OpenAI Misses Targets, Codex vs Claude, Elon vs Sam Trial, Big Hyperscaler Beats, Peptide Craze

0:00 Jason, do you want to tell us about your new favorite podcast?

0:04 Oh, it's so good.

0:06 My feed is now because, you know, since cancel culture ended,

0:11 Sachs, everybody uses the R word and the f word right now.

0:14 My entire feed on Instagram is either gay or down syndrome or bulldogs.

0:20 It's one of those three.

0:21 And then I stumbled upon the Miss Thing pod, Miss Thing.

0:26 And they do a bit called gay name, straight name.

0:29 Here's gay name or straight name for David.

0:32 This good news and bad news freeird.

0:34 Here we go.

0:35 Gay name or straight name?

0:40 David.

0:40 David to me is straight.

0:43 Okay.

0:43 But he has my perfect body.

0:46 It can be confusing because I'm kind of like, are you gay?

0:49 And it's like, no, I just want to be you, David.

0:52 Totally.

0:52 Well, it's so like the Michelangelo's David the male ideal.

0:56 It's like incredible body kind of small.

0:58 Sorry.

1:00 Yeah.

1:01 Oh, it's a little rough.

1:02 [laughter]

1:03 What?

1:03 What are you watching there, Jal?

1:04 They basically nailed these two, but okay, keep going.

1:07 I don't think Chimoth is on their short list,

1:09 but I know Jason will come up at some point.

1:11 Gay name or straight.

1:13 Maybe this is it.

1:16 Chimoth on the count of three.

1:17 Yeah.

1:18 Three, two, one.

1:20 gay.

1:21 [laughter] I'm seeing like Italian sweater,

1:24 like really kind of like a loud [laughter] vibrant sweater.

1:28 He like wears it to like poker night with his like

1:31 his boys and like not I'm not talking like straight poker.

1:35 I'm talking like gay poker nights like at the bar.

1:38 Yeah.

1:39 Always talking about wine.

1:41 Talks about wines.

1:42 Always sort of like Yeah, exactly.

1:45 Yep.

1:46 Also, it's so like the guy at the gym taking off his shirt, taking selfies.

1:53 Yeah.

1:53 And everyone else is kind of like, "Excuse me, Chimoth.

1:55 I'd like to use the mirror.

1:56 [laughter] I'd like to see myself.

1:58 See you at the next day." Poker night.

2:01 Totally.

2:01 You bring the wine.

2:02 [laughter] We'll bring the sweater.

2:05 Yeah, there it is.

2:05 Wow.

2:05 They did do.

2:07 That is fantastic.

2:08 That is fantastic.

2:09 A shout out to my guys at the Miss Ding podcast.

2:12 Wow, that was awesome.

2:14 I think I'm gay.

2:17 [laughter]

2:16 I never knew.

2:18 [music]

2:19 Let your winners ride.

2:26 And it said we open [music] sourced it

2:27 to the fans and they've just gone crazy with it.

2:35 What did you do like cameo?

2:36 Did you pay them to do that?

2:37 Did it for me as a favor.

2:38 So they did it for

2:40 That's awesome.

2:40 Well, thanks to those guys to the miss.

2:43 I've seen those guys before in clips.

2:44 I find them very funny.

2:46 [laughter] It's so great.

2:48 Shout out to my guys.

2:49 That was awesome.

2:50 All right, everybody.

2:51 Seriously, welcome back to the number one podcast in the world.

2:55 It's the All-In podcast with me again, David Freebergia,

2:58 David Saxs, and of course, I'm Jason Calcanis.

3:01 You can call me Jay Cal if you're here for the first time.

3:04 Topic one, open AI.

3:07 They missed their targets for chat GPT Freedberg both on users and revenue.

3:13 Let's talk about it.

3:14 The Wall Street Journal says in a uh a breaking investigative report on Tuesday

3:21 that OpenAI expected to hit 1 billion wows

3:24 weekly active users before the end of 2025.

3:27 They missed that and they still haven't hit the milestone 4 months into 2026.

3:31 Also, Chamath, they missed their 2025 revenue target for Chad GPT.

3:36 Exact number wasn't specified, but as we've talked about here,

3:40 they're at a 2030 billion run rate.

3:42 There's a little bit of accounting nuance that is

3:45 yet to be worked out in the industry.

3:48 Two reasons why this matters.

3:49 Sachs, OpenAI has $600 billion in spending commitments for compute.

3:55 Just to put that in perspective,

3:57 that's about what they're trading for on secondary markets.

3:59 In other words, the entire value of the Open

4:01 AI enterprise equals their spend commitments in the coming year.

4:05 CFO Sarah Frier, who is coming to liquidity, is reportedly worried, hey,

4:10 that revenue isn't growing fast enough to keep up

4:13 with expense and OpenAI wants to IPO later this year.

4:17 This has put Frier and Oughtman in conflict

4:21 or uh maybe there's some natural tension there.

4:24 Frier doesn't think OpenAI is ready for public reporting standards.

4:27 According to the Wall Street Journal,

4:29 Altman obviously wants to move faster, so they released a joint statement.

4:34 this is ridiculous yada yada yada.

4:36 Let's go to you Saxs.

4:38 What do you think's going on here?

4:39 Are these major headwinds or is this just managing expectations as the leader

4:43 of the pack in the most important race of our lifetimes,

4:46 the race towards super intelligence?

4:49 Well, I actually have a little bit of a contrarian take on this.

4:52 I know that OpenAI had a really bad week.

4:55 Like you said, they had that Wall Street

4:56 Journal article which said that they missed their numbers.

4:59 They missed their 1 billion user growth target.

5:02 They missed their revenue numbers.

5:04 That's called into question whether they can

5:05 afford the data center commitments that they've made.

5:08 And then in addition to that, they've also

5:10 had the lawsuit with with Elon happening this week.

5:13 So in the press, it ended up being I think a pretty bad week for them.

5:16 But I have a contrarian take on this, which

5:17 is I think that over the past week or two,

5:20 if you look at kind of what's happening at the product level,

5:22 it's been a pretty good couple of weeks for them.

5:24 They released chat GBT 5.5 and the reviews from, you know,

5:29 people I talked to in Silicon Valley have been really strong.

5:32 You talk to developers, coders, they're very happy with it.

5:35 At the same time, Opus 4.7,

5:38 which is the latest anthropic release, appears to be a bust.

5:41 People are complaining about it.

5:43 They're in a lot of cases are rolling back to 4.6.

5:46 They're saying that Opus 4.7 is rationing compute.

5:49 It's reducing thinking time, not as good.

5:52 there were some bugs and clawed.

5:54 So if you just compare chat GPT 5.5 to Opus 4.7,

5:59 it does appear that OpenAI has had a better couple of weeks on a product level.

6:04 And I think there's reason to believe

6:06 that the product improvements will continue.

6:09 GPT 5.5 is based on a new base model called Spud,

6:14 which is the first base model upgrade they've done in I don't know over a year.

6:17 and having a new base model will pave the way for future improvements as well.

6:22 So I think OpenAI is feeling pretty

6:24 optimistic about their product right now and I

6:26 think you're starting to see on X some of the developer mojo is shifting.

6:30 I'm seeing a lot of people saying that they are

6:33 shifting their their coding usage from Opus to GPT 5.5.

6:39 So I think that SAM may end up being right but for the wrong reason.

6:46 And what I mean by that is that when he made these big compute commitments,

6:51 it was based on those estimates of hitting the billion

6:55 users on the consumer side and hitting those revenue targets.

6:58 The consumer business ended up being weak.

7:00 So they missed those targets.

7:02 But in the meantime, coding has become the allimportant sector of AI.

7:08 And because they made all these compute

7:10 commitments and they built out these data centers,

7:13 they have more compute than anthropic right now.

7:15 Anthropic is token constrained.

7:18 It's reducing their ability to serve mythos, for example.

7:20 It's causing them to engage in compute gating with Opus 4.7.

7:25 And I understand why Daario made that decision.

7:27 I'm not saying I mean it was a prudent business decision.

7:29 I'm not criticizing him for it,

7:30 but I think again I think Sam may end up being right here for the wrong reason,

7:35 which is he missed on consumer,

7:37 but enterprise is going gang busters and is giving him the ability now,

7:41 I think, to catch up on code.

7:44 your poly market

7:44 which is the all important market right now

7:46 of course and we talked about gro and cursor teaming

7:48 up last week Elon and the team over there poly market

7:52 showing now a 32% chance that openai goes public

7:55 by the end of 2026 this is down from 60% in December

7:59 and Shimath you gave a bit of a warning hey

8:02 there's only so many dollars to go around SpaceX IPO is

8:06 obviously getting out first and now if openai doesn't go out

8:11 this year and anthropic does the sets up an interesting dynamic.

8:14 What are your thoughts here generally speaking

8:16 about the massive commitment that OpenAI has made?

8:21 Are they going to run off the cliff or will it wind up being brilliant?

8:25 Uh even if it wasn't strategically for the exact reasons,

8:29 I think they're going to be fine.

8:30 I think this is a multi-t trillion dollar company.

8:32 I think Anthropic is a multi- trillion dollar company.

8:35 I think the thing that's happening right now is uh a complete

8:39 misunderstanding of what's actually happening inside of the world of AI.

8:45 And there is one very specific choke point that is constraining everything

8:50 which is access to the power that's necessary to drive these tokens.

8:54 To the extent that open AI missed,

8:57 I think what that is is an insight to not enough compute capacity today.

9:02 And that problem is only getting worse.

9:04 You've already seen that with Anthropic as well where

9:07 they just found a way to economically induce Amazon

9:12 to give them enough capacity so that you don't have

9:15 to route through bedrock to get to the anthropic models.

9:19 You're also seeing them do differentiated

9:21 deals now with economic participation on top

9:24 of what they already had from folks like Google to give them more capacity.

9:27 What is my point?

9:29 Everything in this market is power constrained.

9:33 The reason that these folks may miss a number

9:35 or a forecast have nothing to do with demand.

9:38 It is entirely 100% due to the supply

9:42 of the power necessary to generate the output token.

9:45 There is a really interesting thing that was just

9:48 announced today that will make this problem even worse,

9:52 which is what you're starting to see now is backlogs

9:55 build up of not just the access to the power,

9:59 but then the componentry that's actually necessary.

10:02 Not just resips and not just NAT gas turbines,

10:05 but now you're talking about transformers

10:07 and all the actual tactical grid infrastructure.

10:10 Why is this important?

10:12 If you look at the actual amount of gigawatts that are under construction,

10:17 we have a huge mismatch now, people have announced all these projects, Jason,

10:24 but less than half of it is actually being built.

10:27 Less than half.

10:28 Most of it is stuck in red tape.

10:31 Most of that is because there are these supply chain delays.

10:34 So there's no credible strategy to turn any of this stuff on.

10:39 Who will this hurt?

10:41 It will hurt Anthropic and OpenAI the most.

10:44 Who will this benefit?

10:46 It will benefit the hyperscalers,

10:48 specifically Oracle, Amazon, Meta, Microsoft, and Google.

10:52 And now what you're going to see is a negotiation and a trade back and forth.

10:57 How much equity do I have to give up?

11:00 How much control do I have to give up to get access

11:02 to the compute versus how badly will I miss my growth forecasts if I don't?

11:07 And now what that means is, and we spoke about this last week,

11:10 that's a huge lane for Grock to just run through and SpaceX

11:14 to run through cuz they have a ton of excess capacity.

11:18 And so I think the cursor deal was the appetizer.

11:22 But if I were Elon now,

11:24 I'd be running all over this market because if the models catch up in quality,

11:28 I think he could also do something really

11:31 crazy with anthropic or open AI right now.

11:33 Maybe not open AI because of the we'll get into the lawsuit in a bit

11:36 the baggage.

11:38 Yeah.

11:38 But man, he and Dario should do a deal tomorrow.

11:41 So you're framing, hey, the the limited resource here is compute.

11:46 The demand is off the charts.

11:48 No, the limiting resource is power.

11:50 power which then powers compute which then

11:53 provides tokens which then services the massive uh

11:57 developer and co-work and all these other projects

12:00 that consumers and enterprises can't get enough of.

12:03 Got it.

12:03 And Jason, the other factor that complicates that for anthropic and open

12:07 AI is all the stuff that's sort of sitting around thumb twiddling.

12:12 40% of that is going to get cancelled because they've done

12:15 such a poor job of creating a good positive halo around

12:18 AI that 40% of all the announced projects get cancelled because

12:23 40% of all projects in the last four years have been cancelled.

12:28 Yeah.

12:28 And there's there there are some bad feelings about data centers, AI, jobs, etc.

12:34 And that's causing some headwind.

12:35 People are you literally doing violent things in society

12:40 and blaming data centers and AI for it.

12:43 I don't want to give it too much air time.

12:44 Freeberg, what's your take on the chessboard we're looking at here?

12:49 Either through compute,

12:50 energy or through going public on a business level, you know,

12:55 the strategic nature of capital,

12:58 compute and energy now playing a role in this massive amount of demand.

13:03 still a ball in the air kind of game.

13:06 BCG had this theory, I think I talked about this once before,

13:10 called the rule of three where they've shown time and again that any stable,

13:14 mature, competitive market evolves to a 4:21 ratio

13:20 of market share for basically 90% of the market.

13:22 So there's a market leader that has four

13:25 times the market share of the second place,

13:27 that's two times the market share of the third place.

13:29 This is the case in pretty much every mature kind of competitive market.

13:33 So you can kind of think about AI probably

13:35 evolving into a consumer market and an enterprise market.

13:38 Open AAI, even if they're not at a billion,

13:40 they're still at 900 million weekly users,

13:43 which is well ahead of whatever Claude is at.

13:46 I think Claude is like probably subund million sacks, you may know.

13:49 And then Gemini is probably closer to them

13:51 at 700 to a billion somewhere in that range.

13:54 Probably pretty neck and neck with open AI.

13:58 So, you know, the consumer market looks like it's trending

14:01 towards a chat GPT/Google fight for first place and second

14:06 place and then probably anthropic in third place and maybe

14:09 Elon emerges and takes off enabled by his compute

14:12 capacity and then the enterprise market is a little

14:14 bit of a different story and that's its own market

14:17 which is kind of anthropic or probably Google in the lead

14:19 actually if you look at all the vertex use.

14:22 Google claims that 75% of GCP customers are active users of Vertex.

14:28 So there's probably a pretty sizable market share

14:32 that Google's captured on the enterprise side as well.

14:34 This is also probably why Google stock has

14:36 absolutely ripped over the last couple of months

14:38 is they're literally in first place or fighting

14:40 for first place in enterprise and consumer.

14:44 But I still think that there's a lot

14:46 of opportunity to Chimoff's point about the compute and energy

14:49 capacity constraints in improving how we actually scale and deploy

14:54 models in both the enterprise and the consumer setting.

14:56 And it is such early days and I just want to highlight this paper that came out

15:00 from MIT from these two scientists and these guys

15:05 published a paper on pruning techniques and neural networks.

15:08 This paper showed that you could actually

15:10 reduce the size of these networks by 90%.

15:13 And get the same accuracy out by pruning

15:17 very large models down to smaller models.

15:19 And then you can make a selection on which model to run for inference.

15:23 And by doing this, you can actually reduce inference costs by 10x.

15:27 You can get 10x the output per energy unit

15:29 that goes into the data center with no loss of accuracy.

15:33 And so it's a really interesting call it algorithmic technique that can be

15:37 applied to the existing large models

15:39 to actually make them much lower energy use.

15:42 So if you think about it,

15:43 you're firing up a very large model to answer a very simple question.

15:47 You can actually prune away that model.

15:49 Now this is probably going to be the case

15:52 in AI applications as it is in traditional Google search.

15:56 There's a long tale of searches,

15:58 but there's a few searches that account for a large percentage of search volume.

16:02 It's like what is the weather?

16:03 What are the movies times?

16:05 You know, what's the stock price?

16:06 Like there's a certain set of things that make up the bulk of consumer energy.

16:10 And there's probably a certain set of things that probably

16:12 make up the bulk of coding output as well.

16:15 And so if you can get that 80% of searches or chat

16:18 interfaces or coding requests reduced down through pruning techniques to smaller

16:23 models and then you have a whole set of smaller models

16:26 that can be called dynamically and you reduce inference cost by 90%.

16:30 you can make much more use,

16:31 call it 10 times the use on data center and energy capacity than we can today.

16:36 So I would argue that we're still in the very early days of getting

16:39 efficiency in terms of output and tokens and we're just in the very

16:42 kind of early stage of that which also unlocks the opportunity for guys

16:46 like Elon to reinvent how this is

16:47 done and potentially compete pretty aggressively.

16:50 There are two ways to win.

16:51 You could throw compute at it or you can do

16:53 SLM's small language models and V SLM's verticaliz small language model.

17:00 So if you had a verticalized small language model for the weather,

17:02 let's say that doesn't exist, but uh you can you can use it as an example.

17:06 They will have one for travel as an example.

17:08 When you hit Google for flight information,

17:11 it's obviously going to route you to something

17:13 lighter and faster that uses Google flights.

17:15 And Google flights has been incor incorporated into Gemini.

17:19 Gemini now is right behind 700 750 million users

17:25 and it's exactly what we discussed I don't know 18 months ago

17:27 on this podcast Freeberg that what if they put it at the top

17:31 and what would that do to their search revenue search revenue is surging

17:36 and they're also surging so they figured out a way to balance those two

17:40 competing forces having search results that are

17:43 AI enabled and still getting people to click on links they've done it

17:47 brilliantly apparently and the stock is rewarding.

17:50 I'll just add one statement to what you said,

17:52 which is like you're using what I would call

17:53 a humanistic on humans don't intuitively know what this model is.

18:01 It's not just a verticalized model,

18:03 but there are going to be models that will be discovered

18:06 through automated pruning techniques that will then be working in concert.

18:11 So, lots of small models that link together.

18:13 And we don't define each model by some

18:15 human heristic like this is a search travel model.

18:18 This is a maps model.

18:20 We don't we don't know why these models work

18:21 the way they do when they get broken down.

18:23 But I do think that that's really where the evolution is happening.

18:26 So effectively a model becomes a macro model.

18:29 It's got lots of smaller models underneath it that can be dynamically called

18:33 and that allows you to have 10x the inference for the same unit of energy.

18:37 Sax, let me just build on your point about Google.

18:38 Jcal, I would say that if there's a single reason why

18:43 OpenAI did not hit its user targets and its revenue targets,

18:49 certainly around consumer,

18:50 you'd have to say it's because Google managed to take meaningful share,

18:54 you know, they were basically nowhere a year or so ago.

18:59 Sergey came out of retirement, helped focus the company, and like you said,

19:04 they did a brilliant job improving Gemini and putting

19:07 it at the top of search, incorporating it.

19:09 Now, that being said, again, I don't think the news is all bad for OpenAI

19:13 because I do think that the 5.5 release was great.

19:15 We're hearing really good things about Codeex.

19:18 I do think that Codeex is taking share in coding tokens right now.

19:23 And I just think we're in a really interesting

19:26 place where these companies are constantly oneuping each other.

19:30 I mean, two weeks ago it looked like

19:31 Anthropic was going to be completely dominant, right?

19:33 I mean, Anthropic was growing at 10x.

19:35 Open AAI was growing at 3x and it looked like

19:38 and then the servers started going down.

19:39 Did you see that this week?

19:40 The server going down.

19:42 People were in my office were complaining we can't get on claud.

19:45 Listen, competition brings out the best in everyone.

19:48 Anthropic forced open AI to compete.

19:50 Google's forced open AI to compete in consumer.

19:53 I just hope the market stays competitive for as long as possible.

19:56 I do think that's what's best for consumers,

19:58 our economy, and for our country overall.

20:02 Let me just say one other area where I think OpenAI

20:05 had a good week is in this red-hot area of cyber.

20:11 Obviously, Anthropic made a huge splash with Mythos.

20:14 It hasn't been commercially released.

20:15 Their compute constraint, but as a proof of concept or training model,

20:18 it hit a new level of capabilities with cyber.

20:21 But now OpenAI has released a new model called GPT 5.5 cyber which

20:26 has just been through a bunch of tests and they've shown this was

20:29 testing done by the AI security institute that GPT 5.5 is the second

20:34 model to complete one of their multi-step cyber attack simulations end to end.

20:39 So it has the same level of capability

20:40 as Mythos and it does appear to be commercially ready.

20:46 You know, they've got the compute to serve it.

20:49 So I do think that that's a big accomplishment.

20:52 I mean, look, we knew that other cyber models were coming.

20:55 It wasn't just going to be Mythos.

20:57 In fact, within 6 months or so,

20:59 all the Frontier models are going to have Mythos level cyber capability.

21:03 But it's impressive that OpenAI got this GPT 5.5 cyber out.

21:08 so quickly and I think 5.5 might be the first cyber model that cyber defenders

21:15 actually get to use because again I don't

21:18 think they're as compute constrained as anthropic is

21:20 and this is an incredible opportunity you

21:23 know for the crowd strikes and PaloAlto networks

21:25 of the world both of which have been on the program they come out and they start

21:30 attacking this space man you could really see

21:34 everything get tightened up and this could be

21:37 an incredible revenue stream for everybody who's got

21:40 whether it's cursor claude or open eye or Gemini.

21:44 This is an amazing opportunity to tighten up as much as it is to get attacked.

21:48 Can I make a point about that?

21:49 Cuz look, there is so much fear right

21:52 now almost the level of panic about mythos.

21:54 People are treating it like a doomsday weapon or something like that.

21:57 It's not.

21:58 is simply that the frontier models have reached the point where they're

22:02 capable of automating cyber activities just

22:05 like they're capable of automating coding.

22:08 But that means that a model could power

22:11 up a cyber attacker or cyber defender the same way they can power up a coder

22:16 and allow them to discover a lot more vulnerabilities.

22:19 So there is obviously a risk there.

22:21 But I think it's important to understand that Mythos or GPT 5.5,

22:26 it doesn't create the vulnerabilities.

22:28 It just discovers them.

22:29 The bugs were already in the code.

22:30 They were sitting there waiting for some hacker to discover.

22:34 If we can now use AI to find these bugs in advance,

22:38 these vulnerabilities and patch them,

22:40 then you actually harden our infrastructure and and you harden our security.

22:46 I also believe that this leap from let's call it preAI cyber

22:50 to post AAI cyber it's going to be I think a big one-time

22:53 upgrade cycle because again you're going to find all these dormant bugs

22:57 and vulnerabilities but I think that once we get past that upgrade cycle

23:01 you're going to reach a new equilibrium between AI powered cyber offense

23:05 and AI powered cyber defense it's going to become a lot more normal

23:09 it's not going to feel like this huge disruption which is to say

23:12 I think you know people are treating this as like some existential threat.

23:16 I don't think it is as long as everyone does what they're supposed to do,

23:19 which is use the new capabilities to harden their code bases

23:22 and infrastructure and security before the hackers

23:25 get a hold of these capabilities.

23:27 Yeah.

23:27 And if Chimath, if you were to look at this to build on Sax's point,

23:32 there are about 5 million or so security experts in the world.

23:36 We talked about token cost.

23:37 40 hours of tokens just pounding it, you know, a week.

23:41 You could create another five million for a hundred

23:45 dollars per chief security officer per security expert.

23:49 So it's the volume of security expert agent saxs to your point.

23:53 Yeah.

23:53 You could have 50 50 million of them 100 million of them.

23:57 They're not finding something unique.

23:59 They're just they never sleep.

24:01 They're relentless in their pursuit of these problems.

24:04 It's a really great point.

24:05 Just kind of just refine that.

24:06 So yeah, there's probably 5 million people in the cyber industry,

24:08 but there's probably only a few thousand really elite hackers.

24:13 Sure, those hackers didn't have the time to go after

24:15 the entire surface area of every possible attack vector out there.

24:19 And so if you train a model to do what they do, obviously,

24:22 like you said, it can operate with a scale and speed that a human hacker can't.

24:27 So obviously, you know,

24:28 what you need to do is get these tools in the hands of the white hats,

24:32 let them do the cyber attacks themselves to then find the vulnerabilities

24:36 and patch them before the black hats get a hold of these capabilities.

24:40 But I think it's just just one last point on this, I'll stop.

24:42 It's just it's really important to understand that the Chinese

24:45 models are going to have these capabilities within approximately 6 months.

24:49 Oh, they have them now in Deep Seek 4 for sure.

24:51 They've got some level.

24:52 Well, no.

24:53 Deepc4, I mean, Dec 4 is impressive in a lot of ways,

24:56 but its capability is not at the frontier.

24:58 It's maybe 80 80 85%.

25:01 Let's call it the American frontier.

25:02 Chimoff, you wanted to get in on this.

25:03 Let's get Chim in.

25:05 Two things.

25:05 The reason that this is even possible is because

25:09 humans are errorprone and when humans code, they create holes.

25:14 And so, humans exploiting humans is where we've been for a long time.

25:18 Now we have computers exploiting humans because the computers

25:21 go and seek out all these bugs that humans wrote.

25:25 In the next phase it'll be machines versus machines.

25:29 And so I think the nature of cyber is going to completely change.

25:32 Probably in the next five or six years there'll be so

25:34 much reason to rewrite all of the software that runs the world.

25:41 In one part because you're going to be

25:42 asked to show more operating leverage and revenue growth,

25:46 but in another part because everything else that was

25:48 handmade in the past is just fundamentally insecure.

25:51 Either way, all roads will lead to all

25:53 the operational software that runs the world will get rewritten.

25:57 More and more of it will be written by machines.

25:59 More and more of it will be impregnable as a result.

26:01 But then the cyber threat actually will only increase because

26:05 then you're going to try to figure out how to use

26:07 a machine to inject something into another machine so that some

26:10 agentic loop inject some malware or injects a bad token.

26:14 And I think that's a very complicated thing.

26:16 What I will tell you is I'm not even sure if I'm allowed to say

26:19 this, but a very good probably the best

26:24 cyber security company in the world run by one

26:26 of the very best CEOs in the world who may or may not be speaking

26:30 at liquidity [laughter] would tell you that they

26:35 have penetrated and can essentially manipulate every model.

26:41 Let me just let me just say it roughly that way.

26:43 Okay, perfect.

26:44 Yeah.

26:44 And I uh at the breakthrough prize uh which three of the four of us were

26:48 at I talked to George Kurtz the other

26:50 person you were kind of describing was not that sitting beside Nash.

26:53 Yeah I'm talking about Nash and George are the two guys leading this Palo

26:57 Alto Networks Crowd Strike and they understand the what

27:01 George told me was there is just a line out

27:03 the door of people who want this product or service.

27:06 And if you look at it, Freeberg, like the murder rate,

27:10 like we're sitting here with the lowest murder rate in the history of humanity.

27:14 It has gone down massively in our lifetimes,

27:16 but massively over the arc of history.

27:18 I think that's what's going to happen with cyber.

27:20 There is only so many attack vectors,

27:22 and the remaining attack vectors are just

27:24 going to be human factors, right, Freeberg?

27:26 That's always been the case.

27:27 And as we make the software more resilient, then the the weak link is,

27:32 you know, the secretary who puts her post-it note,

27:36 you know, with the password there or the accountant who, you know,

27:39 uses their dog's name plus one, two, three for their password, right?

27:43 That's the the historical one.

27:45 Okay, let's any anything you want to add, Free Bird, as we wrap there?

27:48 Oh, that's Why is your bed so messy, by the way?

27:50 Why can't you just ask the room service to come in?

27:53 Listen, I'll tell I can tell you what happened.

27:54 Listen, I'm here in Atlanta.

27:55 And also, why don't you have a suite like where there's two rooms?

27:58 Like, is it just one room?

27:59 This hotel only has one.

28:00 It's just one room.

28:00 [laughter] Yes.

28:01 You know, either you're cheap or poor.

28:03 Which one is it?

28:05 I'm cheap.

28:05 Here's I'll tell you what.

28:06 [clears throat] Here's a situation.

28:07 I'm in Atlanta for the Knicks game tonight.

28:10 You're in one room.

28:10 Here's what I do.

28:11 I just want to explain to you value for value.

28:13 Some people spend their money on private

28:14 jets and they spend $30,000 flying to Atlanta.

28:17 I spend 30,000 on courtside seats.

28:20 I don't want the suite.

28:21 I want to put it into the seat side.

28:23 You can do both.

28:24 I guess I could do both, too.

28:25 I don't I'm I'm in the process of becoming I don't understand

28:29 of embracing my richness.

28:30 Okay.

28:31 If you've already convinced yourself that you

28:33 should spend $30,000 for courtside tickets,

28:36 which I think is outrageous, but okay.

28:38 You've already convinced yourself like 10k each, but yeah.

28:40 Yeah.

28:40 A hotel room that has two rooms.

28:42 Okay.

28:44 Probably cost 15% more than what you're paying.

28:46 It's 2x, but yes, you're right.

28:48 I'll get the I'll get the hotel room.

28:49 20 or 20% more, but you room like 200 bucks a night.

28:53 So you pay 400 a night.

28:54 You get another hotel.

28:55 I mean it's it's Atlanta.

28:56 The most I'm in the best hotel the most

28:58 expensive hotel is 500 a night in Atlanta.

29:00 It's no big deal.

29:01 But everything's sold out because all the Knicks people are coming here.

29:03 So you're selling me double that would

29:04 have been a thousand and you couldn't spend,000.

29:06 Everything is sold out because the Knicks are here.

29:08 So we have to look at your dirty beds.

29:10 It's gross.

29:11 The bed's not that dirty.

29:12 Come on.

29:13 Just deal with it.

29:13 Okay.

29:14 Take it out and post.

29:15 I have a private jet story about flying to Atlanta.

29:20 You reminded me.

29:21 Okay.

29:21 So yeah, there was some event there.

29:22 So I I flew my team there, you know,

29:25 there's a few people on my plane and it's kind of a long flight.

29:28 Was it like 4 hours or something from the back?

29:31 Yeah.

29:31 Yeah.

29:31 So I went in the back to to sleep.

29:34 Well, first, you know, we we started the flight and I had a few

29:36 bottles of Papy Van Winkle on on the plane.

29:39 And so we started off with like a drink and then I went

29:42 in the back and and fell asleep and I woke up basically when we landed.

29:45 So I come out and like all three bottles

29:47 are basically cashed of like [laughter] Happy Van Wink.

29:51 Oops.

29:51 Those were like two grand a bottle.

29:53 No, no, they're more.

29:54 These were like antique bottles.

29:55 Like one of them was I have one of those from your plane.

29:57 I have one of those from the old Falcon.

29:58 Yeah.

29:59 Yeah.

29:59 They were like these vintage

30:01 $4,000.

30:02 I remember.

30:02 Yeah.

30:03 Anyway, you can't even find this anymore.

30:05 So these guys, they asked me like when we land like, "Hey, Sax,

30:09 how much did it cost for you to fly us to this event?" And I said,

30:13 "Well, about $8,000 in jet fuel and about

30:16 $12,000 of Happy [laughter] Van Winkle." Well,

30:20 you gota you got to fuel the the vibes as well as the plane.

30:24 It's uh Is Atlanta nice?

30:26 I've never really

30:27 Do those people still work for you or are they are they uh [laughter]

30:31 they called in Atlanta?

30:32 Um is Atlanta nice?

30:33 Listen, last year I went to the Detroit games and that city was on the rebound.

30:36 Atlanta has an incredible opportunity to rebound.

30:39 I'll say it that way.

30:40 There's a great opportunity for them to upgrade the city.

30:43 I I went to Waffle House at midnight last night.

30:46 There was no shootings.

30:47 Okay, let's keep moving.

30:48 By the way, do you get royalty points

30:49 at the Best Western Atlanta or No, [laughter]

30:52 I get double points because I use my Best Western uh Visa card.

30:56 Yeah, it's everywhere you want it to be.

30:58 All right.

30:59 Use the promo code Jcal and get a thousand extra points.

31:03 In other Open AI news, Musk versus Alman,

31:07 the trial of the century or maybe the decade has started.

31:11 Elon is of course accusing Open AI

31:13 of breach of charitable trust, unjust enrichment.

31:16 He's accusing Open AAI of essentially flipping a nonprofit into a for-profit.

31:22 He's seeking 150 billion in damages that they revert

31:25 back to a nonprofit that Alman and Brockman be removed.

31:29 And there were some fireworks between Elon and the Open AI lawyers.

31:32 Elon kind of leveled up the discussion.

31:34 He said, quote, "If we make it okay to loot a charity,

31:38 the entire foundation of charitable giving in America will be destroyed.

31:42 That's my concern." Obviously, there's a ton of interesting nuances here.

31:48 Specifically, Greg Brockman keeping a diary where he was

31:52 journal maxing his plans uh like a Bond villain here.

31:56 And uh the excerpts from his diary include conclusion,

32:02 we truly want the BC Corp.

32:03 The true answer is that we want Elon out.

32:06 If 3 months later we're doing BCorp, then it was a lie.

32:09 Can't see us turning this into a forprofit without a nasty fight.

32:13 I'm just thinking about the office and we're in the office

32:15 and this story will correctly be that we weren't honest with him.

32:19 In the end, it's still about wanting a for-profit just without him.

32:22 yada yada yada.

32:23 Freeberg, your thoughts on this case?

32:25 Is Elon going to win?

32:27 I just don't know why Greg Brockman's got

32:29 a freaking diary where he's like literally documenting.

32:32 I mean, I love the guy, but what the is he thinking?

32:35 Like, you're just sitting here at home and like,

32:37 let me write about the the crime I'm committing

32:40 or let me write like and let me record it.

32:42 And by the way, let me never delete it.

32:44 I don't understand this.

32:45 It's not just journal maxing.

32:46 It's discovery maxing.

32:48 [laughter] It's smoking gun maxing.

32:51 I don't get it.

32:52 I don't get it, man.

32:54 I mean, do you guys remember from the wire in that scene where the guy's like,

33:00 "Is you taking notes on a criminal

33:02 conspiracy?" [laughter] He's got everybody in the room.

33:05 Can we play that clip?

33:07 It's like, "What are you doing, Greg?

33:10 is you taking notes on a criminal conspiracy?

33:14 What the is you thinking, man?

33:16 If you're going to commit a crime,

33:18 you do not write down the date and time of the crime in your journal.

33:23 Well, look, we don't know it's a crime.

33:24 Let's not.

33:25 Okay, sure.

33:27 A crime, but yes, you keeping shenanigans.

33:30 Jamat, do you keep a diary?

33:32 What do you think, Juel?

33:34 Do you keep a diary?

33:36 I I believe ruminate.

33:38 [laughter] No, I'll tell you right now, rumination is the path to unhappiness.

33:42 Nobody gives a about your feelings.

33:44 Writing your feelings down is only going to make you miserable.

33:47 Talking to your spouse about your feelings.

33:50 Just go to a beautiful dinner, sit courtside at the next,

33:54 and do what I've been doing for 30 years.

33:57 maxing.

33:57 maxing.

33:58 And the register goes up.

34:00 All you have to do is work.

34:02 Start new projects.

34:03 Nine out of 10 foul.

34:04 Place nine out of 10 bets.

34:05 One wins and you're golden.

34:07 Go sit courtside at the Knicks game.

34:09 Keep going.

34:09 Life's too short.

34:10 And just keep moving forward.

34:12 Don't write anything down.

34:13 Period.

34:14 Full stop.

34:15 It's good advice.

34:16 Yeah, I just The biggest surprise to me was this guy's got a diary.

34:19 I just I don't know anyone that has a diary.

34:20 I've never heard of this.

34:22 So anyway, that was shocking.

34:24 Besides that, I have no view on what's going

34:26 to happen with the case or what the judge will do.

34:28 I have no comment on the case either.

34:30 I think it's weird that Poly Market hasn't budged

34:33 even as all of this discovery has been published.

34:35 It's effectively at 42 or 43% that Elon wins.

34:39 So, one of the friends in our group chat said what may just

34:44 happen is that Elon technically wins

34:46 and he's just credited back the $40 million.

34:49 And so, maybe that's what this poll is front running.

34:54 But on a totally separate note, I think Jason, I know you say it as a joke,

34:58 but this idea of just keep moving forward, don't ruminate,

35:03 I think is very good general life advice for everybody to follow.

35:06 The modern-day therapy industrial complex

35:09 and the medication industrial complex, I believe, is around rumination.

35:15 Well, it does pivot around rumination.

35:17 Yes.

35:17 That is the gateway drug to all these things.

35:19 Yep.

35:19 Talk about your problems.

35:21 You know, when these people go to therapy, you ever hear these people?

35:23 Howard Stern's like, "I've been in therapy with the same person 2

35:25 or three days a week for 40 years." I'm like,

35:27 "Okay, what's the incentive for the therapist

35:29 to stop charging you $1,200 an hour?

35:31 There is none." Then they lose a revenue stream.

35:33 They lose a customer.

35:34 It's all a giant fraud.

35:36 Facts.

35:37 Uh, in terms of this case, I wouldn't go that far.

35:40 I do think that there's a lot of value in kind of untying

35:43 some of these Gordian knots that people have because of how they grew up.

35:47 But there's a difference between that and being specific and just

35:51 randomly ruminating cuz I don't think there's a lot of productive.

35:53 You've got an acute issue like in trauma in your life.

35:57 Yeah, sure.

35:58 Unpack it, figure it out.

35:59 I'm just talking about this neverending self-improvement,

36:03 you know, ruminating thing.

36:05 Uh but getting back on topic here, Saxs,

36:09 what's the And we're we're talking about a jury, I believe, in Oakland.

36:13 No, but it's a bench trial.

36:14 This is important.

36:15 It's a bench trial where the jury is advisory in capacity,

36:19 but ultimately that judge, she will make the final call

36:22 and she'll do the damages.

36:23 And so, is this a case sacks of like we've got

36:27 a Bay Area jury judge and we've got Elon who's considered,

36:34 you know, a bit right-wing and people don't all

36:36 agree in that area in terms of his politics.

36:39 And then you have this Sam Alman New Yorker story and people

36:44 finding out that so many different people feel they got screwed by him.

36:48 You put these two things together,

36:49 it's impossible to handicap where this turns out.

36:51 Sachs, your thoughts?

36:53 Well, yeah, I don't think this is about politics.

36:56 I mean, I guess you could argue that what Elon is seeking,

37:01 which is to protect the charity, is if anything a left-coded sort of principle,

37:06 although I don't really think it's left versus right.

37:08 Look, I don't want to take sides on this trial.

37:10 I'm just watching like everyone else.

37:12 The last time I weighed in on some Elon litigation, I got deposed for six hours.

37:18 Remember that?

37:18 Cuz they just assume that somehow I know something, right?

37:21 I've never talked to Elon about the case.

37:23 I don't know anything about it.

37:25 Yeah.

37:25 I'm going to see what happens like everyone else.

37:27 Now, one thing I I will say having just read some of the coverage

37:32 is that apparently the company at some

37:35 point did offer Elon shares in the company,

37:39 but he thought that there was something kind of icky about it.

37:43 Do you remember this that Yes.

37:44 Because at at one point I said on our show when this dispute started happening

37:49 but before it became a court case I said look if Open AAI at a certain

37:53 point decided they had the wrong structure they should just gone and done a make

37:57 right with Elon and he should have been a shareholder on the cap table.

38:01 What I didn't know is that apparently they did try to do something like

38:04 that but Elon turned it down because he

38:07 did want the entity to remain a charitable entity.

38:12 In other had a principled view of it according

38:15 to the reports and was like no we're trying

38:17 to save humanity and then you're giving this keys

38:20 to the kingdom to Microsoft that's all come out

38:24 and I I also have not talked to Elon about any

38:26 of this but my guess is like most of these things there'll be some sort

38:32 of settlement or something here but maybe he takes it to the mat who

38:36 knows Judge Rogers who's doing this 61-year-old

38:39 Obama appointee politics has played a role.

38:43 Saxs, they have had to tell the jury

38:44 like however you feel about these individuals politically,

38:47 whatever, please put that aside.

38:49 But of note is that she oversaw the Epic

38:51 Games versus Apple trial over App Store exclusively

38:55 ruled in favor of Apple with some caveats

38:58 um that they don't have a monopoly, etc., etc.

39:00 So, this is going to be a really interesting one.

39:04 And I think the worst case scenario is open AI for for OpenAI

39:08 is they have to unravel this somehow and that would delay the IPO.

39:12 That would cause chaos in shareholders and I

39:15 guess the best case is some sort of settlement.

39:18 And if Elon put the first 40 or $50 million

39:20 in, he's he's due 10 20 30% of the company after dilution.

39:26 All right, let's keep moving through the docket.

39:27 Lots more to discuss and uh good luck to everybody in their lawsuit

39:31 and those of you betting on market all-in summit selling up fast.

39:36 Our fifth edition Los Angeles September 13th to 15th.

39:39 Go to allin.com/events and uh speakers are going to be top tier.

39:44 Apparently Freeberg is having this as his major creative outlet.

39:48 I heard some back channel chimoff today that he's

39:51 going to be doing Broadway musical uh illusionist.

39:56 tap.

39:57 I got a tap dancing situation.

39:59 He's literally going fullon entertainer.

40:02 This is going to be vaudeville sachs wrapped up.

40:06 He's just going to take it to a whole new level.

40:08 Musical numbers like Nathan Lane.

40:12 I think if you're coding that it's going to be his big gay summit.

40:17 Yes, it could be a big gay summit.

40:20 Might be our last year in LA, guys.

40:23 Why?

40:23 Not might be.

40:24 Might be.

40:24 Oh, everybody wants to go to Vegas apparently.

40:28 Those bones, baby.

40:30 Can you imagine leaving the summit for lunch and going and playing crabs?

40:34 Jimoth, we get a fresh.

40:37 Yes, I can.

40:38 Yes, I can imagine.

40:39 I got some bricks.

40:40 Oh, I got some bricks right here.

40:41 Let's go.

40:43 Yum, yum.

40:44 That way Sachs can come.

40:46 Yeah, [laughter] Sax is like, I'm never setting foot in California, but I will.

40:52 You know, we're doing a couple live events.

40:53 Are you coming to them?

40:55 Liquidity or something different?

40:56 Liquidity.

40:56 And then there's the the all-in summit happens in September.

40:59 Yeah, I'm going to do those, too.

41:00 All right.

41:01 Big tech smashed their earnings on Thursday.

41:04 Google, Microsoft, Amazon, and Meta all reported.

41:07 I don't know why they do this on the same night, folks, but they do.

41:10 And performance was spectacular.

41:12 It was great.

41:13 However, the capex announcements were really the story here.

41:20 Let me just cue this up and show the chart.

41:25 $725 billion in capex guidance in 2026 from but four companies.

41:32 Amazon, Microsoft, Google, and Meta.

41:34 Amazon leading the pack with 200 billion,

41:36 190 billion each for Microsoft and Google, 145 billion for Meta.

41:41 You add Grock, you add OpenAI and some other players to these plans.

41:46 And we haven't heard from the new Apple CEO yet,

41:48 but he's going to be taking over.

41:49 And he's going to have some plans here.

41:50 I'm sure we are going to see the large a trillion

41:54 dollars a trillion dollars in buildout over the next year.

41:57 I don't know if this is even possible,

42:00 but this is all being driven by AI and cloud computing.

42:06 Google Cloud, which includes the Google Suite, that grew 63% year-onear.

42:11 Let that number sink in.

42:13 63% on 20 billion in revenue.

42:15 That's in a quarter.

42:16 Microsoft cloud, that includes Azure, Windows Server, SQL Server,

42:19 they bundled some things together there to get the number to go up.

42:22 Uh that grew 30% on 34.7 billion in revenue.

42:28 Amazon Web Services, the original cloud,

42:30 that grew 28% on 37.6 billion in revenue.

42:33 That's a bit of a pure play.

42:35 Just counts Amazon's web services.

42:38 Obviously, these are all moving to NeoClouds.

42:40 These are all serving AI jobs and tokens now.

42:43 They have a massive customer base and the customers

42:46 from the smallest startups all the way

42:48 to the biggest frontier models cannot get enough

42:51 compute and it is going to the bottom line.

42:54 But this is shrinking Chimath cash flow massively.

42:57 These were free cash flow machines,

43:00 the largest money printing machines in the history of humanity.

43:03 But they are giving up on free cash flow,

43:06 stock buybacks and dividends and the focus

43:09 on those three to invest in infrastructure.

43:12 Amazon's free cash flow down 97% Google,

43:16 Microsoft and Meta down 12, 12 and 8% respectively.

43:20 your thoughts on this free cash flow, the end of the free cash flow deluge

43:25 and the massive massive investment we're seeing in capex.

43:30 I think we're seeing a very important structural shift in the capital markets.

43:36 I think the last 20 or 30 years, well 20 years,

43:40 it's been that the mag 7 just kind of ran away with it.

43:44 that these big companies got bigger and bigger

43:46 and it absorbed all of these investment

43:49 dollars and the biggest reason was that it

43:52 had these very assetike business models, right?

43:55 You just built some more software and it

43:57 just has all this leverage and it all just

43:58 kind of worked except maybe for Amazon cuz they

44:01 needed physical infrastructure for warehouses and delivery and whatnot.

44:04 But by and large it was a very asset light investment cycle.

44:07 Now all of a sudden the pendulum is swinging violently in the other direction.

44:12 And there's something that I think people misunderstand which is

44:15 as it moves back to these asset heavy infrastructure investments.

44:22 The hyperscalers are signing checks that I mean I suspect

44:26 their body can cash but there's a world in which they can't.

44:29 I'll give you an example.

44:30 You know when Microsoft convinced the owners of three Mile Island to turn their

44:37 nuclear site back on?

44:40 Yeah.

44:39 Do you know what their Ford purchase agreement was?

44:41 It was for more than 2x the prevailing spot rate for energy.

44:45 More than 2x.

44:46 The problem is that's not for an enormous

44:48 percentage of their overall energy needs.

44:53 So if you play that out and you think these five or six companies

44:57 all of a sudden are not just spending Jason 700 billion a year of capex

45:03 which they are but then from an operating cash flow they're going to be

45:08 spending 2x the prevailing spot rate because

45:10 they just want guaranteed demand into the future.

45:15 Where's all this cash going to go?

45:17 It's not going to go to the shareholder

45:21 and it's not going to stay on the balance sheet.

45:24 These companies will now get levered.

45:26 They're going to get highly sophisticated around the financial engineering.

45:30 They'll have more debt.

45:32 They'll have all kinds of different vehicles and term

45:34 loans and revolvers and all of this stuff.

45:37 And so, they're going to look like

45:38 this big bulky industrial business in five years.

45:42 And I'm not sure that there's a good valuation case to be made at that point.

45:47 And so I think it may be simpler and this is what I tweeted

45:51 to just follow the dollars like a trillion

45:54 dollars a year going out of the hyperscalers.

45:57 Where is it going?

45:58 Just follow those dollars and buy

46:00 those companies because those companies are already underpriced.

46:03 This is uh obviously reminiscent of something we all experienced.

46:06 Uh Nick, can you pull up the Cisco chart I just sent you and put it at max?

46:10 uh we had a massive buildout of the infrastructure

46:14 of the internet in the late 1990s and into 2000.

46:18 And what that caused was a lot

46:20 of aggressive companies to do massive amounts of spending,

46:23 a lot of retail investors to embrace these stocks like we're

46:26 seeing with people trying to get into these private companies and saxs.

46:30 Look at the 2000 peak of Cisco.

46:33 This is the most extraordinary chart ever.

46:35 It took them 25 years to get back to that peak and uh they had

46:40 a lost two decades and we had a massive

46:43 amount of fiber that wound up getting bought.

46:45 We talked about that a couple years ago on the program.

46:48 But there's something for you to build off

46:49 of here when you look at this massive infrastructure.

46:52 You think it's going to be Cisco systems all over again, World Warcom, etc.?

46:56 No, I really don't.

46:56 The issue we had in 2000 was dark fiber.

47:00 You had all this infrastructure being built out and it wasn't being used.

47:03 There's no dark GPUs today as you know Brad Gersonner likes to say.

47:08 So what's driving the capex now is the voracious demand for compute for tokens

47:16 and the demand is now pulling

47:19 forward this additional um investment in infrastructure.

47:24 So I think what's happened here is that the bull

47:26 thesis for AI just got validated in a single afternoon.

47:30 I mean again you got Microsoft Azure, Google Cloud,

47:34 Amazon AWS, Meta, they're all basically exceeding expectations,

47:39 exceeding guidance in terms of where their cloud revenue would be

47:43 and therefore how much they're going to reinvest in capex this year.

47:47 I think we were supposed to have 660

47:49 billion of hyperscaler capex up from 350 last year.

47:53 I think there's now the new estimate is it's going to be over 700.

47:56 So this is you know again it's more than 2% of GDP.

47:59 This is a huge tailwind to GDP.

48:01 There's another article saying that I think

48:03 in the last quarter AI was 75% of GDP growth.

48:08 And by the way, this is just the capex part.

48:10 This is the physical infrastructure.

48:12 This is not the economic impact of the tokens

48:14 that are generated inside the token factory.

48:17 This is the building of the factories.

48:19 How do those tokens get used?

48:20 like we're seeing they're being used not just to do

48:23 research or to answer questions but to create code.

48:29 And so we're seeing this explosion of productivity in software development.

48:33 And we're seeing an explosion of bespoke software being created

48:37 and that's going to accelerate every part of the economy.

48:40 Every business that now wants to get code will

48:43 be able to get code for the first time.

48:44 Before they couldn't even hire the engineers, they needed to generate it.

48:48 Now they will be able to.

48:49 So that is a huge unlock of productivity across the economy.

48:54 Then you're getting into these new use cases

48:55 like the the co-working use cases and agents, right?

48:59 So the the workflow automations that are happening, it's still early.

49:03 I don't believe that this is going to replace humans.

49:05 We had that um in the past week, we had that crazy case of an agent deleting

49:09 a production database in 9 seconds because because of a bug.

49:13 Look, what that said to me is that

49:16 it's not that agents aren't valuable.

49:18 They are valuable, but they have to be supervised.

49:20 You know, this idea that you're just going

49:21 to be able to like automate all the jobs away.

49:23 It is a massive amount of handwaving

49:26 over the real technical problems and issues.

49:29 The agents have to be supervised.

49:31 Someone has to be accountable.

49:32 It's not going to be the CEO.

49:34 The CEO doesn't want to be accountable for thousands of agents.

49:37 You need people

49:38 despite what Jack had block said.

49:41 Yeah.

49:41 Thousand direct reports is a great like goal, but it's not realistic.

49:46 Yeah, you need IT people who are savvy who

49:49 can supervise this and make sure it's working.

49:50 They have to be accountable to the CEO.

49:52 Someone has to drive the productivity.

49:54 It's like Bology always said, AI is not end to end is middle to middle.

49:58 You have to have someone to do the prompting and you have to have

50:00 someone to do the validating and I would add the supervision and accountability.

50:04 So anyway, the larger point though is I'm

50:06 speaking to the fact that I don't think there's

50:08 going to be this huge job loss associated

50:10 with this productivity boom that we're going to get.

50:13 And in fact, I think what's actually happening now

50:15 is that AI is becoming synonymous with the American economy.

50:20 I mean, the fact that it's generating 75% of GDP,

50:23 you have this capex explosion, this energy explosion that feeds it,

50:28 and again, just the beginning of the applications

50:32 that are being unleashed by these new token factories.

50:35 I think it's all a very, very positive thing.

50:37 and all these doomers who are trying to throw a wet

50:41 blanket on it or constantly scaring the daylights out of people.

50:45 I mean, what do they want the American economy to do?

50:47 Just to stop I mean, they just don't want any progress.

50:50 I mean, like again, you know,

50:52 when you talk about stopping AI or halting AI progress?

50:55 What you're really doing is stopping the American economy now.

50:58 You're basically saying you don't want economic growth.

51:01 AI is now synonymous with the growth of the American economy.

51:05 And if there's no economic growth,

51:06 there's not gonna be money to pay for all the social programs.

51:08 There's not gonna be money to pay down the national debt.

51:11 There's not gonna be money to basically build up our national defense.

51:14 All these things we want to spend money on.

51:15 We have to have a vibrant economy.

51:17 And that is now synonymous with AI.

51:20 So I know that AI may not be popular.

51:22 I see those polls.

51:23 But having a strong economy is popular.

51:26 And I believe that those things are now synonymous.

51:29 It's almost like there was some architect or ZAR who set up the chessboard

51:33 in the first year of this to make sure that it was ultra competitive.

51:38 President Trump set the table on this.

51:39 Absolutely.

51:40 With some good advice, I think.

51:41 Maybe.

51:43 Freeberg, your thoughts?

51:43 It's always good to have good advisors.

51:45 Always good to have good advisors.

51:46 Absolutely.

51:47 Absolutely.

51:47 No, but look, I've said it before.

51:49 The president just wants America to win.

51:51 Literally, there are people who if we were

51:52 looking at this, you know, I don't know,

51:54 a hundred years ago, it'd be like people were like, "Yeah, you know what?

51:57 we shouldn't build the highway system or we half built the highway system.

52:00 Let's stop let's stop building the highways.

52:02 No, the highway system was funded by the federal government.

52:04 There was no competition.

52:05 It was the most expensive on a on a inflationadjusted basis.

52:09 I think it was the most expensive project in US history.

52:13 Yeah.

52:14 And the railroads before that like you can't stop these things.

52:16 They have to keep going.

52:18 It's interesting point you know there is so much demand

52:22 for the resource of tokens of intelligence

52:24 freeberg and it's quite different than

52:26 the fiber situation as Sax correctly points out where we [snorts] built

52:30 all this but we didn't actually have an application here the application

52:34 is pretty um pretty wellnown and you've got a large number

52:38 of people in businesses who are trying to vibe code their way

52:42 to success trying to push this stuff and we had an interesting

52:45 story referenced earlier in the show where uh Claude ate somebody's homework.

52:52 This is the nightmare of all nightmares.

52:55 Somebody was vibe coding.

52:56 Uh it was the founder of Pocket OS.

52:58 Apparently, they make software for rental car companies.

53:00 He was using Opus 4.6 through Cursor's AI platform, their coding platform,

53:06 and uh you know, which is like the most expensive tier.

53:10 Uh and he said he configured it with enough safety rules,

53:13 but the agent was working on a routine task.

53:16 They saw some sort of credentiing mismatch and they decided

53:19 to fix the mismatch by deleting a railway volume without user

53:23 confirmation and uh they pushed the code from a repo

53:26 to a live app and they deleted everything including the backups.

53:30 Literally a scene from Silicon Valley's HBO clip of Son of Anton.

53:36 Hilarious.

53:37 You gave your AI permission to overwrite code in the internal file system.

53:41 Were you going to tell me about this?

53:43 No, I thought that was the company policy these days.

53:47 Okay, well, your AI just failed epically.

53:51 That's unclear.

53:53 It's possible the Son of Anton decided that the most efficient way to get

53:56 rid of all the bugs was to get rid of all the software,

53:59 which is technically and statistically correct.

54:02 But artificial neural nets are sort of a black box.

54:05 So, we'll never know for sure.

54:06 How did they get that so right, Zach?

54:08 Five or six years ago, art and neural networks are a black box.

54:11 So, I guess we'll never know.

54:13 But technically, it was correct.

54:15 Freeberg, when you blow up a hollow system with your vibe coding,

54:19 which you were absolutely showing off in front of Jensen

54:22 a couple of weeks ago about how much code you're pushing,

54:24 who are you going to blame?

54:25 You going to take responsibility yourself?

54:27 Are you going to blame Cla Claude or Kurser?

54:30 Who are you going to blame when you blow up the entire stack over at Ohio?

54:36 Who you blame?

54:37 Yeah, I'll blame Dario.

54:38 You blame Dario.

54:39 Okay, that's what I thought.

54:40 That's a correct answer.

54:41 Correct answer.

54:41 Blame Daario.

54:42 He's the one who says it's a doomsday machine.

54:44 Uh, come on the prodio.

54:47 17th invitio.

54:49 [laughter] I mean, I've invited the guy like 17 times.

54:52 He is totally going to me.

54:54 He wants nothing to do with this podcast.

54:57 Actually, let me speak to that.

54:58 So, I think I think that um there's maybe

55:02 a misperception that this error occurred because of, you know,

55:06 quote unquote AI scheming,

55:09 like kind of in that video that the AI decided that the best

55:12 way to get rid of bugs is to basically eliminate the codebase.

55:15 This is kind of like the, you know,

55:16 AI is going to turn the world into paper

55:18 clips type thing where somehow it'll like miss scheme.

55:21 That's not really what happened here.

55:22 This is a case of just a of old-fashioned bugs occurring at an edge case.

55:28 You know, you've got the fact

55:29 that this API was not designed for permissioned usage.

55:34 You've got the fact that a credential was left kind of lying around.

55:37 Probably it should not be.

55:39 There's kind of like a perfect storm that caused the AI to do something

55:42 or the agent to do something that didn't

55:43 quite understand it was what it was doing.

55:45 I think that if there is a systemic problem here rather than just kind

55:50 of a like a random edge case is that AI still doesn't know what it doesn't know.

55:58 You know, like a human would stop

56:01 before deleting a production database and just say,

56:03 "Oh, I'm about to do something like really serious, really destructive.

56:07 Am I sure I want to do this?" You know,

56:09 and a human would have stopped and said, "Oh, wait a second.

56:11 like I need to be more confident in what I'm doing before I take that action.

56:15 And AI still has this issue where again it can be kind of overcon.

56:19 This is where like the hallucinations come from is it doesn't

56:22 know when it should have a low confidence in its output, right?

56:26 But this is why it has to be supervised.

56:29 You know, the longer the time horizon for a task,

56:31 the more likely it is to go off the rails.

56:35 And a drift.

56:36 Exactly.

56:36 And this is why I think people are starting to realize

56:40 that this idea of eliminating all software

56:42 developers was the peak of inflated expectations.

56:45 Yes.

56:46 Right.

56:46 There was actually a really good tweet on this by Aaron

56:50 Levy who's got the right take on this.

56:52 Aaron retweeted Matthew Glacius who sort of sardonically tweeted that 5

56:58 months in I think I've decided I don't want to vibe code.

57:01 I want professionally managed software companies to use

57:03 AI coding assistants to make more better,

57:06 cheaper software products that they sell to me for money.

57:09 Just lower your prices.

57:10 Don't make me vibe code is the translation.

57:13 Yeah.

57:13 I mean, I think like rare win for for Madaglacius there.

57:16 Anyway, Aaron Levy then says Agent Coding is

57:20 a huge win for software developers that want to get more done and it's fantastic

57:24 for anyone curious to learn how to start coding.

57:27 What it's less great for is casually building complex software that you have

57:32 to maintain on an ongoing basis and take all the risk for upgrades,

57:35 maintenance, keeping up to date with latest security issues, you know, the bugs,

57:39 cyber, those are taxes on most knowledge

57:42 workers who aren't familiar with the system.

57:45 It's not a tax.

57:45 It's a huge risk.

57:47 Yes, it's a risk has to be managed if you don't.

57:50 People will get fired because there will

57:52 be some public companies where some goofball tries

57:54 to vibe code their way out of something

57:56 and they're going to torch the enterprise value.

57:58 It's going to be glorious to watch because we're all going to laugh

58:01 and realize that was stupid and should never have happened in the first place.

58:04 Yeah.

58:04 I mean, it's there is a chance that this improves

58:08 to the point passes trial of disillusionment and becomes

58:11 super productive and you'll be able to get an agent

58:14 to do reasonable things without deleting your data set.

58:17 But we have a way to go.

58:18 Here is your, you know, this is the tech adoption chart.

58:22 Basically, you got a technology gets triggered.

58:23 You have the trial, you have this peak of inflated expectations.

58:26 You go into the trial of disillusionment and then the slope

58:28 of enlightenment invest and eventually it becomes deer and it's an opportunity.

58:33 Hey, uh, Freedberg, you have become reddit tide curious.

58:41 You have and also tell me tell me about rea cuz I want it.

58:46 I want to get on it.

58:47 M I want to use it and I need you to tell Nat that it's okay for me to take it.

58:52 I I have a friend who has some advice as well.

58:55 Freeberg, the coverage is coming out of this phase three

58:58 clinical trial data release that Lily put out last month.

59:01 So everyone's going crazy over the data

59:05 which continues to show pretty amazing results.

59:09 So unlike trazepatide which is kind of Lily's main product today,

59:15 it's a which is a dual agonist.

59:17 It's got two peptides in it that that bind to different receptors,

59:20 the GLP1, the GIP receptor.

59:22 This other one now also binds to glucagon, which is a third receptor.

59:26 And that glucagon receptor binding peptide

59:29 causes the cells to increase their metabolism,

59:31 which actually accelerates fat energy consumption over

59:37 what would typically be muscle energy consumption.

59:39 It's more likely to burn up fat early on, which causes more quick fat loss,

59:46 but also reduces muscle loss.

59:48 And some of the other data that's

59:50 now coming out shows non-HDL cholesterol down 27%, triglycerides down 41%.

59:57 Liver fat down 80% to 80% reduction of liver fat.

1:00:02 A1C drops from 7.9% to 6% in 40 weeks, which is amazing, by the way.

1:00:08 If you're diabetic and your A1C drops that much in a couple of months,

1:00:12 it's literally a life-saving product.

1:00:15 The average user in this phase 3 trial saw their weight decline from 214 pounds.

1:00:21 They lost 37 pounds.

1:00:23 That's compared to six pounds on placebo in 40 weeks.

1:00:28 And you know, modest side effects.

1:00:30 20% people felt more nauseous than the people that were on the placebo.

1:00:34 There's a lot of other separate studies that are being done now that are showing

1:00:37 significant reductions in inflammatory signaling molecules.

1:00:42 So systemic signaling of like hey cells

1:00:46 are in distress triggers this kind of inflammatory

1:00:50 process that can have a lot of other damage to your body can accelerate aging.

1:00:54 And so one of the other conversations is that retride

1:00:57 might actually be kind of a deaging drug as well.

1:01:02 Hercules, Hercules, Hercules, you know, and a lot of the studies, by the way,

1:01:07 are done on the the the very high dose, 12 milligram dose,

1:01:10 but you could probably get this thing dosed down to 2 milligrams

1:01:12 and still see a lot of the anti-inflammatory maintenance and other benefits.

1:01:16 I'm no doctor, but people are going nuts over this being more widely useful

1:01:21 than just for clinical obesity or type

1:01:24 when the FDA when's the projected date for 2027.

1:01:28 Mid 27.

1:01:28 That's what they're saying.

1:01:29 Could happen sooner.

1:01:30 I mean, the data is in the, you know,

1:01:33 the FDA will take their time to evaluate it,

1:01:36 but I think given the way this is all looking,

1:01:39 could happen sooner, could happen sometime later this year.

1:01:42 Swim Chimath Swim said it's incredible and that uh

1:01:47 it's living up to the hype in their experience.

1:01:51 Who?

1:01:52 Swim.

1:01:52 What is that?

1:01:52 What is that?

1:01:53 Someone who isn't me.

1:01:54 Swim.

1:01:56 Oh, this is a Reddit term.

1:01:57 Someone who isn't me said who has a guy

1:02:00 swim has a guy and has cycled on reddatride

1:02:05 and does push-ups and says muscle gain has been

1:02:09 spectacular no muscle loss and a lowering of fat.

1:02:14 If you go on X and you just search up rea Mhm.

1:02:19 it's like incredible.

1:02:20 You see these like 65 year old guys that go

1:02:22 from a dadbod to looking like an incredibly ripped athlete in weeks.

1:02:29 And and I I mean I'm shocked.

1:02:32 And then for me I don't need that help per se,

1:02:36 but my liver health is important to me.

1:02:37 My cardiac health because I'm South Asian and it just looks like a wonder drug.

1:02:41 I can't wait.

1:02:41 When you starve your body, when you turn off the the appetite,

1:02:45 which is the GLP-1 agonist function,

1:02:47 normally your body goes into this kind of mode of starvation and you have

1:02:51 this process by which your body tries

1:02:53 to generate energy from your existing cells.

1:02:56 And because muscle is much denser than fat,

1:03:00 you can have a favoring of muscle tissue

1:03:02 being kind of broken up over fat tissue.

1:03:04 But what this new agonist,

1:03:06 this glucagon agonist that they put into this neutrutide

1:03:10 is it favors fat burning over muscle burning.

1:03:13 And so that actually can drive short-term use at low dose

1:03:17 for people to cut weight and maintain muscle and get ripped.

1:03:20 And so that's why a lot of people

1:03:21 in the kind of fitness community are talking about,

1:03:24 hey, I want to get access to this and get on it for a while.

1:03:26 So you'll see a lot more hype probably

1:03:28 in that community as well as the all the health effects.

1:03:31 It just feels like we're about to have

1:03:34 an absolute avalanche of peptides to choose from.

1:03:37 On November of 2025, Lily cut a deal with the Trump administration.

1:03:40 I saw this to drop the price on Drespatide pretty significantly.

1:03:44 I think it's like 50 bucks on Medicare.

1:03:45 50 bucks from Medicare.

1:03:46 Yeah.

1:03:47 Yeah.

1:03:47 Which is a pretty cheap price point,

1:03:49 but it starts to make sense as you think about the portfolio of Lily products.

1:03:52 You get Tzepide for 50 bucks, but if you want to upgrade, get the Retatride.

1:03:57 That's the high premium product and that's where

1:03:58 they're going to start the Mercedes to the Honda.

1:04:01 I'm sure if I'm Lily and I'm sitting there and I'm

1:04:02 looking at this data coming out, I'm like, "My god,

1:04:04 people will pay for this and that starts to become sort of like

1:04:07 the upgrade to the BMW or the Model S plat if you will." Yeah.

1:04:10 The Trappetide is like the one bedroomedroom messy bed hotel room

1:04:15 and the other one's the sweetide is like the twobedroom suite.

1:04:20 Well, you can also, by the way, you guys know I'm a spokesman for Row.

1:04:25 They also have the Waggoi pill uh row.co/twist cotwist uh to get your

1:04:31 Wait, are you a paid talking about [laughter]

1:04:33 Are you a paid What are you talking about?

1:04:36 We're putting Charles Barkley and we're not having sales team and then you

1:04:43 come over on Allin and you start promoting.

1:04:44 No, no, no, no.

1:04:45 Trust me, we'll get one of those as well.

1:04:47 We'll get a row.

1:04:48 Sponsorship here.

1:04:49 What was the ro pill that you had me get?

1:04:51 What was it called?

1:04:51 Oh, sparks.

1:04:52 Sparks.

1:04:53 Sparks.

1:04:53 Did you take it?

1:04:55 I have taken it and now Amore please.

1:04:57 No, not maybe just a half of a lousy.

1:05:00 I want to hear the story.

1:05:01 I want to hear the story.

1:05:01 Go.

1:05:02 It's so out of control.

1:05:04 I told what I told you.

1:05:06 So then what happens is Nat and I are like,

1:05:08 you can't you can't just randomly use it.

1:05:10 It's scheduled.

1:05:10 We discuss it.

1:05:11 We put it on the calendar.

1:05:12 You need a plan.

1:05:13 You need a plan.

1:05:14 You need a plan.

1:05:15 Can't go in because otherwise otherwise it's too much.

1:05:17 You just can't randomly take it.

1:05:19 What do you mean?

1:05:20 It's going to be a sesh.

1:05:22 It's a whole thing, man.

1:05:23 It's like I don't have the energy for that.

1:05:25 It's an extended session.

1:05:26 You you have to be well rested.

1:05:28 This don't do this at 1:00 a.m.

1:05:29 This is like a 10 p.m.

1:05:31 This is like a No, this is more like

1:05:32 a This is more like on vacation, you know, like 10:00 a.m.

1:05:36 to 12:00 p.m., you know, to noon, you know, you got to really

1:05:40 you got to really schedule it.

1:05:42 Schedule it because kids around otherwise it's got to be empty.

1:05:46 Otherwise, it's unfair to her and it's just a li it's a lie.

1:05:49 Put it [laughter] out.

1:05:50 It's a It's a big commitment literally.

1:05:52 You You look embarrassed, Jim.

1:05:54 Do you feel embarrassed talking about it?

1:05:55 It's just a lot, man.

1:05:56 It's like It's a lot to handle.

1:05:58 It's a lot.

1:05:59 It's If you want to get the extra 20% in your performance, it's a lot, bro.

1:06:04 It's a [laughter] lot.

1:06:05 It's basically over time.

1:06:07 What happened?

1:06:07 What happened was I was like, "Oh,

1:06:09 what is this thing?" Jason's like, "Dude, you must get it.

1:06:11 You must get it." So, we got it.

1:06:12 We tried it and we were like, "What the was that?" And so,

1:06:17 [laughter] then I've been trying to bleed the pills out.

1:06:19 So, I gave some to Stant Tang.

1:06:20 I'm like, "Stanley, you try it." Literally, he's dealing them like cards

1:06:25 but when we're having poker dinner, I'm like,

1:06:27 does anybody want to try these things?

1:06:28 But these what is it?

1:06:30 Rose sparks.

1:06:30 Is that rose sparks?

1:06:31 Shout out to my friends out.

1:06:32 All right, let's keep moving here.

1:06:34 Freedberg, guys.

1:06:35 Friedberg had his own personal Super Bowl.

1:06:38 You see me getting ready for Nick's playoff season.

1:06:40 I get my courtside.

1:06:42 Freeberg had the equivalence acts.

1:06:44 He went to the Supreme Court in order to hear them talk about chemicals.

1:06:50 This was a big deal for him.

1:06:52 The Supreme Court coming together.

1:06:54 Did you wear it?

1:06:56 Yes.

1:06:56 The Monsanto trial happened in the Supreme Court and he went he got courtside.

1:07:00 He went to the Supreme Court and listened in the building.

1:07:03 Have you guys ever seen a live Supreme Court hearing?

1:07:05 No.

1:07:05 I'd love to.

1:07:06 I'd love to.

1:07:07 Tax, have you been?

1:07:08 No, I haven't actually.

1:07:09 I mean, honest honestly,

1:07:10 I think it was one of the most amazing experiences I've ever had.

1:07:13 There was a massive protest out front.

1:07:15 We went through the marshall's office to get in.

1:07:17 And that building, you walk in, it's like sacred.

1:07:20 It's all marble.

1:07:22 It's you're not allowed to talk.

1:07:23 You have to be super quiet when you're in the building.

1:07:25 Like they keep going like you're in some quiet library.

1:07:28 It's like people treat it with this level

1:07:30 of kind of sanctity that that and respect.

1:07:33 And they're like, there is no politics here.

1:07:35 There is no There is no freedom of speech.

1:07:38 This is the court.

1:07:39 When you come into this court, the justices tell you how you will speak,

1:07:43 how you will behave, what you will do, and you will not speak unless spoken to.

1:07:48 You put all your stuff in a locker,

1:07:49 you go up the stairs, you go into the the courtroom.

1:07:52 And the courtroom, it's just so amazing being in there.

1:07:54 They have this amazing marble freeze above the justices

1:07:56 that has some of the great people of human history,

1:07:59 Moses and these kind of amazing historical figures.

1:08:02 And then below them are the nine justices and the court case.

1:08:06 If you guys haven't watched the case, you can listen to them, I think, online.

1:08:09 on you.

1:08:09 Is it worth listening to?

1:08:10 Hold on.

1:08:10 Wait, wait, wait.

1:08:11 I have a question.

1:08:11 So, does Robert sit in the middle cuz he's a chief?

1:08:14 Yes.

1:08:15 And then do all of the right justices sit on the right?

1:08:18 No, they're mixed.

1:08:19 So, they're I think I don't know I don't know the exact seating,

1:08:22 but they're mixed in terms on appointments of the court.

1:08:25 Is that right?

1:08:26 I think that's right.

1:08:27 And then so yeah, that's right.

1:08:28 And then it kind of goes out from the middle with Roberts in the middle.

1:08:31 Roberts occasionally will name the justices and say,

1:08:35 "Hey, do you have a question?

1:08:36 Do you have a question?" if no one's talking,

1:08:37 but otherwise the justices will jump

1:08:39 in with their questions when they want and they'll ask.

1:08:42 Now, honest to God, watching this is like watching LeBron James play basketball.

1:08:46 These lawyers are so mind-blowingly impressive on both sides that you

1:08:52 would just like sit there and I was like in awe.

1:08:54 It was so I I felt like my energy was

1:08:57 completely sapped from me at the end of this process

1:08:59 because you were just so engaged and so caught

1:09:01 into the way that these guys are thinking and talking.

1:09:04 Did you take a rose sparks?

1:09:05 Did you take a rose sparks when you were there?

1:09:07 No.

1:09:07 And if you're familiar if you're familiar

1:09:08 enough with the case or the case history

1:09:11 or the law that's being debated because again

1:09:12 when when you get to the Supreme Court, you never debate the case.

1:09:15 What you're debating is the legal interpretation

1:09:18 of the the decisions that were made on the case.

1:09:21 And so is this constitutional?

1:09:23 How do you interpret this particular act, this law, this federal law?

1:09:27 What's the right way to think about it?

1:09:28 So you don't actually talk about the case.

1:09:29 You talk about the interpretation of American law,

1:09:33 of our laws, of our constitution, of the global.

1:09:36 You're saying the facts have already been determined.

1:09:39 That's right.

1:09:39 Right.

1:09:39 At a lower court, there's questions of fact and questions of law.

1:09:42 The facts have already been determined by the lower court.

1:09:44 It's just Supreme Court is ruling on questions of law.

1:09:47 That's right.

1:09:48 And so they have a full briefing with the full history of the case.

1:09:51 And remember, they only hear two cases a day.

1:09:52 So they're one hour each for each hearing.

1:09:55 So you go in and they only do it Monday,

1:09:56 Tuesday, Wednesday on the last two weeks.

1:09:58 and they only hear cases from October to April.

1:10:01 There's only a handful of cases that are selected.

1:10:03 Wow.

1:10:03 So you're really on a shock clock then to make

1:10:05 you're on a shot clock and you only have and it's

1:10:07 30 minutes aside and then the justices will ask question.

1:10:10 So this was Monsanto and Roundup, right?

1:10:11 So what was the law that was being debated for yours?

1:10:15 The regulatory body, the EPA sets the label for pesticides.

1:10:20 Does this cause cancer or not?

1:10:21 What are the warnings?

1:10:22 This can be damaging for birth defects, pregnancy,

1:10:24 all the things that we're all used to seeing on labels.

1:10:27 when you buy a product, a chemical product and the EPA and their regulatory

1:10:31 authority determined that Roundup does not cause cancer.

1:10:35 When you sell a pesticide, you first have to register it with the EPA,

1:10:38 get it approved, and then the EPA gives you a label.

1:10:41 And the label is written by the EPA.

1:10:42 It says exactly what you're supposed to say.

1:10:45 And in this case, it said all this stuff doesn't

1:10:47 say cancer because they determined it does not cause cancer.

1:10:50 And I'm not going to debate whether or not it causes cancer,

1:10:52 but that's the case that was made is that the EPA

1:10:55 is the regulatory body under a federal act called FIFRA,

1:10:59 fungicide, insecttoide or denicide act.

1:11:01 And that's where the EPA is given their regulatory

1:11:03 authority to put the label on these products.

1:11:07 And all of the cases that have been lost have been state failure to warn cases.

1:11:13 To date, Bayer, which now owns Monsanto,

1:11:15 has paid out $10 billion in these lawsuits,

1:11:19 and they have reserved 10 billion on their balance sheet.

1:11:21 They have 90,000 cases still outstanding in the courts.

1:11:24 90,000.

1:11:26 Wow.

1:11:25 And so, this one case got kind of appealed up to the Supreme Court.

1:11:29 Last year, the White House solicitor general,

1:11:32 and if the solicitor general steps up and asks the Supreme Court to take a case,

1:11:35 it's more likely the case gets taken.

1:11:37 So, the White House said, "Please take this case.

1:11:39 We need to have federal preeemption,

1:11:42 meaning the federal government has the right to set the label

1:11:45 because all of the cases that have been lost and that are

1:11:48 being adjudicated are in state courts where the state has

1:11:51 a law like in California called a failure to warn law,

1:11:54 which means if a manufacturer knows that a product carries a risk,

1:11:58 you have to warn the consumer.

1:12:00 And so the the lawyers have been arguing that Monsanto or Bayer

1:12:04 knew that this product caused cancer and didn't warn the consumer.

1:12:08 And they've been winning cases.

1:12:09 they've been losing cases, but they've won enough cases that this has

1:12:12 now become a multi-dea billion dollar problem.

1:12:15 And so the argument is that the EPA says

1:12:17 it doesn't cause cancer and they have federal preeemption.

1:12:20 So the EPA has the right to determine.

1:12:22 So that's the one argument.

1:12:24 But then when the other attorney came up,

1:12:27 this guy was like literally like watching LeBron James.

1:12:29 And so going in, we're like, "Oh,

1:12:30 63 Bayer's going to win." And then the other guy comes up and he was like,

1:12:34 "Well, hey, you guys overturned the Chevron doctrine last year.

1:12:37 You guys remember that case?

1:12:39 Yeah.

1:12:39 Where basically when the Chevron doctrine got overturned,

1:12:41 it basically said that no longer

1:12:44 does the federal agency get to decide it has to be a direct reading of the law.

1:12:50 Duh.

1:12:51 So now, so he's saying like the states

1:12:53 should have a right to read the law themselves.

1:12:56 They shouldn't have to just defer to the EPA.

1:12:59 And that's what this will come down to.

1:13:00 So at the end of it, we were like, "Oh my god,

1:13:01 this could be a 50/50 coin flip, 54 either way." And going into it,

1:13:05 we were kind of like trying to say, "Hey,

1:13:07 maybe this could be 63." So honestly, the whole experience was incredible.

1:13:10 The case is interesting.

1:13:12 These are very complicated matters.

1:13:13 How are these people able to make a wholesome

1:13:16 argument in like one side gets 30 minutes,

1:13:19 the other side gets 30 minutes, there's a little Q&A,

1:13:21 and then you're done in an hour.

1:13:22 There's this whole art and science and Sax,

1:13:24 you're probably familiar with this on how do you

1:13:26 distill down a Supreme Court case in the briefing dock?

1:13:29 Like what is it you're petitioning around the court?

1:13:31 and you try and distill it down to the exact legal

1:13:34 interpretation you want the judges to rule on, not all the other

1:13:38 And this is oral arguments.

1:13:39 Yes.

1:13:40 Oral arguments, just a discussion.

1:13:41 And then the judges jump in and all

1:13:42 they're doing is asking the lawyer questions,

1:13:44 one lawyer at a time, the one side and then the other side.

1:13:46 And by the way, the solicitor general came up in the middle and kind of made

1:13:49 a few comments and they asked her some

1:13:51 questions from the White House and she sat

1:13:52 down and then the two sides kind of went back and forth and and they just

1:13:55 it's like 30 minutes Q&A each on that one

1:13:58 specific legal question and Katanji Brown Jackson said,

1:14:02 "But what if after the EPA issued the label,

1:14:05 they found out information that it does cause cancer?

1:14:08 Shouldn't they update the label?" And he's saying,

1:14:10 "Well, no, they're not allowed to.

1:14:11 They can only issue the label the EPA says." And he says also

1:14:14 and it's it's a criminal case if they find out that it does cause cancer

1:14:18 and they don't report it to the EPA and then she's saying well what if

1:14:21 the EPA doesn't act and shouldn't the states

1:14:23 have a right to protect their people?

1:14:25 So those are the legal arguments,

1:14:26 the discussions that are going on in all of this.

1:14:28 And there's interesting implications which is fundamentally if the states

1:14:32 get to interpret federal law and ignore federal regulatory bodies,

1:14:36 it opens up a whole new can of worms

1:14:38 in terms of like all the states can start to ignore

1:14:41 federal regula regulatory bodies like the EPA or the FDA

1:14:44 or the USDA or and on and on and on.

1:14:47 So the whole case has a whole bunch

1:14:48 of really interesting implications wound up in it.

1:14:50 when you hear these guys and they're just talking chimat about that exact

1:14:53 like interpretation of the law and that's what this comes down to.

1:14:56 It's not the actual case that matters

1:14:58 and after the sachs they oral arguments and then they have like

1:15:01 a private conference where they'll write

1:15:03 their papers and give their final judgment.

1:15:05 Yeah.

1:15:08 Saxs.

1:15:07 Yeah.

1:15:08 Yeah, I think what happens is that so I guess

1:15:10 there's some discussion that happens behind closed doors and they

1:15:14 figure out where the majority is and then the chief

1:15:16 gets to assign who writes the opinion for the majority

1:15:19 in that meeting nobody is allowed in and in fact

1:15:22 you have a double door system where like if anything needs

1:15:25 to come in and out you have to like kind of like

1:15:27 knock on the door you're led into this anti chamber then

1:15:29 oh is it an airlock is an airlock

1:15:32 it's effectively we I had I don't know if you were there

1:15:34 Jason but we had Ted Cruz come to play in the poker game

1:15:37 uh And Ted Cruz clerked for William Ranquist

1:15:41 and if you want to have an incredible dinner,

1:15:44 ask him about the Supreme Court and Bill Ranquist.

1:15:47 He's a real student of the Supreme Court and it just makes the Supreme Court

1:15:51 free to your point sound like the most

1:15:53 incredible body that's ever been created anywhere.

1:15:58 By the way, more than the White House,

1:16:00 more than the Capital Building, more than any of these other big agencies,

1:16:04 this place has it's almost like being in England,

1:16:07 it has these kind of ways that people operate.

1:16:10 The the the security is so different.

1:16:12 They kind of stand there in the court

1:16:13 and they all exchange places every 20 minutes.

1:16:16 It's very coordinated.

1:16:17 They're dressed very differently than any other courtroom listening.

1:16:20 Maybe like 150, I would say.

1:16:22 How do you take it?

1:16:23 Are they on I actually think everyone is a guest

1:16:26 of a clerk or someone that works at the court.

1:16:28 I don't think that it's like very publicly available to get in there.

1:16:30 You can't line up.

1:16:31 There's no lineup.

1:16:32 There's I don't know if there's a lineup.

1:16:34 Um this was a connection through we got in through the chief justice.

1:16:38 Um he gave us the pass, but I think it was like very um

1:16:42 I think at the Elon versus Open AI case there's you can line

1:16:46 up and then the judge gave like 30 tickets to the press court.

1:16:51 No, no.

1:16:51 Yeah, but I think there's a lineup for the Supreme Court as well.

1:16:54 There's some public access that they're

1:16:55 It did not look like anyone from the public was in this court.

1:16:58 Everyone, everyone is dressed respectfully.

1:17:00 I mean, this court has an incredible amount of like, you know, cool experience.

1:17:07 I would I would just say uh enjoy it while you can.

1:17:10 I mean, I think the Supreme Court is one

1:17:11 of the last highly functional institutions in the United States.

1:17:15 And%

1:17:16 you know at some point we're going to have like 13 or 21 or some crazy number

1:17:20 of justices up there and get jersey after

1:17:26 justices there and so enjoy it while it's still

1:17:29 in the current in the current form it's in.

1:17:32 Can you imagine showing up with jerseys with the justices names

1:17:35 on them and like having sections and like somebody selling cracker jacks

1:17:41 theocracy version of [laughter] the Supreme Court version.

1:17:44 Exactly.

1:17:45 The popularity of the court really depends

1:17:48 on whether it's issuing decisions that people agree with.

1:17:52 That's what it comes down to.

1:17:53 If like if you ask people whether they like the Supreme Court or not,

1:17:57 it really just depends on whether they agree

1:17:59 with the decisions are recency as opposed to

1:18:02 the process of the decisions and how well argued

1:18:04 it is and all these things that you're pointing to.

1:18:07 And actually the the court I mean I just checked the numbers.

1:18:10 The court is relatively popular right now.

1:18:12 I think that it got as low as 35% in the 2024 Gallup survey,

1:18:19 but I think it's back up to, you know, like 44 to 50% favorability,

1:18:26 which for something that's involved in politics is relatively high, right?

1:18:29 Like you look at Congress or

1:18:31 any particular politician, they're going to be lower than that typically.

1:18:35 I just felt so assured of like the institution when I visited

1:18:40 and saw these guys interact and behave and how they behave the process.

1:18:44 It was like man this what an amazing country.

1:18:48 Yeah.

1:18:48 Well, the reason I say what I say

1:18:49 is there was an interview with James Carville recently.

1:18:51 Did you guys see this?

1:18:52 He saidaw he said look when we get power we're packing the court.

1:18:56 So we're not even going to we're not going to worry about it.

1:18:59 And we're going to get to 13, right?

1:19:00 He said we're going to make I think Yeah.

1:19:01 They're going to go from 9 to 13 and then they're

1:19:03 going to create some new states and all the rest of it.

1:19:06 So that'll be that.

1:19:09 Uh enjoy enjoy while it last.

1:19:09 Enjoy.

1:19:10 Enjoy while it lasts.

1:19:11 Uh by the way, end on a high note.

1:19:13 Wait.

1:19:14 Yeah.

1:19:14 [laughter] It's the end of the empire.

1:19:15 That'll be that.

1:19:16 By the way, there is uh a Supreme Court on I was correct.

1:19:20 There is an online ticketing lottery.

1:19:22 So we can all sign up and you can get a fourack of tickets.

1:19:26 I think they should make this I we should talk to Howard Lutnik.

1:19:29 Maybe he can make this an auction.

1:19:30 We get a revenue stream from the US.

1:19:32 We could sell like 10 of the tickets as courtside seats for 20 grand.

1:19:35 Jason, you're exactly what they're trying to protect against.

1:19:38 Exactly.

1:19:38 [laughter] Like, how can we how do we monetize the Supreme Court?

1:19:43 All right, everybody.

1:19:43 That's it.

1:19:44 That's the world's greatest podcast for you for Chimoth Poly Hatia,

1:19:47 David Freeberg, and David Saxs.

1:19:49 I am the world's greatest moderator.

1:19:52 We'll see you chief justice.

1:19:54 I'm like the chief justice of [laughter] the allin podcast.

1:20:00 [music] We'll let your winners ride.

1:20:02 Rainman David and it said we open [music] sourced it

1:20:07 to the fans and they've just gone crazy with it.

1:20:10 Love you queen of winners.

1:20:15 [music] Besties are gone.

1:20:21 That is my dog taking a [music] driveway.

1:20:26 Oh man.

1:20:27 My habitasher will meet.

1:20:29 [music] We should all just get a room and just

1:20:30 have one big huge orgy cuz they're all just useless.

1:20:33 It's like this like sexual tension that you just need to release somehow.

1:20:40 Your feet.

1:20:42 [laughter] We need to get Mercury's already.

1:20:50 [music] I'm going all in.