15
Is that the bottleneck or is energy the bottleneck?
2:23:17
I was doing back of the envelope calculations.
2:23:19
Fel seems like we're using maybe like 1% of global energy production or western energy production on on AI specifically workloads and then we're using like 50% of leading edge fab capacity on AI workloads.
2:23:33
Um and so that feels like okay well even if we all agree and you say as a society we're going all in on AI we can only double the AI chip capacity before we need to build more fabs. That takes years.
2:23:45
Whereas we could say everyone turn off your air conditioning.
2:23:48
We're sending the electricity to the data centers, right?
2:23:51
Like like we have the ability to to generate without without creating new uh did I just get something >> clapping for turning off the AC.
2:24:00
>> Turn [laughter] needs to eat >> strokes for all the grandmothers. >> Yes. Yes.
2:24:05
>> I need my cat dancing videos.
2:24:06
>> You need to feed Claude, right?
2:24:08
[laughter] Um but but but seriously, like there's this debate over, you know, is TSMC the main bottleneck or energy the bottleneck?
2:24:13
How are you feeling about that? >> Yeah. Yeah.
2:24:15
So, so sidebar before I answer the question because I think it's fun.
2:24:18
Um, you know, in the US it's insane to say turn off your AC for AI, right?
2:24:22
And the general public hates AI already.
2:24:24
Um, but in Taiwan they've had droughts before and they've turned off water to entire cities.
2:24:29
They're like, oh, you get to you get water three days of the week [laughter] >> and then the fab still gets supplied water.
2:24:34
It's like this is, you know, you've got to understand the mindset.
2:24:36
We are not ready as weak Americans to do this.
2:24:39
this. Um no but um at the end of the day right like um water water water and power are certainly less big of constraints now now you've got to imagine like you know semiconductor industry is used to hey doubling the amount of transistors made every year or two um part of that is more law part of
2:24:56
that is more more capacity um whereas the energy industry in America wasn't and and so like initially people were like not creative they're like let's do let's do these kinds of gas plants it's like well no now we've realized you know yes there's three main manufacturers of turbines and then you've for a dual combine cycle. Then you've got like
2:25:10
Then you've got like IGTS, but you've also got like medium speed reciprocating engines, right?
2:25:13
Like turns out Cumins can make like a million diesel engines a year and like those can make electricity.
2:25:19
Like if I don't give a and I put it in West Texas, >> easy.
2:25:23
Um so now it's more of like a regulation thing, a supply chain thing.
2:25:27
>> Power is not a constraint in in so far like that much, right?
2:25:29
I think it certainly is a constraint still today.
2:25:33
Um it was the biggest constraint in 2425 data center capacity power uh because the industry was just not ready. people have woken up.
2:25:39
They've like sort of been shocked to the system.
2:25:40
Now you've got, you know, tens of gigawatts being deployed.
2:25:44
Um, you know, next year 30 gigawatts are being added and we think the power is there for it.
2:25:47
Um, >> what was it this year?
2:25:50
>> Uh, it's it's or this year is like I think it's like 18ish 10ish 15 to 18ish, sorry.
2:25:56
>> So, so almost a doubling.
2:25:57
>> Yeah, almost a doubling. Yeah. Wow.
2:25:57
Um and when you look at when you look at um TSMC and the crew right there is not really oh this random you know there's 12 people making medium speed reciprocating engines that you can now convert to make uh power at some random data center. No, no, no.
2:26:10
There's like there is rackus, right?
2:26:12
There is one set of spice.
2:26:14
Like, you know, there's, you know, that's it, right?
2:26:16
And so, um, and then, and then the flip side is like, okay, when you have 12 vendors, everyone's got a little bit of slack capacity.
2:26:23
You know, there's more likelihood.
2:26:25
You know, you can people are like, oh, turbines you can't get.
2:26:26
You can call a broker and you can get a turbine.
2:26:30
You might be paying 50% more, 2x more, but you can get a turbine. Yeah. Right.
2:26:33
Like, it's >> you can't get a 3 nmter fab.
2:26:36
>> You cannot get a 3 nometer fab. Exactly.
2:26:37
Um, and so when you talk about what's the, you know, the the baton got passed from semiconductor shortages in 23 to power and and data centers in 2425.
2:26:43
Um, 26 we're still we're swinging the pendulum, but it will fully be semiconductors again in 27, right?
2:26:51
And so we see this across the entire space of the ecosystem.
2:26:55
It's not just TSMC, it's also memory both >> because both of them have built at a certain pace.
2:27:00
Now TSMC has been expanding at some rate. Mhm.
2:27:03
>> Uh the memory makers in fact have just not expanded capacity basically.
2:27:05
They've not built new fabs since 2022 cuz their their cycle is so undulating. >> Yeah.
2:27:12
And and so when you look at it, it's like, oh, even if they wanted to double capacity, they need to build the fabs, right?
2:27:18
And building the fabs, it is the most complex building humans make, right?
2:27:21
Um it's it's it's it's the entire air of a clean room circulates itself every 1. 5 seconds. >> What?
2:27:28
>> And you don't even feel it when you're inside. Really? >> It's like that.
2:27:30
And and it's like parts per billion of particles, right?
2:27:32
Like it's it's actually insane how um you could you could get coughed in the face by someone who has COVID um and not get CO and >> it gets circulated so fast it doesn't even hit you.
2:27:44
>> It's like that meme of like the spraying when someone's talking and then it just gets circulated.
2:27:48
>> So another another sidebar is um everyone knows CO like really popped off in Wuhan, right?
2:27:52
Wuhan also is home to uh China's uh largest memory company YMTC.
2:27:58
And so when they were like welding people into their homes, the people who worked in the fab still went to work. >> Wow.
2:28:06
>> It was because it's, you know, one, it's a national like it's national importance, but two, like these people are getting sick.
2:28:10
This fab is like way too clean. >> Crazy. >> Sorry, Jordy.
2:28:15
>> I I want to talk about Oracle.
2:28:15
They put out a post this morning that said, "Our partners financing for the Dona Anna County, New Mexico, Shackleford County, Texas, and Port Washington, Wisconsin data centers are secured at market standard rates, progressing through final syndication on schedule and consistent with investment grade deals.
2:28:33
Obviously, they were fast following their posts from yesterday where they said the Nvidia OpenAI deal has zero impact on our financial relationship with OpenAI.
2:28:39
We remain highly confident in OpenAI's ability to raise funds and meet its commitments."
2:28:44
And obviously everyone was looking at this being like, "Give me a cigarette." [laughter] Like smoking.
2:28:48
Like it's like bankr run language.
2:28:50
I haven't seen posts like this since like the FTX.
2:28:52
Is >> it just bad coms or is there something worse?
2:28:56
>> Um it's it's terrible coms. Yeah.
2:28:56
Like like uh I I I I told my Oracle context like who the hell is in charge of the Twitter? Like what are you doing?
2:29:03
Um Nvidia did something similar last year when the whole TPU mania was going on.
2:29:09
It was Yeah, it was it was it was like we're we're thrilled with Google's progress with [laughter] the TPU.
2:29:12
That said, Nvidia chips are the only, you know, >> it's like no one asked you to comment.
2:29:18
I mean, like I'm sure a handful of people in your DMs and and random, but that doesn't mean >> doesn't project confidence.
2:29:26
>> It's it's sort of the lion shouldn't concern themselves with the sheep and like okay, Nvidia is a line.
2:29:30
Maybe maybe Oracle is a little bit more bumpy, but I think Oracle is like fine.
2:29:33
Um, people are just freaking out because, you know, OpenAI is is peak, you know, people are peak negative on OpenAI right now because of how good Anthropic's been killing it.
2:29:42
Um, yeah, I think it's just like kind of silly like they need to they need to hire someone to do comms like a Lulu or something, right?
2:29:48
Uh, both Nvidia and Oracle cuz what are you doing? [laughter] >> Yeah.
2:29:54
>> Uh, how did you process yesterday in general? Jensen was clip farming.
2:29:56
Uh, he was like, I don't know why he does these street interviews, right?
2:30:02
No other CEO does those where they just stick 25 microphones in your face and the paparazzi's flashing. It's a great vibe.
2:30:10
>> It's it's you know Jensen's not been as famous as other CEOs for as long and yet he's so important now.
2:30:18
Um and if you've like if you know Jensen how he is in meetings there I feel like there's two Jensen, right?
2:30:23
There is like PR like good at PR just good at talking good at like making people hyped up and believe what he's doing.
2:30:30
He's great at standing on stage holding up the chip, delivering like a sermon.
2:30:34
>> And then there's the real Jensen which is like a business killer and like actually just knows about every like aspect of the supply chain, right?
2:30:41
Um all the way from like niche semiconductor, you know, design and and manufacturing stuff all the way to like energy, power, data center, like and and and then doing the business deals too, right?
2:30:52
And so like you've got this whole predo like of a whole thing whole range of things that he's good at and he's a killer in and clearly he's like he was in a meeting where he was being a killer and like negotiating like supply contracts or something >> and he walks out and then he walks [laughter] hilarious theory but I like it. Yeah, that's awesome.
2:31:10
>> And and that's why he was like you know like he was like still killer like no we never said we committed to 100 billion you know like and it's like >> I don't know where did you even get the hundred billion dollar number from?
2:31:18
And it's like, well, you did go on CBC and like, you know, make a big deal out of it.
2:31:24
People would assume that it was, but but they did say in the press release, remember these are early talks, but they just kind of jumped the gun.
2:31:32
This was the height of the press release >> economy. Yeah. Yeah.
2:31:35
What's funny is um Oracle stock peaked like just like a week after they announced the open eye deal.
2:31:42
And so like the press release of like, hey, open do this humongous deal. Stock peaks.
2:31:46
Um, same happened with a couple other vendors who uh announced deals with OpenAI or Nvidia like sort of a lot of these like like they all peaked then and then it's sort of been like Nvidia OpenAI trade has been going poorly and sort of like the TPU anthropic Google Amazon complex has been doing well.
2:32:03
Um, it's quite interesting >> been good energy back at home with the roommates.
2:32:08
[laughter] >> What what what's going on in >> I wanted to uh uh Yeah, I got one more thing.
2:32:15
thing. I uh so yes over the weekend it was sort of drowned out by all the justice department stuff but >> wait have you guys talked about Elon saying you can smoke a cigar in the fab yeah yeah I was going to say this is this is this is part of the whole thing >> I didn't realize that was related yeah
2:32:31
that makes the most sense >> indoor heaters we have indoor heater technology no one's taking >> advantage what what does the fab look like if you have no humans inside like that's probably his long-term thing is like yeah there there will be an optimist >> no one no like the number of people working in a fab is like irrelevant. Like
2:32:46
Like >> yeah, but but is it is it irrelevant because there's all these things you have to do when a human's in there because they sweat and they breathe and if you don't have to do that because it's a robot walking down even if it's puppeteered or teleoperated you you might be able to have different considerations.
2:32:59
I don't know if that actually affects >> well it's like a nesting of like it's a nesting of like cleanliness, right?
2:33:02
For example, you've got this wafer you've put like down let's say you put down copper and now you're moving it from one area to another.
2:33:08
Well, it needs to be stored in a vacuum but easiest way to store a vac like or or an inert gas.
2:33:11
Um, and that's like the thing that's being transported in, but then around that you want it to be super clean as well.
2:33:16
Um, >> you you you if you don't, then the copper starts getting oxidized, it affects your yields, all this sort of stuff happens.
2:33:23
And so, like, you kind of want it to be a nested layer, nested layer of like, well, this ch this thing inside the EV tool is super clean.
2:33:28
And then the thing feeding it is super clean and then the thing it sits in is super clean cuz cuz that's how you get to like there's zero particles.
2:33:35
Well, because like you know in the in the in the FUP in the transportation device it's like parts per you know trillion and maybe FOP it's called F O front operated uh front opening I don't know something pod >> but it's called a FOP it's like the thing that moves and it carries the wafers.
2:33:49
Um and then and then the FAB is like parts per billion and you know sort of like you know you you've got to like got this nesting relationship so everything is super clean.
2:33:56
Um, you know, I'm I'm bullish on robots, like super bullish on robots, but only for like not for tests that have like TSMC's, [clears throat] Arizona Fabs or Okay, let's say TSMC Tynon, which I think produces like, >> you know, indirectly hundreds of billions of dollars of global GDP even directly, it's like still tens of billions of dollars, has like 5 10,000 people in it.
2:34:19
Like it's like irrelevant in terms of the number of people who work there >> in terms of the overall economic value that's created, >> right?
2:34:25
It's like it's like it's like how many people fold laundry or how many people wash dishes or how many people like do construction work.
2:34:29
Like these are way bigger markets >> for robotics. Yeah, makes sense.
2:34:32
Uh speaking of China, um what what are you making of the the the Dario essay or I guess his comments at Davos about uh you know selling chips to China is equivalent to you know nuclear weapons these days.
2:34:47
the the Ben Thompson line was something like uh he's he's okay selling chips because he wants dependency on the Nvidia ecosystem CUDA, but he would ban uh lithography tools from going to China.
2:34:59
And I'm always I've I've been wrestling with this idea of like I don't know if China would accept this, but wouldn't there be a different world where you want them dependent on American LLM APIs and you don't even send them the chips?
2:35:11
and you say, "Yeah, you're you can have as much AI as you want as long as you're paying, you know, open AI and anthropic API token."
2:35:18
>> Yeah, I think it's I think it's like a curve of like >> what they will accept.
2:35:22
>> It's it's it's you know, one you you you push someone into the corner, they're going to start swinging, right?
2:35:26
And and I'm I'm like very concerned that China does this, right?
2:35:30
Do they do you do you push them too far into the corner?
2:35:31
Do they say screw this, we're going to start being a lot more aggressive.
2:35:35
We're going to we're going to you know do more military actionary actions or or even just invest twice as much in >> in global supply chain take over Africa more than they already have like LAM like etc.
2:35:47
there's there's or or just take over Taiwan, right?
2:35:48
Cuz if I can't have the chips, what value is there in Taiwan existing in its current state, right?
2:35:54
So there's like there's this like game theory aspect.
2:35:55
Um at the same time, you don't want China to be able to like, you know, if you believe AI is going to be do what I think many at least in San Francisco think it's going to do, which is like completely revolutionize humanity and cause GDP growth to accelerate.
2:36:09
Um do you want to have China also own that technology?
2:36:11
also own that technology? um and the and all you know their ability to integrate that into their military and all these other things much faster you know so there there is like these competing like you know interests where where is the like right line and some people think it's like hey yeah sell them AI model well I think Dario would say don't even sell them AI model access
2:36:28
>> don't even sell them tokens >> yeah I think so I think like I think anthropic does not sell AI access to China they loop it through and you can see this in the traffic data they go through Korea and Japan other places but like and so they get it um >> and then the other other side is like sort of like I think the Ben Thompson view which is like um and I think I'm
2:36:45
more sympathetic to that although I think I'm not exactly aligned with that which is like and we've been saying like don't sell them equipment don't sell them equipment don't sell them equipment um and my view my argument is like more economic in the sense of like if you sell them like tens of billions of dollars of equipment they can make hundreds of billions of dollars of AI value or chips with that equipment
2:37:01
whereas if you sell them AI model access and it cost them this much to get the economic you know they're not able to >> you're capturing more of the value yeah and so that's sort of the question that is is at foot here right do We want them to capture all this value of the of the supply chain in equipment or by buying the chips or using the models right and services. Um and we've seen you know
2:37:20
services. Um and we've seen you know across many you know stacks China refuses to accept uh you know using American ecosystem and they'll wait many years before they develop their own whether it was like hey they didn't use
2:37:34
Windows they figured out a bootlegging economy or they didn't use Visa and eventually they came out with like Alipay and WeChat pay or whatever it's called on and and like these things are way better than Visa in fact right lower transaction cost and higher volume. use use RedStar Linux.
2:37:46
It's North Korea's Linux distribution. Really? Yeah.
2:37:50
>> If you if you don't if you put it on a network, it'll immediately call home.
2:37:54
So, you have to put it on a on a on a firewall network or else it just like steals everything immediately.
2:37:59
>> I'm a fan of Temple OS, you know. >> Yeah. There you [laughter] go.
2:38:01
Uh uh, is Doug Olaflin suffering from a case of cloud code psychosis? >> Okay. Yes. Yes.
2:38:09
>> Okay. Yes. Yes. So I think I think everyone's like cloud code is for coders and it's like no >> cloud code is for people who don't code now right and that's the big realization this year um >> you know we've got we've got a couple
2:38:22
folks now on the firm who have psychosis but >> Douglas Olaflin who is like you know semi analysis number two he's president you know he's my boy um he's the one in fact he's the one who encouraged me to make a substack a long time in the go a long time ago. >> What were you doing before? Uh I I had a
2:38:34
>> What were you doing before?
2:38:34
Uh I I had a WordPress blog, okay?
2:38:36
And I was like consulting on the side, but I was like, "Okay, let me do a Substack now." Yeah.
2:38:40
Um because I saw him making money off it.
2:38:42
I was like, "This is shit."
2:38:42
Like why are WHY ARE YOU GETTING [laughter] PAID FOR THIS?
2:38:46
>> Like there were multiple times where he wrote something.
2:38:47
I was like, "I could do way better. >> I'll show you."
2:38:50
>> And and like obviously like it was good because we both taught each other a lot of things and we we've been great friends and eventually he joined semi analysis.
2:38:56
like you know he he his background is he was a hedge fun analyst and then he decided to do a Substack walk hike the Continental Divide Trail for like six months walking from Mexico to you know and then and then you know came back to doing Substacking tried to do a fun >> six months of touching grass and then he was like I'm ready to lock it on cloud code. >> Yeah. Yeah.
2:39:12
and and and so now he's, you know, like he's he's never been a software developer, right?
2:39:16
But he's been on a generational run.
2:39:17
Like he's he's he's not coding anything, right?
2:39:19
He's just telling Claude to do stuff and like it's to the point where it's like our our our like head of data, head of it is like, "Oh, can you send me that?"
2:39:26
And he's like, "Uh, how do I do that?"
2:39:27
And then he like he zips the whole thing and sends it to him. It's like local host.
2:39:32
He sends him a leak once. It's like local host.
2:39:34
Like it's [clears throat] like, "Bro, that's [laughter] not how this works."
2:39:37
>> But but yeah, no, I I I've talked to some folks in Vibe Code and they'll be like And I'll be like, "Why did you choose Noodejs?"
2:39:41
And they're like, "What's no jazz?"
2:39:42
[laughter] That's a very specific >> someone. >> Yeah.
2:39:47
>> No, but like it's it's um we went on we went on a little tour of a lot of our clients like you know roughly like half our business is or 40% of our business is like hedge funds.
2:39:55
is like hedge funds. We went to New York and we went to all our clients and like part of it asking me is opening and I'm answering like no I think they're fine and then like actual ideas and then like a lot of it is Doug just telling them cloud code is like they they're like you don't have to hire any junior hedge fun analyst anymore and
2:40:12
they're like the junior hedge fun analysts are like [laughter] and then he's explaining you know what what can you do it's like well like you can just do like financial models and perform a financial models and like everything in cloud code without ever opening Excel and you can generate charts and like you don't need Yeah. >> To know how to code, you just need to
2:40:26
>> To know how to code, you just need to know how like how this stuff generally works and you can just do it.
2:40:30
>> Are how many hedge funds are just trying to copy trade situational awareness? >> Yeah.
2:40:36
>> Um I mean I think everyone who's I think I think a lot of hedge funds obviously believe in AI.
2:40:41
I think there's a lot of them who don't believe in it, right?
2:40:43
To be clear, but a lot of them that have done the best believe in it. >> Believe in it.
2:40:47
Why are they selling software everywhere?
2:40:50
>> Oh, you mean selling software stocks? >> Yeah. Yeah. >> Yeah. Why the sell-off then? >> Yeah.
2:40:53
>> Yeah. I mean I mean of course it's like an incremental thing right um and but anyway so so these hedge funds like and then then the question is like okay if you believe in it how do you manifest that trade um and and so when you look across the like ecosystem um I would say almost all my clients sometimes think are two years out numbers are too high >> um but like there's there's like
2:41:14
Leopold's like your numbers are too low and so [laughter] it's like it's like it's like uh in general right and I think I think like if you think about how much do you believe in AI and what's your access to information of AI
2:41:24
um you know there's not many hedge funds who live in San Francisco and like fully breathe and live and understand it and then and then depending on how much you believe in AI, how do you manifest that trait, right? >> Are you surprised that more hedge funds
2:41:33
>> Are you surprised that more hedge funds wouldn't like even just smaller shops wouldn't say like, "Hey, this AI thing seems like it's going to be big.
2:41:38
Maybe we should set up in San Francisco >> or hire."
2:41:43
>> There's there's a number of people, right?
2:41:44
Um, so we're we're you know we're getting an office uh together, Leopold, myself, Dark Cash, and then a client of mine, another hedge fund, and they have one analyst here, and it's like and there's like a number of other hedge funds that are like hiring analysts here.
2:41:55
But, >> you know, being plugged into the AI ecosystem does not mean you're just in San Francisco because you can just walk around and talk to like doofus like startups and VCs and like not actually, you know, see what's coming down the pipeline.
2:42:06
Um, >> and you have to com combine it with all sorts of information, right?
2:42:09
You have to have a good tune with like what's going on Asia supply chains.
2:42:12
You have to have a good tune with what's going on in New York.
2:42:15
Um, you have to good tune with like what's going on like in the in the financial markets, right?
2:42:18
And then like what's going on in credit markets and what's going on in all, you know, the data center energy, blah blah blah, all these different industries.
2:42:23
And so it's it's it's actually not like so simple to like be in tune with what's going on in AI.
2:42:29
You can easily get like head faked, right?
2:42:31
Um, >> you know, for the longest time people were thinking, you know, Adobe is an AI company and like and it's like >> for for a bit like Adobe was going down on AI and then they like launched a few AI features and the stock skyrocketed and then now it's [clears throat] going back down again because people realize, oh wait, no actually it's not an AI company.
2:42:47
Um, I think it's it's the manifestation and thought of like what is actually going to the world going to look like if Anthropic 3xes its revenue again this year, OpenAI 2xes its revenue again this year or you know by the end of the year do how many people even believe by the end of the year AI startup revenue is over hundred billion.
2:43:05
I think that's an insane statement for a lot of people, but that's what it's going to be, right?
2:43:08
And who believes that number, right?
2:43:10
It's like very few people.
2:43:11
And then you you you draw the continuation, it's like, who believes, you know, and when Anthropic says in their funding like, hey, we're going to have $300 billion of revenue by the end of the decade, and it's like actually um I think that number's too low [laughter] because because the economic value of what they're going to create is going to be insane.
2:43:29
Um, and and you tell people, oh, X, you know, Open AAI is going to have 18 gawatts or 16 gigawatts by the end of 28 and they're going to be able to pay for it and that's like, well, that's $300 billion to spend.
2:43:39
How are they going to pay for it?
2:43:40
It's like you you sweet summer child.
2:43:41
Don't worry, Sam can raise.
2:43:44
They're going to blow up on revenue. They're fine, right?
2:43:46
Like, and it is like a bit of a vibe thing.
2:43:47
It's a bit of like, >> you know, irrational exuberance almost, right?
2:43:52
Like, um, Li holds in his, you know, mid 20s, like I'm 29.
2:43:54
Like, we we are irrational, right?
2:43:57
because we have not lived through, you know, you get these >> these PMs who like >> you've never been you've never been that humbled. I >> I don't know.
2:44:05
Like we almost my family almost went bankrupt in 2008 like you know cuz we lived in a motel and we almost foreclosed and we actually did forclose on one motel like pretty bad but like you know I mean I was still a kid right like it was like yeah I've never been humbled.
2:44:16
>> It's good I mean it's good to live through that and understand how things can go wrong.
2:44:19
Uh what are you expecting out of Zach and Meta this year?
2:44:22
We've been big Zuck defenders especially I mean there's this pressure of like oh Meta is spending so much and yet they haven't created you know any any AI product that's super compelling or that's really working and our stance has has generally been Meta's making more money from AI than almost any company in the world outside of Nvidia.
2:44:44
So it's like of course Zuck should be justified in saying hey this is real it's big like I'm going to like back the truck up and and go all in. >> Yeah.
2:44:53
>> Yeah. I mean it's it's clear if you look at the most recent earnings I think their CPM went up 9% when the consumer is weak which means like if you were to like try and strip out like what is consumer spending increasing for CPM of ads versus what is the effectiveness of
2:45:06
their algorithms algorithm got better by double digits in one quarter >> right it's like actually insane how good >> the algo is getting right um at serving you the slop and the ads right um so so so in that sense like [laughter] the pig sound the trough off. I love
2:45:21
I love >> I got [laughter] >> a slop for the slop.
2:45:28
>> We're going all in on that on [laughter] the slopers. >> So, >> I love it.
2:45:35
>> Uh so, so you know, if you if you think about it, right?
2:45:37
Like, okay, Meta, where are they going to like win, right?
2:45:39
Um you know, I think if you have the Galaxy brain take, it's like, well, they've got the best like wearables coming down the pipeline.
2:45:47
They're going to put AI on it.
2:45:49
uh Apple won't be able to put good AI on their wearable so they'll seat it all to like micros Google or >> the other people people have had this narrative oh uh as AI gets better the the value of real world experiences will increase and I think that's a cool theory but if you actually play it out
2:46:04
AI getting better means more content that's more uh like effectively crafted for you more personalized a hundred times more con thousand a million times more content that would imply to me that people will just use digital products more, which means more time on site, more time in the app for meta. So, I So, I don't know.
2:46:25
>> I I mean, I I'm I'm with you entirely.
2:46:27
Um, but I think I think like >> the galaxy brain intake is that you're just going to have a wearable and that's going to have AI assistant.
2:46:32
Open is trying to make it wearables, you know.
2:46:35
Um, you know, there's there's, you know, everyone's trying to make wearables, Google is, etc. , etc.
2:46:38
I think metal will actually execute and then they'll have a good AI.
2:46:41
Um, and then you you stack on like a few things, right? How do they get users?
2:46:46
Well, we've seen at least if you look at the user metric charts, Google's use, you know, Open Eyes users were growing, growing, growing.
2:46:52
They were going to hit a trillion by the end of the year. They 800 billion.
2:46:54
Why did they not keep growing in the last quarter?
2:46:57
It's because Nano Banana came out and they took all the incremental users, right?
2:47:00
Um and and likewise, if you go look at like, you know, Gemini 3 didn't actually make Google grow that much.
2:47:06
It was Nana Banana and then Pro or two or whatever it's called, right?
2:47:09
Those were the ones that made them really grow.
2:47:10
uh Meta's licensed all of Midjourney's uh code data models, right?
2:47:16
One, two, they were like actually just like focusing hardcore on >> is that was that a a billion dollar plus deal?
2:47:24
>> The number is undisclosed.
2:47:24
Mjourney still exists as a company.
2:47:26
Um >> no, it felt it looked to me like effectively a massive exit, but the best case scenario where they can just keep kind of being artists.
2:47:36
>> I I think I think if you had me guess, I would bet it's a over a billion, right?
2:47:40
Every deal that Meta did was over a billion [laughter] basically like whether it's an employment contract, a licensing deal, an acquisition, everything had a B after it.
2:47:49
>> Well, so so the interesting thing is >> that is you're missing a zero again.
2:47:52
[laughter] Don't never miss the zero again. >> Yeah.
2:47:54
Every discussion was how many billions are we spending on hiring this person, buying this company?
2:48:00
>> Well, you met Meta interestingly um has gone down market for compute because they there's not enough compute in the big size deal.
2:48:06
So they've actually gone and like bought like small clusters. >> Oh.
2:48:10
>> Because it's like well I want more comput >> from like from like longtail neoclouds or >> Yeah. Just like Yeah. From a longer tail. >> Okay.
2:48:16
>> Um because that's the only place they can get the compute they need. Interesting.
2:48:19
>> Because you know they've already like went out and signed big deals with Google and Corewave and so on and so forth.
2:48:22
>> Is Cluster Max 3 going to be a smaller chart because of consolidation in the industry?
2:48:26
>> No, it's there's more.
2:48:27
>> It's going to be bigger.
2:48:27
>> It's going to be bigger bigger.
2:48:27
Um but but you know so so Meta >> some thunder.
2:48:33
[laughter] >> That's ominous. It's ominous.
2:48:34
>> That's ominous. It's ominous. Um so so I think meta will you know capture consumers through generative um if there's more content people are just going to go to the content marketplace right um the creator of the content captures less value as there are more content creators and more diversification of content right um
2:48:52
>> and so I think meta just wins by being a platform right Google does too and bite dance does too right but like those three win by having a platform and then the real question is can they get in the assistant uh productivity game right and I think this They drew that effectively search like if you're an assistant it means that you can like there's some commerce happening. >> Well, they span out and poached a bunch
2:49:11
>> Well, they span out and poached a bunch of people from Google.
2:49:12
So, this wasn't in the media much, but like they actually poached Google search people with similar sized deals as like these crazy research. >> Yeah.
2:49:20
And I always I always I you know demoing demoing any of the the the wearables.
2:49:24
You can imagine like Meta wants you to walk around in the world and see like oh what are those headphones?
2:49:29
And like while we're talking I just hit my little thing and buy it, right?
2:49:32
And it's like you didn't even necessarily know that it happened.
2:49:33
Uh but like of course Meta is going to want to monitor.
2:49:37
>> Anyone knows those are the Sony MDRX272s 462.
2:49:41
>> Dude, I've been I've been screaming about them like doing some proper marketing >> branding so far.
2:49:46
>> It's it's literally like they're over here is like WHX 1000XM5 and then their inear is like WF100 XM1000.
2:49:52
It's like dude just call them like Bravia buds and Bravia like headphones or some Well, China just just bought Sony, right? >> Yeah. Yeah.
2:50:03
Bravia brand is actually a Chinese company now.
2:50:07
>> Sony sold their TV and >> PlayStation butts. >> Yeah. Yeah. Yeah. PlayStation >> Walkmans. [laughter] Come on.
2:50:15
>> Something something >> for sure.
2:50:16
Anyway, uh anything else, Jordy?
2:50:20
>> I'm excited for this weekend. >> Yeah. Yeah. Super excited.
2:50:22
You guys, uh >> What are some plays?
2:50:24
We don't watch a lot of sports.
2:50:26
[laughter] >> What are some football guy, right? >> Yeah. Yeah.
2:50:29
I grew up yeah rural Georgia. So I like football.
2:50:31
High school football was the thing.
2:50:33
College football was the thing.
2:50:35
I think NFL is a little less soulful.
2:50:39
>> But you know now now college football has has has the NIL and so it's also soulless to some extent. It's fine.
2:50:43
We we enjoy it you know um primal desire of seeing heads clash you know and and and sometimes that manifest in like you know Twitter drama and sometimes it manifests in real football. >> Yeah.
2:50:55
>> Um all I can say is the Patriots. >> Okay. >> Whoa. Okay. Okay. Uh, I'm kind of bummed.
2:51:00
We're we're going to we're going to since we're going to be at the game, we're not going to really get the great experience seeing the ads.
2:51:04
I'm going to be like glued to I'm going to be like glued to my phone.
2:51:07
I I want to see all the AI uh the different >> Well, don't worry.
2:51:11
I got some more ads for you.
2:51:12
Thank [laughter] you so much for coming. >> Thank you so much. >> Great segway.
2:51:17
>> This message has been brought to you by >> public investing for those who take it seriously.
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co where DDC brands, B2B startups, AI companies advertise on streaming TV, pick channels, target audiences, and measure sales just like on Meta.
2:51:33
Um, what else is in the news, Jordy, before we get out of here?
2:51:39
>> We never somehow never covered this.
2:51:40
Whimo confirmed that they raised 16 billion at 126 billion post according to Ed Ledllo over at >> saw a bunch of VC victory >> Seoia getting in the mix. DST global dragging.
2:51:51
It was sort of a bold move for a lot of VC firms to go in so early as the company was sort of spinning out.
2:51:57
>> And of course uh Google via Alphabet did 13 billion of the round themselves.
2:52:01
So this was [laughter] >> the Medal of Honor on themselves.
2:52:06
That's what you're referring to. Yes.
2:52:09
>> While you pull up the next one, let me tell you about Figma.
2:52:11
Figma make isn't your average vibe coding tool.
2:52:12
It lives in Figma so outputs look good, feel real, and stay connected to how teams build, create codeback prototypes, and apps.
2:52:18
pull up this post from redaction.
2:52:21
It is Twitter every time a minor AI advancement occurs and [laughter] like this new block meta changes everything.
2:52:29
[snorts] >> This new block meta [laughter] >> there's there's a lot of this going on.
2:52:37
Well, you know what else changes everything?
2:52:38
Gemini 3 Pro, Google's most intelligent model yet.
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Standard reasoning, next level vibe coding and deep multimodal understanding.
2:52:44
YC wants you to start an AI rench.
2:52:47
They want you to >> just do it. Get out there and do it.
2:52:53
Start an AI rench and then get on Phantom Cash.
2:52:56
Fund your wallet without exchanges or middlemen and spend with a Phantom Card. And that's our show. Goodbye.
2:53:01
[laughter] >> Thank you for watching. Leave us five stars.
2:53:05
We will see you tomorrow. I had to finish this.