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This week we'll be recapping WWDC.
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Apple released uh Liquid Glass.
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It was a little bit controversial.
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We talked to people that were very optimistic about the new design direction.
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People settled down and they're I think they eventually settled down thinking to themselves, all right, this thing might be pretty nice looking.
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It's probably we'll get pretty good and we'll all move on and it'll probably transition well to VR and other platforms. Yeah.
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It also it also tied uh really well to Apple's kind of retreat.
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Uh Ben Thompson wrote about this.
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Uh Apple is uh turning over the reigns to developers to develop some new AI applications.
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So we're very optimistic about that.
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Um we also had uh we had our intern Tyler Cosgrove.
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He's making Legos right now, but on Monday we had him assemble the first American iPhone from scratch.
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Uh and by scratch I mean seven pieces that he had to kind of glue together. But he did it.
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It was way more than some credit.
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It was way more than seven, maybe 12.
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Um, but it was fantastic.
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Um, we have uh we had Andrew Huberman on the show. That was fantastic.
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We got to talk to him about what's going on with NIH budgeting.
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The proposal is that research funding will drop by 40%.
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We tried to dig into what is what what what pieces of the research budget should we keep um how what is the dynamic between uh the different types of research that are funded?
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and he really broke it down for us on the back of his 4-hour interview with uh Dr.
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Jay Badacharia who is the current NIH director.
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Uh that was a very fun interview.
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Uh we also went to Y Combinator demo day 2025.
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We were live from San Francisco.
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We talked to dozens of teams, dozens of founders.
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We talked to high school dropouts, college dropouts, tons of AI agents.
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About 90% of the new YC class is AI driven.
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Um but we had a bunch of interesting conversations.
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We also talked to some venture capitalists there and we shot a lot of all the hard tech founders.
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We did not see many of them.
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There were a few but we uh but we mostly AI deep dive. They were among us.
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Uh and then and then we also discussed the meta and scale acquisition or investment.
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Uh Meta is investing uh something like 15 billion dollars.
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The deal actually just closed today.
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And so we had commentary from a number of uh founders and and uh pundits and uh journalists and also venture capitalists talking about the nature of that deal and how it came together.
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And so uh we hope you enjoy this year this week's recap and uh it gives you it gives you a good overview.
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The timeline's reaction to WWDC liquid glass whatever says Juan.
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The most important thing in iOS 26 text is finally left aligned.
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You know, I saw people posting about this.
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You said this about like the center alignment, left alignment thing. I hadn't noticed it.
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It looks a lot better left aligned. I don't know why.
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I didn't know what I was missing. Huge improvement. We'll take it. Uh yeah.
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So now text in iOS notifications left aligned. Huge news.
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Also, all the different version numbers are now uh they're doing car models. So it's iOS 26.
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They jump forward a bunch.
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I believe we were on like 18 before, so they just skipped a bunch.
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I can't wait to be in the thousands. We're in 2025.
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It's the 20 and it's the iOS 26 across everything. So, unification there.
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Uh it's happened in in in Android a while ago, but now it's uh it's come to iOS. Um love it.
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Uh again, a lot of people with negative reactions.
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We're going to take you through some of the positive reactions.
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Uh, Steve Jobs would have cornered you in a dark alley and beat you to with a metal pipe if you suggested something like this. That's what Kitsy says.
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This was yesterday's reaction.
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Today, some of the designers are coming out and saying, "Hey, let them cook."
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There's there's a silver lining here.
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There's certain there's certain examples that are really, really gnarly.
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So, there's that one super viral one that's like really hard to see and then people put it next to the Oppenheimer with the the blast.
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I think that's a fake photo.
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I think they adjusted the brightness on that.
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I don't think that's I think No, not a deep fake.
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I don't think there's any AI involved.
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I just think that that someone went in there and was like, "Let me turn up the brightness for for effect."
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Uh, and I think that that's fake.
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And I think that also again, this is this is a beta.
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This is something that they're going to be iterating on before it goes out to the masses.
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It's honestly a bit weird for trillion dollar company to launch a beta instead of they should just say, "No, this is an alpha." Yes.
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But but but it makes sense in terms of like like if you are a developer and there's a and there's a significant change to the user interface.
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You want to bake that into your app so that you don't look out of place on day one when everyone updates.
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So you got to get the beta into the hands of the developers so that the Airbnb app and the public. com app and the ramp.
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com app save time and money. Time is money. Save both.
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Easy to use corporate cards, bill payment, accounting, and a whole lot more all in one place. Go to ramp. com.
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But yes, if you're designing an app, you want it to feel somewhat seamless with the rest of the UI.
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And if there's UI changes, you got to you got to test drive those beforehand.
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You got to make sure that those are right.
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So, the big question, should we have our intern Tyler Cosgrove over there install iOS 26 beta?
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You know, it's potentially Hey, he's on the green screen. Something happened.
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He's on the green screen.
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Uh, should we make Tyler install? Oh, there. There we go.
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that that's where he is really like I'm glad that you did the false background for a minute and make it look like a green screen instead of showing the real Yeah, we wanted him to be more approachable.
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Yes, we just put a green screen behind him as a fake background.
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Yeah, but uh but but anyways, back to you. Is that uh Mkos?
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Where where are you right now?
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Oh, this just my my pool in my backyard. Okay, cool.
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This is where you take sometimes, you know, sometimes fellow some of my friends will will go on podcasts, they'll just use my backyard. Yeah. Yeah. Oh, yeah. That that's what it is.
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Um, anyways, I would love uh I would love to uh watch you risk it all and install the update.
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So, why don't you Are you willing to do it? I'll do it. Yeah. Do it for the team.
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He's taking one for the iOS 26 beta.
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It could go terribly wrong because the bugs, you know, that the the thing with the beta is that you can't go back.
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You can't you can't roll back to the non beta. Yeah.
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And so you could be living with with a with minor grading bugs for like months until they fix it.
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Now they fix it pretty quickly.
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I've done it like once or twice gone on the beta cuz I'm so excited.
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I want to try the new thing.
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This is basically intern hazing. It is hazing. It's the modern era. It's the WWC versioning.
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Set up the All right, Tyler. Well, you are brave.
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Okay, good luck with that.
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And uh get Why don't you get started and we'll get started and then give us uh give us the review.
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We'll be checking in with you to see how the iOS 26 installation process goes and um and and any any problems you run into there.
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Um we also have some other I can't I can't read anything on my phone anymore.
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We also have some we also have some big show updates.
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Not only do you have intern cam now, we also have the breaking news camera. Let's go to it now.
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This is crazy too because I can see you see this.
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So this is the breaking news camera.
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When we have breaking news, we cut to the printer. Cut and it prints out.
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You can read the breaking news.
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Okay, you want to read that to us? Hold it up.
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We have some breaking news.
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If we can switch cameras. What is it? So breaking news.
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Snap to launch smaller, lighter augmented reality specs smart glasses in 2026.
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I'm excited for this from CNBC. Thank you to CNBC.
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Uh, Snap on Tuesday announced its plans to release a sixth generation of its augmented reality glasses in 2026 as competition in the smart glasses market continues to heat up.
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The maker of Snapchat said that its next generation glasses will be called Specs, breaking with the company's spectacles branding that it used for previous Drop the ales. Drop the ales. Just do Specs. It's cleaner.
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Well, they they dropped the T, too.
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So, it's technically Drop the tackles. Drop the specs. Drop the tackles. just specs.
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Uh larger tech rivals like Meta Apple and Alphabet are investing heavily into cutting edge headworn devices. Yep.
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Uh so the last computer will be on your face.
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So the most recent the most recent edition of Spectacles were released in September 2024 to developers only.
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That addition of the glasses was available under a leasing model that that required users to commit to paying 99 a month for a full year if you wanted to develop for them.
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So that's $1,200 guaranteed.
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Um, and then if you want to keep it more, it's probably continues to be $100. So it'll be interesting.
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It'll run Snap OS operating system, which they developed inhouse.
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Um, Snap said the developers will be able to incorporate Google's Gemini AI models into programs that they developed for smart glasses.
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So some sort of Yeah, exactly.
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So you could see some where it's like, okay, you're going to get the glasses, you're going to build out, Google says, hey, that's where you're going with it. Okay. I don't I don't know.
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I I I think very unlikely but um but still you know well Snap's market cap exactly what uh Meta just paid for uh a 50% stake in Scale AI 14 billion. Anything's possible. Spicy. So Snap's at 14 billion.
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Um the the stock hasn't really moved over the past 5 days on this news.
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Uh it's clearly Snap, you know, uh staying in the game with the WWC news, getting something getting a story out there that hey, we're still in the game.
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We're moving and we're doing cool stuff and and I'm excited for this.
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I think we need more uh more innovation here, more people testing different stuff.
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And it's clear that with a new format, with a new uh paradigm of like what the product is, there's a whole bunch of interesting directions you can go.
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Whether there's an augmented reality display, whether it's voice only, whether it's glasses, prescription, glasses, whole bunch of different things.
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Social media goats, both of them are just not the idea of not being a part of the next platform shift is just an idea. Yeah.
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They don't It's a world they don't want to live in, right?
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It's they are, you know, both both um Zuck and and Evan are going to be, you know, competing. Yeah. And so fun.
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Uh could be interesting from a developer angle.
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Obviously, um you know, nowhere near the budget of Reality Labs for what Zuck is working on with Meta, but constraints. They can be good, John. I agree. I agree. They can be good. I'm optimistic.
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I certainly want to try it.
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Um anyway, let's go back to Liquid Glass.
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Ben Thompson gave a whole uh posted a massive article on strategy called Apple's retreat.
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Apple retreats something like that.
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You should give it a read.
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You should subscribe to strategy obviously.
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Um Apple's retreat to its core competency.
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The headline feature of WWDC this year.
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This is from the latest strategy article.
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Was liquid glass a new unified design language that stretches across its operating systems.
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I will reserve judgment on liquid's glass aesthetics and usability. Gruber likes it.
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I am journalist on the horizon.
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But I am not one to install developer betas on my devices.
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But Tyler will but Tyler will.
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That's why we have Tyler because I agree with Ben Thompson. It's extremely risky.
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But he's willing to do it.
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Taking one for the team over there.
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I mean the whole the whole TBPN army.
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He's he's really saying I will I will sacrifice my sword developer beta. The developer beta. It's okay.
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If it goes really south, we'll get we'll get a new phone.
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It's always always a possibility, but good luck.
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Hopefully you make it through.
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Tyler will be checking on you throughout the show.
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Um, so uh first let's read Gruber's uh Gruber's uh overview of his reaction to Liquid Glass because we saw Timeline hated it.
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They said Steve Jobs would hate it. Let's go to John Derek.
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I think part of it is is it was popular to hate on it. Totally.
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And because everyone loves when someone's on a downswing, they love just piling on downwards.
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And when everyone's on upswing, they love just piling on upwards. It's not No.
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And I think I think it's also normal if a if a widely used consumer product does any type of meaningful upgrade.
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I mean, the default is that typically it's bad.
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Meta or Facebook back in the day launching the feed.
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They're like, I want to go back to just randomly looking at your profile.
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I don't want to see random all everything, every change is always met with some sort of push back and then years later, uh, people wind up loving it.
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I feel like this is something that could grow on me.
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I'm I'm reserving until we've fully beta tested it on our intern.
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If he gives it the thumbs up, I'm gonna install it. Uh let's go to John.
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I mean, it's kind of it's it's it's kind of fun.
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It's it's daring and brave and bold to do something like that.
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And uh you know, just because Tyler's doing it, you know, doesn't mean you shouldn't, you know, this this type of bravery that Tyler's exhibiting today, I think it might be one day like an exhibit in the computer history museum. Totally.
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bravery he's he's exhibiting today will be in the history.
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They might even name the next operating system. Cosgrove. Cosgrove. Mac OS 27.
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Mac OS Cosgrove after Taho.
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It's got an amazing ring to it.
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It's got a nice ring to it and it just loads with this image.
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Like that's the default wallpaper for the daring technologist who installed the beta on day.
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Did you know what everybody said? You're crazy. You're crazy. It's impossible.
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You should never do that. But he did it. He took the leap. He took the leap.
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He lives to tell the tale.
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So first, this is going back to Ben Thompson's.
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These first handcrafted UI overhauls are the polar opposite of a probabilistic world of generative AI.
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One uh is about deep consideration and iteration resulting in a finished product.
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The other is about in the- moment token prediction resulting in an output that is ephemeral and disposable.
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Both are important and creative.
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But the downsides of that creativity, unfamiliarity and edge cases versus hallucination and false confidence are themselves diametrically opposed.
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Apple's historical strengths have always been rooting and designing for finality.
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In my first year of strategy, I did a SWAT analysis.
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Let's give it up for SWAT analysis.
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The big tech companies and said about Apple and and I I think we've talked about this a bunch on the show of like generative AI is inherently imperfect.
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Apple is about pixel perfection.
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Uh so uh Apple's product development process is wonderful for developing finished products, but that same process doesn't work nearly as well when it comes to building cloud services.
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Cloud services are never done.
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They are best developed by starting with an MVP and then iterating based on usage.
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This is precisely opposite of what it takes to design a piece of hardware and it's a big reason why Apple struggles so much with cloud services and why other services companies struggle with products.
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The canonical example of this of course was when mobile mi was the mobile mi launch which was de delivered fully formed in which when faced with real world usage crashed and burned.
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Apple's latest offerings are better but still suffer from too much internal development time per release.
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This is a hard problem to fix because it touches the core of what makes Apple.
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I think it matters I think it matters whether or not liquid glass is good because it will be a testament about the state of Apple strengths.
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Uh the point of this article, however, is that WWC was a retreat to those strengths, which is good. This is a bull case.
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They're not getting over their skis and and they will eventually I mean, if they're to win, probably defer to the other companies and play to their strengths and and and let let the other companies win in the places where they are.
15:17
Tim just calling up Sam and saying, "Sam, I'm going to need 50 billion for the Siri, it's yours. 50 billion billion." 50 billion.
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You know, if you and Mossa can pull together the capital, we can make it happen.
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I mean, I I keep thinking about Siri and I I I keep running through like what would it have taken to to just to just replace Siri with a Frontier level, you know, simple response model, not even the reasoning stuff, just GPT40 level, you know, you ask it a question and it and it and it and it reads you like a basic response and not lose the other Siri functionality.
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And I think that it need they need to kind of flip the paradigm because right now Siri is only it's basically all tool use. It's basically a router.
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So it takes in uh you know you ask Siri uh what what's the weather going to be like tomorrow and it knows that you just and all it's doing is picking out keywords weather and it goes to the weather API internally and then tomorrow and it feeds that in and then it delivers that back.
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like like uh chat GBT kind of flips that around by having the default interaction be much more generalizable and it can return kind of any result.
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It can return a paragraph, it can tell you about a historical event or just talk to you and then if it needs to use a tool like web search to get you the weather for tomorrow, then it goes and calls that.
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And so you need to kind of flip that around.
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around. I was wondering about like like why hasn't uh like what what if if you if you rolled back the clock and you were like okay Apple just wants to be in the game what would it take to launch a v2 of Siri and I was like well okay if they if they try to partner with someone under the hood without like they don't
17:06
even have the option of just saying we are going to partner with Anthropic under the hood and it's going to be and it's going to be basically white labelled and no one will ever find out because there are just too many leakers like it will find out immediately and so Mark German will have the story immediately and everyone will know. So
17:17
So they won't be able to say like this is magically ours when really it's someone else's even if they're paying them to be and there's ton tons of NDAs like like they do with cloud services, right?
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I don't actually know who's under the I think they I I I think they do a lot of that and then I think a lot of it comes down to the privacy angle is that they want to make certain claims about privacy and ESG and net zero and stuff.
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And so if they were to just white label another company that didn't have the same reputation in privacy or or environmentalism, like people would just say, "Wait, wait, wait, but like Anthropic isn't isn't known for their privacy rules as much as possible."
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So maybe they're training on my Siri results now.
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And that's a whole that's a whole thing.
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And they have to, you know, build up Anthropic's brand to have the brand of Apple, which is just I'm sure Anthropic is doing great, but they're not known like Apple is in terms of how seriously they they take it.
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And then in terms of uh in terms of acquisition, obviously there's all the antitrust stuff.
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Uh we'll get into this with a scale AI deal, but uh every major hyperscaler except for Apple now has basically done one of these like interesting aqua hires of a foundation model lab to kind of juice up their AI efforts and uh and and it's kind of been a mixed bag.
18:29
Maybe we see one from Apple.
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They certainly have the cash, but uh it would be a very very different it would be a departure from their current strategy.
18:36
Anyway, let's go to uh John Gruber over at Daring Fireball with his review of Liquid Glass.
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He says, "I've got iOS 26 installed on a spare phone already." Oh, no.
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He didn't even use the real phone.
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Tyler, mistake number one.
18:53
Never install it on your main phone. How's it going so far? Well, okay.
18:58
So, so I'm I'm going to download it. It's 19 GB. Okay.
19:00
So, my phone is not, you know, I only have 64 GB on my phone, so I'm going to I gotta do a um you know delete everything.
19:09
Yeah, I'm gonna delete everything. Okay. Yeah. Yeah. Yeah.
19:11
So, you delete just go to your photos app uh select all the images all and then clear the trash and you'll make more memories like memory.
19:19
You could always make you're making more memories all the time.
19:20
Clear out clear out those images and um yeah, good luck.
19:26
If you on a more serious note, if you need um we'll cover your your sort of Apple iCloud plan if you need to get those images in the cloud so you can get if you need if you need a backup feel on your ramp card.
19:36
Yeah, put it on the ramp.
19:38
Um so, uh Gruber says, "I like the new UI a lot.
19:42
In addition to just looking just plain looking cool, Apple has tackled a lot of long-standing minor irritants.
19:49
For example, the iOS contextual menu for text selections, the one with cut, copy, paste.
19:53
For years, there uh for years now, there have been a lot of other useful commands in there, including share at the very end.
20:00
But to get to the extra commands, you had to tediously swipe, swipe, swipe.
20:04
Now, with one tap, you can expand the whole thing into a vertical menu. Elegant. And I agree.
20:10
I noticed that that was getting like it was originally it was just like just copy, just just cut and paste. And it was pretty easy.
20:15
And then they added if you wanted to bold or italics, you had to swipe over and then click on that and then go to text effects.
20:20
And there were like seven different things eventually. Good, good move.
20:23
Minor update, but important.
20:26
And this is what this is, this is the polish that Apple's so good at.
20:29
And then he says, uh, there's some stuff in Mac O mass Mac OS 26 Tahoe.
20:34
I already don't like like putting needless icons next to almost every single menu item.
20:39
But overall, my first impression of Liquid Glass on Mac OS is good, too.
20:43
It's fun and lots of little details are nice, joyful, and useful in an old school Mac. Mac OS Tahoe.
20:51
You'll love Mac OS Cosgrove. I'll just say that.
20:54
It's just going to be a banger.
20:55
It's going to be the best ever.
20:57
Uh there's an interesting article in here that Ben Thompson linked to by Sebastian Dewith uh about physicality, the new age of UI.
21:04
He actually posted this back on June 3rd before Apple refresh this.
21:09
So, there's a lot of Oh, do we have some breaking news?
21:11
I just I just printed this out because So, this was a good post from Signal.
21:15
He says, "Watching this WWDC."
21:17
Thank you, uh, Signal for the uh, for the content, and I'm just happy to be back printing. It's amazing.
21:24
It's It's I don't know how we went a few months without it.
21:27
So, he says, "Watching this WWC felt like watching a dinosaur try to do ballet.
21:34
Technically and aesthetically competent, but spiritually spiritually lost."
21:35
They talked about Apple intelligence, like they just discovered the concept throughout the presentation.
21:41
There was zero ecosystem thinking and no real attempt to rethink a single interaction model.
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More importantly, there was zero sense of how to be relevant in a world where interfaces may disappear, apps get abstracted, and AI is the OS.
21:53
Apple still thinks in icons and widgets while the world is moving towards awareness, intent, and delegation.
22:00
It'll be interesting to see how much of a penalty they will pay for missing this boat.
22:04
Um, uh, another negative post.
22:08
Now, Signal did have a couple other posts that were more positive talking about good stuff.
22:14
No, and I think I think this is generally uh why I'm excited about Johnny and Sam's uh hardware project is it's an opportunity to rethink the device from the ground up with AI at the heart versus Apple which I think going back to uh you know Ben Thompson's position is smart to play their own game right now and understand that they do have the option of aux auctioning the the sort of uh Siri out to the highest bidder at some point.
22:42
Yeah, I mean Alex Heath, who we had on the show yesterday, had a good post who said, uh, this is so very clearly designed for an interface that sits on top of the real world, not a phone.
22:52
Something you would need for AR glasses.
22:54
And so, uh, this idea that it's a little awkward now when you see these images of these glass over the background of your phone display, it can look a little too transparent.
23:05
The the contrast ratios are all off.
23:08
Um, but if you think about if they're really going all in on AR, VR, and they want to project these screens over the real world long term, setting themselves up to really dominate there and not need to do another refresh. Yeah. Could be big. Could be big. Anyway, Mark liked it. Spent some time. Yeah. With Trump. Yeah.
23:33
Trump is known for playing 4D chess.
23:35
Maybe Tim is playing a little 4D chess with liquid glass.
23:37
He's like, "Screen is going to be everything's going to be a screen.
23:40
We're just going to put They have they have a patent for a glass iPhone that that you can see through or something like that or or something that actually um would would would appear more like glass or appear transparent.
23:54
And a lot of the transparent I feel like a lot of the transparent technologies, it's like the closest we're going to get to just true holograms is if you have a variety of devices that are that are projecting holograms into glass sheets.
24:07
And so maybe it doesn't start with your phone immediately because the battery needs to be right there. That needs to be solid.
24:11
But if you think about a display and you're projecting on that and doing kind of the the same wave guides that are happening in uh reflected augmented reality glasses, uh you could do that on a monitor or something like that.
24:22
And so that could be very cool.
24:23
Uh Mark German had a more positive take on WWDC.
24:25
He says, "Excellent WWDC at cohesive story, deep integration and continuity across the devices, zero false promises, impressive new UI, and significant new productivity features on the Mac and iPad, but the lack of any real new AI features despite that being my expectation is startling."
24:45
And so people were expecting, you know, maybe there will be a new Siri, maybe they will move the ball down the field in AI, and they did not.
24:50
Um, Ben Hilac, of course, taking credit for it as always.
24:56
You know him from taking credit for the Jaguar rebrand.
24:58
Now he says, "So excited to see my my uh to finally see my work from Apple ship.
25:03
I was the head of design for the WWDC app notifications."
25:07
There's still a work in progress.
25:08
We've only had the last year to work on them. Stay tuned.
25:10
So, was this was this uh is this fake or is this real?
25:16
I I think this is the brightness adjusted.
25:18
I think that they I think that whoever shared this image uh turned up the brightness or brought up the brought up the lows uh de basically decreased the contrast in the in the photos app like uh to make it look worse than than it actually would.
25:31
Uh but I mean even it doesn't even matter because even if this is the way it looks, this isn't the way it will look when it will ship to to to people because it's obvious and and and Apple doesn't make these like obvious mistakes like this, especially when it comes to design.
25:43
comes to design. um they they they might do something that is like rough in the short term but better in the long term like what I think is happening with the photos app where in the photos app they are understanding that in the future you
25:56
won't want to scroll at all you will just search and search will be so powerful because every photo and video will be tagged so thoroughly from AI and it will understand the context so much that you will just ask it to pull up a photo and it will do it immediately. The
26:09
The problem is is that right now people a aren't they haven't switched that UX paradigm to actually go search first. Yeah.
26:16
And second um and and and second just the tagging is not that good.
26:19
Like I had a video clip of Arnold Schwarzenegger from a movie on my phone.
26:24
I wanted to pull it up and so I searched like Arnold uh Arnold video and it couldn't find it because the clip that I had started with a black frame and clearly it's only indexing like the first frame.
26:35
And so if if you have like a fade in to a picture of your dog, it won't get text. Something like that.
26:40
I don't know if that's exact.
26:41
I use the text search in photos quite a bit.
26:43
I I find it like searching Kugan if there's some document. Yeah, it has. Oh, totally.
26:50
The text search is fantastic.
26:52
And even even things like objects like I I there's a picture of a vending machine.
26:57
You type in vending machine, it it'll do a pretty good job.
26:58
Sometimes it'll miss it though.
27:00
But that these these like generative AI things, they get to 99.
27:04
9% accuracy pretty quickly.
27:07
Apple's just a little bit behind on them, I think.
27:08
Anyway, if you're trying to upgrade your design chops, head over to figma. com.
27:11
Think bigger, build faster.
27:14
Figma helps design and development teams build great products together.
27:17
Uh we'll go to on Figma now.
27:20
We'll go to we'll go to Will.
27:20
He uh gives his review of Apple's liquid glass.
27:24
clearly hints at a clear interface, AR glasses, and unifies OS beyond devices.
27:28
Like iOS 7, I would be shocked if this version of Liquid Glass ships to everyone later this year.
27:33
Too many accessibility features for a company that pioneered it.
27:37
Unfortunately, this means it's going to be muddy/blurry.
27:40
UI design feel used to be about function. This is decoration. Late empire feeling.
27:44
Uh, hate the trend of floating menus that seems to be happening everywhere.
27:48
It puts useless noise on the edges of the screen.
27:52
Ultimately, little of this matters when you can now just talk to the computer. Everything is computer. Anyway, um interesting.
27:58
There's an interesting deep dive from uh this Sebastian Dewith talking about some of the history here.
28:04
Speaking of iOS 7, uh he said on June 10th, Apple showed off what this was in the spring of 2013.
28:10
June 10th, Apple showed off what would be the greatest paradigm shift in user interface design ever, iOS 7.
28:17
I remember exactly where I was and how I felt. It was a shock.
28:19
I love a designer who's just so in the weeds they remember where they were when iOS 7 dropped.
28:26
Uh if there is indeed a big redesign happening this year, it'll be consequential and impactful in many ways that will dwarf the iOS over iOS 7 overhaul for a multitude of reasons.
28:35
The redesign is rumored to be comprehensive.
28:39
A restyling of iOS, Mac OS, iPad OS, TV OS, Watch OS, and Vision OS in the intervening years between iOS's 7, iOS 7's announcement.
28:47
And today, iPhones have gone from simply a popular device to the single most important object in people's lives. So true.
28:53
The design of iOS affected and inspired most things around from the from web to graphic design to any other computer interface.
29:03
That's why I figured I'd take this moment of obscurity, this precious moment in time where its changes are still shrouded in fog to savor something wholesome naive writer of where things are going so I can let my imagination run wild.
29:16
What would I do if I were Apple's design team?
29:18
What changes would I like to see?
29:20
And what what do I think is likely?
29:21
And then he breaks it down. The shaded age.
29:23
He goes back to the skuorphic era.
29:24
iOS started out as Can we pull up this?
29:27
iOS started out as the iPhone OS, an entirely new operating system that had very similar styling uh to the design language of the MacOSS Tiger dashboard feature. Yeah, I remember this.
29:40
You could just put the widgets anywhere. It was very messy. All the sticky.
29:43
This was so fun as a kid. Yeah. Yeah.
29:45
messing around with all the different widgets.
29:47
All the widgets and then you would discovering discovering great widget.
29:51
I I I couldn't I I legitimately felt like a kid in a candy store with widgets.
29:56
It was it was a beautiful time.
29:58
So he says the the icon layout on iPhone OS1, it wasn't even called iOS then. It was just iPhone OS.
30:06
Uh was a clear skuomorph.
30:06
Skeumorphism was the was the buzz word at the time.
30:10
You might have heard that word being thrown around.
30:12
It might surprise you that it doesn't mean it had lots of visual effects like gradients, gloss, and shadows.
30:16
It actually means to that to make it easier for users to transition from something they were used to, in this case phones typically being slabs with a grid of buttons on them to what they had become, phones were all screen, so they could show any kind of button or interface imaginable.
30:30
And yes, there was a whole lot of visual effects in the user interfaces from iPhone OS 1 to iOS 6.
30:37
In this age, we saw everything from detailed gradients and shadows and simple interface elements to realistically rendered realtore tape decks and microphones for audio apps.
30:46
I remember this the realtore tape deck in the voice recorder.
30:51
Pretty pretty remarkable.
30:53
Uh and like when when the directions would actually be like a like a physical sign that you would see on a highway, it was it was such a cool design language.
31:02
I mean, it it feels so not Apple now that they've spent 10 or 15 years post skumorphism, but it was definitely unique at the time.
31:10
Having actually worked on some of the more fun manifestations of it during my time working at Apple, I can tell you from experience that the work we did in this era was heavily grounded in creating familiarity through thoughtful, extensive visual effects.
31:24
We spent a lot of time in Photoshop drawing realistically shaded buttons, virtual wood, leather, and more materials.
31:29
That became known as skuorphic design, which I find a bit of a misnomer, but the general idea stands.
31:36
Of course, the metal of the microphone was not in fact metal.
31:41
It didn't reflect anything like metal objects do.
31:43
It never behaved like the object it mimicked.
31:45
It was just an effect, a purely visual lur lacquer to help users understand the voice that the voice memos app worked like a microphone.
31:55
The entire interface worked like this so as to be approachable as possible.
32:00
Notably, this philosophy extended to even the smallest elements of the UI.
32:04
Buttons were styled to visually resemble a button by being convex and recessed or raised.
32:08
Disabled items often had reduced treatments to make them look less interactive.
32:13
All of this was made to work with lots of static bit mapap images.
32:17
The first signs of something more dramatic did begin to show on iPad.
32:21
Some metal slider slider sheen could respond to the devices's orientation.
32:26
So, as you uh deleting a note or email did not simply make it vanish off the screen, but pulled it into a recycling bin that went as far as to open its lid and close as the document got sucked in.
32:37
I remember there's one that would like shred things when you deleted it.
32:40
They had a bunch of these fun ones.
32:42
And you can imagine the Apple designers in that era being like, I'm uh today I'm moving on from Apple, you know, I was the designer that made the lid open and the thing swoosh into the can.
32:55
It must have been so delightful to just be a designer and just like focus on just that one interaction for like weeks, you know?
33:01
Yeah, that that became a critique of uh Silicon Valley was just like you're working on something like the idea that like something like this doesn't matter in in the grand scheme of things.
33:13
But beautiful things matter. They do. They do.
33:17
And even a feature like this, especially in the notes app of of an iPhone was something that was being used millions and millions of times a day and and is worthwhile to make it beautiful.
33:27
And so there was push back when they went to flat design.
33:28
The Flat Age started in iOS 7, introduced an entirely new design language for iOS.
33:33
Much uh much was written on it at the time, and as with any dramatic change, the emotions in the community ran quite high.
33:41
I'll leave my own opinions out of it mostly.
33:43
But whichever way you feel about it, you can't deny it was a fundamental rethinking of the visual treatment of iOS.
33:48
iOS 7 largely did away with the visual effects for suggesting interactivity.
33:52
It went back to quite possibly the most primitive method of suggesting interactivity on a computer.
33:58
Some buttons were nothing more than blue text on white background like the inbox button.
34:03
The home screen, once a clear reference to the buttons on phones of yester year, was now much more flat looking.
34:10
One part owing to simpler visual treatment, but also a distinct lack of shadows. It is.
34:14
It is hard to look at this after looking at the at the last era.
34:20
Geomorphic era was was amazing. Goed. It was amazing. It was amazing.
34:25
Like I see why they had to move on in some ways cuz it was just like the the the the previous era felt, you know, like it started to feel dated. Yeah.
34:32
It felt like it feels vint it looks vintage today. Totally.
34:37
I'm sure the kids today will like jailbreak their iPhones back to to looking Yeah.
34:41
I mean, I saw I saw a a comparison of the of the original photos app, which which looked like a camera lens, and then the the flat design camera app had just a camera icon that was completely flat.
34:54
And now we're back to a camera lens in the new era.
35:00
And so, some of that some of that skumorphism seems to be coming back with the glass redesign, liquid glass redesign, but uh not fully.
35:05
You know, we're kind of splitting the difference.
35:09
We're going in a different direction.
35:09
I just looked up, do people still jailbreak iPhones? Yeah.
35:11
And uh Reddit says it's still possible.
35:15
The big thing is there's not much need for it.
35:17
A lot of the things jailbreaking was for have been implemented into iOS. Yeah.
35:20
Cuz the app store is so robust. Yeah.
35:22
So you can get you can get an app there's an app for that now.
35:26
There didn't used to be an app for it.
35:27
There were many many many apps that were not available and on the first iPhone there was not even an app store.
35:33
So uh but why did shadows have to go?
35:36
They had they they had an important function in defining depth depth in the interface.
35:41
After all, looking at the screenshot above actually does it no justice.
35:44
The the new iOS 7 home screen was anything but flat.
35:47
The reason was that the shadows were static.
35:49
iOS 7 embraced the notion of a distinct visual layers and using adaptive or dynamic effects to distinguish depth and separation.
35:57
Why render flat highlights and shadows that are unresponsive to the actual environment of the user when you can separate the icons by rendering them on a separate plane from the background?
36:06
Parallax made the icons float distinctly above the wallpaper.
36:09
The notification center sheet could simply be a frosted plane pane above the content which blurred its background for context.
36:18
Johnny IV spoke proudly at the iOS 7 introduction on how quote the simple act of setting a different wallpaper affected the appearance of many things.
36:27
this was a very new thing and so they're not losing the depth.
36:30
They still they're bringing that depth and they're actually making the depth more more complicated with the liquid glass feature.
36:35
And some people are frustrated by that.
36:37
But one thing that they're that they're highlighting is that it's intense from a computational perspective.
36:42
Everyone was saying like rendering all these new features is definitely going to hurt your battery life because it's so much more.
36:49
Although Apple is fantastic at integration and they're fantastic at this type of hardware and software integration and this these types of renderings, they will probably be very quick.
36:57
Uh and so I wouldn't I wouldn't expect a disaster, but I would I would I would expect uh a little bit more of a load just from the the graphic layer, although it will be heavily optimized.
37:07
Um also a new thing in the interface was that the UI Chrome was able to have the same dynamics.
37:13
Things like the header and keyboard would show some of the content they obscured shining the through as if it fashioned as if fashioned out of frosted glass.
37:21
While it was arguably an overcorrection in some places, iOS 7's radical changes were here to stay with some of its dynamic effects getting greatly reduced.
37:31
Parallax is now barely noticeable.
37:31
Over time, its UI regained a lot more static effects.
37:37
I I wonder how much parallax there is.
37:39
I don't really notice any right now.
37:41
I mean, I guess when you swipe around, you can kind of tell they're on a different plane, but I I might have it turned off. I don't even know.
37:47
Um, more interface elements started to blend with the content through different types of blur, like the new progressive blur and the button shapes were slowly starting to make a comeback.
37:56
It settled into a stable state, but it was also somewhat stagnant.
38:00
For bigger changes, there would have to be a rethink.
38:02
What would come next couldn't simply be a static bit again.
38:05
It would have to continue the trend of increasingly adaptive interfaces.
38:09
So he goes on to talk about um the age of physicality and vision OS and kind of where Apple's going and predicts a lot of of of what wound up happening.
38:19
So Ben Thompson to go back to him, he talks about Apple's retreat to empowering developers and partners.
38:24
This was your point about maybe Apple will be auctioning some stuff off.
38:28
We'll see how their service revenue grows over the next few years.
38:33
Um but first let's tell you about Vanta.
38:34
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38:48
It's like a it's like a AI golden retriever for mitigating risk and automating compliance.
38:53
I mean it's literally a golden retriever turned into software. It's fantastic.
38:57
What the golden retriever does for your yard bant does for your business. Yes. Yeah.
39:02
It's like having a golden retriever in the office.
39:05
Like you just feel safer. You you do. You do.
39:06
and you are more and you are safer.
39:08
Uh so uh Ben Thompson writes about Apple's retreat to empowering developers and partners.
39:14
Says that's not to say there weren't some notable AI announcement in announcements in Apple's keynote.
39:20
First Apple announced the foundation models framework.
39:22
Uh this is a quote from uh the Apple team.
39:24
This year we're doing something new and we think it's going to be pretty big.
39:28
for opening up access for any app to tap directly into the ondevice large language model at the core of Apple intelligence with a new foundation models framework.
39:37
models framework. This was what I was asking Alex about Alex Heath from the verge the question of hey there even if even if the models aren't frontier just the fact they're happening on device the speed is very important for a lot of these models being able to use a model without a connection to the
39:54
internet people are still they're still missing internet on planes occasionally or or or they have patchy service bu building an app that takes advantage of that and and opening that up to the developer ecosystem where you can get really creative and kind of start finding those like niche cases of where AI can help for certain things. You
40:09
You know, we we we need the beer app, the I beer app of the AI era. It's coming.
40:16
We've been this development.
40:16
For example, if you're getting ready for an exam, an app like Cahoot can create a personalized quiz from your notes to make studying more engaging.
40:24
And because it uses ondevice models, this happens without cloud API costs.
40:28
So if you're this like kid developer who doesn't who who maybe doesn't have an economic model yet, you can build an AI product that only uses ondevice compute and you have zero you have zero uh like cloud costs which is amazing.
40:44
I mean of course there's way less attention should have. Yeah.
40:48
I mean everyone was expecting it and it's great but um it is very cool in terms of just you can now vibe code an iOS app that has some sort of AI functionality.
40:58
It's not going to be incredible.
40:59
It's not going to be Frontier, but you can get something that's served into the store that only uses Apple's APIs and creates an experience that is that that is zero ongoing cost.
41:09
It's entirely hosted by Apple and runs on the device, which is amazing.
41:13
I I I think that that's very very exciting to see where people go with this.
41:17
Um they give another example of uh if you're camping off-rid pouring over the hikes you downloaded to all trails just describe what mood you're in uh the mood you're in and all trails can use the ondevice models to suggest the best option.
41:31
So um doing doing things like recommendation algorithms on device much much faster much easier.
41:36
Uh we couldn't be more excited about how developers can build on Apple intelligence and so it's important not to oversell the capabilities of Apple's ondevice AI models.
41:44
Of course, developers who want to create something that is competitive with the output of something like Chat GPT will need to use cloud-based AI APIs.
41:52
Of course, you got to burn you got to burn up the H200s to get the really good stuff, but um you know, it's like mid midw midwit intelligence on too cheap to meter.
42:00
Not quite not quite super intelligence too cheap to meter, but like you know, it's a midw that's helpful. Two digit IQ. Two digit IQ. Too cheap to meter. Too cheap to me. It's really free. It's bullet. It's on the device.
42:13
Uh the real that reality however applies um to Apple as well.
42:19
Part of the folly of the initial Apple intelligence approach is that Apple was promising to deliver beyond state-of-the-art capabilities on the cheap using its users processors and power.
42:28
What is compelling about the foundation models framework is how it empowers small developers to experiment with ondevice AI for free.
42:33
This is what I was talking about.
42:35
An app that wouldn't have AI at all for cost reasons now can.
42:40
And if that output isn't competitive with cloud AI, then that's the developers problem, not Apple's at the same time by enabling developers to experiment.
42:49
Apple is the big beneficiary of those that discover how to do something that is only possible once you have an Apple device.
42:55
Second, Apple deeper deepened its relation its reliance on open AI, incorporating chatt's image generation capability capabilities into image playground and adding chatpt analysis to visual intelligence. That's cool.
43:07
There is still no sign of the long rumored Gemini integration or the ability to switch out chap GPT for the AI provider of your choice, but the general trend toward relying on partners who are actually good at building AI is a smart move.
43:21
Third, Ben Thompson writes, "Apple is incorporating chatbt much more deeply into Xcode, its integrated development environment.
43:28
This is their VS Code competitor that you need to use to write Objective C apps for the uh if you're building iPhone software uh for building apps for Apple platforms.
43:38
Uh developers can also plug in other models using a API keys.
43:42
Xcode still has a long way to go to catch up to AI AI first idees like cursor, but again partnering with a foundation model maker seems like a much smarter strategy than Apple trying to do everything itself.
43:53
These are to be sure obvious moves but that doesn't make them any less important both in terms of Apple's future and also with regard to the theme of this article.
44:01
Apple's initial success with the Apple 2 was because of thirdparty developers and developers were critical to making the iPhone a sustainable success.
44:07
Trusting developers and relying on partners may be a retreat from Apple's increasing insistence on doing everything itself but is a much is a is very much a welcome one.
44:16
And so, um, people are also, you know, he quotes Marquez Brownley here, "The Windows Vista update with Aeroglass was a huge part of my childhood."
44:26
So, I'm getting serious flashbacks.
44:28
It does have a little bit of like the Windows glass vibes.
44:30
The biggest part of WWC was about with Liquid Glass, which has drawn some compar unflattering comparisons to past Microsoft operating systems releases.
44:40
Again, I'll withhold judgment until it ships.
44:42
But there is another Microsoft OS comparison I've been thinking about recently.
44:47
To me, last year's Siri disaster was a lot like Windows 8.
44:51
Windows 8 was an attempt to micros to leverage Microsoft's large PC install base into a competitive position in touch-based devices. And it did not go well.
44:58
Consumers, particularly enterprises, hated the new UI, and developers weren't interested in a platform without users.
45:04
Microsoft was forced to retreat and eventually came out with Windows 10, which was much more in line with the traditional Windows releases.
45:11
Apple has clearly missed the boat on cutting edge AI, but what I'm open to is the argument that this was a ship the company was never meant to board, at least when it comes to products like Chat GPT.
45:20
Meanwhile, I've long been convinced that Apple has gone too far in its attempt to control everything, even tangentially related to its devices.
45:29
To that end, I understand why many people were underwhelmed by this WWDC particular particularly in comparison to the AI extravaganza that was Google IO.
45:39
I think it was one of the more encouraging Apple keynotes in a long time.
45:44
And that's what Mark German said as well.
45:46
Apple is a company that went too far in many areas and needed to retreat.
45:50
Focusing on things only Apple can do is a good thing.
45:53
Empowering developers and depending on partners is a good thing.
45:57
giving even giving even the appearance of thoughtful thinking with regards to Apple App Store uh is a good thing.
46:04
Of course, we want and are excited by tech companies promising the future.
46:08
What is a prerequisite is delivering in the present and it's a sign of progress that Apple retreated to nothing more than that.
46:14
Let's hear for Ben Thompson. Another great article. Nailed it. Very, very good. Hall of Famer.
46:21
Anyway, future Hall of Famer.
46:21
If you're looking for the apple of sales tax, get on numeral. Numeral HQ. Sales tax on autopilot.
46:28
Spend less than 5 minutes per month on sales tax compliance.
46:31
It's AGI sales tax, baby. Get on numeral. Yes.
46:34
Now, so Sarah Guo at Conviction, former uh former guest on the show, friend of the show, uh said that Apple transcription is still so bad, so so bad compared to what's available on the market, they should be embarrassed. Okay, so I agree.
46:49
I am now in the pattern of if I want to send you a long text with a lot of thoughts, I will go to chatbt.
46:57
I will click the dictation button.
46:59
I will talk to it and then I will copy and paste that into the iMessage window.
47:04
Do you instruct chat GPT to synthesize it and organize it or use I just use it for the trans?
47:09
I just use it for the transcription.
47:12
I open up the chat GP up. I let it transcribe.
47:13
I don't even one of those John hits me with one of those bubbles that's like this long.
47:20
You actually don't do that, right? I I don't really notice.
47:22
You don't notice that I that I do it.
47:24
But even if I'm just saying like, "Okay, I I I'm I'm walking around.
47:27
I can't really stop in time."
47:29
Here's what Here's what we're hitting in the gym.
47:30
Uh you wake up you wake up at 5:00 a. m. first thing. Transcribe the workout.
47:34
Five for five, 225 bench press. Yeah.
47:36
Uh so basically, um I agree it's bad.
47:40
The question is why is it bad?
47:49
out there that they could work with, partner with a million different ways to solve this problem. Why is it bad?
47:53
And I was thinking that part of it is let's say they buy a company uh that has amazing transcription and it is discovered that that company trained on data that it didn't fully have access to. Interesting.
48:09
All of a sudden, instead of going after some startup with a hundred million dollars raised and 50 million in the bank and you're going to take them for all your for all they're worth, you're going after Apple. Yeah.
48:20
And you can maybe get a billion dollar settlement.
48:22
And so that's like a huge liability and it's also potentially a PR nightmare because a lot of Apple customers care about the the AI training.
48:30
Adobe's had to be very thoughtful about this in the sense that like they're like, "Hey, look, we will let you opt into letting letting us train our image our models on your images if you're on uh Behance or one of these other services."
48:43
Um, people don't like the idea of of training just happening on their hard work generally.
48:51
And so I think that there's some PR risk, there's some legal risk, but I agree that they need to figure it out.
48:57
way less risk if they're just leveraging a vendor that is giving them even that even that is like it's tough for Apple because if they if it's like oh well they're working with this vendor and the vendor is not above board okay well now they're they're linked in the yeah but
49:11
they should also be able to go to somebody like scaleai or Mor and just say like hey we want to build a transcription product and we're just going to make it ourselves I mean they face they like they face they face backlash when Fox Con has bad working conditions, right? And so if they are partnering
49:29
And so if they are partnering with another company and then there's some viral news story, whether or not it's real, that that those people, but everything Apple does carries risk. Yeah.
49:41
Even if they have great intentions and I don't I need to figure it out.
49:43
And this is something that that just feels it feels so easy to move the ball down the field because they already have a transcription product.
49:51
They just need let's just make it better.
49:53
They just need to make it better.
49:54
What are the ways in which we can make it better?
49:55
It's clearly not the modern transformer-based architecture.
49:56
Yes, they need to do a training run.
49:58
Yes, they need to build some GPU data centers for it.
50:02
Yes, they need new training data, but there has to be a way to do that in an environmentally friendly PR compliant like like do it do it the Apple way.
50:10
I'm fine with that, but like you got to do it because this is such a basic functionality of a phone now.
50:14
And the fact that I'm going from one app to the other just to you just in just because the Siri button isn't the transcription is just not good.
50:21
Um and and and that would be such an easy thing to just say, "Hey, hey, we're we're state-of-the-art in this because this is something that is actually uh improves the daily experience of the of the of the phone user." For sure.
50:35
Uh and same thing with so so backwards to have to go in another app just to send a me transcribe a message.
50:42
I yeah I mean I'm I'm fine with them not inventing like the newest paradigm of like deep research like that doesn't that product doesn't need to come from Apple. Yeah.
50:54
But we've had good dictation and transcription for years now and it's open source and it's not even it's not even that it's just like open source.
51:02
I'm sure they can't just like vend the open source model in because of the license and all.
51:05
I'm sure there's restrictions on that.
51:07
Like you're not just going to give Whisper to the previous company.
51:10
We should have somebody that that's a a big dictation head and and have them apply to get a job. No, no, no.
51:18
It's just somebody just a fan go, you know, an Apple fan goes joins Apple purely to just, you know, beat the dictation drum internally because I feel like it just needs to be a handful of people that uh that are really power users.
51:32
Anyway, I wanted to highlight this post from Pirate Wires.
51:36
Uh they did an interview on April 16th with David Hannimir Hansen.
51:38
Uh that's DHH of Rails fame.
51:41
Uh he also works at 37 Signals with friend of the show Jason.
51:48
Uh screen recordings of Apple's new iOS 26 liquid glass software design appear to show a jumbled difficult to read apps and incoherent home screen app animations.
51:57
For a company that built its brand on seemingly impossible feats of technology, Apple now finds itself stuck in a self-perpetuating cycle, falling behind in the AI race, struggling to attack attract talent needed to catch up and fumbling product releases.
52:10
Uh, Pirate Wires writer Blake Dodge diagnosed the company's problems in a May interview with 37 Signals, DHH.
52:16
Read the interview in full on the site, link threaded.
52:21
I recommend you go read it. I won't read it here.
52:23
You have to go subscribe to Pirate Wires.
52:25
Also, it has a lot of bad words, but yeah, it does.
52:29
It he he really let loose.
52:29
And I'm sure there's a lot of Instagramed state of affairs at Apple. Yes.
52:33
Not not quite uh not quite family friendly material, but uh if you're an adult, head over, hit the subscribe button, check it out, and read in full because there's a lot of insight there.
52:44
Um anyway, that is uh the WWE reset recap.
52:50
We are going to be assembling the first Americanmade iPhone. First ever. First ever.
52:54
They said it couldn't be done in the USA. Made in the USA.
52:56
We have him set up over there on intern cam.
53:01
Introduce yourself, Tyler. How you doing?
53:02
Coming in from Shenzhen, you know, situation um you know, place.
53:05
Um yeah, so I'm I'm today I'm building an iPhone 4S. It's uh 14 years old.
53:10
Um but I have He can't start at the 16. Yeah. Yeah.
53:17
Trailing edge for a while.
53:17
But if he can do a 4S, then tomorrow he gets to the five, maybe the six, maybe the seven, starts working through them.
53:24
Uh C uh production team, can we show what what he's working with right now? What he has?
53:29
Okay, so he has he has uh the front, the back, the screen, some inards.
53:35
I don't really know what's in there.
53:37
It seemed like the battery might have been made out of rubber plastic.
53:40
Long stream ahead of him. Yes.
53:40
So, we will be checking in on him throughout the show, learning uh what it takes to assemble an iPhone in America.
53:47
They've said it's impossible, but I think today we might just prove them wrong.
53:51
I mean, Tyler's conviction this morning has been insane.
53:54
I asked him, "Are you are you nervous? Are you afraid?
53:56
Uh are are you you know afraid at the possibility of failing on the on the sort of the the world stage?" Yeah.
54:03
And he said, "No, I got this. I got this."
54:05
So, let's uh let's see how it plays out.
54:08
Anyway, Tyler is uh going to be welcome. Oh, he's working. He's He's slicing.
54:13
Let's go to the slice cam.
54:15
Let's go to the slice cam. What's going on, Tyler?
54:17
He's trying to remove the the pieces from the board because this of course was a uh this was a piece of art that you buy and it comes.
54:28
So, he's the first golden retriever mode.
54:31
First, you got to get the pieces. Yeah. Yeah.
54:33
You have to get the pieces off the board because it's been stuck there as a as an art object.
54:36
And then he will have to assemble them.
54:38
He told us that he doesn't have any screws, which could be a major setback.
54:42
I have I have some glue here. I've got some glue.
54:44
Um I'm I'm I'm feeling really confident right now, guys. Okay, good. Good.
54:48
This is going to be the most Americanmade iPhone.
54:51
There's just going to be glue everywhere. It's going to be a mess.
54:52
Any any politician says, "Hey, you know, we saw Tyler, TBPN's intern, make an iPhone in America.
54:59
We think you guys should actually, you know, take this seriously.
55:03
Why don't you take a plane?" He's making progress. Making progress.
55:04
He's got two and three down. Look at him go. That's great.
55:09
I've almost got everything deconstructed.
55:11
Soon I can start putting it back together. There we go. Amazing. Amazing. Doing great.
55:16
We knew you were going to crush it, so we we we we we tried to make it a little bit harder, force you to deconstruct it first and then put it back together.
55:23
So, uh, this is hilarious.
55:25
A lot of the reinforcement learning is now done on like extremely hard math problems.
55:30
And so, you might need to go to PhD programs to recruit.
55:33
We should offer, you know, like maybe physical intelligence or another lab.
55:38
the data from Tyler, you know, making this iPhone to to to train on.
55:42
Definitely because I wouldn't want potential training data to go to waste. Yeah. Yeah. Yeah.
55:46
A bodysuit so that he's really able to to track all the mechanics.
55:51
We need every every every digit on his hand tracked so that we can This might be the first time an iPhone has been assembled by somebody wearing a a tailored suit as well.
56:00
We're breaking all sorts of uh records here.
56:06
Can you talk a little bit more about what it actually takes to produce a solar panel in America?
56:09
It doesn't it doesn't seem as complicated as building an iPhone potentially, but maybe it is. It's much simpler.
56:16
I don't think iPhones are are even that complicated. We have our intern here.
56:19
I don't know if you can see iPhone in America.
56:20
He's making the first USA made iPhone uh this morning. Cut to Tyler. How's it going, Tyler?
56:28
Give us the update, guys.
56:28
I'm I'm now attaching the volume buttons, but I've got the front on.
56:32
I've got the home button on.
56:34
Okay, I'm feeling really good right now. Feeling really good.
56:35
Okay, we're making progress.
56:36
Typically, once you once you're done with that assembly, uh, TBPN intern, you need to go back to starting with rocks and then make the iPhone from rocks.
56:44
I've got rocks in my backyard, so I'll give you some rocks.
56:47
I'll mail them to you and then you turn that into an iPhone.
56:49
That's the next That's level two. Next level two. Yeah.
56:53
In other news, we have our intern here in the studio assembling an iPhone from scratch.
56:58
uh and not really from scratch, but I I don't know if you can see, but he's he's getting it together.
57:03
He's assembling the first iPhone in America.
57:06
And so I think that's that's more of our focus today.
57:09
Oh god, I said iPhone manufacturing would never happen in America.
57:12
I think this is happening.
57:12
This is the first Americanmade device.
57:18
You're witnessing history. He's starting. It's a 4S.
57:19
So we didn't we couldn't go all the way to, you know, whatever the 16 or the 17, whatever we're on now, but we had to start somewhere.
57:27
We have our intern We have our intern in the studio today uh making the first iPhone in America. It's happening. It looks like he's done. Give me the update.
57:36
I'm getting pretty close.
57:37
He's getting pretty close.
57:37
Missing I most of the kind of, you know, external external things here.
57:42
I got the volume buttons, home button.
57:43
I got to put in the battery, the main kind of chip, motherboard. Okay.
57:46
Um but feeling pretty good.
57:49
I'm probably 80% of the way there. 80% of the way there.
57:50
Less than an hour and a half in.
57:52
I think this is uh uh this is going fantastically.
57:55
I wasn't sure if this is possible, but it looks like it's looks like it's happening.
58:00
We're bringing the iPhone back to America. Yeah. Okay.
58:01
Give me give me any any advice, Aaron, for for Tyler. I was about to ask. Yeah. Yeah. Yeah.
58:06
What what's the advice for our intern who's assembling the iPhone in America for the first time?
58:12
Uh what tools does he need?
58:15
Uh you know, what are the key what are the key steps to really reshore manufacturing?
58:19
There's lots of pieces that didn't go in that phone. Yeah. Take it apart. Not done yet. I'm not Take it apart. Put it back together. Oh, he's not done yet. Okay. Yeah. Yeah. Yeah.
58:29
Aaron, what does it take to restore the iPhone?
58:31
Cuz he I mean, uh, are we going to give him a gong ring for this?
58:35
Cuz I mean, it's a lot of it's a lot of grit, right? We're watching history.
58:38
So, cultural shift is what I'm hearing. Yes.
58:43
I mean, massive cultural shift, which is, you know, underlied by a lot of other factors, too.
58:47
So, um, I think that infusing and transforming the industrial base through technology gives, you know, a lot of incentive for people to jump back in and join.
58:56
We're not we're not trying to go back to the 50s here.
58:58
I mean, you said the the the plants are smaller, like not an entire island, but I imagine that it's not something I can put on my desk. How big are we talking? Yeah.
59:09
No, unfortunately, your intern cannot make one of these at his standing desk like the iPhone. Not yet. I don't know. I don't know. Look at him. Look at him.
59:17
He This guy can do anything.
59:17
The first American iPhone.
59:20
I think he's he's doing well.
59:23
He's making He's making the first USA made iPhone. He's doing well.
59:28
He had to take it back apart to put the battery in.
59:29
Oh, the battery is going in now. Looking good. The battery's in. He's doing great.
59:34
Will Apple announce iPhone assembly in the United States before September? We beat him to it. Well, we beat him to it.
59:43
Let's go to Tyler and see uh h how is the project going, Tyler? Break it down for us. Well, guys, it's done. It's done.
59:50
The first American iPhone, manufactured in America, assembled in America. Wow.
59:55
You know, it's, you know, maybe not the cleanest, maybe not the most perfect.
59:59
It's kind of a coin flip whether it would pass the, you know, inspections, but um quality assurance, but you did it.
1:00:04
I I genuinely can't believe you did it.
1:00:07
I was staring I was staring looking at the pieces earlier and and I and I was kind of concerned because I thought we've given Tyler an impossible task. Yep.
1:00:16
And we're now 3 hours and 20 minutes into the project and it's successful. Yeah.
1:00:21
I mean look I you know I'm more of a software guy, machine learning engineer kind of guy. Yep.
1:00:26
Not not big on hardware but you know this process I'm incredibly bullish on bringing manufacturing back to the United States.
1:00:33
You think it could be done? Okay.
1:00:34
If I can do it, you know, not necessarily anyone, but if you have a college degree. Yeah.
1:00:42
Uh I we got to kind of get into the economics now and understand like, you know, Tyler's market value.
1:00:46
Tyler's market value probably, you know, Yeah.
1:00:51
quarter million a year, took him 3 hours.
1:00:53
We're getting run through it.
1:00:56
Potentially some margin problems. Yeah.
1:00:57
We've been talking about the $10,000 iPhone.
1:00:59
I think we might be looking at it.
1:01:00
You get an iPhone 4S handmade by a college educated machine learning engineer.
1:01:05
But imagine, you know, you know, in in in a lot of cars, they'll like the person that made the engine will sort of sign it.
1:01:10
Uh having an iPhone that was sort of signed on the back by Tyler.
1:01:14
Why don't we get why don't we get a Sharpie for Tyler?
1:01:16
You can sign the back of that with the iPhone hands.
1:01:18
I'm thinking of starting like a kind of boutique, you know, handmade phone company now. I think that's good. Made with love.
1:01:24
Just buy the new iPhone, take it apart, put it back together, sign it. Sign it. Yep. Assembled here. Handmade iPhone. Assembled here. Amazing.
1:01:31
Honestly, genuinely incredible work. Uh great.
1:01:36
They they said it couldn't be done and we proved him wrong.
1:01:38
And you proved him wrong.
1:01:39
Anything is possible in America with enough dynamism. With enough saw today. With enough Tylers.
1:01:43
With enough Tylers and we can we can accomplish anything.
1:01:46
Welcome to the stream, Andrew. How are you doing? Great. Great. Great to see you.
1:01:50
Thanks so much for joining. Yeah.
1:01:52
What's new in your world? Uh what's new? Um goodness.
1:01:54
This week we have a big episode of the Hubman Lab podcast out with the current NIH director, Dr.
1:02:00
J Bacharia who's a MD and a PhD has a unique background because he has a background in medicine obviously the MD uh but he also has a background in economics. Yeah.
1:02:10
And um incidentally did his undergraduate master's PhD and medical school training and then was a professor of medicine at Stanford.
1:02:19
Stanford um you know he's he's Stanford the whole way and um as NIH director right he he holds a ton of power over what happens for the future of basic and clinical research and he looks at all of that through the lens of an economist um
1:02:35
but mostly through the lens of a you know public health official so it's a very unique um perspective and folks on X will recognize more vocal um I don't want to speak for him and and label we have to be careful labels, but totally antilockdown. I I don't know.
1:02:51
Um yeah, you know, not not a huge fan of the lockdowns for most people, right?
1:02:57
He he does say that there are certain populations that he felt should have been uh kept indoors, but other other folks probably in his view should have more actions.
1:03:06
Well, it's a long episode.
1:03:08
I wanted to basically get a preview of it.
1:03:10
You guys focus on a couple things.
1:03:12
one how to fix the issues with science and two the importance of funding research uh both sorry basic research and then uh applied research.
1:03:21
So why don't we kind of cover a couple of those different my question was like the the narrative right now is that uh research funding is being cut like in immensely and I was wondering if you if you got a feel for how severe the cuts are.
1:03:38
Is the narrative overblown?
1:03:40
what is the balance of cuts between applied and basic research and then we can kind of go into some of the implications of that. Sure. Okay.
1:03:46
So, um there is an an upcoming vote in Congress uh in September, I believe, and it's on the table to cut the overall budget for research for NIH by 40%. 40. That's huge. Which is huge. That's huge.
1:04:02
I want the the budget for research at NIH.
1:04:07
there there multiple dimensions to NIH and I don't want to get lost in the weeds of it.
1:04:10
It's a you know NIH is something I'm very familiar with.
1:04:11
I was on grants panels for you know over a decade.
1:04:15
My lab was funded by NIH.
1:04:15
We could go really deep into the weeds but let's just keep it pretty simple.
1:04:19
NIH funds basic research which is research that it's not specifically geared toward understanding the or trying to solve a particular cure or treatment for a disease.
1:04:28
So think um all everything we understand about cell biology, not everything but much of what we understand about cell biology was it was because of NIH funded research into the functioning of the cell over the course of you know 50 or more years and that led to important implications for treatments and cures for cancer.
1:04:46
Um do we we don't have a quote unquote cure for all cancers but many cancers now can be cured.
1:04:52
And to be clear, these are non-commercial.
1:04:56
This is like non-commercial research.
1:04:56
So things that we cover a lot of Yeah, we cover a lot of cannot be patented.
1:05:00
So basic research is you know a laboratory uh wants to understand um how cells work, how neurons work, um how the immune system functions, gut brain access um uh studies on uh you know um how stress impacts health, sleep etc.
1:05:18
Then the applied work is also funded by NIH.
1:05:21
So clinical trials are funded by NIH.
1:05:23
This is an enormous portion of the overall budget.
1:05:25
The exact division between basic and um clinical trial funding is hard to demarcate, but let's just for sake of this conversation just agree because it's true that most of the basic research and clinical trials that are run in the United States are funded by the NIH.
1:05:42
The NSF is a separate entity, right?
1:05:44
Um but NIH's its specific goal is to improve the health and longevity of American citizens and by extension the rest of the world. Right?
1:05:53
Because it it is fair to say that while there are excellent uh you know research funding bodies in the UK and Germany and Switzerland and all over Asia and around the world that the NIH when people say it's the crown jewel, it's the one that's devoted the most billions of dollars to basic and applied research.
1:06:11
And it's no coincidence that the majority of Nobel prizes um has in physiology and medicine uh and chemistry and related fields have come from work that was either initially seated by or certainly supported by the NIH.
1:06:23
So you can't overstate the importance of NIH funding basic and applied research. Mhm.
1:06:30
That overall budget is facing a 40% cut in September.
1:06:34
Now you know I'm hearing two things out there right I wear many hats.
1:06:40
One is my lab ran on NIH money.
1:06:40
It no longer depends on NIH money.
1:06:42
There um I'm a podcaster so I have the ear of folks who are like this is really scary, right?
1:06:51
Um that it gets very political because the current administration seems to be taking a more um like that they're going to revise the way that NIH is structured potentially 20 plus institutes down to eight.
1:07:02
So all of it looks at like downsizing.
1:07:04
There are a lot of people who are terrified about this. Okay.
1:07:08
There's another camp that I hear from a lot um and I can't even say particularly on X and I want to be very clear this camp doesn't always lean right.
1:07:17
It's it's pretty even across the board that are saying, "Wait a second.
1:07:21
Why are we giving so much money to these universities to do research uh from our tax dollars and I know this will upset people who are very science-minded as I am who care about science, but they're saying why why are we doing this?" Right?
1:07:32
Some of these universities, not all, but some of them, Stanford, Harvard, Yale, Princeton, UT Austin, etc.
1:07:38
The private universities often, although some public ones do, have very large endowments, and they're thinking, why are we funding so much of this work?
1:07:44
Maybe these tax dollars should go elsewhere.
1:07:46
There's another key issue and this came up during the discussion with Dr.
1:07:49
Dr. Bacharia which is there are many many people okay I'm just who voiced to me that they don't want to give their tax dollars to basic or applied research at all okay this is a large and growing crowd because they feel that there needs to be for lack of a better way to put it
1:08:09
some truth and reconciliation right they want two things acknowledged and I've and I've actually heard this from many of the let's just say the the highly recognizable names in the in the world of Silicon Valley um super tech or or founders and funders and um and um investors etc. And those two things are
1:08:27
And those two things are the following.
1:08:29
One they want the NIH to acknowledge or CDC andor others in government to acknowledge that there were in their mind failures during the pandemic in particular lockdowns that impacted the non-laptop class.
1:08:42
Okay, we're talking about janitors, um, uh, staff, uh, teachers, kids, etc.
1:08:47
that, um, basically had to halt their work and their income.
1:08:52
They're they're they're pissed off about this, right?
1:08:54
That no one kind of acknowledged this.
1:08:56
The other thing that they're very angry about is the lack of acknowledgement from the scientific community that the science community makes errors.
1:09:04
Sometimes errors that, for instance, in the field of Alzheimer's research, I want to point out, not all the work in the field of Alzheimer's is bad.
1:09:12
much of it is is very solid or or excellent but they they are frustrated by some recent kind of uh unveiling of the fact that there were findings that later were found to be fraudulent basically and that it was never checked up on and then that opens up a whole discussion which I also discussed with Dr.
1:09:31
Bachari about what's being done to solve the so-called replication crisis.
1:09:34
So they're upset about the about the public messaging around health and science, right?
1:09:38
They would have preferred, it sounds like that um people in government say, "Hey, listen, you know, we have a virus that we don't understand."
1:09:47
Um and we we have ideas about what might control it, but we don't fully understand this.
1:09:52
So it was very iterative and a lot of people are pissed off.
1:09:54
are pissed off. kind of like when you're, you know, 15 or 16 and you're being told you can't stay out late and then your parents are staying up late or, you know, you or like we had governors who were saying you had to wear masks but then we're taped in fancy
1:10:06
restaurants on the northern coast and you know at restaurants and like much in the way that a teenager goes wait a second like you smoked pot in college and you're telling me not to smoke pot like this there's a logical flaw here and we at least need to talk about it. So there's this kind of notion that um
1:10:19
So there's this kind of notion that um that the this isn't my stance necessarily.
1:10:24
I'd be happy to share my stance, but that the scientific community has kind of cloaked its errors.
1:10:28
Y and and so there are a lot of people in the general public, they're like, don't give these universities a dime.
1:10:33
Let them dip into their endowments.
1:10:34
And the last thing I want to say about that is not every university has large endowments.
1:10:40
Most public universities do not have large endowments.
1:10:42
Um it's also true that universities don't like to spend their endowments and the typical way that they hide behind uh their their endowments or endowment spending is to say that money is earmarked for other things. Right?
1:10:54
So it's which is not to say that it isn't but and then the the last thing and I know I'm going kind of fire hose here but I want to make sure this comes out is that the big issue that is really on the table as well is this notion of indirect costs.
1:11:06
You guys are finance guys.
1:11:08
So basically a laboratory get might get a grant of a million dollars across four years.
1:11:13
So 250 a year for four years.
1:11:14
And the university then gets what are called indirect costs.
1:11:18
Um they get let's say 500k.
1:11:21
Typically it's an anywhere from 300k on the million to up to 750k on the million.
1:11:26
There are a few cases of even more than that.
1:11:28
The so-called indirect funds that take care of administrative cost and the the basic cost of doing research.
1:11:33
And earlier this year, the Trump administration said, "We're cutting that to 15% across the board for all universities."
1:11:40
And that was very prominent on X uh because Elon retweeted it and a number of other people retweeted it.
1:11:46
And I would say um while the indirects have been very controversial, I I just personally, it's my view.
1:11:54
Okay, this is my personal view is that a a severe cut to the indirects, while on the face of it might sound good, that's going to disproportionately hurt the public schools without large endowments, okay?
1:12:05
Because they don't have money to dip into.
1:12:07
And you know, something more in the range of 30% seemed reasonable to me based on my understanding of what those funds are used for.
1:12:13
But historically, there have been some challenges with indirects.
1:12:15
You know, there were universities, I won't mention which, caught for spending some indirects on things that were unrelated to the science.
1:12:22
You know, perhaps keeping the lights on in the English department.
1:12:25
That's typically not the case now.
1:12:26
It's for disposal of radioactive waste. It's for, yes, janitors.
1:12:30
It's for painting the walls of the building, but it's not for making, you know, a lavish lifestyle for the administrators, right?
1:12:35
If administrators have a lavish lifestyle, it's presumably through some other mechanism, not IDC. Yeah.
1:12:41
there was some example, I don't know if it was UCSF or some school where they had like a multiund million administrative budget and I think people really wanted to push on that and um but I I I think your concern is like obviously there's like this massive um trust issue between the public and new parts of the administration and science and how do we rebuild that trust without destroying the entire system, right?
1:13:05
Is that is that kind of you nailed it and and I I would like to just highlight in the backdrop of all of this there there are is one very major emotional issue that I'm going to catch a lot of heat for this but I don't really care anymore.
1:13:19
Um you know and we're on X.
1:13:19
So a lot of I put out a um a post recently that said hey listen I I I'm I'm very concerned about this 40% overall cut as the most severe uh damage to to science.
1:13:35
We're not talking about allocation.
1:13:36
We're not talking about indirects.
1:13:36
You we're not talking about what's going to happen with that with that body of money.
1:13:40
Uh we're just accept this 40% cut.
1:13:43
And I'm very concerned about this, but I don't want to talk about, for instance, the fact that federal funding to Harvard or to Colombia has been frozen.
1:13:50
And a lot of people said, well, why not?
1:13:52
I have a lot of friends at Harvard Med and at Colombia and elsewhere, and they're like, wait, this is why why won't you talk about that?
1:13:58
Well, that's actually a different issue, right?
1:13:59
The reason that money has been frozen is that in the eyes of the administration, they are Harvard and Colombia are violating civil rights laws.
1:14:07
Until that's resolved, there's no discussion about NIH to be had. That's not an NIH issue.
1:14:13
It it funnels through NIH money. Yeah.
1:14:15
People are tying the issue even though just because it it has some of the same buzzwords. That's interesting.
1:14:21
I I have a question about the 40%.
1:14:22
Um I mean there is a world where where like where what becomes important is the ranking of what gets cut.
1:14:30
And so my question is like within the within the basic research that you're seeing what should we be fighting for hardest to keep what basic science or applied science research are you most optimistic about going forward?
1:14:43
optimistic about going forward? because we saw this with uh GLP1s I mean I'm sure crisper these stuff lang this stuff was NAHH funded at one point that there was a ton of research and then we get this big boom like but it's 10 years later what's on the frontier what are you most excited about and what should we be fighting for let's keep the
1:15:02
funding there no matter what yeah great great question uh in fact the most important question so regardless of whether or not this 40% cut happens if it's less if it's kept the same um a couple of things first of All despite the fact that NIH funds basic research and that many laboratories including my own for for many years focused on basic research like how does the visual system work. We always had an eye every
1:15:23
We always had an eye every laboratory has an eye toward you know what the translational implications could be. Okay.
1:15:29
So we were actively involved in trying to solve you know blindness due to glaucoma even though we were focusing on some basic questions.
1:15:36
So I think it it it's a statistical issue if you step back and you say okay in the field of let's just say vision my my former field um what is the leading cause of blindness cataract but you know what you can fix cataract you can slide out the lens and you put in a new lens that's being done now great what's the second leading cause of blindness more
1:15:55
than 70 million people worldwide glaucoma you can test for eye pressures but once the cells in the eyes start deteriorating people go blind there's no recovering those cells so you can say wow so okay so let's take the top let's take the the statistically that the the the top causes of blindness and let's let's fund those. Okay. Um retinitis, Okay.
1:16:11
Um retinitis, pigmentotosa, etc.
1:16:13
You could do this for pretty much any field.
1:16:15
So in the field of of brain health, you'd say, okay, dementia, you'd say um uh major depression, you'd say a stroke, uh you'd say, you know, I'm going to piss off some people because I'm not going to mention their their particular suffering, you know, here, but it's not hard to find Parkinson's, right? Um MS.
1:16:35
I mean it's not hard to know what the problems are to tackle.
1:16:36
The problem is it deciding what the targets are. Sure.
1:16:41
And as you mentioned like with crisper I mean in many ways it was a fortuitous thing right um down in a lab was working on bacteria.
1:16:46
Uh I mean is you know it's very hard to predict what basic research is going to lead into these different areas but we we know what the critical areas are.
1:16:55
So I'm not trying to dodge your question.
1:16:57
And I do want to say that the emphasis from MAHA on chronic disease and chronic health issues I think has concerned some of the people in the scientific community because listen I as a podcaster that covers science and health will be the first to say that if you're not sleeping well your mental health and physical health is not going to be good.
1:17:15
You can miss a few nights sleep but if you're chronically sleepd deprived you're sicker than you normally would be.
1:17:19
If your gut health isn't proper if you're not getting exercise if you're not getting sunlight.
1:17:25
If you're not doing these things, of course, right?
1:17:26
But I think the people who are in the the kind of hardcore mechanistic science community are like, great, we're all going to strive to do those things and we should, but not at the exclusion of figuring out signaling pathways that are vital for uh like the GLP1s are a beautiful example, right?
1:17:42
This peptide acts at the level of the brain and the gut to make people feel more full.
1:17:45
It also, by the way, shut down the debate as to why people are fat.
1:17:49
It's because they eat too much, right?
1:17:50
They eat more than they burn.
1:17:54
Remember people used to argue about it. Yeah. Yeah. Totally. No. No. GLP1 silenced that. It's really true.
1:18:00
Now, are they eating too much because of a hormone issue related to having too much fat?
1:18:04
Maybe there's some depression feel. Sure.
1:18:05
Like I fully acknowledge like I'm not trying to be completely insensitive, but like but we we isolated the problem by understanding that this peptide discovered in heal the discovery of this is worth spending two sentences on.
1:18:17
aila monster, a reptile that doesn't need to eat very often, makes a lot of this peptide that it turns out in is manufactured by humans too and if you increase it thousandfold in humans, you know, you're not hungry anymore.
1:18:29
And so I think that, you know, the guy studying Hila, the Hila monster, I doubt was thinking about curing obesity, but there you go.
1:18:36
Now, and and of course there's a debate to every one of these things.
1:18:40
No medication should ever replace lifestyle factors, right? Yeah.
1:18:45
There are many I think we sometimes fail as healthy fit people.
1:18:48
We fail to understand that lifestyle factors are very hard for people to implement even if they have copious amounts of disposable income and some free time. It's it's just tough. It's just tough. It's hard to do.
1:19:04
And so I think that the the emphasis on nutrition and exercise is wonderful.
1:19:08
I think I don't think anyone on either side of the political debate would argue that MA in principle make America healthy again isn't a great thing.
1:19:16
I think what they probably would appreciate hearing now I'm speaking for kind of the more science mechanistic biology-minded folks.
1:19:22
They'd probably like to hear a little bit more about hey you know there probably signaling pathways in the brain that are relevant to um you know to depression that maybe a a next class of anti-depressant drugs would probably be good.
1:19:35
I don't think anyone would say there's no need to develop anti-depressant drugs.
1:19:38
We probably take too many of the ones that don't work and have too many side effects, but as a country, but when so when you say what are the most important things, what we really need is a is a very rationally grounded planning committee to sit back and say, you know what, we're going to put x number of dollars towards this, x number of dollars towards that, x number of dollars towards this.
1:19:57
And this is where I think Dr.
1:19:58
Badachara really um shined on the podcast.
1:20:00
He said, and I will totally agree, and I got I have already my phone's been blowing up with some anger from colleagues about this.
1:20:09
Much of the work that's funded by NIH, here's the dirty secret, is already completed because they tend to fund things that are very certain to be completed.
1:20:19
And so people put forward grants based on work and preliminary data showing I can do this and it's already kind of worked out.
1:20:25
Then they use the money for the next iteration.
1:20:28
This is the dirty secret of every NIH funded lab.
1:20:30
and it tends to favor a kind of pedestrian more pedestrian science.
1:20:35
Then they use foundation money and they use um other sources to kind of do the the high-risk stuff. Mhm.
1:20:40
But if you were to look at like one of the the more impressive funding bodies in science like the Howard Hughes Medical Institute, right?
1:20:46
Um gives the equivalent HHMI folks as they're called never they always say it's not that much money, but let me many we can do the test.
1:20:55
We can take away their money and we can see how well they do or don't do.
1:20:58
They don't like doing that. Um, yeah.
1:21:00
It's it's like being on academic steroids, right? Yeah.
1:21:01
The equivalent of three grants per year, basically. NIH grants per year. Yeah.
1:21:06
And more money translates to when when it's excess money.
1:21:11
I know that word excess money, you know, scares investors, especially if they're taxpayers.
1:21:15
But if that money is being used to fund really like really bold hypotheses, like it's my opinion that every single peptide, not just GLP1, every single peptide shouldn't be getting tested in the gray market of like gyms and like biohacking.
1:21:28
That's frankly that's Yeah.
1:21:30
And isn't the reason that there we don't have good studies on a lot of peptides is it's not profitable to study them because they're effectively naturally occurring and you can't patent a like correct there. like like BPC57, right?
1:21:46
You hear about that all the time is will it accelerate healing?
1:21:48
You know, the animal studies are solid.
1:21:50
There's no human studies.
1:21:52
My good friend Peter Aia is like, I'm not going to touch that stuff.
1:21:56
There's no clinical data in humans.
1:21:58
Um and the rest of us are like, well, I take it and it works.
1:22:01
And there's there's no reason for a uh for a drug company to take.
1:22:05
I'll take it transiently for like a like a joint issue. Yeah.
1:22:09
But the the core issue patent it I've I've taken it too. I've benefited from it.
1:22:14
I recommended it to John a couple weeks ago.
1:22:15
He had a he had an injury.
1:22:17
I just said, "Hey, I'm not going to like this is not something I'm going to take, you know, weekly for my entire life, but in in certain instances, but but break down that specifically that you're saying because it's because it's effectively science, there's not there's no real ability.
1:22:34
Would would a drug company have to create some variation of it in order to patent it? Is that is that Yeah.
1:22:39
In fact, um, and I here I'm going to catch heat from everybody, but you know, the pharma companies that make GLP1s, I mean, most people have realized that GLP1s are very expensive and at the dosages that they're typically prescribed, people get some discomfort.
1:22:53
Um, and so the new thing is people get it compounded at a compounding pharmacy and they're micro they're micro doing GLP1s. Mhm.
1:23:00
Um the number of women that I know who are micro doing GLP-1 uh in service to the spring and summer.
1:23:06
Um it's outrageous, right? Outrageous. Yeah, it's outrageous.
1:23:12
And and the idea is that it's GOP1 is a good example.
1:23:14
I mean again, insulin is a peptide, you know, but but there are many peptides like for instance, I'm very um excited about this peptide pinealin for for sleep, right?
1:23:25
And it's for some other properties, too.
1:23:26
But there's no reason why a drug company would go patent pineal.
1:23:29
They're busy patenting uh drugs like the recent class of drugs the Doras.
1:23:34
This is an interesting story work on on a peptide called hypocratin.
1:23:36
Um uh this peptide is it involved in generating wakefulness.
1:23:42
It's also in the feeding pathway.
1:23:43
And there's a new class of sleep drugs that suppresses the wakefulness pathway as opposed to making you more sleepy.
1:23:48
So lower abuse potential.
1:23:50
The doras have been released right.
1:23:52
They maintain the the architecture of sleep pretty well compared to other sleep drugs like ambient etc. Here's the issue.
1:23:56
They're about $325 a month.
1:23:59
So for a lot of people that's prohibitively expensive and they're patented. Yeah. To put it in context.
1:24:06
That's that's a luxury gym membership.
1:24:08
You know it's pine probably cost you.
1:24:11
And I'm not suggesting people take pineal. I'm not a physician.
1:24:12
So talk to your physician and then ignore him or her if you choose.
1:24:16
Don't don't do uh what don't do that on my uh on my suggestion.
1:24:20
But, you know, it would cost you about $15 a month, right?
1:24:22
And and you know, it's it's for me for for me personally has allowed me to I get two and a half hours of REM sleep a night in a six-hour sleep bout.
1:24:30
So, I've shortened my sleep bout to about 6 hours a night with a lot of deep sleep and and the pineal. Is it completely safe?
1:24:38
Reasonably, but we don't know.
1:24:38
We don't have clinical trials.
1:24:40
Now, I'm willing to run that experiment on me. Yeah.
1:24:41
But are you willing to run that experiment on you is the question.
1:24:45
And that's where it gets down to personal freedoms and and that's a you know I don't want to cover too many things here but the I will say that Robert Kennedy has been pretty vocal about at least before the election about wanting things like peptides and stem cells and supplements to be more widely available.
1:25:02
The truth is they're pretty widely available now and I will put an asterisk on stem cells.
1:25:08
I know a very prominent public facing physician who was almost paralyzed permanently from a stem cell injection into the disc of his back.
1:25:14
I talked to a neurosurgeon friend who ultimately saved his life by the way.
1:25:17
Uh this is a well-known story within the the the wellness community and he said yeah you know the discs can't accept stem cell injections but if you go down to Mexico or you go out of country and you say you know my back is hurting they'll inject stem cells into the disc of your back.
1:25:33
So is this to say that stem cells are bad? No.
1:25:35
They're not ready for clinical use yet.
1:25:37
And the United States has been very careful about things like this because before they were careful, there was a clinic in uh down in Florida that injected some stem cells into the eyes of people with macular degeneration who were worried about going blind. And guess what?
1:25:52
They all went blind right away permanently.
1:25:57
So I do want any discussion about peptides and these kind of things when you get into the realm of stem cells, it starts getting to be a serious matter.
1:26:04
So, I don't want to give the the impression that I'm like, "Oh, yeah, try this, do that, you know, shoulder hurt, put some stem cells."
1:26:09
And when you're talking about stem cells, you're talking about cells that can become essentially anything, including tumors. Yeah. Talk about I'm curious.
1:26:17
Talk about I'm curious. I mean, you you and and uh Rob, I feel like do such a good job of of threading the sort of needle around having conversations without giving endorsements for specific things that are untested and and really focused on um understanding uh helping
1:26:36
people understand the world, the science themselves and then take the actions um that they themselves as well as um you know their their doctors uh believe they should like do you think that uh podcasters generally in health uh don't really understand broadly the responsibility they have? I I remember
1:26:54
I I remember when I was in college there was a popular tech podcaster at the time that would pretty much widely endorse micro doing right and I I looked up I looked up to this guy right psychedelics. Right. Yeah. Psychedelics, right?
1:27:09
And and and in hindsight, I look back on that and I'm like that is so wild wildly irresponsible to to basically recommend, you know, that there's m micro doing some people report tremendous benefits, but to sort of broadly endorse something in hindsight feels insane and I don't think people fully grasp the responsibility that they have and just how much impact they can have on an individual or or or how a community thinks about something. Yeah.
1:27:36
I mean, Rob and I, we take it super seriously.
1:27:38
I mean, I've probably taken I've taken heat for a bunch of things in the larger science community, but probably one of the things I've taken the most heat for is um my belief that certain supplements can be helpful.
1:27:50
Um not as a replacement for for um you know, good behaviors and for prescription drugs.
1:27:55
I'm a fan of certain prescription drugs.
1:27:57
Listen, I did an episode on ADHD and talked about the prescription drugs and took a ton of heat from the kind of natural uh folks.
1:28:06
And then I did one on behavioral tools for ADHD and took a lot of heat from the folks who said, "Hey, listen, my kid got a lot of benefit from taking stimulants for ADHD."
1:28:13
So, we we do weave back and forth and on that kind of knife edge.
1:28:18
Um, and it can be tricky in terms of micro doing because I saw it come up in the comments before today's discussion and I was just kind of flagged in my mind there.
1:28:24
The data on micro doing psilocybin are basically clear that it's not known to have any major effect on major depression or some of the other things that psilocybin is being tested for clinically um including major depression PTSD etc.
1:28:38
depression PTSD etc. the data from clinical trials out of Robin Cardard Harris's lab at UCSF and elsewhere on highdosese psilocybin so macro dose right you know two two and a half grams uh four grams is the quoteunquote heroic dose if you're speaking Northern
1:28:55
California language um you know uh uh you know those dosages done in in pretty uh maybe you know two two weeks apart or so with the support of a clinical staff going into it through it out of it etc have been very successful in those trials for the treatment of of depression. Micro doing has shown very
1:29:12
Micro doing has shown very little effect.
1:29:14
Um MDMA was was put up to the FDA last year as a potential um treatment for PTSD.
1:29:21
It has not been approved.
1:29:23
It did not pass approval.
1:29:23
Uh unfortunately as a component of those trials um there were some sexual improprieties in one of the clinical conditions that you know all it takes is is you know one bad incident, right?
1:29:34
And then it was very but it cued people to this larger theme.
1:29:38
How are you going to protect patients who are really incap they're not incapacitated but they're not in a position to really advocate for themselves that you need probably multiple clinicians in the room and checks and balances.
1:29:47
So I think ultimately it will be approved.
1:29:49
Um the remission rates on PTSD by the way from uh properly dosed and spaced MDMA um is remarkable up to somewhere between 60 and 70% remission rates on PTSD.
1:29:59
And the new thing that people are very excited about is Ibagane or IBOGA.
1:30:04
uh 22-hour psychedelic journey.
1:30:07
This has been tested largely from Nolan Williams lab at Stanford um for the seems to in one or two sessions.
1:30:15
It has uh led to a significant number of veterans um essentially ceasing their alcohol use disorder, what is typically called alcoholism and opioid use disorder.
1:30:23
But it you need to be heart rate monitored while you do this.
1:30:27
The point here is that any discussion that we have on the podcast is as you can tell like I'm not known for being very succinct.
1:30:33
It's and it's for a reason, right?
1:30:34
You have to flesh out the conversation around these things.
1:30:38
You can't just say psilocybin is good.
1:30:39
MDMA, yeah, like works great.
1:30:39
You know, people can really get hurt.
1:30:42
And I think um discussions around whether or not you take magnesium 3 and8 or bislycinate be before sleep like okay you can probably speed through those a little more quickly but when you get down to things about psychoactive drugs in particular schedule one illegal psychoactive drugs when you get down to things about um hormone therapies you know you're just starting to get into the into the realm of where people can really screw themselves up.
1:31:07
really screw themselves up. That said, there's, you know, there are a number of things that I hope that the NIH and the and the public health messaging uh going forward will be more expansive about, which are drugs that are currently
1:31:19
prescribed at at enormous rates in the United States and elsewhere for which there are very severe side effects like like the there's this whole thing about people taking finasteride and dutasteride men to to keep their hair and getting permanent sexual dysfunction, right? Permanent sexual dysfunction. Some of
1:31:34
Permanent sexual dysfunction.
1:31:34
Some of them killing themselves as a consequence of this. These are young guys.
1:31:37
It's clear those drugs have very different effects in young people versus old.
1:31:40
Some people can use them safely. Some people can't.
1:31:43
These are FDA approved drugs.
1:31:43
As well as, you know, I think we're finally coming around to the idea that the SSRIs can be very useful for things like OCD in certain cases for depression, but it's it's a double-edged blade.
1:31:54
A lot of people suffered at um as a consequence.
1:31:58
So, I I hope that um there I hope very strongly for three things.
1:32:02
strongly for three things. one as a scientist who essentially has my podcasting job because of the way the NI supported me coming up through graduate school postoc my lab I think a 40% cut uh would be uh too severe uh regardless
1:32:16
of the IDC issue I I think it's just too severe it's going to kneecap science throughout the world and health the growth of new discoveries throughout the world I'd like to keep that funding level I really would happen to IDC what happens to Harvard and Colombia separate matter within there. But then I would
1:32:32
But then I would very much like a a very thoughtful committee to go in and look at every single grant.
1:32:39
Every single grant by area. Yeah.
1:32:42
You have to have a scalpel, not a scalpel.
1:32:43
And you could say, I mean, listen, in the field of neuroscience, am I the most qualified to decide what grant should be funded or not funded? No.
1:32:50
But I can tell you that I sat on study section for a long time and I can tell you there were clusters of grants that didn't solve what we call what good labs call the deletion test.
1:32:59
If this laboratory didn't exist, would it change the direction of science?
1:33:01
You have to pass the deletion test, not solve.
1:33:05
You have to pass the deletion test.
1:33:06
If you didn't exist, would it matter? That's a harsh test.
1:33:09
Nobody wants to be subject to that test.
1:33:11
But anyone that's getting millions and millions of taxpayer dollars, and by the way, millions of taxpayer dollars often to work on and kill, and we could argue about the I think most people are specists.
1:33:23
They'd rather see a mouse or a rat life or a non-human primate life.
1:33:27
But, you know, we're killing animals.
1:33:28
We're um we're occupying the lives of graduate students and postocs with taxpayer dollars.
1:33:32
The the work needs to be justified by passing the deletion test.
1:33:37
And the idea that AI could, you know, do that maybe, but the idea that a jury of close peers are going to decide the deletion test and if you pass, that's flawed in my opinion because the in culture, we don't have time for this, but the inculture of science is, you know, people aren't like helping each other out just to help each other out.
1:33:56
But when you're very close to something, it's very hard to make a really clear decision about it.
1:33:59
And when you're too far from it, you're not you're not qualified to.
1:34:02
So we need people that are in the middle who can really go in and say, "Okay, in the field of of let's say visual repair and neuroscience, give me a scalpel.
1:34:09
I hate to do it, but also, you know, some are going to pass."
1:34:12
And then I do think that instead of cutting people's funding entirely, there should be initiative saying, "Hey, listen.
1:34:18
If you want to run a laboratory, would you be interested in working on these important projects that maybe the Americans paying for this research ought to be able to to vote upvote? Why not?
1:34:27
I mean, it's 2025 and the the NIH has done beautiful work over the last uh funded beautiful work over the last hundred years, but I think an update in the way these things are handled is great.
1:34:37
As I say that, every single one of my colleagues is probably quaking that they're not going to pass the deletion test, except the ones that know they would absolutely pass the deletion test.
1:34:48
And then the question is who's making the decision?
1:34:49
And I'm not saying I should be making that decision, although I have some strong opinions about what's great, what's meh, and and what's like lousy.
1:34:57
But I do think uh we need we need to be more discerning the same way that a bunch of VCs would sit around and say like, hey, what are we getting for this investment? Well, yeah.
1:35:07
I think the question they ask is if we don't make this investment, is the is the world going to be, you know, worse off?
1:35:12
like does this company, you know, I think it's not the quite the same test, but I think it's important for investors to run that that process and understand are we really funding the future that we want or is this kind of a rounding error and the dollars be better elsewhere?
1:35:26
Can you give us a white pill?
1:35:28
Like are there any other organizations that can come in and help?
1:35:32
I I I always think of uh what happened in computer science and artificial intelligence.
1:35:37
Uh Google created the transformer architecture, not patentable.
1:35:42
It created large language models and we got chat GPT and we got all this great stuff.
1:35:46
It would be amazing if we could find a way that the big pharma companies that are big and profitable wind up stepping in and funding some of the gap.
1:35:54
Maybe there's nonprofits.
1:35:56
Maybe we just, you know, the the sheer will of the American people, we wind up voting for more funding.
1:36:01
But how do we really make science amazing and ensure really strong progression over the next few decades?
1:36:08
Yeah, I mean it's that's the the key question, right?
1:36:13
So I mean listen, I grew I was born at Stanford Hospital, you know, I did my training much of it at Stanford.
1:36:17
I'll probably die at Stanford.
1:36:18
Hopefully I don't in the hospital.
1:36:20
I'd like to I don't know die get in morning sunlight or something.
1:36:23
But my dad's my dad's a physicist turned computer scientist, right?
1:36:27
Worked at Xerox Park which was an incubator for ideas, right, for many years.
1:36:31
years. So I grew up in that landscape and you know he always said you know eventually degrees in computer science um will be important for starting companies right but when I was young that wasn't the case right people dorked around on computers and made video games and and then we saw cell biology migrated into the world of biotech and
1:36:48
for the treatment of certain diseases it has been I mean we can't forget that there are certain basic discoveries going back to our earlier discussion that have led to tremendous treatments like the drops to lower eye pressure and glaucoma if you catch your eye pressure elevation early and you take those drops, you will keep your vision. Wow. Wow. Okay. Right. You'll keep your vision.
1:37:06
So, it's not like we can't cure glaucoma, but you have to that's based on basic work.
1:37:09
So, the real key here is to ask, you know, so where has it been done successfully before?
1:37:14
It's been done successfully in in the world of computer science and AI, right?
1:37:17
Growing up, I heard about AI.
1:37:19
It was kind of like like no one no one took AI seriously growing up or brain machine interface was like sci-fi. That was so seriously.
1:37:25
Now, my good friend from from childhood, Eddie Chang, who's the chair of nuros surgery at UCSF, I mean, he's using um BMI, brain machine interface and AI to get people with locked in syndrome to to speak and to and Elon's going to get them to move.
1:37:40
I fully believe that Neurolink is going to solve paralysis. Yeah, I really do.
1:37:43
I'm not saying that because we're on X.
1:37:44
It's I know I know the head neurosurgeon there.
1:37:46
They're well on their way, right? Yeah.
1:37:47
So, the question is what when should it move into biotech?
1:37:51
And you know, Peter Teal had the breakthrough labs idea.
1:37:54
He was paying people to not go pursue graduate degrees in labs to get the PI like me papers and advance their careers and then maybe go get their own lab but to take great ideas and go to through breakthrough labs.
1:38:07
I don't know what happened to breakthrough labs, but I do think that incubators like that where you have enough money to test um you know to move fast, right?
1:38:15
You know, move fast, break things model, figure out whether or not um something is promising and then advance advance that. And here's the key.
1:38:22
As soon as something looks promising, you want to be able to pour human power onto that.
1:38:26
And the problem in academia is every graduate student and postto needs a first author paper in order to have the potential to get a job.
1:38:35
And so one thing that I'm I've you know been in Jay Badacharia's ear about is you know I think I think that the independent investigator model of science in this country where it's the Huberman lab or it's the whatever lab named after the PI.
1:38:48
I think that's a flawed model.
1:38:50
This is going to really upset some people.
1:38:52
There should be labs named after a particular mission like curing blindness laboratory or solving Alzheimer's laboratory and then people can collaborate within that lab without the idea that they're necessarily going to go start their own lab.
1:39:04
In in Europe typically there's a lab head and it's it's very hierarchical but people who get PhDs often stay in those labs as permanent careers.
1:39:12
They get paid better and better.
1:39:14
The NIH has not had a mode to keep paying people as staff scientists and most people don't want to go off and run their own labs.
1:39:20
So, I think the ability to iterate more quickly, much like a a tech company funded by NIH would be enormously beneficial.
1:39:27
And I I could be wrong, but I think it's going to be music to most people who would like to go to graduate school because what it means is that you can be on the bench next to someone and they get the thing that's really interesting and rather than feel like I got nothing, you can now start working together to accelerate the progress of that work.
1:39:43
And so you now who can go off and run a lab becomes a separate issue, but it's really becomes discovery based science.
1:39:49
And and we can't forget that the taxpayers are funding this, right?
1:39:54
So this is like, as much as we'd all like the next generation of scientists to all have their own labs, I think what we really want is for the next generation of scientists to have the opportunity to make fundamental discoveries that seed health and cures and treatments for disease, right? Yeah.
1:40:09
I mean ultimately do we really care about the careers of scientists? No.
1:40:13
That we care about treatments for cure treatments and cures for disease the careerist model.
1:40:17
Well, I do think it I do think it matters that that young people aspire to be scientists and I and that is a concern that I have about the destruction of of trust between the public and the science community via co.
1:40:31
The concern is that you'll have a a you know a 10year period a decade you know a decade where young young people say I'm not going to go into science because they have some and and I think that that's the work that that you do with Huberman Lab has potential to heal that divide between the public and the science community and and one of the reasons why I think it's so important. Yeah. Yeah.
1:40:53
I mean, I listen, one of the most the three most gratifying things I can hear um as feedback for the podcast are, "I can't believe this is free."
1:41:02
Um, you know, that that feels good.
1:41:04
Um, that people are sleeping better.
1:41:07
Like, oh goodness, I made a few morning sunlight and dim the lights and a few things in the evening and like I'm sleeping better because I know that catalyzes many, many other positive changes.
1:41:16
And then the third one and the one that's most heartening to me as a scientist less as a you know a health podcaster is when people say like you really turned me on to like neuroscience or you know uh or I'm going into the field of psychology.
1:41:28
I want to be a therapist but I'm really starting to incorporate some physiological tools with my patients.
1:41:33
That to me is those are the three most gratifying things.
1:41:35
You know I as you can probably tell I'm very impassioned by this right.
1:41:39
Um folks close to me know this is how I talk all the time.
1:41:41
No I haven't had any caffeine in the last you know four or five hours.
1:41:45
just trying to we we have we have we do I'm glad you love this stuff. All right. We love it. We drink it every time.
1:41:52
We debated I I we didn't have time but I debated having you join the call and it was just stacks of cans that you couldn't even see us.
1:42:00
Four of these a morning minimum.
1:42:00
But I have a very high caffeine tolerance and uh um you know fast metabolizer. The fast metabolizer.
1:42:08
And and you know there are many issues I care deeply about but what you just put your finger on is so key.
1:42:14
If this 40 I know people are like don't give them this money they up during the pandemic, excuse my language or they lied.
1:42:20
Listen, these kids that would potentially become scientists, we can harness their energy. They didn't lie.
1:42:26
They didn't do anything wrong.
1:42:28
And if you if we train them properly and we put them into an environment where where the spirit of that environment is just discovery based, maybe a little less careerist, a little more discovery based, you know, and we really fund the most important work.
1:42:42
it we can really transform the treatment and cures for all these diseases that everyone is so concerned about.
1:42:48
I I know that because it's proved to be the case every single time.
1:42:52
If you look at AIDS, there are two things that led to the treatments and you know AIDS isn't fully cured, but it's a very different landscape now than in the '8s. Two things, right? Money and emotion.
1:43:02
And it came emotion first and money and then a bunch of scientists and labs and a bunch of funding. Okay.
1:43:06
So where the reason we don't have a cure for schizophrenia even though it's 1% of the world's population is sadly it tends to run in families there's not a strong lobby for for research on schizophrenia.
1:43:19
The director of NIMH told me that former director Bob Desimoon the reason there's so much interest in autism is that any human being with half a heart looks at a kid and I'm not talking about kids that are on the spectrum or a little neurody divergent who you know I work with those people like we're not talking about that.
1:43:34
I'm talking about kids that have like stereotype motion, can't be in an open environment, will forever need support.
1:43:39
I'm not I'm not trying to like split the spectrum, but there there are kids on the spectrum that really struggle, right?
1:43:44
And so the reason there's so much interest in in finding treatments for things like that or for major depression is because many many people suffer, right?
1:43:53
And what's required is money and scientists working on the right problems and ditching the things that aren't promising.
1:44:02
the the faster you can iterate the better.
1:44:04
And so I do think the Silicon Valley model has something to offer.
1:44:07
And I don't know what the latest update is on Breakthrough Labs and if Peter's moved on from that.
1:44:12
I haven't heard much about it, but that doesn't mean that they don't have something interesting.
1:44:14
But look, I as you Yeah, the key is like if if you have one, you know, one car that that comes out, it's not so great.
1:44:22
You don't destroy the car industry, you know?
1:44:26
You know, you don't like basically dismantle the entire system.
1:44:29
this car, you know, hurt people, so we should just eliminate all cars, you know. Yeah. Yeah.
1:44:36
Look, I think an engineering, we're on X after all, like an engineering um more of an engineering stance on on basic research is helpful.
1:44:45
It's also true that, you know, I can name off countless discoveries in biology where, you know, an accident of leaving something, you know, on the shelf too long or something led to an interesting discovery.
1:44:55
I mean, there is some fortuitous aspect to it.
1:44:57
But now with AI you can also run lots of hypotheses in silico.
1:45:02
Um it more in vitro then move to invivo models.
1:45:05
I mean there is a way to make the the money go further but ultimately there's no replacement for great ideas and there's no replacement for the for human energy and the the best source of hum human energy that I know is youth.
1:45:19
And so you you need to catch people when they're coming out of college not send them to work in a lab for 3 years before they might go to graduate school.
1:45:27
then realize that they can't make a good living compared to their friends in finance.
1:45:31
Like you're not going to get rich doing science.
1:45:33
Although some scientists do get rich, right?
1:45:34
I mean, Janentech was started by scientists.
1:45:37
I mean that there are companies, you know, but ultimately you want to harness that that energy of youth and the spirit of discovery and give people the resources.
1:45:49
I had wonderful mentors and and one of the most important things they taught me was you get money, you give it to young people, and you get the out of their way and let them try.
1:45:56
And and then when they don't work, you you guide them and you say, "Listen, like you're wasting your time. Move on." Like, "No, no, no, no."
1:46:04
And they get bound to it. No, move on.
1:46:05
It's like a bad relationship. Cut and run.
1:46:06
And then that you just start the next thing.
1:46:09
And so, and eventually you get hits.
1:46:11
And then if you can pile human power onto those hits, right?
1:46:15
I'm just describing what everyone knows to be true.
1:46:17
But this is not the way that the system has been designed.
1:46:20
It is it is old and clunky and it doesn't need to be scraped or you know hacked in half almost at a 40% cut but it does need to be revised. It really does.
1:46:32
Yeah, makes a lot of sense. Well, this is fantastic.
1:46:35
I want to hit the gong for you. Can you give us a stat?
1:46:38
How big is the Huberman Hubman Lab podcast now?
1:46:42
Can you give us an idea of the scale of the impact you've had?
1:46:44
We like to hit the gong when we hear big looking over.
1:46:48
He's like, "No, no, don't leak any even some rough numbers. Just any number.
1:46:52
May maybe just one episode hit a million downloads.
1:46:54
Give us some number something to hit the ground."
1:46:57
Those are rookie numbers.
1:46:57
Those are rookie numbers. Yeah.
1:46:58
Um, so well, can I say one thing first? Please.
1:47:03
Anything that that you know, rankings reflect acceleration, not absolute reach. Yes.
1:47:07
Like Rogan, like when people go like Rogan's not Rogan's reach is like beyond I I know the numbers.
1:47:15
like I'm not going to talk about his numbers, but it's it's like it's an order of magnitude greater than all the other major news hubs combined. Oh, yeah. Unbelievable.
1:47:21
Uh we're but no, we we are very blessed to reach um you know 20 million people. 20 million people. Let's go. Let's hit the gong.
1:47:34
Thank you so much for coming on.
1:47:34
This is uh this was uh an important conversation.
1:47:40
I'm excited for people to get the uh get into the entire episode. Yes. Yeah, it's fantastic.
1:47:44
It's over four hours long. No.
1:47:46
So, if nothing else, you know, with the Hubman Lab podcast, I I always say if nothing else, I'll cure insomnia.
1:47:51
But no, the um in all seriousness, I you know, it's time stamp so you can break it down.
1:47:56
If you want to get right to the vaccine stuff, skip to that bounce. It's fantastic.
1:47:58
I listened to it last night.
1:48:00
Can be digested however you want.
1:48:02
I want to say thanks to you guys.
1:48:04
I started watching your show since you had Rob on. Fantastic.
1:48:07
What you guys do is great.
1:48:07
I also think that you're transforming the way that media is is, you know, dispersed each week and, you know, and and it's awesome.
1:48:14
You guys are on X doing what you do and elsewhere. So, thanks so much. We appreciate it.
1:48:18
Yeah, you guys are most welcome. Anytime. This is really fun.
1:48:22
You ever need more matino? Let us know. Oh, yeah.
1:48:23
We can always We're going to park a fridge right here. We're going to do it.
1:48:28
The guys the whole the whole crew back there is is cheering.
1:48:31
Where are you guys based?
1:48:33
We're in Los Angeles in Hollywood. Yeah. All right. Yeah. We'll come right over.
1:48:36
It's a nice We'll see you soon.
1:48:36
Thanks so much for helping us. Awesome. You're the man. Cheers.
1:48:39
We are live from the Palace of Party Rounds. Oh my god. It's YC Demo Day.
1:48:46
Welcome to our YC Demo Day stream. That is insane.
1:48:49
We have Gary Tan joining us.
1:48:51
Wow, you absolutely destroyed my laptop.
1:48:54
Gary Tan, welcome to the stream. There he is. What's going on?
1:48:59
The president of Y Comer, Gary Gary Tan, good to see you. Great to see you. Great to see y'all.
1:49:04
Uh the uh there's a line around the block still. Is that right? People are pumped. Oh my god.
1:49:10
Yeah, it's it's absolutely slammed. Uh take us through. What are you seeing? How's this one going?
1:49:14
Uh what's actually changed cuz we're in a different location. Break it all down.
1:49:18
In the middle of uh this is like our HQ.
1:49:20
You know, this is uh you know, we uh we got the spring batch together.
1:49:25
This is the first spring batch that we've ever done. X25 now. Yeah. Yeah.
1:49:29
Paul Graham doesn't like X, so we're slowly replacing it with spring. Spring. Okay, there we go.
1:49:33
You know, when you create a place like this, I think you get to dictate little things like, you know, not liking X, you know. So, yeah.
1:49:41
So, we got to write out spring, but you know, I don't know.
1:49:42
What do you think it should be? It could be P.
1:49:46
Maybe if it's not X, it's pron mathematical notation for this stuff.
1:49:52
Some like different, you know, we have the Y combinator algorithm.
1:49:53
We need a different algorithm to define the different seasons.
1:49:57
I think I think just forward, you know, winter, spring, summer, fall. Yeah, summer, fall.
1:50:00
I mean, the the energy from the founders is really electric. Honestly, it's insane. Fantastic.
1:50:07
Like downstairs, uh we got a Well, the fun thing about hosting all of the investors in our house is that uh I got a whole who's house, our house chant downstairs.
1:50:15
So, so yeah, we got Give us an overview of the We We heard a little bit of your talk earlier, but give us kind of a breakdown of of how you introduced today. Yeah, absolutely.
1:50:25
I mean, dude, it's like 90% AI.
1:50:27
It's about 10% 11% hard tech, which is awesome. Yeah.
1:50:31
And then the really crazy stat is you know how the last four batches about the last year uh you know the batch itself as a whole has been growing revenue by 10%. Uh this time it's 12. Wow.
1:50:43
So we're actually inlecting up and you know that's what you would expect.
1:50:47
We're in the middle of the age of intelligence.
1:50:50
You know uh 6 months ago 9 months ago you the models were 90 IQ you know then they were 110. Now they're about 130.
1:50:58
You know we're sort of entering super intelligence zone and IQ incoming,000.
1:51:04
I don't even want to talk to that, but yeah, probably very valuable.
1:51:07
I thought um Sam's essay yesterday was very precient and that um like that's that's sort of the silver lining like everyone's sort of worried, you know, what's going to happen to the jobs.
1:51:18
Like to me, what's going to happen is the exact uh I mean it's actually an an uh an opening, right?
1:51:24
like uh the wild stat that we're seeing is actually the number of 18 to 22 year olds applying to YC and getting it is up 110%. Year on year, right?
1:51:33
So, you know, is that driven by people liking to drop out of college or skipping college entirely?
1:51:38
Is that because they're they're viewing college as kind of like I mean there was always a meme about like oh what you put YC in the education section of your LinkedIn.
1:51:48
Now it's like oh wait no YC could actually be the replacement. Is that intentional?
1:51:51
Well, the extreme meme here that is interesting to think about like I don't believe this, but you know the memes among uh you know the 18 to 22 year olds in particular is uh this might be the last time you can start businesses. Oh, interesting.
1:52:02
Because you know once super intelligence hits then uh you know the moes that you you know the businesses that will exist will sort of uh ensconce like the seven powers of the moes out there. Sure. Sure. Maximally efficient. Yeah. Exactly.
1:52:16
They just reach their terminal value immediately and maintain it forever.
1:52:19
So you're trying to get network effects, you're trying to build uh we're trying to we're trying to get brand, you're trying to get a cornered resource.
1:52:26
Uh and then the wildest thing here is that you know this uh perfect uh someone was telling me yesterday uh right now if you look at college grads uh the rate of unemployment for CS grads is actually 2x that of art history majors.
1:52:41
I have not looked this up yet.
1:52:43
Everybody everybody's been quoting that. We've heard that too. We talked about it.
1:52:48
might there might just be something crazy going on with art history majors found they found some hack and they're all getting employed it's full employment for them.
1:52:54
Uh very interesting um question on the AI side.
1:52:56
How are the companies feeling about the current battle between little tech and big tech we saw with WWDC it feels like Apple's kind of retreating from some territory.
1:53:07
We could be seeing more opportunities for developers for like mobile apps.
1:53:09
So, we could see another mobile app boom because Apple's saying, "Hey, look, maybe we're not the company to build every single AI experience on the iPhone."
1:53:18
We're seeing new hardware maybe come from OpenAI.
1:53:20
There's different stuff going on with open source.
1:53:22
How what's the interplay between the average YC company and and the Mag 7 right now?
1:53:28
Oh, I mean, the good thing is we still have, you know, we have net neutrality.
1:53:32
So, and you know that thing we've you know we have seven companies in the you know mega tech world but you know what like anyone can just put something on the internet and they get distribution right so uh I think that you know the most important thing right now that I think all of us should really be
1:53:48
considering is we need platform neutrality right you know the second that you know today you open your Siri and you don't get to choose you know do I want perplexity do I do I want you know chat GPT on there you know no like that this is the next sort of unfolding that needs to happen. We actually need
1:54:03
We actually need the platforms to allow other people to enter.
1:54:07
But if we do that, we can actually have tens of thousands or hundreds of thousands of companies each of which can get to a billion dollars net revenue, you know?
1:54:15
Uh and like you could do it probably with 10 people, right?
1:54:19
So if you link up these two super mega trends, like that's the future that we want to live, right?
1:54:23
Like you can be 18 to 22.
1:54:25
You you don't necessarily have to go and like the credential matters a lot less.
1:54:30
You know what matters now?
1:54:30
It matters, you know, your agency and your taste. Yes. Right.
1:54:34
And so, and you don't have to like go and get anyone's permission.
1:54:38
The nature of LLM, you could actually get bet a better tutor from an LLM than you could from at times working under somebody at some big company who would maybe explain here's how you make a financial model for a software company, but now you could just talk enough with Chad GBT that you could probably figure it out even better than having like a mentor in some cases.
1:54:54
I want to ask about applying to YC.
1:54:56
Have you noticed that the apps are becoming more GPT driven?
1:55:01
Are people using AI to write their apps?
1:55:04
Do you recommend against that? What do you think?
1:55:05
I mean, if anything, like I think the best apps, you know, in the past, we might say, "Oh, this is AI slop."
1:55:11
I think we're because the models are now smart enough like my writing process for instance has totally changed to this point.
1:55:18
Like I am, you know, you're actually able to come up with better ideas.
1:55:23
I think, you know, I I would rather um I would rather people actually prompt to get like all of the ideas out there.
1:55:33
You know, one of the things I've been using in chat GPT lately is like give me 10 options for different concepts or ideas that might fit here.
1:55:40
And then what I'm doing is I'm using my T like I'm using my prompting to do that.
1:55:44
And then on the back end it'll give me like 10 things.
1:55:46
Five of them make no sense.
1:55:48
Two or three of them in my brain I'm like, "Oh, I didn't think about that.
1:55:52
that actually is a useful thing that I can put in with these other concepts that are in my head. Yeah.
1:55:57
head. Yeah. And so I think it's actually uh you know the computer is a bicycle for the mind like if you can prompt really really well um you know using it just I mean you have to treat it like a vehicle right it is not a destination of
1:56:12
its own it's actually you know this is like the self-driving car for the mind right I mean even just treat it like a coworker all that matters is the end output and if you're working on any type of project not working with a smart collaborator is going to lead to a worse output. Yeah. Yeah. Yeah.
1:56:29
Condensing down these ideas because I' I've read a lot of apps where it's like, wow, there's actually like some really incredible KPIs in here, but you buried it in paragraphs of exposition that were completely unnecessary.
1:56:37
And I know that the people that are actually reviewing the YC apps are not going to read this far.
1:56:40
And so just having it as a co-pilot seems like a really really how have you and the other group partners push to guide uh the batch around re reporting on revenue, right?
1:56:51
There's a lot of conversation around what is ARR?
1:56:52
It's sort of this flexible definition.
1:56:54
Yeah, there's obviously a lot of pressure for every founder coming into demo day to like show results.
1:57:00
How how do you guys kind of uh guide? Absolutely.
1:57:06
What we'd like people to do is put a contracted ARR if it is and then if it has an opt out clause where you know people can sort of get out they should just have a little star on it and it's like you know opt out at 30 days or whatever this I think it is very very
1:57:21
important that founders um are just don't engage in securities fraud is like kind of a basic thing right so you know I think that it is a you amazing thing about uh the tech today is like the demos are so impressive that people are willing to take a big chance to sign up for big deals and yeah they
1:57:41
obviously want some flexibility but being really clear about that I mean these are non-GAAP metrics you know there is some gray area but we need to define new metrics as we move forward are you seeing companies able to pivot more times within a single batch and just iterate faster
1:57:55
out because historically it wasn't unreasonable for a company to pivot a few times to get to something great and maybe they only have two weeks at the end to really sprint and show progress now I could imagine certain companies like really really really pushing it. Have you seen that at all? Yeah, abs. I
1:58:08
Have you seen that at all? Yeah, abs.
1:58:08
I mean something like 30% of the companies change their idea during the batch and that's actually great.
1:58:15
I mean you start as you're researching and building. Yeah. Yeah.
1:58:18
And I I think that that's always been true.
1:58:20
I mean some of the biggest companies end up being you know literally pivots from like 2 3 weeks ago and then I think that you know people might say oh that you know that kind of sucks or you know why is that?
1:58:29
I mean I think it's good.
1:58:31
The good news is like you know uh you can just do things.
1:58:37
The good news is like so so when I think of the YC mantras I think build something people want uh talk to your customers is you can just do things.
1:58:44
A third that we're adding is that important to have that valition that that uh uh that a agency.
1:58:49
It's the distillation of agency right I think so. Yeah.
1:58:53
I mean a lot of it actually I think the most important thing that uh I feel like everyone has to learn the hard way is uh to learn you know the sound of your own voice to say uh you know not to get all bicameal mind about it but you know like that's sort of where we're at. Well, no.
1:59:10
Well, no. I mean, if you uh read, you know, PG's old essays, like one of the things that really jumps it out at me is like to what degree, you know, your childhood, your schooling, like he has so many essays about, you know, what high school is like, being a nerd, like all these things are like, you know, really spoke to me and that um the
1:59:28
process of becoming actually a founder is actually a journey inward to actually learn the sound of your own voice that like you can make your own way, you can have agency, you know, just because I And actually by definition the startups that create new categories require a level of courage that like people have to find within themselves. Actually you
1:59:47
Actually you have to say actually you know what like I know the Wall Street Journal says that and you know the hater del journalists say this and all this you know you have to actually separate yourself from the default view of how the world works.
2:00:01
And then you're actually trying to divine secret knowledge by going into the markets going to talk to people that have never, you know, even touched chat GPT yet, right?
2:00:12
Like they don't know the re it's like that meme where you're, you know, you're the tech guy like in the corner and then like everyone's dancing and they just don't know.
2:00:21
They don't like that's where we're at still.
2:00:22
Like it won't be like that for another year or two, but like for now that's still true.
2:00:27
You go into any business in the world, they don't know this revolution is about to happen to them and we get to create it. Yeah. Yeah. Yeah.
2:00:33
Like last demo day, we talked to a company that was doing uh AI voice interactions to help the elderly process Medicare receipts essentially.
2:00:43
And they discovered that the the elderly were talking to their chat bots for like 3 hours.
2:00:48
And that's just something that you might you might be able to predict intuitively, but I thought it was just a very funny like discovery that only happens from actually trying to build in that in that market with the latest and greatest technology.
2:00:59
And that's what I love about YC Demo Day.
2:01:01
What's your latest thinking on batch sizes?
2:01:05
Is that is that are we are we staying, you know, where we're at?
2:01:07
Uh the high level is like we want as much prosperity in the world as possible.
2:01:10
You know, Brian Chesy is on my board.
2:01:12
Um you know, Paul Graham and Jessica Livingston, Carolyn Levy.
2:01:16
uh the board has given me my directives.
2:01:19
It's uh you know what we need to this is the tree of prosperity.
2:01:23
Let's have the tree of prosperity grow.
2:01:25
But you know I think the YC fundamentally is the managed marketplace.
2:01:28
You look at an Airbnb I still super I'm still you know bullish on Airbnb and that you look at the amount of space in the world.
2:01:34
Like the amount of space that has been listed on Airbnb is still a tiny fraction of what it could be.
2:01:40
And what does that unlock?
2:01:42
and it unlocks new experiences, travel like a human, like all these things, you know, it's hundred billion dollar company from nothing.
2:01:47
And uh I think that the same thing is about to happen to innovation and venture.
2:01:52
Um but we have to do it thoughtfully, right?
2:01:55
Like you know, we're at demo day here.
2:01:57
We have uh more than a thousand of the top investors in the world all congregated here, right?
2:02:02
We have a thousand people in this building. That's amazing.
2:02:04
And um you know, we need to grow them thoughtfully.
2:02:08
Like we need, you know, what I need is I need YC to continue to provide returns, right?
2:02:12
And you know, uh can't comment on, you know, the rumors about scale this week, but like we need a lot more things like that and we're getting it.
2:02:21
Like this is sort of uniquely the community where that happens.
2:02:24
Talk to us about the two new partners that were added to the YC partnership.
2:02:27
Oh yeah, there's three actually. Yeah, sorry. Two. Yeah.
2:02:28
Uh Andrew Nicholas and John Shu actually. Okay, cool. Sorry about announced. No. Yeah, Tyler 2. Okay. Yeah. Yeah, that's Tyler too.
2:02:36
Um I mean all of them created uh companies that exited for you know hundreds of millions to you know pedag duty as you know a public company uh you know north of a billion is that's remarkable that those are exactly the kind of partners that we want at YC.
2:02:52
It's like they've been there and they've done that.
2:02:53
So roughly how big is the partnership now? It's 15 partners total. 15 partners. That's great.
2:02:56
So you can still keep the actual uh like group sizes fairly small within the batch.
2:03:01
I mean that's the main you can just basically guess at what the batch size will be because each partner can do 10 to 25 companies depending on uh how hard they want to work.
2:03:14
But uh you know I I don't think it's a numbers game.
2:03:16
I think it's you know we want to fund all the really really good founders and I feel a little bit embarrassed about you know we're accepting companies at a 0. 8% rate right now. Wow.
2:03:31
I think that we're behind the eightball actually like you know I I feel really good about this like we're going to keep growing what we got here um but I also need to grow the partnership and then we need to fund better and better companies and then you know this is a 10 20 50 100redyear thing like we're trying to
2:03:48
build Y combinator into a multiundredyear institution and um we we need it have you had any companies go through this batch and decide not to raise additional capital at this point because they're just making so much revenue that they they have no need for famously uh Tom funded Axiom last uh last last batch. Yeah. Yeah. And they're Yeah. Yeah.
2:04:04
And they're doing hundreds of Yeah.
2:04:06
Hundreds of millions of dollars in pure profit.
2:04:08
So I saw that and I was like trying to look back through our stream and like did we did we I mean crypto is wild man. Yeah. A wild west. It's a wild west. That is insane.
2:04:17
Well, we'll let you get back to it.
2:04:19
Thank you so much for coming on the stream. Always honor. Welcome to the stream. I'm John. Nice to meet you. Nice to meet you. I'm John. How you doing?
2:04:29
PMF for or die season soon. They everyone died. Everyone died.
2:04:32
You have a story about PMF or die. They died. They died. It happens sometimes.
2:04:38
Never never lock yourself in a room for the 90 days.
2:04:43
Move to sunny San San Francisco. Do YC.
2:04:46
That's our from here on out.
2:04:48
So anyway, uh please introduce yourselves in the company. Yeah. I'm Tom. I'm Eric.
2:04:52
You guys, you guys look you're not related at all, right? We're not related.
2:04:58
You could go by brothers.
2:05:00
People ask us, people ask us.
2:05:04
Uh what does the company do?
2:05:04
We're doing a gentic people search.
2:05:06
So we have a database, people database of around 200 million people. Interesting.
2:05:10
Um, we use a genic search to search over that for uh companies and businesses to do like sales recruiting uh GTM and uh so so are we talking about specifically like I am a recruiter and I need a saleserson and I'm going to yo to you to try and hire them. No. No.
2:05:26
So basically like you can put in a criteria like essentially what we do is pull the hold the microphone up.
2:05:31
Here push the microphone a little closer. Yeah. There you go. Yeah.
2:05:33
I mean essentially what we do is we like like deploy an LLM, assign every profile to that LLM, and then given your criteria that could be like one paragraph long, we we just ask the LLM if if this profile like meets that criteria and then and then we built like a load load distributor to run like 10,000 LM calls in parallel. Wow. Do do that at scale.
2:05:52
You know, we can we can search over like 5 million profiles in like 30 minutes. Okay. Insane.
2:05:56
Uh so so walk me through one of the key examples. I'm sure this is live. You have customers.
2:06:01
What's an example of someone using this?
2:06:04
So, for example, um you know, we had a Cory come in yesterday actually.
2:06:08
It was just like every founder that was acquired that was a CTO of their startup was acquired by data bricks or snowflake in the last 3 years and they and they still work there.
2:06:17
And there's only probably like 25 people in the world that fits that profile and we found all 25 of them just by using because we can use an LM going just like because we have like we can throw as much comput at the problem as we want and then and then and then the LM get better the more comput.
2:06:29
It's interesting to be able to find you're able to find information that is historically effectively impossible to find without doing all the work that you guys did ahead of time. Makes sense.
2:06:39
So, uh, who's who what buyer or what buyer archetype within an enterprise or business is most excited to buy your product? Yeah.
2:06:50
I mean, like mega recruiters by far recruiting firms. Yeah.
2:06:55
Uh, no, just like people that you know or like a big recruiter at Yeah.
2:06:56
know or like a big recruiter at Yeah. I mean, we work with Mer you know for example yeah so so you know like like just like people hunting for talent but it's like a generalized type skill right so for example you know like an I lab's training a new voice model and they need like people that speak Cantonese then
2:07:13
it's like okay find me every Cantonese speaker in the US that has like a podcast presence and like okay great these guys can come train our voice models right like that's kind of or they might even need someone who speaks Cantonese and also is an expert in biology so they so they can talk about the biology terms and that's something
2:07:27
that how are you going to search that on so it's so funny cuz I you I run these type of queries in my own head where I'm like we need a videographer who's in LA that has experience in film but you know but he's also part of teapot that's basically what we need often times has a sense of humor it's like how would you even know that well if you look through
2:07:47
their post I'm sure you can figure out it's basically like as close as you can if you just give like a criteria to human recruiter so you can imagine like 5,000 human recruiters like manually looking over profiles and then you know okay uh business model are you Uh most recruiting firms they charge on like a per fee basis $30,000 to place an engineer somewhere. Um are you are you
2:08:05
Um are you are you doing like a seatbased pricing consumption based pricing?
2:08:09
This sounds expensive if you're talking about running 20 million LLM queries at the same time.
2:08:14
It's actually not as expensive as people think because because you know open source models have gotten so good. Yeah.
2:08:18
Um like like I'm guessing like you know any like deep research query cost us like $10 maybe. Um okay.
2:08:24
But then um you know like we we charge for conversion with with our customer conversion. Okay. Yeah. Yeah.
2:08:31
We charge by conversion customer.
2:08:31
So that's probably pretty expensive.
2:08:33
And also um we have like a platform just that's just available to everybody.
2:08:37
So they can pay us a subscription fee search as much as they want.
2:08:40
Do email enrichment phone numbers.
2:08:42
I feel like we could use this. This is we might be. Yeah.
2:08:46
We need a guest that doesn't hate tech that we're talking about this. How did you two meet?
2:08:51
Uh what were you doing before YC? Yeah.
2:08:53
So we met in elementary school actually.
2:08:55
So we were originally from Canada. Let's go.
2:08:56
Um we went in elementary school and we became close friends. Yeah. It's been great here.
2:09:04
But um uh we became close friends when Eric he uninstalled Windows on my computer.
2:09:08
Um and I was really pissed at him for a day and then we fixed it.
2:09:11
So then we became great friends after that.
2:09:13
Windows couldn't load anything homework.
2:09:20
That was it was during school and then uh the ultimate prank is uninstall Windows on your best friend's computer always being boys.
2:09:25
We spent one semester of college each. So I was at Penn.
2:09:28
He was at UC San Diego and then and then come January we were both like like why are we even there? Let's 18 19. We're both 18 right now. Here we go.
2:09:41
What's What's the youngest team that you've met here besides yourself? 17.
2:09:45
There is a high school senior. Okay.
2:09:45
I didn't remember when I It is getting younger. Yeah. Yeah. Yeah.
2:09:53
We have a bottle of wine here.
2:09:53
We're gonna give it to you. Yeah.
2:09:59
Hopefully you're not going back through YC in 3 years.
2:10:00
Hopefully you're at the NASDAQ or something.
2:10:04
Talk to me about traction metrics.
2:10:06
Anything that you're sharing here at demo day, anything to get the venture capitalist excited. Yeah. 270 paying customers.
2:10:11
First version of the So, I don't know if you guys heard about LinkedIn. Linked. No. Yeah. Yeah.
2:10:14
So, so, so, so a very early version of this product we launched which is like rank Stanford like like Stanford rank which was so we basically built this we basically scraped the entire alumni database of Stanford. I'm sure they love that.
2:10:29
Yeah, they were okay with it.
2:10:29
We put it online and then uh and then and then and then we had this uh we had we had this app where people can like see two random alumni put next to then next to each other and then you and then you vote for who's more cracked. who's more crap.
2:10:45
And that version and that version of the app picked up like 80,000 users was how we got into YC and stuff. Very cool. Very cool. And then Yeah.
2:10:52
And then you know when we came down here uh 27 paying customers about 16,000 my monthly recurring revenue since ago. Yeah. No. But yeah. Wow. Just you two.
2:11:06
It's so there's three of us.
2:11:06
We're two two our founding engineers from high school. He just graduated. the same high school. Yeah. Yeah.
2:11:14
We always the same high schooling.
2:11:19
No, I'm super excited for you guys.
2:11:21
Congratulations on We're going to get on the product and I actually have a bunch of people to send this to. Yeah, this is fantastic.
2:11:26
You should give it a try. Fantastic. Thank you so much.
2:11:27
We'll talk to you soon guys.
2:11:31
So, we were trying to make this happen for the last week, but he just announced his series A today. 17 million, right? 17 million. Congratulations. There we go.
2:11:38
I mean, we have stuff to celebrate. I already got the hat. I got the hat. Hey, congratulations.
2:11:47
It seems like you've done that a couple times. Yeah. Yeah.
2:11:51
This is the biggest round of the day. Uh, who did the round?
2:11:53
Uh, how far along are you? How big is the company? Give me some stats. Yeah. Yeah. Okay.
2:11:57
Uh, so ABC 8 ABC did the By the way, guys, this is awesome.
2:12:01
I did like the interview with Venture Beat and this I'm way more starruck by this.
2:12:03
So, like I'm not just saying that, but I'm like, "All right." Uh, yeah. So, ABC led the round. Let's go. Absolute dog.
2:12:10
Uh what what partner are you working with? Uh uh Jack Moshkovitz. He's great. He is fantastic. Um yeah. Yeah. So how far along?
2:12:17
So we were uh And were you in the last batch?
2:12:22
No, we were we were a couple batches ago. We're summer 23. Um great vintage.
2:12:26
Was that still excellent? What was that?
2:12:28
No, we were the first batch.
2:12:28
We were Yeah, we had an advantage.
2:12:29
Um but yeah, so Outset does AI moderated research. So what does that mean?
2:12:35
It's like when when big companies have to do a ton of research like Nestle has to go figure out what you know whether they should launch this weird version of DeJouro pizza or something. They go do research.
2:12:45
So either they're running massive surveys and they're getting like very low fidelity data.
2:12:48
They don't really know much.
2:12:49
It's just a bunch of numbers or they're running interviews and that's where it's a one-on-one interview with everybody, right?
2:12:53
Which not actually cost effective. So totally.
2:12:55
So now you can run AIE interviews.
2:12:57
That means AI is leading the conversation, digging in, following up, probing deeper and synthesizing all that for you. Got it. Uh we work. How big is that?
2:13:04
How big is the legacy market imag?
2:13:06
So, so the research market is like 140 billion.
2:13:10
It's it's actually a really big one.
2:13:10
Uh just like Yeah, there's like huge companies that you probably may have never heard like Ipsos and Contour and like these like big guys that are uh just kind of sitting there.
2:13:20
So, so there's a lot of opportunity and uh yeah, we work with like Microsoft, Nestle, Weight Watchers.
2:13:25
So, we've been super enterprise focused and uh yeah, we're a couple years in.
2:13:28
What are the interaction patterns that you like or you think are kind of unlocked by AI?
2:13:31
I imagine that voice phone calls is top of mind versus like forms which was always like available and already computerized essentially.
2:13:42
It's actually I think the the cooler thing is what you can get from participants and synthesize.
2:13:44
So it's like so now we do video interviews and they do audio and they can even share their screen, right?
2:13:49
So all of that is being ingested and then like people ask like to say I have an avatar when it's interviewing and we actually did research and people don't want that.
2:13:57
They don't want the avatar.
2:13:57
No, they don't want the avatar.
2:13:58
The avatar is something like weird uncanny valley stuff where I'm like and you're just like a used to phone calls.
2:14:02
Yeah, people are used to phone calls and they're fine sharing their video, but like if you suddenly see an AI, you're like, I'm trying to make, you know, 15 bucks doing some research.
2:14:10
You like suddenly see that, you're going to be all weirded out.
2:14:13
So, it's actually much better to have AI come through with voice and text. Yep.
2:14:16
Talk to me about recruiting. You said 15 bucks.
2:14:17
That's enough to get someone to jump on, but how do they even find out that 15 bucks is on the table? Yeah.
2:14:22
So, we partner with a number of different like companies that do nothing but panels.
2:14:25
Like nothing but recruiting. Nothing but recruiting.
2:14:28
You have like uh user interviews and prolific some demographics and some basics.
2:14:32
They get all that and they have millions of people that are like ready to it's like gig work, right?
2:14:36
And and so they Okay, I need 100,000 people that drink energy drinks to give me feedback. Exactly. Exactly.
2:14:42
And you can Yeah, you can do all that through our platform.
2:14:43
Is there a certain unlock when an AI doesn't need to like, you know, if a human is scheduling research calls, you know, you can imagine they do like four 30 minute blocks and then they take a little break and they do some more or something like that.
2:14:57
I I don't know how it works, but an AI could hypothetically talk for hours and hours and hours and hours like kind of 100%.
2:15:03
So there's like, you know, I think a lot of AI stuff is like, oh, where can it replace the human thing and now it's cheaper, but actually like this this is uh taking on the stuff that like humans can't physically do.
2:15:13
We only have 24 hours in a day.
2:15:15
And so it happens that people use it to like interview 500 people, right, in a day or two.
2:15:19
And that's like it's literally not possible.
2:15:22
It's like just the laws of physics don't allow it.
2:15:23
And so they're able to do that.
2:15:24
They can do multilingual.
2:15:24
Oh, they kind of do it all at once.
2:15:26
So, you can wind up like unlocking actual like stuff you've never heard.
2:15:31
I don't Can I curse on this?
2:15:32
Uh, we usually don't, but uh stuff that they like would not have uncovered anywhere else.
2:15:36
Um, and they actually are able to get that like in this way. Yeah.
2:15:40
What's your what's your stack under the hood?
2:15:43
What models are you using getting the most value out of?
2:15:46
Yeah, we're using a combination.
2:15:46
So, it's, you know, it's like we're hitting multiple models constantly, but Azure we we're using a lot of Azure actually.
2:15:52
to OpenAI models under the hood through Azure and it is their model router helpful?
2:15:57
I saw they announced that at Build.
2:15:58
I haven't talked to anyone that's actually used it.
2:16:00
Um I I don't know yet actually.
2:16:02
I so I don't think we rolling it out.
2:16:04
So basically like what we need is just incredible amount of reliability because what we'll have is like three customers are all running like 500 simultaneous interviews.
2:16:12
So we have to actually scale very very quickly.
2:16:13
Um and also like a lot of our customers like the Nestle of the world like they care a lot about like super safe, super reliable.
2:16:20
we kind of like wind up the kind of Yeah.
2:16:23
And then you're also probably going through like peak LLM usage hours in the middle of the night.
2:16:30
No, we can't process it on our own time.
2:16:32
It like has to happen in that moment.
2:16:34
But then we also fall back to OpenAI. We use Gemini for stuff.
2:16:35
So we're like hitting a lot. Yeah.
2:16:37
Talk about the uh uh the is the voice modality beyond the uncanny valley at this point?
2:16:43
Is that why voice is valuable?
2:16:44
Are you using voice a lot?
2:16:46
because you can imagine that uh this might have been possible via text interactions.
2:16:50
You're texting your responses back and forth, probably get more out of a voice interaction.
2:16:54
Uh are we just if we if we talk to you again in like 2 years, do you think it'll be like okay yeah now I'm a believer in the in the avatar thing because it is photoreal. Okay. So so two things.
2:17:05
So one right now what matters most is obviously the modality of the participant.
2:17:08
Like if you think about like all we care about is the most deep like like uh thoughtful in-depth data that we can like pull from you. respond.
2:17:17
So that matters way better than text and that's way better than text.
2:17:19
Our first product that we like. Yeah.
2:17:20
Our first like pilot with Weight Watchers back in 2023 was like all text and it was actually pretty good, but like it's nothing compared to what people actually say.
2:17:28
Um and so that's what matters most is like video screen share uh uh uh voice from the participants from AI. Yeah.
2:17:36
Like so we use voice a lot.
2:17:36
So there's a voice to voice mode where there's like no buttons, it's just conversation and like people like it but like not as much.
2:17:42
And I think there's still if it's not a person then it's still got the minor imperfections, the kind of slight bits of latency.
2:17:49
And so I do think in 2 years we'll talk more about that.
2:17:53
But ultimately like people know it's a computer.
2:17:55
Like they know it's AI and like they're kind of cool about it.
2:17:58
Like we we've done a bunch of this research where like do you care that it's AI?
2:18:02
They're like no like I I get it. Yeah. Right.
2:18:03
In fact they like have even expressed that like they'll share more because there isn't a person.
2:18:09
It's this idea of like social desiraability bias.
2:18:10
You're like, I don't want to come off as a person that is X, Y, and Z to another human.
2:18:14
I will just tell you, look, I want this to be faster. Like, yeah.
2:18:20
Or or like or like the real reason I'm not using your product is this.
2:18:23
I'm not going to say that to a PM or a researcher who's like, right?
2:18:27
But instead, you'll tell the real truth.
2:18:28
And that was like our initial pilot with Weight Watchers was all about weight loss.
2:18:32
And like that was like a thing people don't want to share a lot about.
2:18:35
But with AI, they like shared everything about their lifestyle. Yeah.
2:18:39
I mean, it sounds like you've done like you've worked with really big companies and that's where the money is, but uh when you went through YC or if you're here, uh you know, there's a big theme of talk to your customers.
2:18:46
Has there been any pull at the lower end of the market? Yeah, it it's funny.
2:18:50
I I sometimes you get a found reaching out like I I really want to use this cuz I don't want to talk to my customers like talk to your customers.
2:18:57
We don't want you as customer. Yeah.
2:19:01
There's basically I like I think a good a good reason not to use an AI moderator is like if you could sell to that person probably don't outsource that, right?
2:19:09
Like maybe to build the relationship yourself.
2:19:10
So the truth is especially all the B2B stuff there's not as like you usually turn around and say come back when you're like a couple hundred people and like you're kind of scaling that out.
2:19:19
Um but yeah, with with some consumer stuff where you really have millions of people you're trying to learn from, it can make a ton of sense.
2:19:27
uh talk to me about the like data processing and the intelligibility that goes into once you have all that data.
2:19:33
Uh it's very nice to be like hey we have like you know 5,000 hours of video we prepared a 200page report.
2:19:38
Yeah but nobody reads that.
2:19:40
So how are you thinking about actually compressing that down because that seems uniquely suitable for AI but there's still a lot of art in terms of like a lot of people say oh yeah I did a deep research report and then I uh the next query was give me 10 bullet points about that because I didn't actually want the full report. Yeah. Yeah. So so all right.
2:19:55
So, so our vision here is like we should be building we are building the like deep research but for primary research.
2:20:00
So you think about deep research is like all right all desk research now commoditized.
2:20:05
commoditized. So what if you just ask a question you just want to like learn from real people that's much more up to date that whatever like your own proprietary data it should be the same kind of idea right end to end agents are doing that but today like the premise of AI kind of uh uh scaling your qualitative research is like you also have to help them do something with all
2:20:23
that conversational data right because otherwise you're like left with just like hours and hours of transcripts I'm like I'm not going to review that the application of this is I know this is not what you're focusing on but if I was a a a VC that wanted to write a a multi multiund million growth check being able to like get live interviews with like 500 customers. I mean people do this
2:20:41
I mean people do this with GLG and Yeah, that's right.
2:20:42
No, this is a huge opportunity actually.
2:20:45
So, so um yeah, I have a whole side thought like I think one of the challenges there is with the expert networks.
2:20:51
It's all about finding the right people.
2:20:53
But if you're in a situation where you can find the right people and you can find a hundred of the right people.
2:20:57
Well, we just had Plato AI on you. You should talk to them. They're in this batch.
2:21:01
They do like LLM based people search.
2:21:01
So you can find like every person that at at data bricks that was a former founder, you know.
2:21:07
No, there is an opportunity to connect all of that through through that.
2:21:10
But what I was going to say with the synthesis is is what we do is basically taking all the video stuff and then we like process it and we give you basically reports, but it's like not just a like here's what it's telling you like deep research style, but what you can do is like start like uh uh slicing and dicing it.
2:21:23
You could like we quantify it, we give you breakdowns, we like build highlight reels for you, and then like people can like cut it and segment it.
2:21:30
And so it's like a whole analytics suite on top of qualitative data which is like not a thing that has ever been done cuz what were you doing what were you doing before this?
2:21:39
So before this I was VP of product at triple bite if you know yeah harsh hired me. Yeah. Exactly. Exactly.
2:21:47
He uh yeah he he anyway so so uh I I worked at Triple Bite at at uh Jumpstart another company.
2:21:52
So I was like leading product and design teams but earlier in my career I was like a I was a consultant where I was doing like this work non-stop where I was like are you a are you a nominative determinism guy? Yeah. Yeah.
2:22:03
He's looking for capital to fire a billion dollars into research. Yeah.
2:22:10
I have not capitalized on that.
2:22:10
I was looking at big we're big uh we're big into the names or the truth the name. I like that. Yeah. It works well for you.
2:22:18
I have a friend who always calls me the loose cannon.
2:22:22
You can never know what to expect.
2:22:22
It's a little bit too much.
2:22:24
Dial it in the capital cannon.
2:22:26
You can trust him with your capital.
2:22:28
Just give it to fire in the capital cannon and boom.
2:22:30
Yeah, I'm going to pivot off of loose cannon on capital. Aaron capital cannon. There you go.
2:22:38
Anyway, this has been fantastic. Thanks so much. Congratulations.
2:22:39
We'll have you back on sometime.
2:22:42
Can you introduce yourselves? Yeah, I'm Cinnamon.
2:22:46
I'm a mechanical engineer by background.
2:22:47
uh built hardware for Apple, Google, Slack. Uh wow.
2:22:50
Hardware hardware hardware for Slack. Yeah. What do they have? RFX way. Oh, sorry.
2:22:56
The Stanford linear accelerator center. Oh, that SL.
2:23:03
Okay, that makes more sense.
2:23:03
I was like, what what hardware device did I clarify? Cool. Awesome. So, I'm AJ.
2:23:08
Uh I worked as a researcher at Stanford, researcher at Harvard.
2:23:13
I was like an intern at Intel. Um cool.
2:23:14
Built a lot of stuff, I guess.
2:23:17
Can you uh pull up on the mic a little bit more and then tell us what your company does? Yeah, so we're Godella.
2:23:22
We're building a frontier physics model for mechanical engineers.
2:23:26
So currently AI models can't handle physics accurately cuz a lot of them are language based. Ours are different.
2:23:30
Ours are built to handle physics accurately which means it can be used as a faster cheaper replacement to simulations and physical prototypes. Okay.
2:23:37
This is something that I think a lot of labs like to to promise right solving you know they like to bring up Yeah.
2:23:43
They like to bring up this idea of of solving these problems in the physics.
2:23:47
So, so walk me through it sounds like you're actually training a model.
2:23:50
Is it all uh reinforcement learning with verifiable rewards or are you generating uh a whole bunch of training data?
2:23:57
Is there a human data labeling component like what is the pipeline to create what you're creating?
2:24:03
So, at a core we extract embedded physics from data and that makes it generalizable. Okay.
2:24:06
Did you want to share more? Yeah.
2:24:08
So, we don't use reinforcement learning or anything.
2:24:09
It's basically like this sort of like you know encoding framework like where we actually like take the mesh itself like we work with meshes right because we're like in simulation and stuff.
2:24:18
So we take the fluid mesh and then we like encode into like this low dimensional space and that allows to like learn the actual physics of the system. Interesting.
2:24:24
And this allows and when you do like symbolic regression on like the latent space you get like a lot of you know like you get a lot more generalizability than you would get with like regular models. So yeah.
2:24:34
When did you guys start working on this?
2:24:36
Did you bring it into YC or did you pivot to this at some point? We brought it into YC.
2:24:39
So we were teeing a computational mechanics class together at Stanford which was teaching undergrads in mechanical engineering how to use the traditional simulation software kind of bred a hatred for those softwares.
2:24:50
Meanwhile is doing insane research at Stanford to use ML to model the physical world in these crazy accurate ways.
2:24:56
He's like building ML models for Intel that are replacing months of trial and error in their plasma edge process.
2:25:02
And I realized like hey there's this huge opportunity to out with the old out with these old simulators.
2:25:08
Yeah, let's bring physics informed ML to the broader group of engineers who could really benefit from these faster, cheaper answers. Okay.
2:25:12
Uh try and make it a little bit more concrete for me.
2:25:16
I'm familiar with like CFD, computational fluid dynamics.
2:25:17
I have a I have an engine and I'm trying to simulate how the air will flow over the jet that I'm building.
2:25:23
Is that an example that we could use to kind of build off of?
2:25:27
Is it just is it just faster inference than calculating everything uh deterministically? Is that the goal? Absolutely.
2:25:33
Like all of simulation, every time you start with a simulation tool, the engineer starting with a question, right?
2:25:39
You've got a question, your 3D model.
2:25:40
You want to know how is what is drag on my Yeah, exactly.
2:25:44
How is that going to change as I change thickness or angle of attack of my air foil?
2:25:47
Now, imagine instead of needing to learn a simulation software, you can ask with natural language, drop in your CAD, and get simulation quality results instantly.
2:25:56
And we say instantly, it's 4,500 times faster than a benchmarked uh GPU accelerated solver that we tested against. Wow. Wow. Wow. Okay. Very cool.
2:26:04
What's the go to market like?
2:26:06
I mean, I imagine you're selling to like very large aerospace defense companies or who else is building stuff?
2:26:12
I mean, Apple, Google, these companies could buy this. Yeah, exactly.
2:26:13
I think the huge benefit of physics informed ML is we can tackle problems that traditional simulators cannot tackle.
2:26:22
Multi-ysics, multi-cale, make it extremely feasible to tackle those problems.
2:26:25
And we can also fill in the gaps where your idealized equations don't suffice to capture the complexity of the problem.
2:26:30
So these problems that Apple, Google, maybe aerospace are throwing millions at in terms of R&D and building and testing, we can give you accurate physics models that can replace your need to physically build and test the product.
2:26:41
So and they can probably like reality check your faster results with the traditional system that they have in place whenever they need to and build that confidence run that overnight instead of while they're designing traction selling it. So far it's been great.
2:26:57
So we launched two weeks ago.
2:26:57
We entered a 25k a year contract with an engineering firm to replace ANIS which is $30 billion incumbent.
2:27:03
Um however I think our stronger pull right now is we have some exciting opportunities with enterprise customers to again tackle those highest value problems where you don't there is no solution for for modeling something like drop simulation right like there is no great simulator that gives you that fast accurate answers even though it's you know governed by software gets like 70% accuracy or something. Yeah.
2:27:23
And it's also very slow though.
2:27:25
It's like 2 weeks to compute a drop simulation on a 14-inch MacBook Pro today.
2:27:29
So like these really high value problems for enterprise customers.
2:27:32
We have the opportunity to go apply our software and they'll give them more accurate answers. Very cool. How big is the team?
2:27:37
Where are you going next?
2:27:39
How's the fund raise going?
2:27:40
Yeah, it's three core one advisor.
2:27:42
Uh the fundrais is going well.
2:27:45
We're just about Well, it's very exciting. We're just uh Yeah. Yeah. Yeah.
2:27:49
Whoever whoever's like, you know, bidding, I'm sure they're, you know, going to watch this.
2:27:53
So Congratulations on a fantastic demo day.
2:27:55
We've been giving out hats to folks who come on the stream. Thank you so much. Great to meet you guys. Congratulations.
2:28:02
Can you introduce yourselves? What are you building? Sure. So, we're Morpho AI.
2:28:05
We're building a software tool for engineers that are building new robots and new machines. So, interesting. Yeah.
2:28:10
So, I came came from the manufacturing tech world.
2:28:12
Um, we both met at Harvard. I was an MBA. He was a posttock.
2:28:14
And you know, you should talk about yourself too. Yeah, absolutely.
2:28:17
And yeah, I met her when I was at Harvard.
2:28:19
Like she said, we got a introduction from a mutual mentor.
2:28:22
Um I did my PhD at MIT focused on automating the design of robots.
2:28:26
So it seemed like uh you know wanted to bring that into some sort of product show people in the world how useful it was and how it could change the way people design and she was super excited about changing all the pain points in manufacturing. So we're doing this.
2:28:37
What is exciting in terms of robotics in manufacturing?
2:28:38
There's a lot of noise about humanoids but we talked to a lot of people who are just saying robotics in manufacturing or is it a tool to accelerate the manufacturing of robots? second one.
2:28:47
The second one, but the main beach head market is really in industrial robots. Yeah.
2:28:52
So, so what those look like? Yeah.
2:28:55
So, the crazy stat that we found is, you know, if you're actually buying a robot off the shelf, you can't actually just like put it in the factory floor like 90 plus% go through a customization process.
2:29:03
So, that's like 6 months of lead time and, you know, it takes up a lot of engineering hours.
2:29:08
So, one of our customers, they were trying to build a full new industrial robot arm.
2:29:11
Um, 6 months gone in, you know, arm's not lifting.
2:29:15
They came to us and we basically redid their entire design.
2:29:17
So quite literally we're allowing to build new hope and the future in a matter of minutes.
2:29:25
How concentrated is the robotic arm market?
2:29:28
Are there just a few companies that you really need to integrate with deeply or do you need to create something more generalizable out of the gate?
2:29:33
Um so we're starting out with a little bit more of the OEM side of things but also these integrators that are buying these arms and saying I now need a new hand.
2:29:40
So out of like the what 50,000 robots that get deployed in a given year, most of them have a new hand that's made.
2:29:47
And so now you have a journalist engineer trying to make a new hand for 4 to 6 weeks in a new design.
2:29:53
Uh so really plugging in in that early stage of what do I build uh before we even get to the programming part. Yeah.
2:29:59
The whole idea is you just have to input the test specifications that you needed to solve and then as much as possible we're automating of the mechanical and somewhat the control design side of things.
2:30:07
Is there is there a lot of data that you need to feed in?
2:30:09
Is there a lot of data that you need to pull from the manufacturer before you can We work with parts that manufacturers have and that they like to work with.
2:30:16
Um, and aside from that, it's task specifications.
2:30:17
But we unlike a lot of the uh Genai companies out there, we do employ some Genai solutions, but they're all trained from simulation and not from data set.
2:30:27
And that's really important because the data for how do you go from design to some how well it's going to work that that doesn't really exist.
2:30:32
Nobody takes logs and curates data sets of things they've built and how well they worked. Sure. Sure. Sure. Uh talk about traction.
2:30:39
Uh sounds like you guys already are are in market. Yeah.
2:30:41
So uh we actually just got a grant from the UK government 2 and a half million British pounds last couple of months. Non-dilutive. Fully non-dilutive. Congratulations. We love it for that. Jordy. Oh my god.
2:30:54
My ears have in here than out there. Funding from here.
2:31:01
I mean Europe just shooting themselves in the foot.
2:31:02
Not going to be like why didn't we That's going to be at least like quarter point or something. Anyway, congratulations. Thank you.
2:31:12
None of us are British, but they basically just Well, we're setting up an office in London.
2:31:15
Like, we're really excited about about working with them and setting up an office.
2:31:18
Of course, there's benefits to us.
2:31:23
We have a lot of collaborators there as well. That's fantastic.
2:31:24
Yeah, it'll be an amazing outcome for them.
2:31:26
Last thing, bull bull bear on uh humanoids. Yes, humanoids. What's your take?
2:31:31
I'm bear, but like medium bear. I don't know.
2:31:33
It was was like, okay, if you're going to build a humanoid, which humanoid?
2:31:37
Like people come in all sorts of sizes and shapes.
2:31:40
A construction worker is not the same as like a like a toddler or something. Okay.
2:31:44
Not toddler, but like ballerina. Exactly.
2:31:45
Backer looks different than So, you're going to need custom designs regardless of what you do.
2:31:50
But I mean, the other side of things is just like what is the application right now of the humanoids that isn't solved better by re-engineering the things around the humanoid?
2:31:58
And that's that's there's a there's a long tail there.
2:32:01
So, we we hope we can help people to design the humanoids of the future.
2:32:03
I just don't think it's here yet. Yeah. Yeah. Yeah.
2:32:06
I think I think there's Yeah.
2:32:08
You're not saying bearish in a 100redyear timeline.
2:32:12
I just think it's a little I completely agree. Very reasonable stance. Anyway, congratulations. Congratulations.
2:32:15
I'm going to give you a little progress so we don't have to deal with the whole fist like Okay, perfect.
2:32:20
Anyway, who are you guys?
2:32:24
What are you guys building?
2:32:25
And please microphone as close to your face as possible. Yeah. Uh I'm Daniel.
2:32:29
Uh and this is Musa and we're the co-founders of Vibe Grade. What do you guys do?
2:32:33
So we help teachers save time grading papers directly in their existing learning management system.
2:32:38
What's the response been like?
2:32:38
Uh do the teachers love it?
2:32:40
Yeah, actually all of our customers are paying out of pocket, right? Really? Wow. Okay. Interesting.
2:32:46
Uh you said in their LMS. Yes.
2:32:46
So who are you plugging into?
2:32:48
Who's the most dominant platform providers right now?
2:32:51
So the main one, the best integration is on Google Classroom. Oh, interesting.
2:32:53
So a lot of is Blackboard not a thing anymore? Not that much.
2:32:57
There's some we got a request for it, but most teachers are on Google Docs and Canvas.
2:33:02
So like most K to2 which we serve are on Google Docs in classroom.
2:33:06
We have toddle as well, but it's mainly those few. Makes sense. Yeah.
2:33:11
But for universities they like Canvas is great like they can it'll go and highlight actual sections.
2:33:15
So can you add that on as like one of those like if I'm in Google Docs I can I can do an addiction. It's a Chrome extension. Okay.
2:33:23
So we realized that teachers they don't want to have to learn a new tool and go to a new platform to do that.
2:33:27
So we built a integration that works directly with Google Docs and directly with Canvas.
2:33:31
So it's easy as clicking a button and then we open up a window inside Google Docs. Yes.
2:33:37
So walk me through the typical like workflow homework.
2:33:39
I assume it's like the student uses the the teacher Daniel to write the questions.
2:33:44
The student uses chat GBT to write the answers and then the teacher goes back and check it. You guys grade it.
2:33:50
But but what's the actual workflow in the loop?
2:33:52
Are students are students sending uh Google Docs links to their teachers when they're done?
2:33:58
So Daniel was just in high school, so he really knows how this works. I dropped out.
2:34:03
You dropped out of high school. Get the bottle of wine. Congratulations. Yeah. High school dropout. Yeah. You front ran everybody.
2:34:15
Everybody that was like coming into demo day. Oh yeah. Dropped out of college.
2:34:19
It's like that's how it played out. Had to go further. Yeah.
2:34:23
I mean, so you're selling I think we heard about you earlier.
2:34:26
You're selling it back to your teachers now.
2:34:28
Like that you at school you dropped out of sort of.
2:34:30
I mean they don't really like me that much for some reason. Yeah. Like on my final exam.
2:34:34
Well, I mean I I skipped finals to go fly to we we went to this edtech conference in Orlando.
2:34:39
So I guess they don't really like me because of that.
2:34:42
But we we had to bring in some more teachers.
2:34:44
Well, they'll like you when you're the commencement speaker in a couple years. Hopefully.
2:34:47
So the workflow is where teachers usually they students submit on Google classroom or like canvas.
2:34:53
They submit a Google doc or a PDF and then the teacher opens it up the doc and they grade it within by like highlighting certain sections, adding comments, a summary of the feedback.
2:35:03
So we do that whole process uh right in the Google doc and then we have the teacher review everything.
2:35:07
They can speak to our system to understand their tone and their style.
2:35:12
So then we add those comments.
2:35:14
How how is the actual like testing and grading and homework process changing?
2:35:18
Cuz I imagine that the like the solution to chat GBT homework is probably everyone's on a laptop typing the answers in the classroom and there's a monitor watching that you're not just aing it and then it can be AI graded and then that's a win for everyone.
2:35:34
Is that roughly what's happening? Yeah.
2:35:35
Like the thing that we've seen is like people say all the time, you know, like students are writing with chatbt, teachers are going to grade with AI.
2:35:40
Like what's the whole point?
2:35:42
But well, the objective of the student is to learn and if you're just using like chat and mindlessly submitting something, well then you're not doing the right thing.
2:35:49
Um there's always going to be a way to get around it.
2:35:53
Um if you really don't want to learn but like for teachers, the main like objective is to give students the best feedback and instruction.
2:35:58
So what we're doing is just enabling them to do that a lot better because even in school when like our teachers would give us just like generic copypaste feedback. Yeah.
2:36:08
So we just want to give students the best feedback possible.
2:36:09
gives teachers their time back and they really get the value because they're the ones experiencing the benefits. Yeah.
2:36:15
Talk to me about the top five.
2:36:18
Why would you not at one point just give the tool to students as well? Yeah, 100%.
2:36:22
That's actually what we're working on next.
2:36:24
So, we just signed three contracts with with schools last week. Okay. Congratulations.
2:36:29
and they want to be able to give this tool to students so that while they're writing um they're going to get real-time feedback on their Google doc as if the teacher were adding comments in real time.
2:36:40
So it's sort of like you know you you submit a rough draft to your teacher they have to grade it and then give it back to you and then you can do a final draft.
2:36:48
Instead of that why not just have like the assistant give you feedback in real time.
2:36:52
Oh yeah that makes sense on the dock. Yeah that's great.
2:36:54
Uh talk to me about the top of funnel.
2:36:55
How are you telling teachers that this works?
2:36:57
I imagine at the price point you can't do handtohand combat sales.
2:37:02
Uh how are you getting in front of teachers these days?
2:37:06
So all of like we have 100 teachers that are paying completely out of pocket and all of that has been organic through either the Chrome web store.
2:37:11
So they find us when they install another extension like Grammarly and then they tell their friends about it.
2:37:16
So word of mouth has been really strong.
2:37:18
We got 600,000 views on Instagram and Facebook in the past 30 days. There we go.
2:37:24
We just started posting like memes and things like that and teachers really they they share it and then they look at our page and so they find us there. That's great.
2:37:31
So that's the top of funnel up until this point and we're going to start pumping out more like UGC style content.
2:37:36
We're actually going to the biggest edtech conference in the US at the end of this month. The Super Bowl of Ed.
2:37:41
Yeah, there's going to be 20,000 teachers there and we got a really big we bought a 400 foot booth there and we're going all out for it trying to reach as many teachers as possible. Congratulations.
2:37:51
You guys make your first uh money on the internet. Thank you.
2:37:54
How did we first make back?
2:37:54
I used to make I used to make websites for like random people.
2:38:00
I charged like this pastor in New Jersey like $3,000 to make his church a website. Wow. There we go.
2:38:06
That was back when I was maybe 16.
2:38:09
I made like a a mental health chat app in like grade 10 and like a couple of people like one person in Japan found it and they installed it and paid for it. They paid for it. That's amazing. Congratulations. Happy day. I need to hear. Thank you. Bloom. Bloom.
2:38:23
Are you building Bloom filters? No. No.
2:38:26
What do you So, the easiest way to Nope. Okay. Bring it down. No.
2:38:31
So, lovable but for native mobile apps.
2:38:32
The easiest way to love to just like show you. Okay.
2:38:36
So, have you guys tried to build mobile apps? Touch this. What?
2:38:40
I've never seen that interaction before.
2:38:42
He's got a UI from the future.
2:38:44
So, have you guys ever tried to build mobile apps? Yes. I wrote Objective C. It was terrible. I wrote X code. It was awful. Sucks.
2:38:49
So, we love Apple, but you know, we we're very excited about the anthropic partnership. Yeah.
2:38:57
So, typically you'll have to write code.
2:38:59
You'll have to build the app in Xcode or something.
2:39:00
You'll have to submit for apps to review.
2:39:01
You have to get your users to download it on Tesla, enter an invite code. Okay.
2:39:05
So, with us, I can literally just like talk into my phone, build a native app, and then I can send it to you via literally bumping phones. Do you have an iPhone? Yeah, I do. I do. Wow.
2:39:14
And it'll just load the app right onto the home screen. Okay. Malware installed.
2:39:23
All personal information extracted. I don't know. I got I got one. Let me see. Uh, we got half.
2:39:28
I mean, this is just using airdrop.
2:39:31
I think Do you need to turn it on again? No. Oh, no. Live down.
2:39:36
Airrop like maybe we need another.
2:39:39
Are you on the same Wi-Fi? You want to try? Yeah, I'll try. Let's see. Airrop. Airrop is on. Oh, contacts only. Yeah, it's on everyone. Okay, I got it. I got it. Cool. Let's try it now. There we go. Skill issue. Skill issue maybe. Okay. Jesus. Is it going? I got the wave. You saw the wave, right? Yeah. Yeah. Yeah.
2:39:55
I only have ramp public add quick eight sleep wandering installed right now.
2:40:02
Those are going to be app one more shot. Uh you can take it.
2:40:05
Otherwise, we'll have to maybe try on your phone. Yeah. It's okay.
2:40:10
It's not like this is live or anything. Okay. You you try. You try. We'll give it a shot. Anyways, keep keep Yeah.
2:40:17
So, so when someone like tries to build an app with Bloom, right, it's doing but it's not doing that.
2:40:24
I think the internet just sucks.
2:40:24
I think the internet is not great. The internet.
2:40:27
Anyway, so what you would see is you'd be able to open this app on your phones like instantly, right?
2:40:31
And it uses app clips under the hood. Oh yeah. Okay. Okay.
2:40:35
So that's how you're getting the app on without me actually installing. Exactly. Exactly.
2:40:38
And then Oh, they're really hacking the apply. That's awesome. Okay. Exactly.
2:40:41
And so the other thing is like when you prompt to build an app with us, we also automatically deploy a back end for you that real time syncs data between devices.
2:40:50
I as I understand app clips, it's like it's like uh there's there's like one app like the Uber app exists and then there's an app clip that ties to to Uber.
2:40:57
Are you creating like custom app clips?
2:41:00
It's all it's all around one iOS. Oh wow. One Bloom app.
2:41:07
So it's like an app store with an app store. Yeah. Kind of like that.
2:41:12
We've talked on the on our show before about this idea of like ephemeral apps like memes.
2:41:17
Like there's a lot of things that should exist but only for like a day.
2:41:20
We had so many ideas for like funny apps but we don't want to actually go pay.
2:41:24
pay. Oh, it used to be there's so many people people have an idea for an app and you're like well you realize it'll cost like a million to build that it's not actually like with us we built an app for for demo day in 5 minutes that everyone in the audience could just scan to like vote on our valuation in 5 years
2:41:40
and it like had a graph that updated in real time for everyone cuz it has a back end connected to it and so what we want to enable is a creator economy but for software okay under the hood what are the best uh code generation LLMs that you're using what do you like what's exciting And how how is that market developing? Yeah. So I mean right now Yeah.
2:41:56
So I mean right now we're we're obviously for the smartest models we're using cloud for sonet.
2:41:59
Um but we're also experimenting with you know a smart mode smart mode and a fast mode because sometimes people just want to make like a quick edit to their app and like literally you can just speak into our phones right you just go into like this like edit mode and then you can just type hey add this feature or whatever.
2:42:16
Um and so sometimes you just want those changes fast.
2:42:18
Um, and what's cool is like if you had this app open, it would also hot reload on your device. Right. So, very cool.
2:42:25
What uh what use cases are you most excited about? What are you seeing? What categories broadly? Yeah.
2:42:31
I mean, so right now we're seeing people that already think as software as a creative outlet use this.
2:42:35
So, developers, designers, and entrepreneurs.
2:42:37
So, especially for designers and nontechnical entrepreneurs, it's great because they can just like be their creativity gets unlocked with this.
2:42:44
Um, and so we're seeing people build all kinds of things like personal apps, but also apps that I wouldn't have imagined before, like someone in Africa building like a wild life tracking app for their conservation.
2:42:55
Uh, and then people building like funny apps for find the most delicious animals to go after to conserve animals for for my own hunting for my next hunting expedition.
2:43:09
But um, but yeah, what I'm really excited about is like the apps that I can't even imagine, right?
2:43:14
When the YouTube founders put out YouTube, I'm sure they weren't imagining or MKHD just like you know you put out images in Chet.
2:43:20
They didn't really imagine studio exactly happening.
2:43:22
And for us, I mean, it's software, so you can literally do anything. Congratulations. Thank you so much. How's your round going? Oh, and round is closed. Let's go. All right. All right. Good.
2:43:37
Meta is paying 15 billion.
2:43:37
I thought it was 14 15 something like that for a 49% stake in scale AI that pays out investors and employees.
2:43:46
CEO Alex Wang is leaving to join Meta and head up a super intelligence lab.
2:43:51
Feels like those licensed and acquired deals at Character AI inflection and adept to avoid regulatory scrutiny says Tane who's been on the show.
2:43:59
Um this is from the information.
2:44:02
Uh you got to hit the size gong 15 times for Alex Wang.
2:44:03
Let's hear it for a fantastic pulled out my uh your headphones.
2:44:10
I'm going to need to let you hit the size. I will hit the gong. I will hit the gong. Where's the gong mallet? Oh, it's over there. Where's the mallet? It's over there. Here we go.
2:44:25
[Music] Technical difficulties.
2:44:26
Lots of difficulties, but we got we got it done.
2:44:30
Um, congratulations, John, but that wasn't 15 hits, but it was one big one.
2:44:34
Uh, and uh and it's very exciting.
2:44:37
I I'm I'm a big fan of Alex Wang. I've met him.
2:44:39
I interviewed him on stage at an event about a year ago and uh he has a ton of really deep insight into artificial intelligence and I'm sure he's going to be an absolute beast in the board dog.
2:44:48
Hopefully you get a board seat. A boardroom general.
2:44:53
He's a boardroom general. Um yeah.
2:44:53
So, so, so it's funny when when these the these kind of deals uh are relatively recent phenomena.
2:45:01
We saw this with character AI to Google to Google Adept to Amazon, Inflection to Microsoft, and now Scale AI to Meta.
2:45:10
And the interesting thing here, I was talking with an early scale AI investor this morning, and they just still didn't, you know, I think the deal is still in the works.
2:45:19
They didn't have full um insight into how exactly it was structured.
2:45:22
The question is Meta is paying $15 billion for 49% of Scale AI and Scale AI through that is losing their CEO. Sure. To Meta. Yep.
2:45:36
Now, Scale AI is a very big business. Yep.
2:45:39
They have a ton of customers uh that that I imagine are are going to want to continue to work with Scale AI.
2:45:45
The question becomes what happens to the actual EV of scale AI in a scenario where half of it is owned by a hyperscaler who has very different incentive structure than a traditional acquirer.
2:45:58
The positive here is that didn't you talk to somebody at at character or adap or inflection?
2:46:02
character or adap or inflection? You talked to one of somebody who was I talked to I talked to what happened with character is basically like the cap table was effectively wiped out and reset and to my not wiped out cashed out to very high ended up in a situation where they had this massive balance sheet no investors
2:46:22
like no preferred shareholders was effectively uh is effectively to my knowledge community or or sorry employeeowned and I don't think that's what's happening here But it is seemingly clear that um the uh founders, the team and the investors in Scale AI will be getting um paid out I would imagine proada based on that $15 billion um mark. And the and the thing that's
2:46:48
And the and the thing that's wild here is I mean I so it's an up round for everyone.
2:46:51
Everyone gets everyone gets cashed out gets a ton of money.
2:46:55
Alex gets to go work in like a hyperscaler on super intelligence and actually like take a run at some really serious like high technology.
2:47:01
The thing that was ultimately surprising so Scale AI has more than $900 million in cash on its balance sheet at the end of last year according to financial information shared with prospective investors.
2:47:12
Uh they've raised more than 1.
2:47:14
5 billion from investors.
2:47:16
So they only burned 400 five uh 600 million of that like they kept more than half of it.
2:47:21
Uh the company made uh 870 million in revenue last year and expected more than two billion this year and uh it lost about 150 million.
2:47:31
So I mean doing doing pretty well.
2:47:33
I mean there is a world where where like scale AI as a company even though they're not wholly owned by Meta, they are independent and they have $900 million war chess and they can go build a great business and do a bunch of stuff there and then uh that the relationship with Meta can be a fantastic growth engine.
2:47:48
Yeah, this the the only reason this was surprising to me, well, I guess one, it's um scale AI was, you know, if you're if you're trying to build a a new research lab, they're probably going to need to still acquire a lot of other top talent.
2:48:04
You can imagine Meta going and and trying to bring in into you know, people from Anthropic or OpenAI into this new organization.
2:48:13
Um, but uh it felt like scale.
2:48:16
I I I do wonder if if scale, you know, scale's team could go back and and see circle's IPO and the success of that. Yeah.
2:48:25
If they would have still done this done done this deal because scale AI could have ripped in the public markets. Yeah.
2:48:31
Circle IPO showed that the public markets are very receptive to pure play technology bets that that have great narrative around them. Yes.
2:48:41
and and I could easily seeing what circle has traded at I could see scale trading well beyond um uh you know the this valuation they're getting from Meta. I agree with you.
2:48:54
The question is how does the liquidity picture what does the liquidity picture look like in that scenario?
2:49:00
They wouldn't the in insiders wouldn't have been able to unload 15 billion 14 15 billion like does that does that actually play out?
2:49:07
Um because yes, yes, the the stock could have traded up to 100 billion market cap, but can you actually if you're an investor, can you actually get liquidity without crashing the stock if you're putting 15 billion of downward pressure? Uh so yeah, it's a lot. Big question.
2:49:21
We'll have to ultimately huge pickup for for Zuck.
2:49:25
I think Alex, you know, will will continue to be um you know, uh execute uh very well and I could imagine he'll be a huge part of recruiting.
2:49:33
Uh he's done a fantastic job.
2:49:36
He's like been everything.
2:49:38
He's been on everything from like the Theo Vahun podcast to testifying in front of Capitol Hill and lawmakers. He's got a ton of range.
2:49:46
He's very able to storytell and do creative deals which increasingly are extremely valuable in the world where the AI paradigms like the secrets are known.
2:49:57
It's about, okay, how can you actually marshall the capital and and the will to build the hypers scale data center to actually go and and get Jensen to fork over a 100,000 H200s on on schedule?
2:50:05
Um, I'm sure there's a ton of different uh things in the works.
2:50:11
Yeah, it's interesting for Meta, too.
2:50:12
Even if they're able to leverage the scale team uh to simply get better at ads, they can make back the the the 15 billion. Uh oh, yeah.
2:50:24
in there's a ton of different ways this works out.
2:50:26
Uh and I mean they clearly need a lot more reinforcement learning training data for to improve Llama 4 because they need a they need a reasoning model um if they're going to stay in the game.
2:50:38
That's clearly the next paradigm.
2:50:38
And I mean like a big part of what we learned about uh from semi- analysis this week was that verifiable rewards are really important in reinforcement learning.
2:50:50
Scale AI pioneered that humanities last exam.
2:50:52
They've been in like the eval game defining the rewards that you would work against in the reinforcement learning paradigm.
2:50:58
And so, uh, there's a whole bunch of ways that that plugging Alex and the scale team in makes a ton of sense. Meta has to attack.
2:51:04
They need a layer that they can control that everybody else depends on.
2:51:09
And this looks like their play at it.
2:51:11
The other thing I think is interesting about that that I've been thinking about is how many of these big AI companies have effectively been acquired in ways that the government doesn't get to approve.
2:51:22
It's, you know, the big talent acquisitions.
2:51:24
When I saw this, I was like, that looks like an acquisition the FTC doesn't get to approve.
2:51:27
And so I don't know if that's the motivation.
2:51:30
Have not spoken in any of the parties.
2:51:32
We're not in scale AI, but my sense is you're basically seeing the Game of Thrones play out, but the government is one of the houses.
2:51:37
What's very interesting about that is that we are Yeah.
2:51:41
Yeah, Lena's gone and we're in a different regime, but it's the same thing.
2:51:44
Let us let us do some acquisition deals.
2:51:46
Let us let us do some some M&A.
2:51:49
Just just $28 billion acquisition.
2:51:49
Just let us sneak it sneak it in. It's fine.
2:51:53
Let's get it by just one.
2:51:53
Let's open Let's get the M&A window open.
2:51:55
Master I mean the other thing I say is when was the last time you heard a big tech company buy a company for $80 million?
2:52:02
It felt like that used to happen all the time. All the time. Yep.
2:52:03
And now it's a lot less common for they took that from us. They took that from us.
2:52:07
I think I feel the grief. Yeah.
2:52:10
I mean, there used to be there used to be a a really great outcome for a lot of folks where if you're a founder and you start and you still own 20 30% of a company and you sell it for 80 million, like that's that's a house in PaloAlto and kids and generational wealth in some ways.
2:52:24
Um, and yeah, that that has gone by the wayside.
2:52:28
I don't exactly know why because those seem to be easier to get across the finish line from an FTVC perspective, but I guess folks just I mean, at what point does the FTC just say like, "Look, we know we can see what you're doing." Yeah.
2:52:40
I mean, also 15 billion on this company for and and the CEO no longer works there. Works there. Yeah.
2:52:47
I mean, the flip side Yeah.
2:52:47
I mean, the flip side is like when was the last time a company was valued at 80 million?
2:52:52
because every company seems to go straight from straight from 5 million to 500 million in two days and so I I don't know where you're getting 80 million like that that's just hard to hard to find.
2:53:04
Let's talk about the most recent deal.
2:53:06
Uh Meta acquiring Scale AI.
2:53:10
Uh 49% of the company is changing hands I believe at around 14 or 15 billion dollars.
2:53:15
Spiritually the whole thing. Yes.
2:53:17
But technically uh what what what was what has been your reaction and what uh what have you learned from reporting on that?
2:53:23
Yeah, I think you just covered that that uh they they have a new CEO.
2:53:27
So, so bring we just reported that yeah per um you know per our sources that they have picked uh the new CEO to be Jason Droi uh former VP he's currently uh you know at scale being promoted and and he was um formerly an at Uber a VP and formerly also a partner uh in VC at
2:53:44
benchmark so but you know that's just that's just the latest right we we kind of we have really been breaking the news on this front we had a scoop this Saturday night um that we were the first ones to report that these uh talks were happening for Meta to make this multi-billion an investment in scale. I
2:53:57
I think it really caught, you know, a lot of people by surprise in the industry.
2:54:02
Um, this is a huge investment planned um on Metasfront, right?
2:54:05
Probably their biggest ever um in a company.
2:54:07
And I think it's, you know, I think it's a sign that Zuckerberg is ready to go full on founder mode and try to catch up on AI.
2:54:19
And this last Llama 4 release was, you know, widely considered a disappointment.
2:54:23
And so how do you how do you change course bring this big right big investment and the super intelligence lab and he's recruiting engineers we've reported on that they're making crazy high offers right um they will open Mizak will open the the checkbook for the top talent.
2:54:42
Yeah, I think the interesting thing that stood out about some of, you know, people were just reporting the headline numbers of saying, "Oh, you could make, you know, 10."
2:54:51
Nine figures was the number. Yeah. Nine figures.
2:54:54
But you have to look if if you're trying to recruit somebody that joined OpenAI 2, three years ago, they're probably run rating at $10 million a year. Yeah.
2:55:03
Already on on a on a full um fully loaded comp basis. I think it makes sense.
2:55:09
What have you learned about the future of the scale business, right?
2:55:13
Is Jason going to be, you know, pounding the pavement trying to get, you know, more customers for the core business?
2:55:20
Continue to adapt the core business.
2:55:22
There's a lot of scale competitors right now that are saying like, hey, we're going to take a lot of foaming at the mouth. Yeah.
2:55:27
We, you know, we're we're going to use this to our advantage when we go to the other hyperscalers and say, do you want to do business with a company that's owned by Facebook or Meta? Yeah, absolutely.
2:55:37
I think um it's really unclear what's going to happen with the future of scales business.
2:55:42
future of scales business. um you know obviously the meta investment you know that's expected you could see partnerships happening there on that front but what will happen with the other customers I think we don't know um and and what will happen with um yeah other competitors in the space could
2:55:57
they start to right gain ground um as scale deepens the ties with Meta we'll see I think what we do know you know we we have reported that um Alex Wang plans to to join Meta um I think for Meta he is seen as someone who is just sort reinvigorating right their AI team and this renewed AI effort. Um he's a big Um he's a big name. He's incredibly savvy.
2:56:19
Um you know very wellconed and uh built a real business with scale.
2:56:24
So you know I think that's where we're in the middle of you know of the deal.
2:56:29
We're waiting you know to see the all the official details sort of as this plays out. [Music]