OpenAI vs Anthropic IPOs, Anthropic $3T, Zuck’s Price War, China Ends Open Source?, Trump Accounts

Summary

With Friedberg on vacation, Brad Gerstner fills the fourth chair for an episode dominated by the economics of the AI boom. The besties open on the coming wave of trillion-dollar IPOs — SpaceX already went public at $1.75T and trades near its offer price, and Anthropic (confidentially filed June 1) and OpenAI are both slated to follow. Gerstner, an investor in both labs, says Altimeter would be “a buyer at scale” in either IPO and lays out how SpaceX wrote the blueprint on raise size, index inclusion, and staged lock-ups. Anthropic is rumored to be exiting the year above $100B in revenue (OpenAI around $70B), with Gavin Baker’s claim that Anthropic could trade at $3T hanging over the conversation.

The core debate is whether that revenue is real. Chamath keeps dragging the table back to ROI: his own CTO told him token costs are “doubling every 45 days” while productivity gains are maybe 5%, and his analysis pegs the actual AI-driven EPS lift of the S&P 493 at somewhere between zero and 2%. Gerstner counters that intelligence is the largest TAM in history, that adoption is a bottom-up phenomenon hitting every employee at once, and that frontier labs’ share of wallet is rising even as commodity token prices collapse 90% a year. They dissect why enterprises can’t easily route to cheaper open models (memory, context, and harness portability aren’t solved), why the market is consolidating toward an OpenAI/Anthropic duopoly measured in revenue, and how “dark tokens” from open-source deployments hide real usage. The segment folds in a parade of X posts from CTOs at Uber, DoorDash, Databricks, and others, plus Zuck’s new Llama 3.1 price-war play at “1/100th of the cost.”

The back third turns geopolitical and then domestic. Reuters reports the CCP is considering export controls on China’s top models (Alibaba’s Qwen and Zhipu’s GLM going closed after catching the frontier) — which Sacks and Gerstner read as more chess than threat, since it would hurt China more than the US. Finally, Gerstner recaps his week in DC launching Trump Accounts (the Invest America Act): $1,000 seeded at birth into an S&P 500 account, 1.5M accounts and $1B+ in deposits in the first 24 hours, and a $100M personal philanthropic pledge from Gerstner himself. Chamath, before leaving to sell enterprise software, delivers an extended, emotional case that the accounts — modeled on Australian superannuation — are “the most American thing you can do” to rebuild belief in capitalism.

Highlights

”Our token costs are doubling every 45 days”

“I sat down with my CTO today and I said, ‘How are we doing on token spend?’ And he said the most incredible thing. He said, ‘Right now our token costs are doubling every 45 days.’” — Chamath Palihapitiya, 5:18

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”The actual ROI was somewhere between zero and 2%”

“And so the answer as far as all publicly available data was that the actual ROI was somewhere between zero and 2%.” — Chamath Palihapitiya, 15:38

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”Intelligence is the largest TAM we’ve ever seen in the history of the world”

“The thing that’s different is that intelligence is the largest TAM we’ve ever seen in the history of the world. These guys are penetrating it, so yes, the super sophisticated companies… are optimizing their token spend… but it’s not really changing the trajectory that the frontier labs are on.” — Brad Gerstner, 22:45

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Zuck opens a price war: same quality at 1/100th the cost

“Announcing it, he was basically like, ‘Hey guys, I’m going to give you the same quality at like 1/100th of the cost.’” — Jason Calacanis, 35:45

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”The best thing that could happen for America is if China sprouted their own doomer community”

“The absolute best thing that could happen for America in terms of winning the AI race against China is if China somehow sprouted their own doomer community.” — Brad Gerstner, 60:00

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Trump Accounts: $1,000 at birth compounds to $50,000 by 18

“$1,000 for every child at birth that could compound for their life in a privately owned investment account… if you start with $1,000 and somebody matches that, and you save 10 bucks a week, that’s $50,000 at age 18.” — Brad Gerstner, 65:30

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Key Points

• Brad Gerstner fills in for Friedberg (0:00) - Friedberg on vacation; Chamath recaps a UN AI commission dinner in Geneva co-chaired by Marc Benioff, with Jensen Huang and Anthropic’s Tom Brown attending.
• The trillion-dollar IPO rush (3:00) - SpaceX IPO’d at $1.75T, trades near offer at ~$2T (7th largest company); Anthropic confidentially filed June 1, OpenAI to follow.
• Anthropic may be “accidentally profitable” (4:53) - Chamath contrasts Anthropic’s enterprise mix with OpenAI’s higher consumer cash burn.
• Token costs doubling every 45 days (5:18) - Chamath’s CTO reports flat ~5% productivity gain despite exponential spend; models have “asymptoted.”
• SpaceX as the IPO blueprint (7:13) - Raised $75B at $1.75T on ~$35B forward revenue; pioneered staged lock-ups and early index inclusion.
• Once over $1T, “get-rich-quick schemes are over” (12:22) - Gerstner: these are compounders growing revenue 30%+ for years, not IPO pops.
• AI’s real EPS lift is 0-2% (15:38) - Chamath’s S&P 493 analysis; enterprise is “brittle” because buyers will eventually demand ROI above the risk-free rate.
• Consumer as a “safe harbor” (16:22) - Tens of millions of small buyers inoculate a business from the ROI reckoning enterprise faces.
• Bottom-up adoption hits everyone at once (23:18) - Sacks: unlike Excel, every employee in every department is trying AI simultaneously at $20/month.
• Dropping token cost 95% via open routing (25:03) - Chamath moves agents to hourly runs after using OpenRouter, GLM, and a Bittensor (TAO) subnet.
• Jevons paradox and frontier share of wallet (27:24) - Despite 18 months of open-source pressure, frontier labs’ share of economic value is rising (Jesse Zhang tweet).
• Sovereign AI strategies everywhere (30:00) - No country wants to depend on a closed American model; many will accept “95% as good” open stacks.
• Zuck flubs open source, opens a price war (35:30) - Meta’s Llama 3.1 pitched at “1/100th of the cost”; Zuck (@finkd) tweets more in one day than his whole history.
• The consequence of “95% as good” (36:53) - Chamath: replacing a $200/hr consultant, the $3-vs-$15 inference difference is irrelevant if frontier is bulletproof.
• Open source share falling as a % of spend (41:00) - Sacks cites open source going from 19% to 11% of enterprise spend; frontier is the “easiest choice.”
• The AI market is becoming a duopoly (52:42) - Chamath: only Anthropic ($16B) and OpenAI ($80B) register meaningful token revenue; the gap may be growing.
• “Dark tokens” hide open-source usage (53:42) - Sacks: open deployments show up as hosting cost (Nvidia, neoclouds), not revenue, so revenue understates usage.
• CCP weighs export controls on Chinese models (54:29) - Reuters reports regulators met Alibaba, ByteDance, Zhipu; go open to catch the frontier, then go closed.
• Winning the China race is DC’s unifying force (58:08) - Gerstner: the risk is lower-bureaucracy “ham-fisted” moves, not top-level intent; GLM shows Mistral watermarks (distillation).
• Energy is the real throttle (61:43) - Chamath: the US is ~three Californias short of projected load growth by 2050; Taiwan runs on ~2-3 weeks of LNG.
• Trump Accounts launch (63:09) - #1 app in the App Store; 1.5M accounts and $1B+ deposits in 24 hours; $1,000 seeded at birth into the S&P 500.
• Gerstner’s $100M pledge and the philanthropy platform (68:39) - Aims to be the largest direct philanthropic platform in US history; $100B target in 12 months; Dell anchored $6B.
• Better than an IRA (79:52) - Chamath frames it as a “covert IRA” from birth; employers add $2,500 tax-free, friends/family and philanthropists can fund it.

Mentions

Companies

• SpaceX (3:00) - First trillion-dollar IPO ($1.75T raise), cited as the blueprint for OpenAI and Anthropic.
• Anthropic (3:43) - Confidentially filed June 1; rumored >$100B revenue, possibly “accidentally profitable,” Gavin Baker’s $3T call.
• OpenAI (4:53) - ~$70B revenue rumored; higher consumer cash burn; corporate restructuring adds IPO complexity.
• NVIDIA (21:00) - Jensen uses AI to design next-gen chips (“the machine is building the machine”); has open-source models.
• Uber (17:16) - CTO Praveen’s X post on “agentic pods” and forward-deployed engineers; ran through tokens in Q1.
• DoorDash (33:00) - CTO Andy Fang: open-weight models for code review, Fable (Anthropic) for hardest work, Kimmy 2.6 for lower-level tasks.
• Databricks (45:24) - Founder Ali Ghodsi: harness choice alone can cut cost ~2x on the same model (GLM 5.2).
• Meta (35:30) - Llama 3.1 released as a low-price agentic open model via new Meta model API; Zuck opens a cost-based price war.
• Coinbase (40:23) - Cited by Sacks as one of the few enterprises that built its own token-routing middleware.
• Salesforce (1:03) - Marc Benioff co-chairs the UN AI commission; Chamath calls him “the impresario of impresarios.”
• Lovable / ElevenLabs / Decagon (48:00) - Big frontier-lab customers building their own proprietary/verticalized models; Decagon sends 90% to post-trained open models.
• Alibaba (Qwen) / ByteDance / Zhipu (GLM) (54:29) - Chinese labs at the center of the CCP export-control report; Qwen and GLM going closed after catching the frontier.
• Robinhood (1:20:21) - Vlad Tenev helped build the Trump Accounts app; a more elegant version of Gerstner’s original design.

Products & Technologies

• Claude / Claude 3.5 Sonnet (14:40) - Positioned as the enterprise brand; Chamath notes it refuses some health/research queries (“nerfed”).
• ChatGPT / GPT-6 (9:44) - OpenAI’s consumer brand; GPT-6 rumored within 30 days as OpenAI regains “swagger and mojo.”
• GLM 5.2 (Zhipu) (25:03) - Cheap open model used in dynamic routing; alleged Mistral watermarks (distillation evidence).
• OpenRouter / Bittensor (TAO) / Groq / Cerebras (25:03) - The stack Chamath used to cut token cost ~95%; inference improving on multiple fronts.
• Trump Accounts app (Invest America Act) (63:09) - #1 in the App Store; $1,000 seeded at birth into the S&P 500, QR-code funding, designed by Joe Gebbia’s team.
• 529 accounts / IRA / Roth IRA (1:05:15) - Contrasted with Trump Accounts; Chamath’s conversion-in-a-0%-bracket strategy.
• Australian superannuation (1:27:44) - Cited as the forced-savings model that makes Australians “extremely happy.”

People

• Brad Gerstner (0:00) - Guest host; Altimeter founder, investor in OpenAI/Anthropic/SpaceX, architect of Trump Accounts, $100M pledge.
• Gavin Baker (3:58) - Predicts Anthropic ends 2026 with >$100B revenue and would trade at $3T today.
• Marc Benioff (1:03) - Co-chair of the UN AI commission; “a fucking master” per Chamath.
• Jensen Huang (21:00) - Uses AI for all NVIDIA chip design; on the UN commission.
• Tom Brown (1:45) - Anthropic co-founder at the UN commission; “wears it on his sleeve” about open source for countries.
• Mark Zuckerberg (@finkd) (36:05) - Announcing Llama 3.1 and a price war; tweeting more than ever.
• Nikesh Arora / Andy Fang / Praveen / Ali Ghodsi (45:24) - CTOs/CEOs whose X posts frame the model-routing and “model fungibility” debate.
• Michael & Susan Dell / Gwynne Shotwell (1:07:08) - Anchor Trump Accounts donors ($6B and 350M SpaceX shares respectively).
• Joe Gebbia / Vlad Tenev / Luke Pettit (1:33:07) - The “dream team” who built the Trump Accounts consumer app; Gebbia praised for government software.
• President Trump (1:03:52) - Pushing to auto-enroll all 50-70M kids; “make every child a capitalist.”

Surprising Quotes

“My costs are doubling every 45 days, my upside is essentially flat… because we’ve effectively already asymptoted. And so we’re going to take a step back and try to figure out what to do.” — Chamath Palihapitiya, 5:41

“We’re talking from 100 billion to 300 billion. 200 billion of incremental revenue is incomprehensible in the history of Silicon Valley.” — Brad Gerstner, 22:24

“GLM 5.2 has watermarks from Mistral all over it, right? So we know they were distilling.” — Brad Gerstner, 58:08

“We gotta get their P-doom up. We gotta get their P-doom up.” — Jason Calacanis, 60:15

“Put aside your TDS, put aside your valid criticisms… this is the most American thing you can do.” — Chamath Palihapitiya, 1:25:07

“Buffett’s like the secret is to find a really small snowball and a really long hill. But the size of the hill in this country, we’ve cut off the first third of the hill forever. Nobody saves anything until they’re 25.” — Brad Gerstner, 1:40:18

Transcript

Jason Calacanis: 0:00 Alright everybody, welcome back. Number one podcast in the world. It’s July All-In Episode 280. Friedberg is on a little vacay, I’ll leave it at that. And yeah, Bestie Brad is here. How you doing, Brad?

Brad Gerstner: 0:16 I’m doing great. I’m doing vacay in maybe Idaho or somewhere, J-Cal? You know, who knows?

Jason Calacanis: 0:23 Who knows? Who knows? It could be anywhere. It could be anywhere. I mean, there’s lots of things he could be in plenty of places. And looks like you are somewhere in the Northeast, I’ll leave it at that. Having a little vacay for yourself this week?

David Sacks: 0:30 Very patriotic- I’m in my flag room, very patriotic room here, you know, where I work on the East Coast in the summertime. And spent some time in DC this week. And it’s been a great week, been a great week celebrating America 250.

Jason Calacanis: 0:49 Great. And you’re going to be out of there by the by the second week of August. Yeah, so I have it August 10th through the 30th. I’m good. Jason BNB. Jason BNB. Oh, absolutely. You don’t know the half of it, man. I am on a summer bender. I’m like, oh, where’s your- where’s your vacation home? Wait, where are you? Where are you? I am in Paris. I did about eight interviews for the Ray’s conference that’ll be coming out in the All-In feed. And of course, cackling from the factory. Look at him. You’re working in the factory, Chamath Palihapitiya. It’s going to be a hot software summer for Chamath. How’s your hot software summer going?

Chamath Palihapitiya: 1:03 It’s good. Selling enterprise software is hard, but it’s good. Chamath’s like, man, I was such a dick to all my CEOs. Hey, the SAS period, now you know. I went to Geneva. Shout out to Marc Benioff. I went to Geneva. He works out of Europe, he’s all his European customers and he had a dinner in Geneva, which I joined and then me, Jensen, Brad Smith, Anthony Tan from Grab and a bunch of other folks were put on this UN commission for AI that Marc is the co-chairman of. When you see Marc Benioff in action, man, this guy is a fucking master. Holy shit.

Jason Calacanis: 1:31 He is the impresario of impresarios, yeah.

Chamath Palihapitiya: 1:33 You see how he’s built such a ginormous business. It’s impressive, it’s impressive.

David Sacks: 1:37 What is a commission by the United Nations for AI? What is their what is their calling?

Chamath Palihapitiya: 1:43 Open source.

David Sacks: 1:45 No, but no, but it’s like, the United Nations actually do anything? Do they actually do anything? It started already, actually, it’s so funny. Yeah, I mean, Anthropic was there too, some- the one of the co-founders Tom Brown, I think his name is. Yeah. Yeah.

Jason Calacanis: 2:00 Was the Anthropic guy running around saying it’s the end of the world? It’s the end of the world?

Chamath Palihapitiya: 2:02 No, no, no. He was too- Tom’s awesome. Tom’s awesome. In fairness to him, he wears it on his sleeve, which is like, hey, we really believe we’re doing the right thing. Right. And just trust us. And I think the future’s open source for all these countries.

Jason Calacanis: 2:12 Well, we’re going to get into that. That’s on the docket for sure. But let’s start with the IPO up. But, you know, there’s a trillion-dollar IPO rush to the exits and, you know, this was a big topic of discussion Brad at the liquidity summit last month, and we’d never seen a trillion-dollar IPO. We had one this year already, SpaceX, trading right about where it went public, so it was priced, I guess, to perfection. And theoretically, gonna see two more. Brad has the inside information, so I’ll try to get it out of him. OpenAI and Anthropic are slated to go out. Let’s just go quickly over what happened with SpaceX. It ran up to $200 a share. It’s been down a bit, it’s at $150 a share as I said, that’s right at the IPO price. So it’s trading at that 2 trilly market cap, currently 7th largest company in the world. And Anthropic confidentially filed on June 1st. I don’t know why they call this confidential filing when it immediately comes out, but I guess the information is confidential. PolyMarket says 65% chance Anthropic’s IPO will happen this year on light volume, 360K. And two weeks ago, Gavin Baker, another bestie, said he thinks they’re gonna end 2026 with over $100 billion in revenue and very profitable. He said, a couple of us gasped on the program, that he thinks it would trade at 3 trillion right now if it went public. Chamath, you made a great call on the pod, you said, “Hey, good idea for Elon to get out first.” What are the chances here, Chamath, that these other two get out this year or maybe in, you know, say nine months in the first quarter of next year? Let’s start there.

Chamath Palihapitiya: 4:34 Well, I think that these are all great businesses. I think the question is, what is the market clearing price? And I think that’s more of a function of how much appetite the markets have to absorb new issues and at what scale. That’s number one. And I think that’s mostly determined by price. So, I think Anthropic and OpenAI are probably in two different places. The last time we heard from OpenAI, their cash burn was still quite high just because of the diffuse nature of their business and more reliance on consumer than enterprise. I think Brad mentioned it in one of the pods that Anthropic may actually be accidentally profitable, I think he said something like that.

Brad Gerstner: 5:18 Yeah.

Chamath Palihapitiya: 5:18 Let me tell you something really interesting. I sat down with my CTO today and I said, “How are we doing on token spend?” And he said the most incredible thing. He said, “Right now our token costs are doubling every 45 days.”

Jason Calacanis: 5:30 Okay.

Chamath Palihapitiya: 5:31 And I was like, “Ugh.” And he said, “Yeah.” And I said, “Well, what is the downstream productivity?” And he said, “Maybe 5% max.”

Jason Calacanis: 5:41 Okay.

Chamath Palihapitiya: 5:41 And I said, “Okay, so my costs are doubling every 45 days, my upside is essentially flat?” And he said, “Basically.” And I said, “Well, explain why that is.” And he said, “Honestly, what we’re finding out is that you need to use a lot more tokens…”

David Sacks: 6:00 to get to this next iteration of improvement because we’ve effectively already asymptoted. And I said so what should we do? And he said, honestly, we have to figure this out. And so we’re going to take a step back and try to figure out what to do. I don’t know how many other companies will actually go through this reckoning now, but the point is everybody in the next three or four years will for sure go through it. So I suspect that if you can get out now, you should get out now before all of that starts to seep into the water table, because I think that’s probably what allows you to get out at a huge price and raise a huge amount of money.

Jason Calacanis: 6:39 All right. Brad, you are well invested and well known for being invested in these two next IPOs, so you probably have some good insights since you talk to them on a regular basis. Chances they get out in the next 6 to 9 months, both of them? You’d say 100% chance unless there’s some outside event, you know, blockade of Taiwan, some black swan event that we’re not anticipating? What do you think the chances are they’re public when we’re sitting here and I’m skiing in Hokkaido?

Brad Gerstner: 7:13 Yeah, I think, I think it’s very high. But let me first say, you know, the SpaceX IPO where we were also investors and we also bought in the IPO. I mean, it was textbook. It was a hugely successful IPO. They raised $75 billion at $1.75 trillion, okay, so it went out below where we are today, it’s up 25%. You know, and let’s call it on 35 billion of forward revenue. So if you think about that revenue multiple trading at 2 trillion on roughly 35 billion of forward revenue, it’s an incredible achievement. I think it was textbook. I think Anthropic and OpenAI were watching very closely because frankly we had not had an IPO of that size and to Elon’s credit and to the team’s credit, Brad, Gwen, they really pioneered some really smart and interesting things as part of that IPO. So you know, you heard from Gavin, Anthropic’s rumored to be, you know, trending over 100 billion in revenue compared to the 35, right? If they exit the year at 100, that means their GAAP revenue next year could be well over 100. So based on the SpaceX success, I think it would be a blockbuster IPO. And I think SpaceX has shown them the way on things like the total raise, pricing, liquidity, inclusion into the indexes, how to do the lock-up. Like I think they’ve gone to school. It was a staged release in terms of getting out of the lock-up. It has to hit certain milestones and some of those are time, early inclusion in the index, raise 75 billion dollars, like…

Jason Calacanis: 8:41 Early inclusion in the index, let me have you unpack that for a second because people said, ‘Hey, maybe this feels unfair that they should be forced to buy it.’ What’s your take on that? Is that just like haters gonna hate, or is there something to that?

Brad Gerstner: 8:58 I think there was legitimate concern.

Jason Calacanis: 9:00 What is the legitimate concern, yeah?

Brad Gerstner: 9:01 The legitimate concern is that a company that had not been through the process of being vetted post-IPO, there’s a lot of volatility.

Jason Calacanis: 9:10 Sure.

Brad Gerstner: 9:11 You’ve seen that chart, Jason, that the the peak-to-trough drawdown in the six months post-IPO is 50%. We’ve seen a pretty big drawdown here from the peak-to-trough as well. So you don’t want to jam it into an index at the peak and then have a 30% drawdown on top of people, which often happens in IPOs because people get excited, it runs ahead of itself. But they didn’t do that here. There was fear that that was going to happen. So both the exchanges and the indexes, they looked at this and they made some modifications because the the other side of the argument is it’s so damn big and important that it needs to be part of the index.

Jason Calacanis: 9:44 Right.

Brad Gerstner: 9:44 Right. And so the reason the rules had previously existed is because most companies coming public were younger, earlier, less tested, less revenues, less profits, all the things weren’t as important in the overall scheme of things. So I think that they pioneered some really smart things. It’s worked well. It’s traded well. And so I think that that provides a bit of a blueprint for Anthropic. But just in in terms of the enthusiasm, is Altimeter, is a Fidelity, is a T. Rowe an enthusiastic buyer of Anthropic based upon the things we know today around profitability and model improvement and revenue growth, etc.? Yes. Everybody would be pig-piling in. Everybody would be trying to get into the top of the book. And, you know, the last I heard, you know, again, rumor that they would like to get out this year. On OpenAI, everybody knows that Anthropic kind of passed OpenAI on a revenue trajectory, but I will tell you, OpenAI’s kind of got its swagger and mojo back. It’s coming out, you know, just today with a whole new set of models. We know GPT-6, you know, there’s a lot of talk of that coming out within the next 30 days. A whole new generation of models. I think their revenue has really ticked back up. The most recent kind of rumors I see on Twitter is around 70 billion dollars sticks at the year. So just as a reminder, 70 billion may not be over 100 billion that’s rumored in Anthropic, but it’s still twice where the revenue of SpaceX is at. So can they get out at over a trillion on that type of revenue growth, being one of the two frontier premier labs? I think the answer to that is yes. Um, I’m not sure there’s a huge race between the two of them to get out first. I think they’ll both go out when it’s when it’s time. I think OpenAI has a little bit more complexity just associated with the corporate restructuring that they have to go through, etc. So I would be surprised if they go out before Anthropic, but the fact of the matter is I don’t know. But today, as I sit here today, Altimeter would be a buyer at scale and at size in both of those IPOs.

Jason Calacanis: 11:49 At three trillion are you a buyer or are you a hey, you know, it’s obviously going to trade up and down and there’s no rush? Because you I think were the one who said on the pod or it might have been at Liquidity Live when I asked you Point-blank, hey, should retail get involved in SpaceX? What’s your thoughts? And you were like, hey, listen, it’s a 4% float, 5% float. It’s going to trade up and down, but, you know, a year from now, it might be trading at the same basic price. It’s going to be priced not to perfection, which it seems to have been, but I think your position was it’s going to be priced reasonably. There’ll be plenty of time to get in. You don’t have to like, you know, panic about getting your shares.

Brad Gerstner: 12:22 Yeah, I— once a company’s valued at over a trillion dollars, like the get-rich-quick schemes are over, right? Like you and I share a deep passion, J, we got to get retail investors, we got to get the citizens of the United States in on these value-creating opportunities earlier, right? The accredited investor laws are insane that we have in this country and keeps people from participating in these things, but it is what it is, right? So they’re coming public at over a trillion dollars. I still think there’s a lot of meat on the bone on SpaceX, on Anthropic, on OpenAI, but you’re not going to have things that are— I don’t expect that they’re going to be priced in a way where you’re going to get a 50% to 100% durable bounce out of the IPOs. If so, that would mean they were probably mispriced, right, into the IPO. But I do think that these things can be compounders. They’re going to compound at the rate they compound revenue, and I think all of these companies are going to compound revenue at well over 30% for the next many years.

Jason Calacanis: 13:26 And 30% a year, just for people to understand, that— this is high growth in public markets on very large revenue numbers already. You know, growing 30% when you have 100 million in revenue is one thing. Growing 30%, you know, when you got a 10 billion or 100 billion, you know, this is— this becomes a different task. So, let’s talk a little bit about these two companies, Chamath, and what the public’s going to perceive them as. ChatGPT seemed to be the public brand, the consumer brand for, you know, large language models. It’s the AI for, you know, people who are doing their homework or mom and dad are trying to fix the dishwasher, whatever. And then Claude took the lane of, hey, we’re going to be the one for corporate. And it did seem like OpenAI got very distracted with Sora and, you know, Disney relationship, we’re going to make a puck with Jony Ive, everything consumer. Then they realized, oh, wow, the revenue seems to be in enterprise first. Is that going to wind up being the big mistake when we look at it? They kind of gave the Google position, the high-growth position to Claude and Anthropic, and they took the Yahoo position, or do you think they’ll catch up on the enterprise? Or maybe they should just go back to trying to be the consumer version. How are these going to be positioned a year from now? How’s the public going to look at them?

Chamath Palihapitiya: 14:40 The problem with enterprise revenue is at some point the person that’s spending it has to see an ROI. I asked Claude 3.5 Sonnet, Anthropic’s new model, I first asked it: What is the lift of the S&P 500 earnings per share growth since 1990? 2024 from AI. And they answered, ‘Oh, it’s 50%.’ So then I looked through it and I said, ‘Well, no, you’re including the money that Nvidia makes for selling chips to AI.’ So I said, okay, I asked a different question, which is then what was the EPS growth of the S&P 493? And the answer was 9%. And I said, okay, well that’s different. And I said, unpack that. And the overwhelming majority of that was from pricing power sitting on top of inflation, and then the other 3% was from buybacks. And so the answer as far as all publicly available data was that the actual ROI was somewhere between zero and 2%.

David Sacks: 15:48 Hm.

Chamath Palihapitiya: 15:49 So, I don’t know. I mean, I think that enterprise looks really good. The problem is that very smart investors like Brad and Gavin and others at some point will start asking companies, what’s your ROI? What’s the actual EPS lift? And if the answer is, ‘Well, I don’t really know,’ or ‘I’m not sure,’ and, you know, you don’t necessarily have the pricing power to continue to raise prices, enterprise is probably a little bit more brittle because there are fewer buyers and they’re more demanding.

David Sacks: 16:21 Hm.

Chamath Palihapitiya: 16:22 Consumer on the other hand then all of a sudden becomes an incredible safe harbor because you have tens of millions of buyers. And having those two orders of magnitude more buyers at a much smaller price point inoculates you from the vicissitudes of an ROI discussion.

David Sacks: 16:43 Hm.

Chamath Palihapitiya: 16:44 So it all really depends on what the actual ROI is of this money being spent. I think that we’re in the phase of just being astonished, as Brad said, about the scale of the revenue growth. But at some point, you’d have to be an idiot not to ask, well, who is paying you this and can they sustain paying it to you? I just don’t know what the answer to that question is and at some point, it may not be now, at some point people will have to answer that question. If you’re spending [censored] million dollars a year on tokens, and that [censored] million dollars a year is doubling and tripling and quadrupling, at some point you’re going to have to show an ROI that’s above the risk-free rate of return, otherwise you’re going to have some angry investors on your hands.

Jason Calacanis: 17:16 And interestingly, our discussion here for the last couple of weeks on the pod has centered around that, and the industry has responded on the place where all the CTOs, CEOs and capital allocators hang out, which is X.com, formerly known as Twitter. Here’s Praveen, the CTO of Uber. And so when you ask, like, how are they getting the ROI out of this, people are now bringing that conversation front and center and they’re explaining it on X. And he talked, remember… Uber was also the one that ran through all their tokens in the first quarter. So that on the other side of the business, which is legal, operations, marketing, customer support, HR and procurement, which he lists here. He says in this, you know, today 99% of our engineers use AI tools. Okay, great, right? And everybody’s, you know, doing vibe coding and has a coding assistant. More than 70% of pull requests are attributed to local or cloud agents. Our engineers have built 2,500 agentic skills. So how are we bringing agentic AI beyond engineering? And what they’ve decided to do is essentially, he talks about these agentic pods. And this to me seems directionally how this should be done, which is you find engineers and you, as we talked about, forward-deployed engineers—fancy way of saying put an engineer—put them into departments and have them work with the department heads who understand systems thinking, how their process is done. And he says it’s making, basically long and short of it is, they’re making massive, massive progress on the operational side of the business. So Brad, you’re pretty familiar with Uber, had been a long supporter of that. This is a company that knows how to deploy technology pretty well, and they’re an operations machine run by an operations machine, Dara.

Chamath Palihapitiya: 19:01 They should report the EPS gains attributable to AI.

Jason Calacanis: 19:12 Yeah, well, I mean, and this first step seems like they’re really being thoughtful about this. First, hey, this token spend got out of control with the developers. We’re going to need to pause this and look at it. And then second, here’s how it’s going to lower costs and create more efficiency. So Brad, let’s talk about that side of it. Not just token maxing with the developers hitting the slot machine of like, okay, let’s see if this pull request, let’s see if this produces the right code or not, to these departments in a more strategic way. It’s not just the person who works in HR, you know, using Claude code or Perplexity or whatever and trying to vibe code something. This is, hey, we’re sending engineering in to work with your top systems architect and we’re going to, you know, try to find that ROI, yeah?

Brad Gerstner: 19:57 Yes, I, you know, first, I would say Chamath’s right. The only question is on what timeframe. There’s no doubt that there’s a lot of money being spent today that is in the experimental bucket, right? Where I think there probably isn’t direct ROI, Chamath to your point. But I think we’re so early nobody cares. I think we’re so early in terms of enterprise adoption. Remember, the total addressable market here is every single small, medium, large company on the planet. And so we’ve never seen revenue growth like this because we’ve never seen a TAM like this. And if you look at the distribution of revenues across these businesses, it’s not like it’s concentrated with four or five customers. There are millions of customers independently, economically making the decision that is rational for them every day that it makes sense like Praveen at Uber. And of course, they’re trying to find things on both right now, mostly the cost side, cost takeouts to justify the investments that they’re making in, you know, in tokens. But I think we’re on the verge of breakthroughs in agentic AI. Intelligence is going to dramatically change the revenue side of the equation for a lot of these businesses, breakthroughs in life sciences, breakthroughs in product innovation, etc., where they could not divorce themselves from this even if they wanted to. For example, Jensen Huang has talked many times that all of his design work, all of his design work now at NVIDIA is using AI to design the next generation chip. The machine is building the machine, so you can’t get rid of that even if you wanted to, and tiny intelligence advantages at the frontier where he sits are required. Like, there’s no way I don’t think that Jensen is going to use anything but the best models that he can to build out those capabilities. So I just think that we’re not going to see that in the next few years. You’re going to see it under the hood, of course, but that occurred at Snowflake, there was tons of optimization that occurred at Snowflake, but the revenue continued unabated, the revenue growth continued unabated because they further penetrated use cases, further penetrated the enterprise. So let me be provocative here. If these guys end the year over 100 billion, I think that they’re on a revenue trajectory that they could 3 to 5x again next year. We’ve never seen anything like this. Never.

Jason Calacanis: 22:21 Okay, you’re saying 100 to 300, 100 to 400.

Brad Gerstner: 22:24 I’m saying they think… and Jason, like you and I have talked about this, our minds were blown if a company could go from 100 million to 300 million. We’re talking from 100 billion to 300 billion. 200 billion of incremental revenue is incomprehensible in the history of Silicon Valley, okay? And just the fact that we’re even in that—

Chamath Palihapitiya: 22:43 In the history of the world.

Brad Gerstner: 22:45 Yeah, in the history of the world. So the fact that we’re even talking anywhere close to this tells us something different is going on here. I think the thing that’s different is that intelligence is the largest TAM we’ve ever seen in the history of the world. These guys are penetrating it, so yes, the super sophisticated companies that 80/90 and Chamath are helping optimize their token spend that are early adopters, 100% that’s occurring, but it’s not really changing the trajectory that the frontier labs are on.

David Sacks: 23:18 Yeah, and one of the interesting things about this technology, that’s really unique, we talk about intelligence on demand. When you would make a piece of software or you had some technological innovation, it would typically accrue to, I don’t know, one group of people in an organization, you know, maybe two groups of people, right? Excel comes out, okay, yeah, the accounting department’s having a field day with it, but it’s not really affecting human resources or marketing. Okay, yeah, maybe it trickles down eventually. Every single person in every single organization is playing with these tools. So if everybody’s playing with it, everybody’s trying to apply it all at the same time. It’s kind of like, you know, you got a thousand-person organization, people are spending 200 a month. Okay, yeah, Chamath, they double it every, you know, x number of months. Okay, yeah, now they’re spending 300…

Brad Gerstner: 24:00 $400 a month per person. Okay, they’re spending $5,000. Well, if the average salary is 100, 150k at this organization, it’s only an incremental 3, 4, 5% on top of their salary. So the way I look at it is, did it make that person 3, 4, 5 times more effective at their job? And I think the answer is yes. So that’s why there’s so much token maxing going on, and it’s also a bottom-up type product. You can just get into this product for 20 bucks a month and, you know, no CIO or CTO is like, oh no, you can’t spend 20 bucks a month on your corporate card for this technology. So when a bottom-up technology hits everybody at the same time, that’s what would explain this revenue ramp that we’re all having a hard time adjusting to. It applies to every single person. Like, who isn’t impacted by the technology is my question to you, Chamath. Like, in what organization you’re working with with 80, 90, is there a department that says, yeah, the intelligence on demand, not for us? We don’t need it.

Chamath Palihapitiya: 24:54 Well, it’s less about being dismissive that way. It’s more that regulators and other people won’t necessarily allow to use the way you want.

Brad Gerstner: 25:03 Okay, so finance, HIPAA, yeah, there’s HR data, you’re not allowed to put that to work just yet. What I’m finding is, once you start using this and getting some gains, it’s very addictive. And we were sitting here, I don’t know, maybe in January, and I got that open Claude bug. And then, you know, I started playing with this Hermes or Ermez agent, which is not a French company, by the way. They just use French names, Nous Research or whatever it is. I started playing with that. It’s a very peculiar piece of software, but it’s very open piece of software. So I went to Open Router, I got my own keys, I’ve been playing with GLM. Then I talked a little bit about Bit Tensor on the program, known as Tao, dollar sign T-A-O, it’s a crypto project. Somebody who is creating a subnet that is putting GLM 5.2 and other models available at really cheap prices. So I all of a sudden experienced, because they gave me an API key, having my token cost go down 95%. And when you have unlimited tokens, as an exercise, which is going to come to everybody. Eventually, everybody’s going to learn how to drop the price by 95%. And it’s going to happen as well because people like Groq with inference, this is all inference, right? This is what people are using. They’re using inference to do this. Well, inference is being impacted like three or four different ways. The software’s getting better, open source at the same time. You’re going to have distributed networks like Tao, and you’re going to have better, you know, chipsets from Groq and Cerebras, etc. All that’s happening at the same time. Once I got down to 95% cheaper, I started setting my agents, instead of doing daily runs, to doing hourly runs. Then I took my agents from doing one task, and I broke them up into three agents and had them doing three different things on the hour. And when you start doing hourly tasks, and then you wake up in the morning and like 14 jobs have been done, you’re like, wait a second, this is completely…

Jason Calacanis: 27:00 totally different as one example I have it has all the all-in episodes all the this week in startups episodes and we set these cron jobs to go find what the new trends are in technology I have a trend spotting agent running every hour informing me of the top three or four trends and I just give it words really does change your thinking when costs go down what do you think the tokens are going to cost Brad in you know next year

Brad Gerstner: 27:24 I mean that well yeah we’ve we’ve seen we’ve seen 90% reductions in the price to tokens for each of the last two and a half years we’ve talked a lot about Jevons paradox which I think you’re you’re referencing here which is you’re going to use a hell of a lot more when it happens I think the central debate right now in AI is the one that Chamath keeps pointing us back in the direction of which is for 18 months since the deepseek moment right when the deepseek moment happened the markets fell 40% and there was a reason for that many started arguing that the frontier models were screwed that open source was going to kill them that they were closing the intelligence gap that model routing was going to make it easier and easier to route these tasks to cheap tokens but despite all of those arguments and now we’re 18 months into this and I had this back and forth with Gurley a lot I love open source I want all the competition in the world let’s be very clear but despite all of those arguments the facts in the field are just the opposite the share of economic value right there’s this there’s this tweet this week from Jesse Zhang that we ought to pull up here you know the economic value the share of wallet is actually increasing to the frontier labs while the share of tokens these commodity tokens is obviously going up you know to the other guys and I had a little back and forth this week with Nikesh on this kind of trying to suss out why is that the case right because what people would have thought is oh cheaper pretty damn good 90% is good enough to do all these tasks that you’re talking about Jason so nobody’s going to use the anthropics and the openais of the world but despite that it looks like their share of wallet has gone up

Chamath Palihapitiya: 29:08 I think I think it’s not that I think it’s more that when the iPhone was a novelty everybody would keep upgrading because you expected that the new price was worth it and then at some point there’s a moment and you can debate when it happened where people said you know what I’m just going to keep the old phone because it’s good enough and I just don’t see the difference and I think that there’s going to be a moment like that like when I use Claude 3.5 the problem is that it’s nerfed on a bunch of things that I would normally research you know I was with somebody this weekend and he was telling me about some health thing and I put it into Claude and it was like won’t answer you and I’m like okay so I think that everybody will get to a point they’ll get to it at different times where they just say you know what like it shouldn’t really matter what model I’m using if I get an answer that I think is reasonable and I can kind of go about my day separately I think when the cor… or CFO gets involved, that’ll be an entirely different conversation altogether. I think that what I can tell you after this UN commission that I joined with Benioff and Jensen and Brad Smith, there is not a single country in the world that is not trying to figure out its own sovereign AI strategy and I don’t think they believe using a closed source American model is the answer. And so, you know, we—I think we have to keep in mind, there’s three trends. One is just geographic penetration of humans and there are still many, many, many more people that don’t use it than do, which is an upside and an opportunity for everybody.

Jason Calacanis: 30:34 Mhm.

Chamath Palihapitiya: 30:35 And then the second is there is going to be the experimentation as you said that needs to transition to ongoing repeatable usage. And then the third is that all of that then needs to plug into the existing regulatory infrastructure that we use as a societies to run the world. And I think when you put all of these things together, it’s not clear to me who wins except that you’re going to have a lot of diversity of choice. Certain countries, I can tell you after this week, have no desire to subjugate themselves to any technical risk. And so they’re willing to spend the money to have their own. Now we can argue and debate whether that country has any chance, but they would rather take an open source model like NVIDIA’s actually and stand up their own stack soup to nuts for their own people and their own companies inside of their own country and if the models are 99% as good or 95% as good, there’s going to be a claim that some countries make which is it’s just good enough.

David Sacks: 31:29 That’s the question. That’s the question.

Chamath Palihapitiya: 31:31 And then separately, there are companies who will not have the earnings growth to justify this without going on some long protracted carve out of cost and most companies, you know this, they just don’t do it, they don’t have the nerve to do it, they’re not capable, you know, you wrote that famous essay to Zuck, he was pressured into finally doing it. Absent a very few companies, most people just allow the problems to compound. So I just don’t see a world where when you get clobbered over the head you don’t look at other ways of just displacing costs and if it’s like Coke-Pepsi kind of a thing and Pepsi’s one one-thousandth the cost of Coke, I don’t know, I just think it’s—it’s a risk that I think has to be managed in the perception of the market participants and the underwriters.

Jason Calacanis: 32:11 To add to that Brad—just open source is very hard to implement when compared to just firing up Claude and having Claude already approved in your organization. The number of steps it took me in order to—and I’m pretty familiar with technology—it took me hours to configure my new setup to get onto this BitTensor network to get OpenRouter going. And to your point Chamath, it does dynamically route now. So I’m dynamically routing and I have, you know, GLM 4.2 and then if I fall back to Claude, but which Claude am I going to fall back to? And here’s another piece of evidence to your point Chamath please— There are some organizations that just aren’t capable of this, they don’t have the- the team that does this naturally. We just talked about the CTO of Uber, now let’s talk about another CTO, Andy Fang is the CTO of DoorDash. Shout out to Stanley. And so he, as you can see here in this tweet, I- I- I think a lot of people listening to All-In over the last couple weeks are coming out and as I said, explaining what they’re doing to address this exact issue. He says, hey, with our internal coding benchmarks, we’re able to confidently introduce open-weight models into our AI code review without degrading code quality, have the frontier model Fable from Anthropic to do the hardest work, delegate lower-level work to Kimmy 2.6. And they are now releasing their benchmarks. So another group releasing their benchmarks and saying, hey, we know this is an issue. The CTO has been charged to your point, Chamath, again, CFO says, hey, make sure this is- uh, profitable and we get the ROI. They put that on the CTO. Here’s another CTO from another leading tech organization that knows how to implement this. Yeah, the- the really interesting thing we have to forecast right now is what happens in an earnings miss. And I think what happens in a- in a moment where for whatever reason, maybe there’s just an externality, but there are a series of earnings misses. Where are people going to look? And I just think that people find it very difficult to lay off other people. I think it’s much, much easier to cut other costs. And I think that the more successful these companies get in a very quick amount of time without really proving the ROI, I just think the bigger the risk is. It’s complicated. The game on the field when you’re working with these enterprises and just trying to explain it to them is I- I think that they’re- they’re getting smarter quickly is what I would say.

Brad Gerstner: 34:43 I would just say I- I think Chamath’s absolutely right about the sovereign stacks that are going to get built around the world. This is not either-or. We are going to have open source and we are going to have frontier intelligence. Um, the preponderance of the tokens today are already shifted toward cheaper, lower, uh, uh, lagging models out of OpenAI or lagging models out of Anthropic or the other frontier labs that are out there. Obviously we- you know, we talk about those two. xAI’s released an incredible model, you know, in the last two days. Meta’s out with, you know, a- a terrific model today. So Gemini’s still in the hunt. So there are lots of choice.

Jason Calacanis: 35:30 The Meta thing was really intense because I thought, okay, you know, we talked about the game theory, which was Mark should scorch the earth with open source.

Brad Gerstner: 35:38 I think they flubbed that play.

Jason Calacanis: 35:39 But then I think he is now said he’s going to create a price war. And so if you look at the tweet or the quote, there was a post Jason, I don’t know, Nick, if you can find it.

David Sacks: 35:44 I got it.

Jason Calacanis: 35:45 Announcing it, he was basically like, hey guys, I’m going to give you the same quality at like 1/100th of the cost. Now, again, there’s a lot between here and there. There’s a lot of enterprise distribution that’s required and, you know, this- there’s been a couple of misses.

Chamath Palihapitiya: 36:00 before, but I thought it was interesting that the vector of challenge was on cost.

Jason Calacanis: 36:05 Yeah, here’s some Mark Zuckerberg tweet just so I queue it up for you there, Brad. And he’s @finkd, that was his old handle back when he was in college, F-I-N-K-D. And he’s done more tweets today over this Llama 3.1 announcement than he’s done in his history. So he’s getting into the X.com conversation. Quote: ‘Today we’re releasing Llama 3.1, a strong agentic open model at a very low price. It’s available through our new Meta model API and in Meta AI.’ So he’s coming out saying, ‘Hey, we got the strongest agentic tool here, please come use it.’ He also wants to have his own, essentially, you know, he wants to jump into not the hosting space, but he wants to provide tokens as well. So again, I think we’re gonna have a tremendous amount of selection. The competition is great for America.

Chamath Palihapitiya: 36:53 But I think if you look at the things people are doing, let me give you an example. The premium workload. Jason, you talked about summarizing a document. It may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model. But if you’re talking about replacing a software engineer for two hours, that may take 2 million expensive tokens. And the consequence of using something that’s 95% as good is really high, right? Because you have a long running task and if the task breaks early, or it breaks in the middle or it breaks at the end, there’s a huge cost to that.

Jason Calacanis: 37:31 You still burn the tokens, right? You back to this analogy as you’re pulling the slot machine and you lose.

Chamath Palihapitiya: 37:36 And the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So three consulting firms, they’re competing, they need the smartest consultant. It- they’re charging 200 bucks an hour. The difference between spending $3 on a cheap model or $15 on an expensive model to replace a $200 an hour consultant, it’s just irrelevance. That inference cost difference is irrelevant if you’re getting something that’s bulletproof for 15 bucks. And so I think that’s what we’re seeing play out. The best evidence for all of this is just revenue growth. Right? We can sit here and speculate all day long as to what—

Jason Calacanis: 38:13 Revenue growth not from Anthropic and OpenAI, but from their customers.

Chamath Palihapitiya: 38:18 I’m— no, I’m talking about what is Anthropic’s revenue growth compared to OpenAI, compared to the open source models. Millions of independent actors are choosing every single day. The open source companies are growing, right? But they’re growing selling something that is really, really cheap. And there’s a room in every single market for premium products, for mid-tier products, and for commodity products. And I think we see a lot of this token growth, people are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won’t pay for the frontier stuff. There is no evidence of that on the field today. It may develop— Over the course of the next couple years, but it’s not on the field today.

Jason Calacanis: 39:03 Yeah, and just to give people an idea, we keep mentioning what sovereigns are doing. To give you the specifics on that, the UAE very famously has their own Abu Dhabi Technology Innovation Institute shipping Falcon—you’ve probably heard about that. The Saudis have Allam, and they’re doing their own models that are Arabic LLMs. And then this week, Japan is investing $6 billion in a consortium—it’s called the Neotera, N-E-O-T-R-A consortium—and they’re doing that and skipping ahead to physical AI, i.e., robotics. Okay, joining the conversation here, the one, the only, Saxi-poo. Sacks, bringing you into the discussion, talking a little bit here about the debate that we started here on the podcast, getting ROI from tokens, where are the tokens going to accrue to, open source versus the frontier models. A bunch of CTOs chiming in on X this week, and the last couple days in fact, talking about how they’re managing intelligent routing, first to open source models, then falling back to Claude and the frontier models. How do you think this is playing out, and if you’re an investor in the space, how do you think about the frontier models and their growth when you have, uh you know, CFOs coming in and saying, ‘Hey, justify this cost, and do you have a cheaper solution, and what is that cheaper solution?’

David Sacks: 40:23 Well look, I think that enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that that would be more efficient, and they are seeing their compute costs or their token costs skyrocket right now. So everyone’s trying to figure out how do we put the brakes on this or at least control it, you know, make sure we’re getting ROI. You also have the AI sovereignty issue that we discussed last week that Alex Karp talked about where they’re worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. So there’s no question that enterprises would like to diversify, they would like to get off of these frontier models when they can. The problem is, I think in most cases they don’t have the technical ability to do it. I mean, Coinbase figured out how to do it, DoorDash figured out how to do it, which is to say they built a token routing system, a layer of middleware that allows them to sort of send frontier tasks to frontier models and non-frontier tasks to more mundane models. But I don’t think your average enterprise has the technical capability to do that. So I think this is a case of the spirit is willing but the flesh is weak. I mean, they are willing, they would like to diversify off of these closed models, but they are unable to do it. And so this is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year, so…

Chamath Palihapitiya: 42:00 Open source as a share of enterprise spending is actually decreasing. Now, I don’t think that means that usage is decreasing. I think usage is skyrocketing in both these categories. It also may be the case that because the whole point of using an open model is you just pay for the compute cost, you don’t have to pay a lab, so it may be the case that it’s hard to measure that usage in terms of spend. But nonetheless, I mean, anyone who’s saying that these closed models are going to lose or are somehow losing, you’re just not seeing it in the data. Like Brad’s saying, the revenue is skyrocketing. And I think the most you can say is that enterprises that are technically capable would like to gravitate towards hybrid architectures. But at the same time, it takes technical expertise and it is just phenomenally convenient, whether you’re a developer or an enterprise, just to go with the frontier labs. And that’s why their revenue is skyrocketing.

Brad Gerstner: 42:58 It is the easiest choice there of all.

Jason Calacanis: 42:59 It’s the most refined… yeah.

Brad Gerstner: 43:00 Most refined product.

Jason Calacanis: 43:01 Yeah.

Chamath Palihapitiya: 43:02 Yeah, and there’s one other thing here as well. And this was discussed in a really interesting blog post by the founder of Decagon, which is enabling AI-powered customer support for enterprises. And what the founder said is, look, open models are great when you know exactly what you’re trying to do. Why? There’s smaller, cheaper models, but you have to do post-training, you have to have the data set, and you have to know exactly what you’re going to use them for.

Jason Calacanis: 43:29 Totally.

Chamath Palihapitiya: 43:30 But if you don’t know exactly what you’re going to use them for, you want the most powerful general intelligence that you can get, right? So what he said is that for mature use cases, yeah, you want to go open. But for immature use cases, which are all the new things people are discovering right now, you’re just going to want to use the most capable general model that you can. And then once you figure out what the workflow is and what the workload is going to be and exactly what you’re trying to accomplish, then you can use a small, highly trained model. And I think he said something—

Brad Gerstner: 43:58 You’ll optimize in the post to get the gains.

David Sacks: 44:00 For customer support, your model doesn’t need to know physics, you know, for example.

Brad Gerstner: 44:03 Right.

Chamath Palihapitiya: 44:04 And so you don’t need that capability. But enterprises are still trying to figure out exactly what all these workflows are going to do. So I think that’s another factor, which is to say that, you know, it depends on the use case and how mature that use case is, and you really want the most powerful frontier models that you can at the discovery of all the potential for the technology.

Brad Gerstner: 44:33 Yeah. And then let me just wrap up—

Chamath Palihapitiya: 44:35 Then there’s one other interesting post that I saw was by Nikesh Arora, who also said that what he’s seeing is, yeah, enterprises would like to diversify, they would like what he called model fungibility. They would love to commoditize these models, right, and just hot-swap them.

Brad Gerstner: 44:50 Headless is the term being used, right?

Jason Calacanis: 44:51 Yeah.

Chamath Palihapitiya: 44:52 Yeah, that’d be ideal for enterprises, is you sort of swap out the model for the cheapest one that gets your task done. But then what do you do about memory? What do you do about context?

David Sacks: 45:00 Context, what do you do about history? And what he said is no one’s really figured out a way to abstract that stuff away from the model yet. And again, this goes to the technical challenge of creating this middleware layer that would do the most efficient token routing. It’s, well, you know, it doesn’t work unless you can make all of that context and memory and history fully portable to the cheaper model that you want to basically hot swap to.

Jason Calacanis: 45:24 Which means you have to have some technical ability, Sacks, and the people with technical ability, the tip of the spear, the 1% of people deploying this technology are starting to figure that out. Here’s another proof point and some more evidence. This is Ali, the founder of Databricks, I think a company you’re very familiar with, Brad, and what he realized when you start taking apart the harness and you start looking at the skills, you look at the memory and all this accoutrement that you put around your tasks. He said, we find that the same model, using the same model, not using open source versus OpenAI or Claude, we’ve found that for the same model, the choice of harness can significantly save cost by about 2X. So they found with GLM 5.2 that this performs extremely well and that their tasks literally are getting cut in half using the same model but with a different harness. And that rings true to me. Once you’ve built one of these agents, and I was talking about one earlier, I’m running every hour on the hour to find trends, I asked it to please start optimizing it. And when I optimized it, it was like 80% less token use. And now in these apps, you can go to your analytics, Sacks, and you can actually see your token use by hour, by job, and across which models you’re using. This is really sophisticated and hard for a consumer to do of the technology, but it’s definitely a trend.

David Sacks: 46:52 So with that harness that he was using, is that something they built in-house?

Jason Calacanis: 46:56 Yeah, I think it’s, uh, yeah.

David Sacks: 46:58 Okay, got it.

Jason Calacanis: 46:59 So they basically created their own application.

Brad Gerstner: 47:00 He’s using Omnigent in front of these, and that it can multiplex different harnesses and models for different tasks. So he’s not only routing to the right LLM, he’s routing to the right harness. And people don’t even know what skills are, people don’t even know what the memory is at this point. That’s all abstracted into the Claude product or the Perplexity product, etc. Yeah.

Chamath Palihapitiya: 47:22 There’ll be a massive business, there already is. All these inference clouds, you know, the Base 10s of the world, the Fireworks of the world, every single hyperscaler in the world is going to do this. They’re all going to provide, you know, tools that allow you to achieve some level of model fungibility. The big question is, at the end of the day, we’re going to have, it’s going to be very heterogeneous. But what is the mix between these two? I again think the TAM is so damn big here that you’re going to have huge open source use cases, sovereign use cases, etc. You’re going to have plenty of room for the frontier labs. Let me throw something out I’d like to get your opinion on. You know, to a certain extent, there’s this implied assumption in the world…

Jason Calacanis: 48:00 world that there’s going to be this convergence of intelligence, right? And if you look at the benchmarks today, seems like everybody, you know, on the benchmarks, they are converging. But yet, if you look at the revenue distribution, it’s not converging at all. One of the questions I have, will the model router itself be smart enough to overcome David, the inherent intelligence advantages of the generalized of the frontier labs? The non-consensus argument might be that intelligence is not converging at all. That super intelligence becomes fully self-recursive and as it becomes recursive, you actually extend the lead because the smarter your model gets, the more revenue you get, the more compute you can buy, the more compute you can buy, the better the model is that you can build. So I think there’s a chance that over the course of the next two to three years, as we take on much more complex agentic tasks, that the distance between the frontier and everybody else doesn’t converge, it actually extends. We shall see. But, you know, I think there’s this implicit assumption in all the arguments today that everything’s converging. I’m not sure that we’ve really run that to ground. Another piece of evidence to put into this mix. I interviewed Anton, the CEO of Lovable. Lovable is an app that or is a service that allows you to vibe code, you know, different pieces of software. They’ve got a really interesting take on that. They went from 100 to 600 million in revenue over the last few years. They went from 0 to 350 in their first two years. The product’s been out, I think, roughly for like 30 months. Then I also spoke to the CEO of ElevenLabs, Mati, and I asked both of them point blank, are you guys, you know, you’re major customers of, you know, the frontier models. Yes, they’re spending tens of millions of dollars with those frontier models. I asked them, hey, are you concerned about data leakage and them competing, you know, releasing competing products. ElevenLabs is doing voice, and obviously Claude Code, you know, is an obvious competitor to Lovable. And are you going to make your own models? Both of them said that they’re working essentially on their own models. Those are major customers of the frontier labs who they want to get off of the frontier models and they want to have their own proprietary model. The ability to create verticalized models is getting easier and easier every six months or so. So that’s going to be another trend to look for is these verticalized models for voice, verticalized models for building code, and we’re going to see people stop using the frontier labs. And these are major, major eight and nine-figure customers. I think they’re going to just run for the hills and only use the frontier models if they absolutely have to.

Chamath Palihapitiya: 50:41 Yeah, but Jason, Jason… the counterpoint there is ElevenLabs, I love Mati. Do you really think he’s going to use an inferior model? He’s got to have the best voice agent in the world. And if the best voice agent in the world is given to him by using the frontier labs, can he afford in a competitive marketplace to say, I’m going to use the cheaper version, the thing that…

Jason Calacanis: 51:00 I built for myself even though it’s not as good as the other thing, if he builds something better I totally agree with you.

David Sacks: 51:08 Which is what he believes he’s doing. He believes he’s making a better version, yeah.

Jason Calacanis: 51:10 So that’s the question I just put on the table, whether or not you’re going to see this convergence, whether in fact it is that easy. I don’t think it’s that easy, but we shall see. Okay.

Brad Gerstner: 51:19 It may come down to how discrete and sort of predictive the use is. So, like Decagon, the customer support AI company, they said that 90% of their usage now is being sent to open models. But those are open models that they’ve had the opportunity to post-train on and do a huge amount of customization based on all of their learnings and all of the data that they’ve got. So again, it comes back to maturity of the use case. If you know exactly what you’re trying to do, it’s probably easier. But, you know, I don’t think that’s most enterprises though. And maybe there’s going to be a pattern where, you know, all the immature use cases, which is to say all the things you’re figuring out, you’re just going to want to use the most powerful model possible and then once it gets really well defined, maybe you start moving some of those workloads to post-trained open models.

David Sacks: 52:29 Purpose-built models, yeah.

Jason Calacanis: 52:31 Purpose-built, yeah, it could be something like that. That’s kind of what’s happening with the people who are at the tip of the spear, they’re working on the routing of the jobs, they’re working on the harness, and they want to be independent and have that AI sovereignty we talked about last week.

Chamath Palihapitiya: 52:42 Well, but just to take on Brad’s point for a second, I actually agree with what I think you’re saying Brad, which is the market today seems to be pushing towards duopoly, or it has become a duopoly, certainly measured in terms of revenue. If you were to look at market share based on token revenue, there’s only two companies making meaningful revenue: Anthropic at what, 16-something billion… OpenAI at 80-something billion… I don’t know if anybody else even registers. And it may be the case that the more tokens that Anthropic and OpenAI produce—I mean, we gotta remember every token that they’re serving up is on behalf of a use case, right? So they themselves are learning from that, and they’re getting better at then providing whatever offering that is. And so who knows, like the gap may be growing. A year ago it seemed like we had five major labs, you know, now it seems like there’s a top two and then everybody else. So I mean look, I could see AI easily becoming another tech market that becomes a duopoly, which by the way is the trend, the historical trend is like monopoly or duopoly in most tech categories, for better or worse.

David Sacks: 53:42 Yeah, in this case however, we’re using revenue as the metric to determine the winner. Keep in mind when you’re doing open source, those are dark tokens. Those don’t come up as revenue. So we don’t know the utilization that’s occurring at DoorDash when they’re using an open-source model. We do know their Fable and their Anthropic spend, right? And OpenAI spend. As we see that in the Anthropic revenue ramp, the more they deploy these things on their own hardware, using commoditized hardware, using the Neo-clouds, you don’t see that. It doesn’t come up as revenue, it comes up as free. The only thing you’re paying for there is the hosting cost. You know, and that’s, that will come up on Nvidia’s balance sheet. So the gains you’ll see there will be Cerebras, Neo-clouds, Crusoe Cloud, etc. So just keep that in mind when we’re having this discussion.

Jason Calacanis: 54:27 Let’s talk a little bit back to sovereignty here. The CCP said that they might, or there was a report out according to Reuters—Reuters generally does a good job of this—they dropped a couple of anonymously sourced reports about AI in China. And these were published about 15 minutes apart. The big scoop that CCP officials, Chinese Communist Party, are reportedly considering restricting overseas access to China’s top models. So two Chinese regulators met with Alibaba, ByteDance, and Zhipu AI, they’re the ones who are doing GLM 5.2 that we keep referencing. They’re discussing limiting access to the top open and closed models outside of China. Why are they doing this? Well, they’re making any theft or leaks of AI research a national security offense. And they want to control who can fund Chinese AI labs. And we saw this with Minimax, which was a Chinese company tried to go to Singapore, the CCP pulled those employees from Singapore back to China. And so here is their main concern, the quote is that they’re concerned about Meta’s. Chinese authorities are deeply worried about the potential for Meta’s to exploit software vulnerabilities and that Washington might deploy a model against Chinese interests. In fact, last week I proposed the reverse to you, in your previous position as Czar of AI, do you think the United States should be banning those models? Now we have the opposite, China saying potentially, according to these reports, allegedly, that they might restrict them. So explain the game on the field here, if you’re going to look into what China’s thinking, why would they want us to not have those open source models and how is this chessboard developing?

Chamath Palihapitiya: 56:00 Well, last week I explained why it would be harmful to the US to ban open models. So if you’re China and you want to harm the US, maybe you would want to. I mean, it does kind of make sense. Because our companies are benefiting a lot from all this R&D that they’re doing. Now, at the end of the day, I think this story is probably a little bit overstated. I think there are a few Chinese models that were open source that have gone closed source, but I don’t think they’re all—I’d be surprised, let’s put it that way, if they all went closed. So for example, the number one model in China as I understand it is ByteDance’s model which is already closed. That’s kind of like their ChatGPT equivalent. And it’s always been closed. Then you’ve got Alibaba’s Qwen, which was open and now I think is going closed. And Zhipu, which has GLM 5.2, which we’ve talked about a couple of weeks ago because it…

David Sacks: 57:00 They seem to be catching up to what was then commercially available as the American frontier at certain tasks. They, I think, are going closed too after having been open. And so this is, I think, the tactic is you stay open until you catch the frontier or you get close to it. And then there’s a really compelling incentive to go closed because you want to capture all the value for yourself moving forward.

Jason Calacanis: 57:23 Which by the way, is exactly what Sam Altman did famously at OpenAI. Not only did they go from a non-profit to a for-profit, they went from open models to closed models. So this is exactly paralleling what Sam realized three years ago.

David Sacks: 57:33 Yeah. I mean, in a way that was what I think Meta’s original strategy was, was that Llama was going to be open, but then they actually they’ve sort of backed away from open a little bit, but— this is kind of an obvious strategy, right? is that if you want to catch up, you go open. Because by the way, you’re not going to make any meaningful revenue on closed anyway because you’re not close enough to the frontier, so why would anyone buy your product? But if you go open, you get the developer community on your side.

Chamath Palihapitiya: 58:04 And you get utilization. More people use it, which in AI gives you reinforcement learning, yeah. Sacks—

Brad Gerstner: 58:08 Well having spent some time in DC this week and in talking with both the White House and Treasury etc. on this topic, what I can tell you is while there may be some, you know, debates about regulation of US models, the one thing there’s absolute agreement on is doing everything to stay ahead of China. And, you know, and the president, all the way up to the president, very interested: how far are we ahead of China? What are the things we need to do to stay ahead of China? It is a unifying force in Washington. And the idea that we were going to kind of take our frontier labs off the field, off the playing field, while letting Chinese open source models run free, you know, and on top of that, distilling our models—I will tell you GLM 5.2 has watermarks from Mistral all over it, right? So we know they were distilling etc. And I think the US government’s going to take steps against this distillation, which they should do. So I think that, you know, China doing this in some ways, I don’t think it hurts the United States. The United States can spin up open source models. We’ve got Meta spinning one up. Obviously we’ve got the good work going on at Nvidia with their open source models. The labs, I’ve talked to a couple of the frontier labs about open source models. I say why aren’t you guys making open source models? They’re like there’s not a lot of demand for it. If there was a lot of demand for it, we would make it. And so I think the US is in a good position. I think this is probably more chess playing by China than actual threat because it would hurt them a lot more than it would hurt us.

Jason Calacanis: 59:16 Yeah. And then to just back up your point Sacks about when you’re behind, go open, and then once you catch up, start tightening things up. That’s exactly what they did with Android, right? Google released Android. At a certain point they were like, in order to use Android and the license, you have to include Google Search, you gotta use Google Drive, you gotta use Chrome, and they start tightening it up so it’s not really an open source project at this point.

David Sacks: 59:30 Which by the way, I think that’s exactly what Meta’s strategy is.

Brad Gerstner: 60:00 The absolute best thing that could happen for America in terms of winning the AI race against China is if China somehow sprouted their own doomer community.

Jason Calacanis: 60:11 Yes!

Brad Gerstner: 60:12 We need like a Chinese David over there.

David Sacks: 60:13 Yeah.

Jason Calacanis: 60:15 We gotta get their P-doom up. We gotta get their P-doom up.

Brad Gerstner: 60:17 Exactly. We need a lot more people over there freaking out about, you know, job loss or RSI, whatever.

Jason Calacanis: 60:24 Yeah.

Brad Gerstner: 60:25 That’d be the best thing that could ever happen to us is if they start cracking down on their labs in the same way that the doomers want to do over here.

Jason Calacanis: 60:30 Yeah.

David Sacks: 60:32 Brad, let me just say one comment. I mean look, I agree with you that from the President on down everyone wants to win the AI race and in fact that was, you know, in the big AI policy speech the President gave about one year ago, that was the whole thrust of the speech was declaring that we were in an AI race and America had to win it. I think the big risk is more, and this would not be at like the top level, you know, I think if the President could make every single decision it would be perfect. The issue is at a lower level in the bureaucracy do people somehow do things that are counterproductive? Maybe they think it’s going to help us in the race against China, but they end up doing something that’s ham-fisted. They just like ban something or without, you know, really truly understanding all the implications of it. So I think there’s no question that the administration wants to win the AI race, the President definitely does, and at the top levels they will all make smart decisions. The question is whether at lower levels of the bureaucracy you can get mistakes being made. And then you have the influence of Congress and whatever they want to do. Those guys, they’re more responsive I think in a way to the doomer community that’s creating a lot of political pressure right now.

Jason Calacanis: 61:43 Well, and the throttle, you know, paradoxically to all of this might not be the software, might not be the chips, it might be energy. Yeah Chamath, I mean when you look at your data center projects and the other ones that are going out there, if we need more tokens, if people need more inference, we have a gating factor in the United States which is energy.

Chamath Palihapitiya: 62:05 There’s an analysis that my team put together which I think is quite staggering. If you just look at the load growth that’s expected between now and 2050, we are about three entire Californias’ worth of energy short. And that’s just assuming regular consumption of devices and cars, fridges, televisions and computers. So we have a, we have a, we have an enormous problem in the United States with respect to electrons.

Jason Calacanis: 62:35 Yeah, and if you put Taiwan into the mix here where the chips are coming out of, I had a really big wake-up call, I think there was a Wall Street Journal article about this. The amount of LNG, which is what Taiwan runs on, is like they have two or three weeks of it. China decides to blockade Taiwan, they’re going to run out of energy immediately. So this is energy both in China, Taiwan, and the United States. It’s all dependent on that. We have to get…

Brad Gerstner: 63:00 Nuclear running, more solar running, more batteries, more of everything, and that is obviously a regulatory challenge here in the United States.

Jason Calacanis: 63:10 All right, let’s talk about your time in DC. Brad Gerstner went to DC, everybody, and huge congrats, Brad. You’ve been harping on about this, you know, accounts now called Trump accounts, the Invest America accounts, and tell us what happened in DC this week because I think you finally have the number one app in the world. Trump accounts is the number one app in the world. Congratulations and a bunch of announcements. So, what’s the contours of the announcement? And maybe you could take us behind the scenes. There it is, Trump accounts, the official app number one in top downloads. Your kids can invest in the future.

Brad Gerstner: 63:52 You know, there’s been, you know, a four-year mission in the making. Thanks to you guys, you were early supporters, backers. We talked about it on here and you know, founders are crazy and you guys probably looked at what I was working on and thought you’re nuts, you’re wasting your time on this. And so, you know, when it got signed into law last year, that’s a huge moment in a founder journey. That’s like getting your first round of funding, maybe. Like okay, we actually, this thing is going to happen. But on July 4th of this year, the app went live, right? So that means millions of accounts got created, the accounts got funded and to celebrate that and to really kind of take the next step forward. You know, we designed a joint bell ringing, first in history between the NYSE and Nasdaq, from the Oval Office, that was incredible. We had hundreds of CEOs there, kids there, families that were impacted. And the president really, you know, kind of laid out that this is much bigger than just a program to give a few people some accounts. This is really about making every child a capitalist. In fact, the president suggested that we’re going to auto-create accounts for all 50 million kids or upwards of 70 million kids under the age of 18. So he called on us to get the accounts opened faster, to perform more people, to have more impact to make sure no child is left behind.

David Sacks: 65:15 Brad, just slow down because a lot of people don’t even know what a Trump account is. Just explain what it is and then you should contrast it to like a 529 account and some of these other things.

Brad Gerstner: 65:25 Great. So, as you guys know, The idea was very simple. $1,000 for every child at birth that could compound for their life in a privately owned investment account. So you’re born, you get a social security number, and you get an investment account. And if you do that, and you start with $1,000 and somebody matches that, and you save 10 bucks a week, that’s $50,000 at age 18.

Jason Calacanis: 65:52 And that’s invested in the S&P 500?

Brad Gerstner: 65:57 S&P 500. So, when these accounts are created, all that Money goes into the S&P 500, there’s no cost, it’s a free account for the lifetime of the recipient. And that was packaged into the Invest America Act, which was passed into law a year ago as part of the reconciliation bill. So that’s what actually occurred on July 4th of this year. All those accounts were created for all of these kids. That’s the reason the Trump account app is number one in the App Store because parents started hearing about this and saying, ‘Whoa, I need to go download and get this set up for my child.’ We had over a million and a half accounts created in the first 24 hours after the launch of this. We had over a billion dollars of deposits. So I was contributing money into the accounts of my nieces, my nephews, my kids, friends’ kids. Every account app has a QR code, Jason. So somebody can just send you the code for your kid, you double, you know, Apple Pay on your phone, double click, and you send them 25 or 50 bucks. So that is kind of the, the most essential part of it, but we also had a bunch of announcements around philanthropy.

Chamath Palihapitiya: 67:08 And just to be clear, you can get access to that when you’re 18, 19, 20 years old and start putting it towards school, or you can roll it into your IRA, I guess your, your, your retirement account. Obviously, Michael and Susan Dell were the anchors here, over $6 billion, $250 for each of 25 million children, primarily lower- and middle-income kids.

Brad Gerstner: 67:25 SpaceX’s president, Gwynne Shotwell, she joined the party, put 350 million in her SpaceX shares and for children of lower-income communities.

Chamath Palihapitiya: 67:29 So with this, there’s a device or some way to do it so you can target specific communities by geo- or by, I guess, their net worth somehow?

Brad Gerstner: 67:34 No, it’s just zip code and age. Zip code and age.

Chamath Palihapitiya: 67:36 Zip code and age, okay. And then Micron put in 250 million, up to a thousand per employee. So that seems to be a real interesting way to do this, like you can do an employee, I’m sorry, an employer contribution. And Brad, Brad did it for all kids in Indiana, I think, right?

Brad Gerstner: 67:52 Correct. All kids under five.

Chamath Palihapitiya: 67:54 And Brad, this is a big number here. I mean, I—this is the big announcement. Brad, the guy who complains when we make him buy in for 10k after 10 p.m. at the poker game, and who, like, rage-quits the game when he loses $6,000, somehow Brad dropped $100 million. I mean, oh my god, let’s get a round of applause and a golf clap. Brad, this is—I mean, I’ve never heard of you doing any philanthropy. Like, sometimes you show up with a bottle of wine to the game, but this is a big number, this is a big decision for you, huh?

Brad Gerstner: 68:39 Well, I think this will become the largest direct philanthropic platform in the history of the country. We told the president we think we can raise $100 billion in the first 12 months. And so the scale of the philanthropy, the nature of the philanthropy, directly to America’s kids without a charitable middleman that’s directing who goes… It’s wide and and how it’s distributed. So you think about the people who now are, you know, worth 10 billion or 100 billion. How do they give that money away at scale, effectively? Now we have a platform they can do that with. It goes directly into the accounts. The money can’t be taken out until the kids are 18.

Chamath Palihapitiya: 69:19 There was a— there was a bunch of noise about how some people won’t do it because it’s called Trump accounts. And that some people said, you know, this is going to create this weird class divide by people who had TDS and refused to give their kids because I think your website or something said something like 13 million bucks by the time they’re 50. And, you know, I tweeted something to the effect: that is an irresponsible amount of money to not give a kid because you don’t like the fact that it’s called a Trump account. It’s— it’s patently insane.

David Sacks: 69:53 I mean, if you go on Blue Sky, which is like the open-source lib TDS social network, people are like, you know, just put one, ‘Y’all trust those Trump accounts? I sure as hell don’t.’ And so there’s a bunch of people. So speak to that for one second, Brad.

Brad Gerstner: 70:08 So I would say this, you know, it was the enabling legislation is the Invest America Act. Um, a lot of people, a lot of Democrats call ‘em Invest America accounts. They’re officially Trump accounts. And the facts on the ground are that parents aren’t listening to that noise. The parents who are signing up for this are across the income spectrum, across the economic spectrum, they’re across the political spectrum. They know and understand that their first responsibility is making sure their kids have a connection to the American dream, have savings for their life. But yes, they are called Trump accounts and I— I’ve read some of that blowback. But the President himself, let me just make this case very strong. There’s nobody who I’ve talked to about this over the last two years who cares more about every child getting an account than the President himself. In fact, that occupied a lot of— a lot of our conversation over lunch. He said, ‘How do we get more people auto-enrolled in this faster? I want every kid to have the shot to have this. I don’t want anybody being left out and left behind because their parents are too busy working two jobs, or because their parents may have an issue with it being called a Trump account.’ So the President is pushing us very hard, and the Treasury Secretary, to get more kids signed up fast.

Jason Calacanis: 71:30 And he told you, you need to Brad. He gave you instructions, he gave you an order. That he wants you to auto-create the accounts. This is a brilliant move. We know— we know the social security numbers of people who are under 18. He told you: get to work and automatically create the accounts. Are you going to do what the— President Trump has commanded you to do, Brad? Are you going to auto-create them, Brad, or are you going to disobey the president?

Brad Gerstner: 71:51 We— our intention is to get all 50 to 70 million accounts created over the course of the next 90 days using all of this data but— You know, listen, we got to work through Treasury, the White House, Social Security, etc. There’s definitely hurdles that we have to overcome.

Jason Calacanis: 72:07 And you also got to get through Elizabeth Warren and Bernie Sanders and Ro Khanna, who are going to try to stop you. Are they going to try to stop you from doing this? Are they giving you blowback because they don’t want to give Trump the win, which is totally retarded? But okay.

Brad Gerstner: 72:16 No, listen, I’ll give credit where credit is due. You know, Cory Booker’s come out in support of these, and Gavin Newsom, Governor Wes Moore, John Fetterman, Senator from Pennsylvania. So there are plenty of Democrats who are able to get over that hurdle. But you bring up a good point, and I said this on CNBC yesterday. On the one hand, you have Bernie and Mondaire, they want to take and tax all these corporations, they want to control all that money in Washington and decide who gets it, right? It’s a very dependent-on-Washington model. On the other side, you have the President and this administration and frankly, a lot of Democrats who are more in the orthodoxy closer to the center who say, ‘No, let’s set up a private account for every kid in America, let’s fund them, let’s not make them dependent, let’s make them independent of the government to build wealth on their own, financial literacy on their own, more likely to graduate from high school, start a business, buy a home.’ Those are two very different world views for America. And I think the antidote to more socialism is more capitalism. And as I told the President, this is more capitalism.

Chamath Palihapitiya: 73:27 Sacks, if this succeeds and Brad does as he’s been instructed by the President, we’re going to go from 50% of people owning equities in the country to as much as 70, maybe even 75% of the country having access and for the first time being part of equity nation. What’s your thoughts on this, Sacks?

David Sacks: 73:46 Look, I think that’s a great thing. And I think that this is a tremendous new philanthropic platform, and that’s really important, especially in this time of growing anger and backlash and populism against billionaires and people questioning whether the system is rigged and whether they can be successful in America, whether they will be able to be part of it. This is a really important antidote to that. But I almost think that the philanthropic aspect maybe has gotten almost too much attention because people are naturally attracted to the freebies. And the part that I think hasn’t gotten enough attention are all the comments I saw on CPA Twitter, you know, where all these accounts were talking about what an unbelievable, I guess you could say, estate planning strategy this is or—

Chamath Palihapitiya: 74:39 Tax advantaged.

David Sacks: 74:40 —like a wealth management technique, whatever you want to call it, like planning for the future. And there’s never been anything like this before. They were basically saying this is like in the top three. You know, there’s certain things that you just have to do. Like if your employer offers a matching 401k, you have to do it because otherwise it’s just you’re losing out on free money. And if you don’t do a health savings account or you don’t max out your Roth IRA. There’s just certain things you have to do because—

Chamath Palihapitiya: 75:00 because they’re so tax advantaged or you’re getting free money, right? And in this case, you’re getting both. There is the opportunity for, frankly, the free money for your kids, right? But also the tax advantage is huge. So let’s just go through this and Brad, correct me if I get any of this wrong. So you can donate up to $5,000 a year to your kid as long as they’re under 18. And it’s not just you. It’s any friends and family or others can contribute as well, which is new. And then they get tax-free compounding till they’re 18. And your employer can contribute up to $2,500 tax-free. So at a minimum, you should go to your employer and say sign up for this and if you have to, take $2,500 out of my salary and make it a donation to my kid’s Trump account because then that’s a huge tax savings, right? Neither side has to pay tax on it. So you know, like Brad said, this is basically like an IRA. You get tax-free compounding. Then when the kid turns 18, they can get access to it and they can do a rollover into an IRA or into a Roth IRA, which is even better because when the Roth IRA matures, you don’t pay tax on the money that gets distributed out of it, whereas with a traditional IRA, all the taxes get deferred until the end. The difference is that when you do a IRA to a Roth IRA conversion, you’re supposed to pay taxes at that point. And I saw one really clever CPA say that well the best way to do this is wait till your kid is actually not a dependent anymore, like so maybe they’re in college or they just graduated from college and they’re in like the zero percent tax bracket because they’re not making any money, and then do the conversion. And so you’ll be able to convert the Trump account very cheaply into a Roth IRA, and now they’re going to have two to three hundred thousand dollars potentially in that account that they can then do tax-free investing for the rest of their life. Or they could potentially start a company with that. Other things you can do with an IRA is you can use part of the money on a down payment for the first home purchase, or if you get into a health emergency, you can use the money for that. So there’s all these things you’re allowed to distribute money out of an IRA without incurring a penalty. But generally speaking, the point of an IRA is to save for retirement. And this is where I think it gets really amazing, is because if you start with two to three hundred thousand dollars at age 18, you’ll be at $10 million plus by age 60 if you just let it compound. I mean, there’s ranges—

Jason Calacanis: 77:30 A lot of nepo babies we’re creating here. We’re going to have a lot of rich kids with trust funds.

David Sacks: 77:33 Yeah.

Chamath Palihapitiya: 77:33 Yeah, so this is like… this is all you have to do to make sure that your kid is protected for retirement is if you and your family and your friends and your employer can just contribute to their Trump accounts.

Jason Calacanis: 77:46 Let’s go. Guys, that’s like way better than social security.

Chamath Palihapitiya: 77:49 Brad, I have an idea. I have to go sell some enterprise software so I have to leave, but I’m really proud of you. I think this is incredible. You should convince OpenAI and Anthropic— to give the equity of those companies if this is going to be as big as you say, 100 billion, 300 billion, zillion, trillion, put it into the accounts of every kid.

David Sacks: 78:12 Can you just explain how that works against the $5,000 limit?

Chamath Palihapitiya: 78:15 I gotta go. Love you guys. See ya later.

Jason Calacanis: 78:18 All right. Good luck on the sales call.

David Sacks: 78:19 Yeah.

Jason Calacanis: 78:20 Always be closing, Chamath. Always be Chamath closing. ABC.

David Sacks: 78:23 Chamath always be closing.

Brad Gerstner: 78:24 You know, again David, and Chamath, as we build out the platform at scale. So imagine now you have 50 million accounts that are opened. We do- I’ve said on CNBC, you know, I’ve obviously talked with Dario and Sam and Elon and others about making those donations. I don’t like this idea of shaking down our companies, taking their shares and then putting them in some government slush fund that perhaps Bernie or AOC or somebody’s going to control in the future. I’ve said it’s got to be voluntary. And number two, it should go into citizen accounts, right? Privately held in citizen accounts and compound for their life. And so you asked the question David, how does it happen given the limits that you have, you know, the $5,000 per child? That’s why you have to have so- you know, 50 million accounts open, right? Because then you can take dollars in at scale. But we can also set up a pooled account David, where it can be distributed over time. So you distribute it to all the kids subject to the limits that you have today and then any remainder you can distribute to the three and a half million kids that are going to be born next year or the three and a half million kids born the year after that or the three and a half million kids born after that. We are on a trajectory now that we’re going to have over 100 million of these accounts set up over the next decade, okay? So we could have 70 million today and then you’re going to add 3.7 million a year. So you’re going to be at 100 million private individual accounts that are compounding for people’s lives that anybody can donate money into. That the people themselves, I think one of the things that gets lost is moms and dads or somebody working their summer job put in 10 bucks, put in 100 bucks, put in- you know, into these accounts. They get to see it on their phone. You know, this is when we started this, my sons and I designed this app and it’s basically what we ended up with. It’s a- you know, Joe and T-

David Sacks: 79:11 I see.

Brad Gerstner: 79:12 Vlad and- and Joe have implemented a more elegant version of this, but every kid owns a little bit of Nvidia, a little bit of Microsoft, a little bit of Apple. Imagine opening up that for a child David in middle school or high school to the money page. And now you’re getting excited that you’re seeing oh man, I’m in the game. I have ownership. And while you reference what it could be for families who can contribute $5,000, obviously Michael Dell and I and Gwen and everybody else, we’re focused on the 50% of Americans who feel left out and left behind who would otherwise have zero. If you do the math on this over the course of the next 15 years, you could have somewhere-

Jason Calacanis: 80:21 Shoutout to Vlad at Robinhood helped you with it, yeah?

Brad Gerstner: 80:24 Vlad and- and Joe have implemented a more elegant version of this, but every kid owns a little bit of Nvidia, a little bit of Microsoft, a little bit of Apple. Imagine opening up that for a child David in middle school or high school to the money page. And now you’re getting excited that you’re seeing oh man, I’m in the game. I have ownership. And while you reference what it could be for families who can contribute $5,000, obviously Michael Dell and I and Gwen and everybody else we’re focused on the 50% of Americans who feel left out and left behind who would otherwise have zero. If you do the math on this over the course of the next 15 years you could have somewhere-

David Sacks: 81:00 between two and four trillion dollars added to the accounts of families and kids who have otherwise had zero.

Jason Calacanis: 81:08 I just on-

Brad Gerstner: 81:09 Go ahead, go ahead.

Jason Calacanis: 81:10 We talk a lot about- the philanthropy piece is basically, the way the philanthropic aspect works is that other people, philanthropists, can contribute towards that $5,000 per kid, right?

David Sacks: 81:23 Is that so?

Jason Calacanis: 81:24 So, you know, when Gwynne Shotwell contributes two million shares of SpaceX to two million kids, each kid’s getting a share of stock that’s worth 150 bucks. 150 bucks. So now that’s counting against their $5,000 limit. But so that’s what makes it compelling is, okay, look, every family that can afford to do the 5,000 should because it’s just so compelling from a tax and savings standpoint, but then even for families who can’t, they’re going to be beneficiaries of philanthropists who just want to give this type of direct giving. And it seems to me this is so much more efficient and so much better than the whole NGO industrial complex where-

Brad Gerstner: 82:05 Absolutely, where they take 40% for their offices and their salaries. Yeah, they’re just grifting.

Jason Calacanis: 82:12 One of the numbers I saw was kind of amazing is again, it just goes back to the power of compounding, is that if a Trump account had been maxed out and you have the standard market rate of return that we’ve had for say the past 30 years, then by age 28, that kid will be a millionaire.

Brad Gerstner: 82:27 Incredible. That’s right. All the numbers you hear me quote, the $50,000 and the $200,000, it doesn’t assume maxing. You know, that just assumes people are adding $50 a month because I’ve been focused as Michael and others have really on the families who don’t have the capacity to save today. We’re getting all of them into the game. And the President directed us, he said, listen, we have 529 accounts that already help the top 10%. That’s not who we’re focused on. This is about the Main Street agenda. This is about all the families that he ran for, to feel left out and left behind, and we’re reconnecting them to the American dream through universal ownership. They all have their own account. They all have a private account on their phone. Um, it’s a game changer for the country. It’s the largest change to our social contract since 1935 and Social Security. And importantly, I think it couldn’t come at a better time. You know, we have this fight for the soul-

David Sacks: 83:22 Well also just every employer should be signed up to be, you know, an employer that can contribute because again, you could take that $2,500 and, hopefully, look, it’s additive and it’s not just a substitute, but even if it’s just a substitute and JCal, your employer takes 2,500 bucks out of your salary and puts it in your kid’s Trump account, then you’re reducing your taxable income. So it’s a no-brainer for every-

Brad Gerstner: 83:47 If you’re a profitable company, your employees are going to love you. Yeah.

David Sacks: 83:51 Yeah, but I think every employee is going to want to and every employer should do it because it’s a tax savings for both, right? So like this-

Jason Calacanis: 84:00 The tax savings here is huge.

Chamath Palihapitiya: 84:02 Yeah, so just off of the mechanics of it, I just want to maybe level up here for a second.

Jason Calacanis: 84:06 Yeah.

Chamath Palihapitiya: 84:07 I have been often critical, I call balls and strikes, and I can tell you in detail the things that I have a problem with this administration and their actions they’ve done and I have done it here on the pod. I want to address the people who are negging this, and specifically negging it because it has the name Trump accounts on which I told you at the poker game, call them Trump accounts. I don’t know if that was like an obvious thing or I was the person who told you to do it, I’m not taking any credit here, but I remember that conversation where I was like, ‘Just call them Trump accounts.’ Like, if whatever criticism you have of Trump, however valid you may feel it is, this has nothing to do with Donald Trump and how you feel about him. Put your TDS on the side, put your valid criticisms on the side. In this country, we have a K-shaped recovery going on, we have immense tension between the haves and the have-nots, we to the point at which people actually believe that socialism and communism is a better operating system than the best operating system humanity’s ever created, which is called democracy plus capitalism, right? And kids love capitalism, they love building businesses. But we are in an existential moment right now. If these kids believe—young kids, and there’s a couple of generations of them right now who do not believe in America anymore—while we’re sitting here on the 250th anniversary of this amazing experiment known as America, this is the most American thing you can do. So put aside your TDS, put aside your valid criticisms and embrace this. And give the flowers to Brad, to the people donating, like Michael and Susan Dell and Gwen. This is beautiful. This is the most beautiful gift I’ve ever seen to a country and this could be something that’s a unifying principle that brings us back together as a country. That everybody gets to participate in capitalism. And this is the number one way to do it, which is to let kids on their smartphone instead of saying, ‘You know what? Bernie’s right, I should get a free bus ride, I should get free pizza and we should take Ken Griffin’s and seize his, you know, penthouse as a pied-à-terre.’ Fuck all that. You know? And we have CEOs getting shot and their homes firebombed. Well you know what? If you’re one of those CEOs, you’ve done incredibly well. There was something called the Giving Pledge where they pushed, you know, affluent people at the TED conference for decades, Bill Gates and everybody, Warren Buffett, everybody was pushing for this. This is like the perfect version of the Giving Pledge because you’re not just saying I’m giving away my wealth by the time I die. You’re very strategically saying every single person in America gets to be part of the best part of America, which is entrepreneurship and everybody will be part of the equity nation. And my final point is, one of the happiest countries in the world is Australia. If you’ve ever gone to Australia, everybody— People feel safe. And we have a large number of people in this country who do not feel safe. And the reason they don’t feel safe is because they don’t think their kids are safe. To the point at which people do not want to have kids in this country because they feel the system is just too hard. This could change that. If people feel, hey, kids have a shot, and I don’t have to worry about my kids. I worry about my kids and I’m affluent. I can’t imagine being a single parent and what anxiety you must have as a single mother or father and you’re making minimum wage and you’re behind the eight ball for your entire fucking life. And now your kids are set? That’s all people want. That’s the only thing a parent wants is to make sure their kids have a better future. That was the promise of this country, and somehow it went off the rails for the last two generations. This puts it back on the rails. This is superannuation funds in Australia. In Australia, people are extremely happy. The reason they’re happy is they’re forced to put 14k a year or whatever it is, 12 or 14% I think of their income into essentially a 401k that they get to direct to a certain extent. It’s forced saving. This does the same thing at a very basic level. This could replace Social Security, this replaces the Giving Pledge, and I just want to say, Brad… You know, a lot of my friends got involved in politics, some of them on this very program, a lot of people, it’s very divisive. You threaded the needle here. It was a masterclass in balancing these two crazy parties and the divisiveness in this country. I just want to give you, as your friend, your flowers. This is just absolutely outstanding what you did. And you have been incredibly humble in your approach to this. This would not have happened without you, Brad. This is your legacy. Of everything you’ve done in your life, lots of success, and I’ve seen it up close and personal, this is a million times X everything you’ve done in your whole fucking life. You’ll be remembered for this. This is architectural.

David Sacks: 88:58 It’s gonna be as big as Social Security. I mean, it’s a new platform. It’s an entire new platform because it’s not— because it’s philanthropy, but it’s also retirement savings, I mean, right?

Jason Calacanis: 89:09 And everything in between.

Chamath Palihapitiya: 89:11 And this is not static. We— this is dynamic. We can add to this, there could be other features.

Jason Calacanis: 89:14 I’m seeing a lot of people in the comments say why stop at age 18? Why can’t you— I mean, you have the account rollover into a IRA or Roth IRA after age 18, but why can’t you keep it going? And then people can keep that $5,000 contribution going, and, you know, it doesn’t mean we take away retirement benefits that are owed to current Social Security recipients…

Chamath Palihapitiya: 89:31 Exactly.

Jason Calacanis: 89:33 But at a certain point, you could just say that, hey, the next generation’s gonna be on this platform rather than the old one, and it would be a lot better.

Chamath Palihapitiya: 89:40 But sunset it.

Jason Calacanis: 89:41 A lot more efficient than the government running it, right Sacks?

David Sacks: 89:43 Yeah. We don’t want the government running this.

Jason Calacanis: 89:47 Let me ask a question about this, Brad, because I do see one thing that people say, which is what if your kid turns 18…

David Sacks: 90:00 18 and then they just want to blow the money. You know. How do you trust? How do you trust that they’re going to put it to good use? You know, as opposed to I don’t know, you know, YOLO. YOLO on whatever.

Brad Gerstner: 90:14 As you know, these things are always political trade-offs and balances, and I wanted them to have to compound until they were 30, right? Because I figured by 30 you were a little bit more, you had… mature, mentally developed on on financial issues. But you know, the argument ultimately became, you’re old enough to vote, you’re old enough to fight a war, if by 18 we don’t allow you to have control of your own money. So that’s where a political consensus was built, David, but the other thing remember is they can only take up to 25% out to buy a home, start a business, go to college. The rest rolls into an IRA and there are built-in penalties for early withdrawal on an IRA, so there are disincentives for people to pull out. But let’s be clear, we have to do a much better job in our education, our public education system, leveraging this as the platform. You know, if a kid doesn’t have any money it’s hard to get excited about learning about money, but if a kid’s in the game and has 12,000 bucks in the seventh grade, now you got my attention. I own a little bit of Nike, I own a little bit of Apple, I own a little bit of Nvidia. Let’s talk about that. How did it get there? How did it compound? If I added 50 bucks a month, what does it turn into? All of these things will now be present on every child’s phone in America. 37 states require financial literacy, every state should build this into the curriculum. We’re working with a lot of states on that. You know, we haven’t talked about that. We have about 25 states who are going to add money into the accounts of the kids in their state.

Jason Calacanis: 91:30 The states are going to do it.

Brad Gerstner: 91:31 States, state action. Oklahoma, West Virginia, Indiana, etc. So that’s also sweeping the country where the states are looking at programs they already have where they’re spending money for kids that are ineffective.

Chamath Palihapitiya: 91:54 Take out the middle man, yeah.

Brad Gerstner: 91:55 And they’re saying instead of continuing to spend money on these things that aren’t working, why not just block grant the money directly to the kids? Because we know if you give the kids the money, more likely to graduate, more likely to, you know, to buy a home, start a business, etc. So I think that as I said in the Oval, this is day one, and I am fully committed to the next decade, as is my partner in crime on this Michael and Susan Dell. I appreciate your guys’s comments on the on the legacy of this. It has been the most profound work and kind of honor of my life and standing in the Oval Office with my two sons who are really my two co-founders on this. We drafted, you know, we made the sketch of this at our kitchen table in the at the in the fall of 2020. That’s where the conversation started. Lincoln’s been with me in every single meeting with every congressman, senator, president, former president, etc. The president shouted him out, you know… Again when we were there, that journey as a father with my kids has been like, the payback has been really extraordinary.

Jason Calacanis: 93:07 The other thing that’s notable here and again, like you got to call balls and strikes and give credit where credit is due. Joe Gebbia joined this administration. A lot of people in the tech industry were like, oh, you know, oh, you joined the Trump administration, whatever, you know, okay, there’s some criticism. He’s an incredible world-class designer. I was talking to producer Nick, our producer here, also happens to show the same last name as me. He signed up for this, right? He’s doing well, but, you know, his wife signed up for it. The software is fantastic. Let’s pause for a second. The American government has made exceptional software and this all got done in Trump’s first 18 months. Immense credit for this. This is like of all the, you know, challenges this presidency has had and the Iran war and other issues, we made great software. The American government, because of Joe Gebbia, makes kick-ass software. Like, just also major shout-out to him and you know, he could be doing whatever he wants, he’s, you know, he’s done incredibly well as a co-founder of Airbnb, and he’s doing this. Like, that’s a real patriotic thing to do. I’m just over the moon with this. I think it’s fantastic.

Brad Gerstner: 94:13 I would say the dream team. You had, like Michael Dell, myself, Vlad Tenev, Joe Gebbia, the Treasury Secretary, Luke Pettet at the Treasury talking basically every day for the last year and our objective was not we want to build the best thing that the government’s ever launched. We wanted to build one of the best consumer products period that’s ever been launched.

Jason Calacanis: 94:36 Mission accomplished.

Brad Gerstner: 94:38 Mission accomplished. And there’s any Silicon Valley consumer company would be thrilled with the numbers that we’re seeing, the ratings that we’re seeing, the engagement that we’re seeing. So, you know, it’s been fun doing it with that incredible team and you know, everybody’s in this for the right mission as well. And so I agree with you, Jason, it’s rare that government recruits or embraces, uh, you know, that, that mission and I think the tent’s getting a heck of a lot bigger. It is bipartisan. It’s bipartisan in support. You know, this is important what Governor Moore from Maryland said yesterday. He said Republicans and Democrats have been trying to do something that looks like this, feels like this for 40 years. This guy got it done, give him credit. This is great for America, great for our kids. And especially on the 250th anniversary, you know, you’re standing in the Oval Office looking at the original Declaration of Independence and you realize what people laid down for this experiment. And then I hear, I read this chatter in my feeds about Mandami and others literally wanting to set this great experiment on fire.

Jason Calacanis: 95:46 Yeah.

Brad Gerstner: 95:47 Right, wanting to burn the place down because they, they think they have a better formula. No, the answer is evolving and doubling down on the formula that has worked for 250 years and rather than… Making all these kids socialist, we get them all into the game of capitalism. They become owners, owners and shareholders in America. So, that’s what we got to accomplish and, uh, appreciate the chance to talk about this.

Jason Calacanis: 96:05 Let’s go. Let’s go. Let’s go.

Chamath Palihapitiya: 96:12 Listen, incredible Brad, and, um, you know, just amazing to watch you do it. And if you, if you, if you really think about it, the tech industry needs to win and capitalists and creators and the makers, the people who build stuff, this is our chance to say here’s an example of something that helps the people at the bottom, right? And I talked a bunch about, um, the minimum wage here, we have seven dollar minimum wage, like, we need to address that as well. These are the things that if we address what people are scared about, what people who are, who don’t have what we all have here, uh, if we have empathy for those people and we actually care about them and we give them a path to believe in the American dream, they will take that path.

Jason Calacanis: 96:59 100%. We have to give them a better path than these socialist lunatics. And this is so much of a better path. Let’s do it again. Let’s do another one of these things. Let’s keep growing this spirit of getting everybody in the country to have equity in these great companies. That’s why the entire world is trying to replicate what we do here. Every single country I go to wants to recreate Silicon Valley.

Brad Gerstner: 97:25 Well, we’re not, we’re not stopping here and, um, David, to your point, we’re, you know, whether it’s the AI companies, or frankly whether it’s the Intel shares, or whether it’s the TikTok fee, I now have a place where all of those wins achieved by this administration can go, they ought to go directly to all the citizens, you know, the country into their accounts and compound for a lifetime. And then as far as people over the age of 18, there’s certainly a lot of talk about that as well. Um, not again, as Social Security is a sacred promise by both parties, nobody’s going to change that. But there’s a huge opportunity to have a supplement here. Why shouldn’t people, you know, between 20 and 30 or 20 and 40 also have a Trump account that they can begin on a supplemental basis adding dollars that they own and control. Remember, the big difference between this and Social Security, Social Security takes 12.4% of my W2 income and puts it into something akin to the black hole of government. I don’t own it, I don’t control it. If I ask a room of 3,000 people how much they’ve contributed, they have no idea. If I die, I don’t have title, it doesn’t pass to my heirs, etc. So it’s really not mine. But if we created a supplemental IRA, it doesn’t even require new legislation I don’t think, just expanding the age, these are IRAs after the age of 18, right? Then people could start building supplemental wealth and participate in these gains. And so there’s a lot of conversations going on about that as well and so we’re not done, but, uh, it was a hell of a milestone on the 250th birthday of America to ring the bell in the Oval. And to watch all these kids get their accounts lit up, it was pretty special.

Chamath Palihapitiya: 99:04 What I think is really cool about the Trump accounts, I think it’s an amazing philanthropic platform, but in addition to that, it’s an amazing platform for middle-class family planning. That’s the point I’m trying to make, is all the CPAs that I’m seeing talking about this are saying this is like one of the greatest things ever, and if I could only tell my clients to do one thing, this would be the one thing.

Brad Gerstner: 99:27 Finally something for the middle class, right? This is what people have been asking for. Hey, let’s get something for the middle class, let’s get something for the, you know…

Chamath Palihapitiya: 99:33 Yeah, and I think the market gap, correct me if I’m wrong, but basically the market gap that was created here is that you can’t get an IRA, which is basically a tax-advantaged savings account, until you have your first job, right? and get earnings. Which would be for most people at age 22 plus. So for that first 22 years, your kids can’t have an IRA, right?

Brad Gerstner: 99:51 Correct.

Chamath Palihapitiya: 99:52 And you’re effectively giving every child at birth a covert IRA, you know, it’s a Trump account that has certain rules, it’s actually better than an IRA.

Brad Gerstner: 100:03 Better than an IRA. It’s better than an IRA because with an IRA, your employer can’t contribute 2,500 bucks tax-free, can they? I mean, I don’t think so. And philanthropists can’t contribute to it, and moms and dads. But the most important thing, you know, like I say this… You know, Buffett’s like the secret is to find a really small snowball and a really long hill. Okay? But the size of the hill in this country, we’ve cut off the first third of the hill forever. Nobody saves anything until they’re 25. Okay?

Chamath Palihapitiya: 100:30 If that.

Brad Gerstner: 100:31 The easiest compounding in the world, the easiest compounding in the world is between zero and 25.

Jason Calacanis: 100:38 Alright, listen, this has been amazing.

Chamath Palihapitiya: 100:40 Yeah, because they’re dependents, frankly. They’re still on mom and dad’s stuff.

David Sacks: 100:44 Exactly, so you pick up the first third of life in compounding. And so it’s a, it’s really remarkable.

Jason Calacanis: 100:54 Let’s go. Uh, I mean listen, this is an amazing job. Alright, we’ll see you all next time. Bye! I’m going all in.