415: ‘A Good Duck Butt’, With Allen Pike

0:00:00How's your week going, Alan? Well, you know, here in Vancouver, it's Monday at noon, but we don't have the Thanksgiving stuff going on here in Canada. We've covered that. We're fully thanked. That was a month ago up there, right? Yeah. So now we just get to have a couple days or sometimes an entire week where all of our American customers go off and have your festivities and we can just work on product and do all that kind of stuff that we want to be doing when we don't have all the inbound email.

0:00:29I am afraid that I'm going to come off as brutally American-centric here. And I don't mean to, but I do think that the whole Thanksgiving thing is one of the weirdest differences. You know, our cultures are so alike, but also, you know, you have your differences and the Thanksgiving thing just blows my mind. I don't know why.

0:00:56The differences between Canadian and American culture are more obvious to Canadians than Americans, which is a dynamic. Anytime there's like two similar cultures or technologies or anything, but one is like 10 times bigger than the other, then if you're in the 10 times bigger group, you're like, yeah, and then there's this other one, which is pretty similar, but like a weird version of us. But then if you're in the smaller group, you have this, oh, well, obviously these things all kind of stick out. But the Thanksgiving one, I remember reading, this is a terrible way to open a show. Remember, it wasn't Thanksgiving.

0:01:26It was done in Canada before it was done in America.

0:01:28Oh, who cares?

0:01:30I mean, probably some listeners are now mad at me if that's not true.

0:01:34Right. Well, it was all a long time ago, but it just, it's just, I don't know, because every other, well, like, no, it makes intuitive sense that July 4th wouldn't be a celebration in Canada, right? Like it does, even a toddler in America would understand that.

0:01:54But Thanksgiving feels anchored to the period between Halloween and Christmas in a very sturdy way in the American mind.

0:02:09And it just seems bizarre that you guys, you have Thanksgiving, but it's just different.

0:02:16It is a different, because of where it is and where it sits and how it's observed, it sits differently. Like in the Canadian mind, it's anchored to, it's a harvest festival, right? And so are you harvesting a lot in November, the end of November?

0:02:30No.

0:02:31Maybe not. But you think of it more as like, it's end of year visiting your family time, and we think of it as like a harvest related. I think that's the association.

0:02:42Yeah. So what I'm hearing from you, and it makes intuitive sense too, it's sort of like any other religion here in America where you don't celebrate Christmas, even, what's the word, when it's not sort of as a, not as a religious holiday, which is really how I sort of celebrate Christmas.

0:02:59Oh, as like a secular...

0:03:01Yeah, secular holiday. Like famously here, Jewish people often go out and have Chinese food because there's not a lot of restaurants that are open on Christmas, but Chinese restaurants are and just sort of a weird day. Well, we have our day to ourselves. You guys have...

0:03:16Well, no, I mean, Thanksgiving, definitely to be clear, and thanksgiving in Canada, we do tend to see family and we'll do like a Thanksgiving dinner type of thing. It's just not like, it seems like America takes the entire week off. It's, hey, what do we get, what do we get this week? So it's Thanksgiving on the Thursday of that week. So the entire thing is a write off. People are flying from other continents for Thanksgiving in America. So in Canada, it's just a notch below that. It's one of the observed holidays, but it's not like, in some ways, as I understand some American families, Thanksgiving is more important than Christmas, which is why.

0:03:46Which is not, I think, very common in Canada.

0:03:49Yeah, I think in a certain weird way it is. And in terms of getting the biggest coagulation of family together under one roof for one meal, it kind of is. Whereas Christmas is often people dipping in and out with, you know, depending if you have multiple family members who live within driving distance, somebody pops in for an hour and goes away. Whereas Thanksgiving is sort of like, here's the list of the people coming to this house for the whole day.

0:04:17And you still might have some pop-ins who have other obligations or something like that, but it's a little less in and out. And Christmas also is so, just with the gift giving, is so inherently focused on the little kids of the family.

0:04:32Sure.

0:04:33And Thanksgiving is dreadfully boring for little children.

0:04:38I mean, well, yeah, that is true. But I do, part of now as a parent, a part of what I enjoy about Thanksgiving is trying to tread that balance where you do kind of try to instill a little bit of the sense in the kids, "Hey, we're going to be thankful," and like set them up to not spring it to them at the dinner table, but like, "Oh, what are we going to say our thanks for?" and that kind of stuff. I kind of like that as a parent. Of course, the kids are rolling their eyes that we're making them do it, but it builds character.

0:05:01It builds character. That's exactly what I say.

0:05:04Well, it's a good first topic for our Canada-US differences show.

0:05:07Yes.

0:05:08For the next two hours, we'll be discussing contrasting cultural practices.

0:05:13Here's a question that popped into my head when you mentioned that is—

0:05:16Oh, no. What have I done?

0:05:18Well, no, but the fact that because America, by population and everything else, is so much bigger that Canadians are surely aware of when Americans are having Thanksgiving. Americans, I don't, I just know it was like sometime in October. It doesn't affect us.

0:05:35And like you said, you notice America in a way that we don't notice Canada just, you know, it's the nature of being smaller versus larger.

0:05:45And one thing that's always surprising, it's always like a curious fact in America is when somebody becomes typically like a celebrity or an actor or somebody.

0:05:55And then it's like an interesting fact. Michael J. Fox is Canadian.

0:05:59Oh, how about that?

0:06:00You know, and it's like, "Oh, that's interesting. I never would have guessed."

0:06:03In Canada, is it obvious that who's a Canadian?

0:06:07Interesting fact, Taylor Swift, would you believe she's American? We would not be surprised by this. Nobody would be.

0:06:13Right.

0:06:14That's an effect of the media landscape, right? Like, and this is not just a Canadian thing, like other English speaking countries, and Australia has this, will have the same dynamic is that there's a huge amount of presence of English language media that's so US centric.

0:06:27And then we just kind of, it's the water that we swim in to the point that there's laws in Canada, probably also in other countries like Australia, to promote Canadian produced content.

0:06:37Because otherwise, you'll have this like self reinforcing loop that like, again, an interesting phenomenon that happens from this, like, when you when a Canadian is arrested, the police will joke sometimes how they find it funny that they'll say, "You didn't read me my Miranda rights."

0:06:51It's like, you're just watching too much Law and Order, man. That is not a thing in Canada, but Canadians see so much American content that we start to just absorb the world, the person we're not that familiar with. Sometimes we don't even actually realize that we're seeing Americanisms because it's just so pervasive.

0:07:07That's a tip for any Canadians out there that are, I don't know, getting arrested.

0:07:11Don't expect to get to get out of jail for not having your Miranda rights read to you.

0:07:17Right. Yeah, exactly.

0:07:19Well, let's see how much longer we get them read to us here in the United States.

0:07:22Well, we'll see.

0:07:24I invited you here because of all of my friends, Alan Pike, you I think, are the deepest into AI, our current AI moment. And or at least you, you may well be the most knowledgeable and deepest, but I'm quite certain that if anybody who I know who I would like to have on the show, and I've been meaning to have you on the show forever anyway.

0:07:50But I also think you're going to you're able to explain this to me because I still don't really understand a lot of it. And I suspect a lot of listeners out there feel the same.

0:07:59I'm happy to be helpful.

0:08:01Yeah.

0:08:02And very deep into it. Maybe to be.

0:08:05Well, that's a good reason to have you on the show, though, to get some of it out of here. And, and I don't know.

0:08:12It's part of startup life. Like you if you start a startup, which I'm sure you were this way when you started Daring Fireball Incorporated, it just kind of consumes you for a while. And it's the thing that you're doing, you're living and breathing, everything you listen to, everything you want to read is feeding this one thing. And so it's good timing that way.

0:08:30No, it's it's, you either have that mentality or you don't. I mean, like, I was at Joyent when Joyent got started. It was trying to do calendar, email, contacts type stuff. And it's like, I wasn't even an engineer. I was like product design. But it's like, I don't know, I had the intricacies of the ICS format in my head.

0:08:51But it's you don't need you don't need to have that much in your head at any one time. But it's like inevitable when you're when you're trying to soak in the whole current state of the art.

0:09:02Yeah, and when it's the fundamental thing about building, building zero to one product is incredibly hard, incredibly rewarding if you do it both maybe financially, but especially like, it's extremely satisfying thing to take a thing that didn't exist and then make like a real impactful business out of it. And so it motivates this, like mentality of it being kind of consuming for better and for worse, you get both sides of the coin. But I'm happy to I'm happy to be your guide in whatever wherever your curiosity leads you or you think that

0:09:31Excellent.

0:09:32You know, the audience maybe.

0:09:35Yeah, well, before we get to that, the one thing, the one sort of news topic that I've been writing about it during fireball, and I think you have some spitball takes on it too is the announced, but not official pending some sort of perhaps regulatory approval, Apple acquisition of pixelmator, which is a little difficult. I keep running into it. I hope I've done a good job. But the weird thing is that the company is named pixelmator.

0:10:04Their flagship product is named pixelmator. But they also have an app called photo mater. Yes. And so they bought Apple bought pixelmator the company, but it includes pixelmator the app and photo mater the app, which Yes, we did. It all makes sense. And when you are a company that only has one product as pixelmator did with pixelmator app for a long time, it makes sense for the company in the app to share a name. But there we are. Anyway, and as I keep

0:10:34clarifying pixelmator in broad strokes is a photo in the Photoshop competitor for iOS and Mac OS started on Mac OS, and photo mater is like a Lightroom competitors sort of basic sense of what photo maters market penetration is. No, I really don't. And I was I've been kind of hoping that by me writing about it, I get more email from people and I'm getting email from people who use it. It definitely has users.

0:11:04But I really don't have a better sense of where it stands. There's sort of like a dark room, even the name dark room, sort of gives you that strong hint that they're a Lightroom competitor, a little bit of a hint in the name. Right. Dark room is one. There's another one I'm thinking of. But there's a couple of apps like that. Dark room springs to mind as a direct competitor to photo mater, I think,

0:11:33whereas both dark room and photo mater, by building a top the Apple platform ecosystem, unlike Lightroom aren't asking you and in fact, can't I don't even think there is a way in either one of those apps to say, give me an independent library separate from my main system photos library. It's a way of using Apple's API's to

0:12:02if you're a user who's only ever used Apple photos, you shoot photos with your iPhone and on your Mac, you use the photos app. And you just have a library and you maybe use iCloud, probably this point most I bet most users use iCloud photo sync. Certainly listeners of this show probably do. If you use dark room or photo mater, you open the app for the first time. And you have access to that your same library of photos. It's it's

0:12:32they're using, you know this, but for people out there listening who don't, which is super convenient. So it's

0:12:39Jared Ranere: especially with the phone sync, where you're taking photos and they're appearing in your library, any product in 2024, where it's you take some photos, and then you have to do something in order to get the photos from your phone to your computer. It's you've now become like a 1.1% for ultra enthusiasts product, and even the ultra enthusiasts, we don't want to do that. He is still

0:12:58right. And I kind of think that's going back 10 years or more than 10 years, I forget when Apple, I guess it was like 2012 or 2013. When Apple first made the shift. Prior to Apple photos, Apple had iPhoto for consumers, and Aperture, which was a direct competitor to Lightroom for pros. And they are what's the word deprecated both at the same time.

0:13:26And, and to move towards this future based on Apple photos, and photos is definitely the new version of iPhoto. It has a new name. And it's sort of a new concept for library management. And in my opinion, is just better overall. I think it came out of the gate as a very strong replacement for iPhoto. And after a few years of iterative development, I, to me, there's no looking back.

0:13:56I mean, and maybe there are specific features people miss from iPhoto.

0:14:00There's specific features, I'm sure Donald Trump's recuse it could tell us some specific things that it does worse.

0:14:04The bookmaking, right?

0:14:06Bookmaking.

0:14:06But

0:14:07Which was great. I use that too.

0:14:09But sure, but if you tried to go back to the iPhoto that we had, the version that was before photos, and you tried to use it now, you would be baffled by, I mean, even just like quality of life stuff about like, oh, I can't edit my live photos or whatever the things are that have come in since then.

0:14:22Yeah. And sync is such a hard problem in all of computer science.

0:14:27Infamously forever.

0:14:28And is like a lot of infamously hard problems in computer science. When you get it right, you just take it for granted as a user.

0:14:38Even if you're an engineer, and you've worked on applications or things that involve sync, like I did when Brent Simmons and Dave Whiskus and I made Vesper, and we made our own sync engine.

0:14:51We knew we had it when we just stopped thinking, oh, this is pretty fast.

0:14:55It's like it just becomes invisible.

0:14:57And the sync between devices with Apple Photos through iCloud has been really, really good for years.

0:15:05And at this point, I would say is very close to perfect.

0:15:09I really don't have many complaints.

0:15:12And every once in a while, I'll make a change on one device and expect to see it instantly or nearly instantly on another device. And it's not.

0:15:21And then I look at the other device and it's paused for system optimization or something.

0:15:26And it's like I can manually make it start the sync, but I don't even have to do that anymore.

0:15:31It's really, really been good.

0:15:34But I think basically, I think that was one of the reasons that Apple abandoned Aperture was that it was just based on a, it really would have taken an awful lot of work to move it to this new back end infrastructure.

0:15:49I don't know, there might have been.

0:15:49Well, they were probably, you know, as part of that photo library migration and moving to hit up all beyond the cloud, they needed to do this huge retool of the model layer, what I would call the model layers programmer, but like the back end of this whole thing.

0:16:00And so if they're going to do that, which app are they going to do it first for? Obviously the consumer app.

0:16:04And then if you think about it, well, how are we going to, if we wanted to have an Aperture successor, where like evolve Aperture, we would have to fork photos and then get photos working and then fork that and then have a shared code that did that for both platforms.

0:16:18And I'm sure that was on a whiteboard somewhere is the thing that they might do.

0:16:21And then when push comes to shove is, well, what do you prioritize? We prioritize getting the consumer one out and then it's pretty compelling to say, well, let's try to add 80, 20, 80% of the more pro features to the photos app over time that Aperture might've had, which of whether or not they did that and is then kind of left up to history.

0:16:37But you can see how you get there.

0:16:39Yeah.

0:16:39And I know by me writing about it the last week or two, I'm hearing from people who still say they miss Aperture and I can't blame them.

0:16:47And anybody who's been using computers long enough and is even vaguely a power user has at some point lost an application, probably multiple applications.

0:16:58Just companies go under apps, get acquired and get lost in the acquired company or the market changes or stuff happens.

0:17:08And it kind of hurts more in a certain way in the back, in a petulant, selfish way when it's a company like Apple where they didn't go bankrupt.

0:17:21Yeah.

0:17:22Yeah.

0:17:22Yeah.

0:17:22If you're like, well, of course this thing, it never was feasible anyway.

0:17:26And yeah, I got a free ride for a while on this startup that never was going to work out and they went bankrupt.

0:17:31Then it's just kind of the shape of the world, but these billion trillion dollar companies have the ability to completely change the course of any given market if they decide on their whim.

0:17:40Right.

0:17:40And if you're, if you're creative profession, when Aperture was truly a professional tool or just your very serious hobby from pro serious prosumer to true.

0:17:52This is what I put on my tax return.

0:17:55I'm a professional photographer pro.

0:17:58It hurts to see that Apple still is committed to the video editing market with Final Cut and the professional audio editing market with Logic.

0:18:10And why not photos?

0:18:12You know what I mean?

0:18:12Like why am I left out as a photographer?

0:18:14Would you think that were there photographers that were feeling that they're like, Oh man, I just really wish Apple would enter this market.

0:18:20I wish that we could be in the same boat as Logic or I don't know.

0:18:24I don't actually, I'm not very close to how like music producers feel about Logic, but like those of us that edit podcasts and Logic have this love hate relationship where it's, this is a tool that is like very good in some ways and has a few janky bugs that they don't seem to be fixing.

0:18:38And like you accidentally press some hot key and then your whole interface gets screwed up.

0:18:41And it's this kind of like, I'm glad it exists.

0:18:44But if some company's entire livelihood was like, if Logic was completely independent company, I don't know, we can, we can speculate what it would be like.

0:18:52But I'm not sure if there was a lot of photographers that were just wishing, Oh, they were so sad that Apple didn't have other than the people who miss aperture.

0:19:00They just want aperture the way it was.

0:19:02But yeah, I, I don't do it.

0:19:05I don't even edit the podcast.

0:19:06So I, I don't, I've, I, as far as I know, I've never actually even used Logic.

0:19:11I've seen it though.

0:19:12And Apple at times has like, I think it, I guess it was the Mac pro, like the last Intel Mac pro, the one where they debuted the current Mac pro.

0:19:29Yeah, it was.

0:19:30So it was 2019.

0:19:31Uh, I guess at WWDC where they announced it.

0:19:35And the thing I remember the most are the wheels.

0:19:38Yeah.

0:19:39Classic.

0:19:40Forever.

0:19:41I asked at the hit, they had a nice, they'd rented out a building across the street from the San Jose convention center to demo it, the Mac pro hardware.

0:19:52And I asked if I could get on one and kind of ride it around as like a little sit down scooter.

0:19:58Of course I would ask the same.

0:19:59And they said, no, you can't, but that you, but in terms of the weight, it would be fine.

0:20:06Not that it would break the wheels, but we're not going to let you do that.

0:20:10Yeah.

0:20:10And, uh, you know, I wasn't really serious.

0:20:13Although if they had said yes, I might've tried it, but they had a demo station pulled up and I don't know, remember his name, but somebody who was, they, I guess they had hired him as a consultant, but he was like a professional movie score.

0:20:28I don't even know if he wrote the scores, but he had something to do with big budget, major Hollywood motion picture scores and had this ridiculous logic program or, or, or project open with just a ridiculous number of tracks and was explaining that.

0:20:47Until recently, a score like this would have had to have been broken up into several projects with fewer tracks.

0:20:55Yes.

0:20:55And then it was a lot of work and just easy to screw up to get them to emit the final score and put it all together.

0:21:04And now that it can all be one project, it's just, I think you just, it's life-changing in terms of the efficiency.

0:21:11And I was there, Jim Dalrymple was still writing the loop website and Jim knows music and I don't.

0:21:18And I looked at Jim and he was like, yeah, this is, this is, this is really impressive.

0:21:21And I was like, okay.

0:21:22So I, you know, I think Logic has huge fans who are super serious users, but I kind of feel like podcast editing falls through the cracks as being so simple, technically, that, that it's not really an optimized tool for that.

0:21:38It's sort of.

0:21:38It's just different enough from this, like what the, this Composers for Hollywood Hans Zimmer is doing something so different than what the podcast editor is doing that really it justifies its own UX, but it's not quite a large enough market to have a tool of the polish and quality that Logic.

0:21:58And obviously there, there are smaller indie teams that are building tools in that space and we're all watching them and cheering for them to overtake Logic, maybe for that use case.

0:22:07We've gotten way off track.

0:22:09I know we have.

0:22:10We're talking about things like major acquisition.

0:22:11But it is sort of like back in the day before.

0:22:15And I was in that boat.

0:22:16I used Lightroom for a couple of years.

0:22:18And in fact, when Lightroom and Aputure were at their peak competitive, I chose Lightroom, not Aputure, even though I tend to go the Apple route because I appreciate the Apple specific UI conventions and sticking to the platform.

0:22:33But I loved, I haven't used it in years.

0:22:36And I, but I get the impression that it's still pretty good UX wise.

0:22:40I loved Lightroom's interface.

0:22:43It wasn't Apple style, but it didn't feel foreign or Adobe-ish.

0:22:48It just felt Lightroom-y and very custom, but it was so much faster than Aputure.

0:22:54And it's one of those things where in the alternate universe where Apple had stuck with Aputure, Moore's law would have just helped Aputure catch up.

0:23:01And certainly the shift to Apple Silicon, I'm sure by today, if they're in the alternate universe where Apple stuck with Aputure, Aputure would be, would, nobody would think it was slow.

0:23:12But like 2012, 2013, it felt slow.

0:23:15Like I tried it and imported some photos and it's just like going from one photo to the next.

0:23:21Had a little bit of lag that Lightroom just didn't have.

0:23:24Lightroom just felt like too fast to be true.

0:23:28Isn't it, isn't that, did it really just make the change?

0:23:31I asked it to make cause it's transformational for those kinds of workflows.

0:23:34If you're doing a thousand of something, the difference in between it being a 20 millisecond lag and a 200 millisecond lag, that's only, you know, 180 milliseconds.

0:23:43So it seems irrelevant.

0:23:44But if you're trying to go like next and then a keyboard shortcut, next keyboard shortcut, that kind of workflow that it just can makes it feels like a totally different experience.

0:23:52And Lightroom had that.

0:23:53And I, you know, so I used Lightroom for a couple of years, but it was overkill for my needs.

0:23:57I mean, I'm like an, not even a prosumer photographer, I'm an enthusiast.

0:24:03And I eventually just sort of, it was like, you know what, I'm just going to switch the photos and I'm happier for it.

0:24:10And it really did coincide with me shooting the tipping point where even at things like Christmas or Thanksgiving or birthdays or vacations, like I still have standalone cameras I use, but I use them less and less.

0:24:26And I shoot more, even like special occasion things with my iPhone.

0:24:30And you just can't beat the, it's already in my system as soon as I shoot it.

0:24:36Like you said a couple of minutes ago, that whole, okay, step one, take your SD card and import your photos to your photo library.

0:24:45It's like you've already lost, you've already lost people.

0:24:48Right.

0:24:49Yeah.

0:24:50But I kind of feel like the dark room, photo mature model of building on the photo library.

0:24:57Photo mater, photo, photo mature.

0:25:01That's a callback.

0:25:02Some listeners are laughing that God, that was so long ago, but the backstory on that, cause there's gotta be, I wonder how many readers are listeners to get it way back when on this podcast.

0:25:14I mean, this is when I was still doing it with Dan Benjamin.

0:25:17We had them as a sponsor, I think.

0:25:20I think it was a sponsorship.

0:25:21I don't even remember.

0:25:22And I literally, until that point had only ever read their name and I already had the app installed and was super impressed that two brothers had made this app that was like a full image editor on the Mac.

0:25:37And then I realized while recording the show, I wasn't quite sure how to pronounce it.

0:25:41Haven't took my guess.

0:25:44Pick, yeah, you're like probably, probably picks them pixel mature.

0:25:47That sounds like probably.

0:25:48Well, I knew, I didn't even know quite where they were from that.

0:25:50Turns out they're from Lithuania, but I knew they were from somewhere central to Eastern Europe.

0:25:58And so I thought, I don't know, pixel mature, maybe that's whatever language they'd speak natively.

0:26:03Maybe that's a thing.

0:26:04I don't know.

0:26:04But now it's burned in my head.

0:26:07It became a running gag on the podcast.

0:26:09And at least a third of the time I try to say their name without even trying, it comes out wrong.

0:26:14But anyway,

0:26:15So we're talking about photo mature.

0:26:18Photo mature.

0:26:19For pro photographers, that library management step isn't a hassle.

0:26:28It is like, oh, no, I need this.

0:26:30I can't, I need to have separate, complete libraries and projects within libraries.

0:26:36I can't just have all of my photos in a soup.

0:26:39I mean, just think about like a wedding photographer.

0:26:41Yeah.

0:26:41Just each customer is a client.

0:26:45I mean, any kind of pro photographer has like clients and projects and you don't want to have all of, you don't want to, it would be catastrophic to mix photos from one to another.

0:26:56You want to have the same in Final Cut.

0:26:58In Final Cut Pro, you don't just, it's not like iMovie where you just like drop in stuff.

0:27:02It's create a library and then create a project within that library.

0:27:04And like an event, it has three nested layers of keeping the stuff separate.

0:27:08And you need that when you're all day, every day creating this media.

0:27:11So it makes sense.

0:27:12I don't know.

0:27:13So there's two different apps that Apple acquired.

0:27:15What they'll do with PhotoMator, which again gets complicated to talk about because the one that they have with photo at the start of the name isn't the one that's like Photoshop.

0:27:25It's PixelMator.

0:27:27Yeah.

0:27:27It's like Photo, Pixelmator.

0:27:30That's like Photo, I'm trying, now it's up to 50/50.

0:27:33You've cursed yourself.

0:27:34Every time you refer to the joke, you give yourself another year of, but yeah, so the two products.

0:27:39One is the sort of well-established.

0:27:42It's this, like you say, Pixelmator.

0:27:45And then there's this kind of new growing one that's the in the dark room genre.

0:27:51And I think what you're getting at is where are they going to take these two?

0:27:54Are they going to just invest it?

0:27:56Did they just buy it for Pixelmator?

0:27:58Did they buy it for also for PhotoMator?

0:28:00And how is that all going to play out?

0:28:01And then of course, people who are fans of one app or the other have feelings about it and reasonably so because something that they've been depending on, especially if they're professional or even as a enthusiast,

0:28:12may get neglected and or discontinued.

0:28:17Right.

0:28:19So why do you think Apple bought them?

0:28:25Well, they have like a specific perspective on this having worked at Apple a long time ago and then having been an Apple developer for many more years than I was in the Apple ecosystem.

0:28:38So I've seen so many of these, like some of the big, obviously the big acquisitions, Beats and PA Semi where this is like a giant strategic thing for the company with a ridiculous amount of money involved.

0:28:47And they have a different set of considerations.

0:28:49But then at the, well, I don't know, I'm not sure exactly what the size of the Pixelmator acquisition is, but it's like probably not at the, it wasn't announced, was it?

0:28:57No.

0:28:58And yeah, it's the sort of thing I know some people love to speculate on that.

0:29:04And I don't blame them, but I, that's not my game. And so I just leave it to others.

0:29:08But I think I get the impression that they were doing fine financially, you know, as a standalone company.

0:29:15So I'm guessing it was fairly good for the team that owned Pixelmator.

0:29:20And that's one of the things when these acquisition stories as like end users, we just see the announcement go by.

0:29:25Often, if you're not in the industry or you're not like a founder, you haven't been involved in acquisitions.

0:29:30Sometimes people won't consider some of the reasons why a company might get acquired. And one of the reasons could be they're failing, right?

0:29:36And then so like an ecosystem partner like Apple who benefits from Pixelmator and maybe also Photomator continuing to exist might step in a situation like that.

0:29:45They might step in a situation where, I have no information about this to be clear, but they might step in a situation where someone else is going to acquire the company where, and they would rather not, it be Microsoft owning them or whatever or some goal or something, Adobe maybe.

0:30:00Who knows?

0:30:01And so you can imagine that kind of thing happening.

0:30:04You can imagine it being a concern that maybe Pixelmator is like, oh yeah, maybe we'll add Windows support.

0:30:11And then Apple gets wind of that.

0:30:13You can speculate a bunch of different reasons, but like strategically, I think for Apple, the biggest, like clearest reason to acquire a product like that is to continue to develop.

0:30:22It gets big enough and the cost would be high enough, it's probably to continue developing and preserve or maybe enhance its position as a differentiator for people to use a Mac rather than just to the thing that people tend to worry about is just to get the team.

0:30:35It's, oh, we want to build, like you can imagine it, but this wouldn't probably happen with a product as successful as Pixelmator, but it does happen is that companies like Apple, including Apple will be like, we want to build a product.

0:30:47Let's say they wanted to build Aperture, right?

0:30:50Then they're like, let's bring back Aperture.

0:30:52Let's take the photos code base and build a pro app like Aperture.

0:30:55You would need a team to do that.

0:30:57And a way you could get that done potentially would be to acquire a company like Pixelmator and then quietly wind down the product they were working on and then ramp them up with, they would be the sort of team who could execute well on Aperture reborn.

0:31:09But I think that would be such a weird thing to do.

0:31:12Like it wouldn't make any sense.

0:31:13Like maybe rebrand, but like to throw away the code base of this working app that has a loyal fan base, then obviously that would upset so many people.

0:31:20So it seems like keeping that product rolling and improving has to be the current mindset.

0:31:26Obviously acquisitions tend to go in all sort of different ways where you have this great vision when there's acquisition and then a huge kind of like embarrassingly high percentage of acquisitions end up failing for various reasons, including we tried to merge our code bases and then we turned out there's a whole bunch of demons inside or the person who championed this ended up leaving the company the month after the acquisition closed.

0:31:44There's all sorts of ways they fail afterwards.

0:31:46But my instinct is that it's they have good intentions for the product.

0:31:50Yeah, I think so too, especially Pixelmator, just because there aren't that many apps that do that.

0:31:58The other one, I'll just throw it out there just for anybody who hasn't looked at it and tried is Acorn from Flying Meat Software.

0:32:06Gus Mueller.

0:32:06And our mutual friend Gus Mueller, which is one of those, oh man, I in theory, I could do it or someone else could do it, but like a just compare and contrast Acorn versus Pixelmator would be such a fascinating.

0:32:22You could do like a whole semester long user interface study just based on those two apps and the differences between them and the way that they could both be good or great and both sort of the same basic idea.

0:32:36It's a bitmap image editor for Apple platforms.

0:32:39And the priorities is being a good citizen on Apple platforms.

0:32:42Right, and both from the user benefit perspective of, oh, you're going to be you're going to find your way around this app because it uses the idioms of the Mac platform, but also how can an seemingly incredibly small team make an app that does so much?

0:32:58It's because both apps base so much of what they do on the, I guess, mostly core image, but the frameworks that Apple provides that don't really have a user interface, but give you all sorts of things like file format support and all sorts of stuff.

0:33:15And then I don't mean to downplay how much custom processing code because I know especially Acorn because I'm friends with Gus, just how much work it's not.

0:33:24Oh, it's just a thin front end on core image.

0:33:27That's not true at all, but I don't think it's so inextricably tied to it that it would be difficult.

0:33:35It wouldn't just be like, oh, recompile it for Windows.

0:33:38It's well, we're missing all the core image.

0:33:39I think it's probably almost intractable to, or I don't know, but that's a longstanding strategy in the Mac.

0:33:47What I think of is the indie Mac world is like you as a team or as a product company, you say we're going to be the Mac asked version of this thing or for iOS, we're going to have an iOS app and it's going to be really going into the platform conventions.

0:34:02And obviously those companies have a weird relationship with Apple because obviously Apple celebrates them because they're helping Apple make this case that our APIs are good.

0:34:09They tend to be early adopters of APIs.

0:34:11They tend to be platform advocates, but then also they have this weird tension where those companies are so dependent on Apple that if Apple has a lacking in one of their frameworks or they maybe deprioritize something that that team depends on, it's like an existential risk to your company.

0:34:25Apple's like, well, yeah, we'll get into that in iOS 19.

0:34:29You're like for an entire year, we're going to have this huge hole or bug or whatever.

0:34:33And so obviously those parties are motivated to work things out and stay on the same page.

0:34:39But often these teams are so small that sometimes they get buffeted around a little bit.

0:34:42So it's interesting.

0:34:43Right.

0:34:44It's a very different set of trade-offs than the, oh, we'll just do it all ourselves and make it cross-platform, but then it all feels weird and foreign or electronic on every platform.

0:34:56Right.

0:34:56Yeah.

0:34:56And obviously every product team has all these certain economic trade-offs that can make that path, like that Electron path, which obviously is problematic for Apple because if there's nothing special about the Mac app, then that's not good for Apple.

0:35:11But that Electron path can be extremely beneficial for a team where they're actually providing some service where it's okay.

0:35:17We're an online HR platform where it's like, oh, you can set up your payroll and you can do all these things and help your team and get feedback or whatever.

0:35:24Oh, and then we should have an app.

0:35:26And this, are they going to actually build a really nice Mac app using all the latest Swift UI or whatever, and also a Windows app and also an Android app and also an iOS app there?

0:35:35No, the alternative would just not be to have an app at all, which in some cases, we could talk about the Clod app if you want, if you've tried that.

0:35:41Yes, we will get to it soon.

0:35:43And it's weird because Apple, for all the Pro tools and the other apps that they've made over the years, and like the iWork suite is still a thriving suite that went through its own similar iPhoto to Photos transition around the same time, where they kind of hit the reset button to get the file formats and the user interfaces to in sync between the Mac and iOS version.

0:36:13Which was painful for a couple years, but I think they've successfully pulled it off and I don't really do a lot of slide presentations, so I don't use Keynote.

0:36:23That's the one, I'm still a Keynote actor.

0:36:25One day I'll tell a bit of the, I don't know if the statute of limitations has fully expired yet, but one day I'll write this to some of the stories of, because I was on the team, the iWork team at that time when the transition from the classic iWork, which like iWork 09, which is how long ago it is.

0:36:40It's like maybe the statute of limitations has gone by by then, but iWork 09, too, it was like, let's bring iWork to the iPad and I won't say anything like dramatic, or there wasn't, it's not like there was dramatic, dirty laundry or whatever, but it was a really interesting challenge where it's like, okay, we have this thing that's always been a Mac app and it's made these assumptions and it's, the code base had been around for a long time by then.

0:36:58And it's, can we get this to work on this tablet that has a microscopic amount of memory and doesn't have any of the, because it was iOS 3 at the time, or iPhone OS 3, right? And they were going to bring it to the iPad OS.

0:37:11And so iPad OS 3.2 offered a few new affordances, but it didn't have even like background, like you didn't have, it was iOS 4 that brought the background behavior.

0:37:21And so it was like when the, if you wanted to build an app at that time that was as complicated and powerful, when you hit the home button, you would get a timer.

0:37:30It was something like a second and a half or two and a half seconds, and you had to finish writing out to disk everything that you had done.

0:37:37It was in memory before your process got killed.

0:37:40And obviously being Apple, like you could have maybe tried to be like, oh, well, we're going to give special entitlements to the iWork apps to run longer.

0:37:47But the thing is, even if you did say, oh, well, the apps have longer to save to disk, then the whole OS is bogged down with you slowly doing whatever it is you're serializing out or something like that.

0:37:57So that was like a really interesting engineering challenge that the team rose to was like, hey, we need to like get the under layers of this productivity software that's desktop class into this small memory and this small time window basically to persist out, which was pretty interesting.

0:38:14Yeah, and I forgot, I actually knew that, but I actually didn't even bring that up.

0:38:18Remembering, I thought you were kind of poking me to see.

0:38:21No, I had literally forgotten it.

0:38:23And then when you said that, I was like, oh, yeah, I did know that about Alan.

0:38:26I think it was one of the first things when we first became friends years ago, I knew that because it was fresher.

0:38:31It was a more recent thing.

0:38:33Yeah, that's serendipity.

0:38:35Maybe it was subliminal in the back of my head.

0:38:37But, you know, when iOS first came out and it's instead of porting AppKit over, which I don't think could have worked because of AppKit makes so many assumptions about the overall environment.

0:38:51But UIKit was meant to be familiar to AppKit programmers, but with some modernizations in very broad strokes, some modernizations and some concessions to the different application runtime environment of the very constrained early phones and iPads.

0:39:15But there's all sorts of other stuff in AppKit that just didn't make sense to port over.

0:39:21But if the iWork apps were depending on it, well, then how did they run on iPad, right?

0:39:25And for me personally, one of them was that Pages supports really rich typography features, like not just picking font, but when you go to the font panel and then open the sub panel for typography.

0:39:39You can choose alternates and stuff.

0:39:41Yep, alternate glyphs, you can get proper small caps if the open type font you're using supports them.

0:39:47You can get advanced letter spacing and kerning controls.

0:39:51You get all this neat stuff.

0:39:52And that stuff's all built into AppKit.

0:39:55And again, it's like core image.

0:39:57Like you can build a really rich typographic app in AppKit without writing the code to do all this stuff because it's right there in AppKit.

0:40:09But it's not in UIKit, so how do you share a document where you might do these fancy typographic things on the Mac and then you open them on the iPad?

0:40:17And they're dependent on OS features that are no longer there.

0:40:21It's all very complicated.

0:40:23And it sounds like, well, duh, what they did is the obvious path forward.

0:40:27They just hit the reset button and kind of rewrote a lot of stuff.

0:40:31But infamously in software engineering, that is the path…

0:40:35"Reset" is a bad word.

0:40:36You avoid that word if you do not need to invoke it.

0:40:39And then often it ends up getting euphemized to an overhaul.

0:40:42It's like, is your overhaul a rewrite?

0:40:44So we learn as leaders in software to avoid it.

0:40:48But sometimes this is the only way forward.

0:40:50And it's not that there was no code shared or anything, but it was a big engineering lift and they pulled it off.

0:40:55And again, it helped being a first-party app where if you really needed to get some code out of AppKit and just put it in the app, you could do it in a way that a third-party app doesn't even have the source code.

0:41:06Yes.

0:41:06But long story short, the iWork apps are today, I think, a thriving success.

0:41:13I think it's great.

0:41:14And I think they really do the, "Hey, you can just open these same documents on your phone, your iPad, your Mac, and save changes just show up everywhere."

0:41:24It's great.

0:41:26So I think that the notion that Apple doesn't care about apps like that, I don't think it holds water.

0:41:35I think there's a sort of knee-jerk fear that Apple only cares about what makes the most amount of money, stuff.

0:41:42Well, I think that the fear, you can kind of go with one layer of more nuance where Apple does care about these products.

0:41:56They care about it.

0:41:57And also, obviously, you learn over time that Apple isn't a thing.

0:42:01Apple is a whole bunch of people that are told to try to do their jobs well.

0:42:04And what does it mean to say Apple cares?

0:42:06But from the outside, obviously, they purposely project a uniform, spherical, polished image.

0:42:12But Apple, in abstract, cares about these products, despite the fact that they are not the highest money-making things, from a strategic perspective.

0:42:21So obviously, they use iWork and they want iWork to be good.

0:42:24And they use, I don't know if they use Final Cut Pro, they must use Final Cut Pro for their own internal video products.

0:42:29So when they're using these tools, they care about them on that level.

0:42:33But the more, I think, bigger way that they care about these products is that it's a systemic risk to their platforms.

0:42:41If, I mean, it goes all the way back, I know you like to go back on the history of Steve Jobs, like, getting held over a barrel for, there's no,

0:42:48there were like, Microsoft had complete control over Apple because you had, there was no alternative at that time, way back to Microsoft Office.

0:42:56So Microsoft Office kind of had complete control of if they wanted to kill Apple, which was bad for them for antitrust reasons.

0:43:01But if they wanted to kill Apple, they probably could have done that, and as could have Adobe, because you, they, Apple didn't have any backup.

0:43:06Like, these products were so important to having a viable platform at that time that you had, it was just wise for Apple,

0:43:15and strategically a good thing for them to do, to say, well, let's not have all our eggs in this basket.

0:43:19Let's also fund what we think are high-quality versions of these products.

0:43:23And what, now 20 years later, what are the products that we're talking about?

0:43:26We're talking about iWork, and we're talking about Pixelmator, we're talking about, effectively, things in Microsoft Office and Adobe's domain,

0:43:32still, 20 years later, still very important things we do with computers.

0:43:35And so, definitely Apple cares about these things, but it's, I think, a little bit different,

0:43:39and to maybe give a little bit of credit to the people who are, who get a little nervous when these acquisitions happen,

0:43:44is that sometimes a company caring about a product in order to protect themselves from potentially bad action or undesirable action

0:43:52from other competitors who neglect their platform, the type of caring that that results in is a little bit different

0:43:58than the type of caring of an independent company that is entirely there.

0:44:02Every dollar of their profit comes from that product.

0:44:05And so, if they can make that product have 10% more market share, that's like the entire focus of the CEO of that company,

0:44:12versus the really fundamental, most important thing for Apple is to make sure that their whole platform strategy is working,

0:44:19and sometimes it can be slightly less critical that maybe podcasting features are really good in Logic,

0:44:25whereas if Logic was a completely separate company, maybe there would be, I'm just very mis-speccing, I don't know if that drives that,

0:44:30but I think that's the mentality.

0:44:32Right, it's a different thing.

0:44:33That people accuse Apple of having.

0:44:35Right, but they've never had, or I shouldn't say never, because I even called back to it,

0:44:40I don't think they've had anything since MacPaint that counts as a bitmap image editor.

0:44:45And MacPaint is a really long time ago.

0:44:48MacPaint never supported color, or I don't think it did.

0:44:53Really?

0:44:54I don't think so. I don't even remember it supporting MacPaint.

0:44:57I've been in the Mac world for a long time, but MacPaint was even before my time.

0:45:01MacPaint is-

0:45:02MacOS 9 is my, like-

0:45:04There was a third-party app before Photoshop, I think the first version came before Photoshop,

0:45:11before Photoshop just sort of was like, "Ah, throw all these other ones away, Photoshop kills them all."

0:45:16But there was an app called SuperPaint.

0:45:18There were a couple of them.

0:45:20It was a Macworld magazine of the era was full of competitors.

0:45:25SuperPaint was like MacPaint with color.

0:45:28Oh man, there were a couple others.

0:45:29I love that era.

0:45:30I know we're already like, we're an hour into not even me talking about the show.

0:45:33But I love going through old magazines and ads for when technology was so new,

0:45:41the things we think of it just like totally just like plain, obvious checkbox features that wouldn't even occur to you,

0:45:48you'd not have, were like an entire app.

0:45:50There was an app that's like, if you buy this package software, you can print vertically as well as default horizontally across the page.

0:45:59Yes.

0:46:00Right?

0:46:01Some plugin for your word processor and there's like, "Oh wow, that'd be cool."

0:46:04Because then we could do like landscape instead of portrait, posters or whatever.

0:46:07But I forget the name of that app, but I think that sold, it wasn't really a Mac thing.

0:46:12I think Macs didn't have it.

0:46:13It was pretty bad, maybe Apple II era or something.

0:46:14But it was like a, no, it was like a DOS thing for like Lotus that you could print your spreadsheet sideways.

0:46:21It wasn't even like, it wasn't even that you could print anything sideways.

0:46:26It was like an extension to Lotus 1, 2, 3 to print Lotus spreadsheet sideways.

0:46:33But because spreadsheets are often so wide and it most many printers of the time were dot matrix printers,

0:46:40where you could just go right over the perforation and make it as long as you needed.

0:46:45You had infinite length.

0:46:47It was a game changer.

0:46:49And then it was like the initial Sherlocking.

0:46:52It was like, well, then of course, every spreadsheet added the ability to print in portrait.

0:46:57And it's like, whoa, well, is that unfair to the company that had an entire business selling a three or four hundred dollar per seat floppy disk software program just to print sideways?

0:47:09But that happens with every technological revolution.

0:47:13And that's part of why I enjoy looking back on those because they're so obvious to us now.

0:47:17But in 2024, there is 10,000-ish AI startups all building stuff.

0:47:22And some of them, a lot of them, are building the equivalent of the print the spreadsheet sideways.

0:47:27There was a whole bunch when these models for like chat GPT moment, GPT 4 comes out.

0:47:31There's like multiple startups whose main focus was, well, if you're a programmer and you want to use this AI model, then you often might want it to output JSON.

0:47:41And so we'll, our whole product, make sure that the model always outputs JSON, even though sometimes it won't and it's inconsistent about that.

0:47:48And like, obviously, when you're a founder, if you're thoughtful, that's not going to be the end of your whole company and that's the only feature you ever remember to do.

0:47:54It's like your foot in the door.

0:47:55But there's a whole bunch of companies that that was like how they initially marketed their thing.

0:47:59And it was really useful in like early 2023 for all the developers.

0:48:04And now it's obviously every model that's just, we assume, of course, you can enable that as a developer.

0:48:09That's just table stakes.

0:48:10And that was only like 18 months ago.

0:48:11Yeah, but see, we've combined, we're doing what, you know, who calls the weave?

0:48:17We've brought it all together.

0:48:18Oh, no.

0:48:19I refuse that branding, but thank you.

0:48:22We have brought it back together.

0:48:25I will not be referred to that way, but.

0:48:29But there is, but you're right, though.

0:48:31But you're right, though, that whenever there is a breakthrough, there is this explosion in startup ideas.

0:48:40And we're seeing that with AI right now where, and most of them aren't going to make it, no doubt.

0:48:46Many of the ideas that they have, like you said, oh, just if what if we could just guarantee that everything's in JSON all the time?

0:48:53At the moment, that is actually has use cases where there's somebody out there who hears that and is like, oh, my God, that's what we need.

0:49:00We're having such a hard time because that's what we really want is everything in JSON.

0:49:04And we have been struggling to get it and try their thing.

0:49:08And it's like, yeah, it works or it works more often than our in-house solution does.

0:49:13Well, 18 months ago, almost every app that a developer was making on top of AI would have one or more all caps only respond in valid JSON.

0:49:23Because they were trained so that they could do it in theory in the understood JSON, kind of.

0:49:29But there's a difference in between it's statistically likely to usually have valid JSON and it 100% always you can just depend on that it's going to do that.

0:49:37So it was super. It's not just like some people found that useful.

0:49:40That was like a table state, like almost everyone who's building on that needed some way when we were building these pieces of software to be able to reason about, okay, well, what's coming out of this and how do I display it in my UI?

0:49:50How do I parse it?

0:49:51So, yeah, and now let me start off so moved on.

0:49:55All right, let me take a break.

0:49:57That's the money bell.

0:49:59Have you listened to the last episode?

0:50:01Yes.

0:50:01Joanna, this is a new gimmick.

0:50:02What do you think of the new gimmick, the money bell?

0:50:04I think it works.

0:50:05I think it needs to be loud enough, though, that it feels like at first you had it off in the distance and it's almost like it's background noise.

0:50:11It should feel like it's part of the recording.

0:50:13So I don't know.

0:50:14Let me try this here.

0:50:15Here, you can see me.

0:50:16Better?

0:50:18I think that's good.

0:50:19I mean, for me, that's good.

0:50:20That's putting it right up to the mic.

0:50:22Well, anyway, this episode is being brought to you by our friends at Work OS.

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0:51:23Really, really interesting. Work OS.com is the URL.

0:51:26And just this last week, they had what they called Launch Week, where they announced seven new features amongst them.

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0:51:39PassKeys, I've been writing about that at Daring Fireball, but now they've added PassKey support. Radar as your SAS, software as a service app, grows in popularity.

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0:52:49That's it, no special slug from the show, but if maybe there's like a poll or something after you sign up, you can tell them it came from the show.

0:52:57You always joke, Jon, that you don't know what SAML and SCIMR are. Do you want to know what they are?

0:53:02No.

0:53:02Or is it more fun for you?

0:53:03Oh, way more fun.

0:53:04Because I can explain it.

0:53:05No, no, you're going to explain so much.

0:53:06I won't swell the mystery.

0:53:07I could guess, but I don't want to know.

0:53:11I enjoy it. It's humorous to me as someone in like the startup world.

0:53:15Like an S-A-A-S app. I think they call it SaaS.

0:53:19SaaS? Yeah, I have heard SaaS, but see that I worry about other people like me who may not know what it means.

0:53:26But there's also SaaS, which is like a CSS framework, but I think the target demographic knows what you mean.

0:53:31Anyone who's going to buy WorkOS isn't going to be like, wait, SaaS, what do you mean by that?

0:53:38Yeah, but then there's also like the SaaS, which is like the way my wife talks to me.

0:53:42Well, I mean, I don't know, maybe they support that too.

0:53:45I don't know. All right.

0:53:48Where do you want to start talking about generative AI?

0:53:51So I've been immersed in this world as someone for the last few months.

0:53:55I've been building software, but I think for this audience, Apple intelligence is kind of like the core motivator for a lot of the conversation around it.

0:54:06And so I think there's two high level lenses that I think are interesting, or at least examples of lenses that are interesting to think about Apple intelligence.

0:54:15One is, is it useful yet? And the other is, how does it compare with the state of the art, the other offerings?

0:54:25And those are two very different questions. When I first started using it, installing the 18.2 beta and playing around with this stuff,

0:54:31at first I would have instinctual reactions to these because I'm over exposed to the state of the art.

0:54:37A day ago, something came out on Sunday and I'm still kind of itching. I feel behind because I haven't tried the latest cursor updates, whatever, for new AI features.

0:54:44But most people are not like that. Most people are not aware of what state of the art is or wouldn't even think in terms of state of the art.

0:54:50They just open this feature and then if it's useful to them, then great.

0:54:53And if it's not useful to them and the state of the art thing only starts mattering when it's competitive.

0:54:58Like if Android has a feature that's better and then a friend says, "Hey, my Android phone does this better."

0:55:05That matters more than if some insider AI people happen to know that, oh, if you wire in a different model or OpenAI natively offers this functionality that maybe people aren't familiar with.

0:55:16So I think those are the two kind of lenses that are interesting for this stuff.

0:55:21Yeah. So where do you think, well, how many models out there should people even care about?

0:55:28If you were like a listener of this show and you're like, I don't know, and you wanted to do your own evaluation of where Apple intelligence stands versus the competition.

0:55:38Who's on the short list of apps of models you should try?

0:55:42ChatGPT obviously is the one that everybody talks about. It's like the Kleenex brand of the industry at the moment.

0:55:48So there's a distinction that's worth making. And I think when you're trying to think about this stuff that's useful and it's very easy to miss is the distinction in between a model and the product that's built on top of the model.

0:56:02So you go in to ChatGPT, it's going to serve you the current free, I mean, if you're a free user or if you're paid, then you're paid.

0:56:09But the models that OpenAI has developed for use on that service, which currently the frontier models or in the jargon in the AI industry, they'll call the frontier model is like the biggest and best model available.

0:56:22So their frontier model is 4.0 for general usage, GPT 4.0. And then there's the 0.1 model, which is not GPT 0.1, which is another like one of those jargony things because OpenAI is just as bad at naming stuff as every other AI company.

0:56:38But I think like a little inside baseball on that, I think OpenAI is actually backing away from GPT as a term because they're struggling to trademark it.

0:56:46So they don't want a GPT 5, GPT 6 and everything is based on GPT, but then they don't own a trademark for GPT.

0:56:51And you see other startups trying to call themselves GPT something else. And then if OpenAI can't own that phrase, then that's a problem for them.

0:56:58So I think that might be a motivator why they're 0.1 is their like future generation model.

0:57:02Interesting. So it's not even about persnickety precision to the term of art. It's just as simple as trademarking.

0:57:10I mean, they haven't said that.

0:57:11Or it could be a factor.

0:57:12I've heard someone push one of their people on like, oh, why is it called 0.1 and not GPT 5?

0:57:18And they're like, well, you know, 0 stands for OpenAI and we think this is a 0.1 is a new paradigm and so we thought it needed a new.

0:57:25But it's like, really? Is that the only reason?

0:57:27And so my speculation is that the trademark they're moving and like they recently bought chats.com.

0:57:32So they don't just have chat GPT.com, but chat.com, which I'm sure cost a ridiculous amount of money, but they have a ridiculous amount of money.

0:57:38Right. So they do.

0:57:39So just for very briefly, 0.1 is I would say for most people, 0.1 is like not going to be the reach for model.

0:57:46But it's interesting because it is in OpenAI has definitely been hyping it as the next generation of how they want to make these models better.

0:57:54So GPT 4 and GPT 4.0, which is what most people, the most common model people interacted with via chat GPT.

0:58:01It responds to you right away and you sort of its dreams in its response.

0:58:06But as soon as you ask it a question, it starts answering.

0:58:10Whereas what 0.1 and future models of that series are starting to do is conditionally spend some time, quote unquote, thinking about the response before it responds.

0:58:20It's still kind of janky. It's like a pre, they call it a 0.1 preview. It's not really fully baked yet.

0:58:24But that's the like really if the state of the art, like if you're trying to solve a physics question and you're like, which AI LLM that exists is most likely to answer that question successfully, then that's probably the OpenAI 0.1 model.

0:58:38That's accessible all through the chat GPT app.

0:58:42But something that's really easy to miss and you see it when you interact with chat GPT within Apple intelligence is that there's a difference in between in talking to GPT 4.0, which is the model, and using chat GPT.

0:58:56Because when you use chat GPT, it's powered by this GPT 4.0 model.

0:59:01And then in January, it'll be powered by whatever they call the successor model.

0:59:05They're calling whatever they keep improving the model.

0:59:08But on top of all that, they build all this extra functionality that improves over time.

0:59:12So recently, we were talking about how they have search wired in.

0:59:16And search isn't something that's like fundamentally part of GPT 4.0.

0:59:21It's application features that they have added to the chat GPT app that helps GPT 4.0 give you better answers.

0:59:29And they also have the ability for it to generate images and the ability for it to interpret images and the ability for you to upload code and the ability to,

0:59:36there's a canvas feature in chat GPT where you can like work on something and then text will appear on the side and you can iterate it back and forth.

0:59:42A lot of those are like extra affordances that are in the product, but that isn't necessarily part of the model, if that makes sense.

0:59:50Yeah, that does make sense.

0:59:52But it also, and we'll get to it, I think, it also further confuses the line of who's solving what where when you ask Apple intelligence question and it bubbles up to chat GPT.

1:00:05Is it coming out of the model or is chat GPT itself then doing the action of going out and searching the web to get the answer?

1:00:13Have you noticed, in my experience so far of using the beta chat GPT integration, is it seems like, even though it's calling it chat GPT,

1:00:21it seems like Apple has mostly implemented using the GPT 4.0 APIs and not all of the extra search, web search and citations and image generation.

1:00:32I don't think if you ask, hey, Dingus, can you have chat GPT make me an image of a sloth? Like, I don't think any of that stuff is wired in,

1:00:41which is the same experience you would get as a developer if you were like, oh, I'm going to make a chat with GPT app as a web app or an iPhone app.

1:00:49And then you just took the APIs that OpenAI gives you to as a developer to make something, you wouldn't get any of that extra stuff.

1:00:56You would have experienced that similar to Apple's, basically.

1:00:59It's all changing so rapidly that all of this is going to be just a snapshot in time from late November 2024.

1:01:07Yeah, we should say this is November 25, 2024, and we should get this edited out as fast as possible so that when any of these various models,

1:01:15I might say, oh, O1 is the best, Preview is the best advanced model now, but in three days.

1:01:21And, you know, iOS 18.2 and the various other, the Mac OS 15.2, God, I wish those major version numbers were in sync,

1:01:30have only been in beta for like six weeks or something, but they've already gotten better.

1:01:34And a friend of the show, Paul Kefasis, on his One Foot Tsunami blog had a thing when he first upgraded to 18.1.

1:01:44So he just got regular Apple intelligence out of beta, was like, couldn't be bothered to try it in beta.

1:01:50Like most people, and asked who was playing at the TD Bank Center in Boston, the big basketball concert arena.

1:01:57And Siri was, I forget what happened initially, it was like Siri gave like a horribly wrong answer with Apple intelligence, like not even close.

1:02:06And ChatGPT would be like, let me look on the web and let me look on the TD Bank website and gave like the correct answer for whether,

1:02:15I don't know, it might have been a Celtics game that night or something like that.

1:02:17But that's already been fixed, you can ask Apple intelligence questions like that and it'll somehow search the web.

1:02:25It's moving that fast.

1:02:27Does it though? Because the thing, I don't know for sure, in like a few weeks, but the sense that I get of the approach that they've been taking so far with Siri,

1:02:35I know I'm supposed to say Dingus, with Dingus, with this technology is that they are not yet giving the engine,

1:02:46it's a web search, they are trying to give it useful context from databases.

1:02:51So like the feature that allows you to ask, hey Dingus, what is the score for the NHL game?

1:02:56It's doing that from a database, it's not going out to the web and then doing a search and returning the search back to the agent,

1:03:02which then reads you the answer, displays the answer.

1:03:05But so you have this kind of whack-a-mole problem where when Apple hears, oh, well people are trying to do this thing,

1:03:11they're asking Siri about this thing and they're getting bad results and this is a common use case,

1:03:16let's improve the recognition of the system so that it knows, oh, I should look at this source for the location of the next NFL game.

1:03:24You can settle those really common requests, but with a general, which I assume eventually they'll do,

1:03:31with a general setup where it can just search the web on your behalf and then it can reason about those answers and then give you the answer,

1:03:38you could ask kind of an unbounded question where you could say, oh, well, what is the latest episode of the talk show?

1:03:45What's going on with John Gruber? What number is it? And who was the guest? I forget.

1:03:48And like Siri is never going to have a custom handler for that.

1:03:51But there's this kind of infamous thing about Google where they say like something like 20% of Google searches have never been searched before.

1:03:57And so, and that's true just every day because people are always have more and more niche things that they want to know.

1:04:03And so the solution of creating these databases and stuff and fixing the individual problem, yeah, okay, you fixed Paul's problem.

1:04:10But then I asked something, well, there's also an underlying problem with Siri that it's been true forever,

1:04:15and it continues to very much be true with the 18.2 beta and probably will continue at least until the rumored iOS 19 Transformer AI-based version of the product,

1:04:25is that it's just inconsistent, which is also so with ChatGPT for some of the same reasons.

1:04:30But like I was trying to test on my way today to record this, I was trying to test again something that I had test a couple of weeks ago,

1:04:37which I asked it to contrast, verbally I asked, to contrast the words tacit and implied.

1:04:44Because on a dithering episode, Ben was like, oh, well, tacitly this tacitly this, I'm like, what's the difference between tacit and implied?

1:04:51And I tried that a couple weeks ago. And it like sort of halfway went to ChatGPT, and then it stumbled and it wouldn't read me the results.

1:04:59Today, I did that and it misheard me and it read me ChatGPT results for what it had misheard.

1:05:06So I tried it again. And then the second time it gave me, here's what I found on the web for can you contrast the words tacit and implied.

1:05:14But it wouldn't read it. It would only display it on the screen.

1:05:17I mean, you can chalk some of that up to just the growing pains of they're working on 20 different AI features all simultaneously and trying to bring them to the OS.

1:05:24But also that's kind of been the behavior of this sort of voice interface on iOS for 10 years.

1:05:32Those sort of experiences do happen when you push it outside of, no one's ever asked that question before.

1:05:36I'll never ask that question again. You need a sort of general more ChatGPT-like system in order to handle requests like that.

1:05:43Right. Basically, the first thing you want people to take away is that there is a difference between the underlying model and things that are built on top of it, which includes the branded first-party chat apps that the makers of the models tend to make.

1:06:00Yes. And so if we go to the Clod app, which arguably Clod being Anthropic is the company behind it.

1:06:07Of course, each one has a name of the company, a name of the model, and then versions of the model. So there's like names everywhere.

1:06:13But Anthropics, Clod 3.5, the latest version of that, which is out in the last few weeks, is like argued.

1:06:20A lot of people seem to be arguing that are spending a lot of time with these models that for a lot of things, it's maybe the best.

1:06:25It surpasses 4.0 in some ways for certain task coding, it seems like especially.

1:06:30And on top of that model, which we can use as developers to build stuff, if you use like the cursor IDE, and I'm sure we'll get into programming tools maybe a little bit today.

1:06:40But if you are interacting with the Clod website, you're interacting with that specific model and all the features the Clod team has put on top of in their equivalent of ChatGPT, which has some features that ChatGPT doesn't have.

1:06:53And it lacks some of the features that ChatGPT does have, and then they're sort of playing cat and mouse on the product level as well as on the model level.

1:07:02Whereas if you're looking at a company like Apple who is using these models to potentially build products, they are mostly consuming the sort of the AI part, the model part, and not all the stuff on top.

1:07:12And I get the feeling that there is a parallel to the earlier era, which is still going, but like when you're building an app for iOS, or you're building an app for Windows, there is a difference between being a third party developer and a first party developer because the first party, like you, we were just talking about in the previous segment,

1:07:38where if Apple really wanted to special case numbers on iPadOS in 2010 to give it more time to serialize the data to disk just because it got interrupted and was put in the background, they could do it and others couldn't.

1:07:54ChatGPT, the app, can do things with OpenAI's model that you can't necessarily do from the outside through the API, or at the very least, even if it's the same thing, they don't have to worry about paying for the tokens.

1:08:09Yeah, and that happens all the time, so it's very common that when a new user-facing functionality, like when OpenAI is, oh, okay, a good example is just in the last week or two, Anthropic Cloud released computer use, which is sort of, so maybe if people haven't seen this, this is like fascinating, where you can say, hey, just use a computer and it will click around, it'll type in fields, it'll scroll, and you just, you ask it to do something, oh, please fill out this form for me on a website, and it will attempt to do something.

1:08:38It's pretty beta, a little janky, and obviously has huge security for all the implications. They strongly encourage you, start up a little sandboxed virtual machine that doesn't have any of your actual information on it and let it play around with that and watch it, what it's doing.

1:08:52But it's like this glimmer of a future-aware, which, and product, there's entire startups that are very involved in just doing this, and they're getting more advanced results than what Claude's doing.

1:09:03But Claude, my point with all that is that they launched this computer use thing a few days ago, simultaneous with a model update that was tuned with examples so that it was better at performing that task than the model that had been available to everybody else the day before, basically.

1:09:19And that happens all the time.

1:09:20And it is kind of funny where you just sort of, like a modern desktop computer is a very powerful device but that needs a human being to drive it, which limits how fast certain things can go, because you actually have to do the typing.

1:09:36And if you could have a robot doing it for you, the robot on its own can do more by driving an existing non-AI Windows or Mac PC than it could on its own, right, and can do it faster than a human being can.

1:09:55Remember, was it Star Trek IV, where Scotty sits and they travel back to the '80s time and Scotty sits at a Macintosh and everybody remembers the joke where he's talking to the mouse.

1:10:08Yeah, talking to the mouse.

1:10:09But then once he's like, oh, okay, and it doesn't really make sense. The implication is that somebody like a smart engineer from the 23rd century, of course, finds a 1987 Macintosh trivial to use as opposed to archaic and cryptic, which is how it would be.

1:10:29Yeah, you're actually how it would be.

1:10:30But then all of a sudden he just like starts typing and clicking real fast and boom, he's got like the molecule from…

1:10:35Transparent aluminum.

1:10:36Transparent aluminum.

1:10:37But that's sort of what the near future vision of like having Claude drive your computer for you is, is like being able to sit Scotty in front of your computer and boom, do something much faster than you could.

1:10:50Or maybe something mundane.

1:10:52Certainly that's the eventual, but the thing that's closer is that you can have it do mundane, like to your point, mundane, boring stuff that you would lose attention span on that you just don't want to do. That's not a super high use for your time. Even like just like a really, really dumb thing is like running a startup, there's all these administrative tasks where it's like in order to do my monthly update for my finances, I need to do a sync of my bank feeds.

1:11:16But I have to initialize that and then once it's initialized, it takes like 10 minutes and then for that 10 minutes, you're just sitting there because I don't know, legacy systems.

1:11:22And then eventually the data comes in and then there's a couple simple tasks I want to do. But I can in theory have Claude wait for that and then once and just watch.

1:11:30And then once it's done, it does the couple simple tasks that I otherwise forget. I switched to another browser tab or I got interrupted by a Slack message or something.

1:11:36And so the starting point, and this is true for almost all of these tools, is that the things that are the first stuff that becomes easy for them to do is the stuff that you would have an intern.

1:11:46Like imagine just the most diligent intern that never says no and they sometimes make mistakes, but they're incredibly well read and they will work around the clock.

1:11:54And so if you expand from-

1:11:56You don't have to feel guilty that they're working around the clock.

1:11:59No, no, they're happy to do and you could spin up 10 of them, right?

1:12:02And so this is the first low-hanging fruit for these things is what those interns can do.

1:12:07And to some degree, that's not specific to this. The frontier models like Claude, the most advanced models, that goes all the way down to the Apple intelligence features where they have a much smaller, less powerful model than what what Cloud is.

1:12:20But there's still some of these tasks where in theory you could have an intern transcribing summaries for all of your WhatsApp group chats.

1:12:27And they would probably do that pretty okay, even though they're maybe not the PhD level or whatever, but it's a relatively straightforward task.

1:12:34All right, so there's Anthropix, Claude, and then you've listed LAMA 3.2. Now that's META's-

1:12:40Yeah, so I would say the two other LAMA 3.2 are, I mean, 3.2 is the current version, but like LAMA's large models, so like they have what they call a 405 billion parameter model, which is a way of saying very large, like particularly large model, which is open source.

1:12:57So that's the really interesting thing about what META has been doing is they're releasing these open source models and they are continuing to push the envelope.

1:13:04I'm not just happy with what previous generations of open source models had been like 6, 12, 18 months ago was like, well, there's this open source model and it's not as good as the other ones, but it's like way smaller and obviously not as well crafted.

1:13:15But you know, it's open source and you need to say anyone or there's arguments about what the definition of open source, it's open weights, or that is actual code used to produce the model open?

1:13:24No, but like you can download it and use it as long as you are not Apple, Google, or Microsoft, I think is the way their license is written, which is a bit of a musing will turn there.

1:13:35And so that's one of the other sort of frontier models. It's not thought to be quite as good as the Anthropic or CluD, but if Anthropic and OpenAI disappeared tomorrow, there would suddenly just be huge demand to use this model because it is in the same ballpark.

1:13:50And then the final one is the Google Gemini models, which there's fans of for sure, and there's certain few tasks that they're very good at.

1:13:58If you're a developer and you need to put a million tokens of just huge amounts of volume into one API call, then the Google models can do that.

1:14:06They're trying hard and they're releasing new versions all the time.

1:14:08I think the kind of vibe among developers is that the Google models are for most tasks not quite up to the same level as those other models, but those are kind of the four heavyweights, I think, in the minds.

1:14:20And then there's dozens, hundreds of other either completely separate models where they have a really specialized thing that they do well or fine-tuned variants.

1:14:31There's like many, many variant models that are fine-tuning off of open source stuff.

1:14:34But I would say if you're an enthusiast or curious about this stuff and you want to kind of follow the horse race, those would be the four to care about.

1:14:41And if Apple wanted to like integrate other models in, those would be the ones that they're probably calling.

1:14:47Right. And that it wouldn't make much sense unless, but it's probably not the case that there is some secretive dark horse out there who's going to decloak and have a competing model that will join.

1:15:02Well, there are absolutely secretive dark horses out there that are telling VCs that they're going to decloak and dethrone one of these people.

1:15:10We just need a few more billion dollars and we will just blow everyone's minds in the style of these.

1:15:16You know, there's always some startups out there that are doing a humane thing where it's hard to tell from the outside is this going to be the next big thing or not.

1:15:24But I think the smart money is that probably we're not going to have someone come out of nowhere and dethrone all these people.

1:15:32But one of the things that makes this moment different than previous, I've called back and I have some other ideas that call back to similar explosions or new opportunities in the computing industry.

1:15:46But one thing that's different here is that we just need billions of dollars, right? And even taking inflation out of the equation, that just was never the case for startups before.

1:16:00I mean, and hardware is always a little different than software, but I can't really think of a software startup idea that required billions of dollars in previous times.

1:16:14Because what would that money even have gone to in previous eras?

1:16:19Well, we have the mythical person month problem where you can have an infinite amount of money, but you'd be hiring more and more and more engineers isn't going to make you go faster past a pretty small team size.

1:16:29And in fact, will soon start slowing you down.

1:16:31Yes, rapidly.

1:16:32But if you think about, so certainly in software, yes, but then there's some argument that are these AI startup software? Well, I mean, the end artifact they're creating is actually just a kind of a single giant data file that then can be used.

1:16:47Right? It really the kind of nut of all what these companies are making is software in that way.

1:16:52But in order to do all that, they're creating or using these huge training clusters.

1:16:57And so there is a hardware component. And so Sam Altman, the founder of OpenAI, which you sort of have to take everything he takes with a grain of salt, because obviously he has a huge vested interest in shaping how we think about this stuff.

1:17:08But he likes to when people say, oh, it's like the internet or it's like this invention or that invention.

1:17:14He will talk about as being like the transistor, where when the transistor was invented, then they obviously there's all this VC and like Silicon Valley is named after it.

1:17:25And it's not as much the mobile revolution, right? If you try to map what we did in 2008 to build all our businesses and the curves and the paths, it's quite different because of it's an enabling technology.

1:17:36I think in his argument, it's enabling technology that we all start to eventually take for granted underneath everything.

1:17:42But that obviously after $100 billion or so of VC investment.

1:17:48And it is also, I guess, one of the main knocks against this whole thing are the environmental costs of training these models.

1:17:57That it is extremely mind bogglingly computationally expensive to train these large models.

1:18:05If you're a small startup and you're trying to convince VCs to invest in that you're going to be the next Claude or something like that.

1:18:11That's what the billions go to, right?

1:18:14Yeah.

1:18:15And obviously as we as an order to mature.

1:18:18And it's not just expensive, it's rare to it. There's only limited number of server farms that could do it.

1:18:23Yeah.

1:18:24Well, obviously there's a compounding problem where there's way more demand for training hardware from Nvidia than their supply.

1:18:31And therefore, obviously that gives Nvidia pricing power, which means that if you want to raise VC to be yet another company that's competing for that same limited pool, you're driving the price up more.

1:18:40And eventually, I think that's why there's been a big downtick over the last year in interest from VCs to be like, "Yeah, okay, sure.

1:18:47Here's yet even more billions of dollars to go and yet another parallel track."

1:18:52And you see instead doubling down from to some degree VCs, but now at the scale that they're at, increasingly Amazon just had a huge investment in the Anthropic.

1:19:00Because obviously Microsoft and OpenAI have relationship, Anthropic and Amazon, there's sort of like a natural pairing where Amazon has the thing that Anthropic needs, which is a whole bunch of infrastructure.

1:19:09And so we're kind of more, I think, at the middle stage of that.

1:19:12There isn't a lot of startup founders going into Y Combinator and be like, "Oh, what's your idea?"

1:19:16It's like, "Oh, we're going to make a new foundation model that's going to beat OpenAI."

1:19:20We're way down the path now where the vast majority of startups are thinking about, "Well, how do we apply this stuff?

1:19:25How do we build really great experiences and features for people that are just take it, assume that, yeah, there's going to be some great models for us and they're going to keep getting better?"

1:19:33The big callback, and again, maybe it's just because I just, for years I had had, if you ever have a TV show like this, you're like, "I think I'll probably like that and I'd like to watch it, but I don't feel like starting it yet."

1:19:46And then years go by. And for me, one of those is Halt and Catch Fire.

1:19:51Oh, yeah.

1:19:52Yeah, I feel like I would probably like that, but I feel the same way. It's like, it's in my wheelhouse.

1:19:57It was on my maybe list, and I know Kotke liked it, and I often, you know, I share a lot of overlap with things he likes.

1:20:05But there was some, I don't even know how to describe it, but I had a misconception about what Halt and Fire would be like that was totally wrong.

1:20:15And it is, I would describe it as being a very Mad Men-esque show.

1:20:21It is, and I wish somebody would have told me that, because Mad Men is probably my favorite television show of all time. I've watched it.

1:20:27It's that and The Sopranos. And Matthew Weiner, who was the showrunner and creator and main writer of Mad Men, came from The Sopranos.

1:20:37So it's, there's a certain similarity. And Halt and Catch Fire feels like that type of show.

1:20:43Except it's about a little scrappy electronics company in Texas in 1982 or '83 who's going to try to make the shift into making personal computers.

1:20:54And I've only through season one, I don't know where it goes from here.

1:20:57But I was like 10 at the time, but I remember those years of personal computers.

1:21:03And the show, I think, from my recollection, does an amazing job. They have great technical advisors who are making the characters' lingo seem true.

1:21:14Just the way that there would be like, I don't know, you think you're going to use this chip from Intel as the CPU, but did you hear that they're working on the whatever and it's got like one and a half times higher megahertz clock rate?

1:21:27And it's like, what? Are you serious? How do we get that? And you say, oh, you got to know somebody.

1:21:30And it's like, yeah, like six weeks later, six weeks later, the chip that you thought was the hot shit is now old news.

1:21:37Yeah. And if you bet everything on that, now you're doomed.

1:21:39Yeah. Like you've, we've already locked in like this huge buy of motherboards from Taiwan based on that chip.

1:21:46And you're telling me that by the time we get them back, we're going to be selling yesterday's news?

1:21:50But that's happening again with these models, with the AI moment where if you had just gone away for a six month sabbatical and just tuned off the internet, you'd find yourself way behind right now from where you were six months ago.

1:22:07Oh yeah. On this stuff, like especially trying to like be a developer that's building on this stuff and what are the approaches and because what you're constantly, you take for granted that you have this mental model of what isn't possible as a developer or someone who's building a business.

1:22:21And when that's changing every few weeks, that's very different than the environment.

1:22:27Like we went through this pretty calm period where through a lot of the last 10 years since really mobile kind of settled in that you sort of got this orthodoxy about here's the default tools you use to build a product and here's the different approaches.

1:22:40And one of the famous ones is like this kind of almost like received wisdom in the startup world, which is like consumers don't really pay for SaaS.

1:22:48Like they don't really want to pay a subscription. Like, yeah, there's some, yeah, they have Netflix, but like you can't just make a software company and then people will subscribe for it and make a business that way.

1:22:56But now suddenly with the turn of all these LMs and image generation models, there are like some pretty meaningful companies being built.

1:23:03I mean, obviously OpenAI is an extreme example where they have $2 billion revenue supposedly, and a lot of that is consumer subscriptions.

1:23:10But also just like image generator apps and things that automate things for people's church sermons and people are suddenly, the tools are so useful that suddenly we have this huge uptick in consumers paying subscriptions and we'll see whether or not that shakes out.

1:23:22But like it goes all the way up the stack in terms of assumptions being broken and that's the fun of it. But yeah, you have to kind of, you feel like you're always behind.

1:23:30So that's the fun and the curse.

1:23:32So one of the things I've picked up in recent weeks is this sense that everybody in the industry thinks that these large models are sort of running up against the wall, and that's why they're switching to other technologies to add reasoning because the leaps that were made going from GPT-3 to 4 aren't going to happen again.

1:23:57Yeah, there's definitely, I think it was Information or Bloomberg or one of these had a story where they'd had sources from multiple of these four labs that I mentioned, maybe all four of them, talking about how the scaling law seems to be slowing down.

1:24:10And it's hard to tell from the outside how much of that thing is.

1:24:14We've been talking in the inside baseball, people were really plugged into this stuff. There's been discussion for almost a year about the data wall, which is this idea of, okay, we're not me, but they are training these giant models.

1:24:27And one of the ways that they've been getting huge gains from the GPT-2 to 3 to 4 and similar for the other models is putting more and more and more data that they've pulled from the internet or transcribing YouTube videos or all these sources of data and putting in more data is one of the main things that is making them better.

1:24:42And then eventually you have this huge diminishing return in how much effort does it take to get the next token of data.

1:24:49And by no means are these models being trained on literally all human data, but eventually when you're being trained on more or less all of the useful data that you can straightforwardly scrape from the internet, then suddenly each bite becomes way more expensive.

1:25:04You have to have a licensing deal or in a lot of these cases they're having, there's a company called Scale AI and there's a few competitors of them that are like a billion dollar company that are creating training data.

1:25:14So if you're open AI or anthropic, you can say, hey, I need a thousand examples of a senior chemist analyzing this type of thing and talking through the reasoning about why it wasn't wasn't that way.

1:25:28And it needs to be a PhD and then here's the thing. And then the company will go off and find those chemists and just multiply that by everything that they find that there just isn't enough examples of.

1:25:37There's not enough examples of people being uncertain on the internet because everyone on the internet just claims that they know what they're talking about.

1:25:42And so they're like, okay, give us more examples of people who are expressing, well, it seems like this, but here's how I would consider the right or wrong and to try to train the model.

1:25:49And that's way more expensive and slower.

1:25:51And so one of the things that's not clear is, okay, well, obviously we were getting these huge gains and we were getting easy gains and it's not obvious where we are in this moment in November 2024.

1:26:02Is this like the various times where in silicon where we are getting faster processors and then suddenly like for a year it's like, oh, maybe Moore's law stops.

1:26:12And maybe just the 333 megahertz G3 or whatever it is that we're stuck at for a while is just as fast as computers are ever going to be.

1:26:21And then of course that silly looking back where we've been at the same number of gigahertz for 10 years, but they're getting better and better using other approaches.

1:26:28And that, yes, that's more expensive, but because we're at this scale, it's worth that extra expense.

1:26:33Or is this something that historically we'll look back on like, hey, remember those two years where everything got way better and AI just roll at once.

1:26:40And then now it's still not that much better than it was.

1:26:43My instinct, it's more like the former, if not primarily because we've proven out so much economic value from these things getting better that there's now a whole bunch of resources to get to that next notch that wouldn't have been mustered if we hadn't proved out that these things are extremely useful tools in so many different verticals.

1:27:02So that's something that we have to kind of like watch and wait.

1:27:06But even, I mean, I'm just kind of going off and on all tangent now, but like I find personally this argument about, oh, if they hit a wall and are the models going to get that much better?

1:27:15Like it's interesting and it makes an impact to how we're building our businesses and our products to some degree.

1:27:20But even if the current models only get really incrementally better and we somehow this is like a really big wall and that it's only just kind of like going to get five or 10% better every year from here on out.

1:27:32There is still many years to really fully use these things.

1:27:36It's like we're all the baby versions of actually building products on top of this stuff.

1:27:41So it's not maybe as existential, I think, as some people make it out to be.

1:27:44It just harks back to other years of computing.

1:27:47Oh, has Moore's law hit a wall?

1:27:49I mean, I can't tell you in my years of enthusiasm for computers how many times I read, I mean, going back to when you had to read them on print, like get used to it.

1:27:58Chips aren't going to, you know, they've reached a maximum there, chip production.

1:28:02Oh, sorry, by the time I got to the end of writing this column, there's been a breakthrough.

1:28:07But you get used to its human nature where when you get used to an 18-month-old computer feeling way too slow year after year after year, that all of a sudden you expect when that doesn't happen again, you think, well, that's it because that's the way it's always been.

1:28:25And they're not getting faster, they only get, like you said, 10% faster a year.

1:28:31But you forget compound interest, 10% annually doesn't take long for all of a sudden something is twice as fast or twice as good or whatever.

1:28:41Yeah, even one of the aspects of this that some people at the time can be easy to forget is that even if the models don't end up getting way better in meaningful ways, which we don't know what the path is going to track.

1:28:52My sense is like the successors to Claude and 4.0 and these are already meaningfully better in the lab, they're just not as much better as the previous generations, like more than 10% better.

1:29:02And we as humans are very bad at appreciating that. We're like, "Wah, wah, I wanted more."

1:29:12It's a big deal when you are a business and you make a bet that it's like, well, GPT-2, 3, and 4 were each this far apart and they were this much better, and so we're going to make a big bet that depends on GPT-5 being this much better on this path.

1:29:26So to some people it is really a big deal. But my point was that even if the model architectures need to change in order to like this thing about GPT-01 and then everyone else is going to be taking the same pattern where,

1:29:38yes, the model can get bigger and be trained on more data to get better, but it can also spend more time thinking.

1:29:44And some people don't like it when I call AI's thinking, but we know what I mean when I say it's thinking. It's not the same way if I type something into my Mac and there's this rainbow spinner, I call it thinking even though it's not actually thinking.

1:29:54Or you can argue about whether or not it's thinking, but that's not the point.

1:29:57We're using these other, even if these other approaches are what they use in order to continue to make things better, the incredible investment in making the silicon and the hardware better and cheaper and all of the incredible economy of scale of running these,

1:30:13which is still a very new thing, like Ben Thompson was talking about how Nvidia is just now rolling out the first architecture, this Blackwell architecture, which is based on the first post-ChatGPT architecture for even running this stuff.

1:30:28And so that is so obvious to me that we're going to continue to have economies of scale of being able to run these things cheaper and faster, even if we're getting roughly the same order of magnitude of intelligence out of them.

1:30:40And there's so many usages where we don't even think to use it for this or that either as developers or end users, because even though it's amazingly fast compared to what you might think two years ago, it's amazingly slow for other use cases.

1:30:55Or expensive, just prohibitively expensive to have it maybe go off and write a thousand scripts and then you compare them all and then it comes back to you and here's the best one or whatever.

1:31:03When things get cheaper, demand for the thing goes up, so I think there's a lot of that to be had also.

1:31:09Interesting.

1:31:13Explain to me, because I feel like I have a pretty good layperson's understanding of how text-based stuff works with these, and including the fact that they seem particularly good at writing computer programming code.

1:31:34I kind of get why. Everybody I think knows that it's like, "Oh, it's like predictive pattern matching based on, 'You give me this input, I can guess what you want to do going forward in prose."

1:31:50And in computer code, it's helpful because computer code is so much more rigid than just language prose.

1:32:01But it still absolutely boggles my mind that image generation works as well as it does, including photorealistic image generation.

1:32:12Yeah, there's a distinction there that often gets lost in these systems, because users, we interact with a whole system, a product, and then underneath there's these models. But there's traditionally been totally unrelated or mostly unrelated architecture in between the way that we would generate language and the way we would generate an image.

1:32:33So if you look like the year ago state of the art around language generation is this transformer model, a large language model, and it's effectively predicting the next token, and that generates prose or code or whatever.

1:32:46And then we have these diffusion models, which is a different architecture, a different approach for creating an image.

1:32:52And so it's very vaguely like it makes a random field of pixels, and then it tweaks them over and over again to make it look more and more like the thing you want it to look like, which is a duck or whatever.

1:33:04And those two technologies were fairly separate. Now, there's an enabling technology of the transformer, which gives the system's ability to kind of reason about text, that makes those diffusion models more useful, because then I can say something like,

1:33:17three ducks sitting on the bay with the sunset in the background, and the transformer can help convert that into something that's meaningful in the space of this image model.

1:33:28But then most of the work that's happening to create that image is happening in this diffusion architecture.

1:33:33So I think that's one of the things that's like non-obvious. When you ask chat GPT, "Hey, make this image," at least for a long while, it would shell out to what it would call DALI, which was its different architecture system that was making images.

1:33:45One of the things that is very new and not very super well understood yet with the limits of it are, though, is what they call multimodality, which is a $5 word for a large language model that has a shared architecture across different types of things it can output.

1:34:03And so in addition to us training it on a bunch of code and other textual things, we also train it on a textualization of an image, of a video, of audio.

1:34:15When we have it all in one model, then we can get things that you sort of have transformative examples like chat GPT's advanced voice mode, which if there's anybody who's interested in this stuff that hasn't actually tried,

1:34:28not the chat GPT voice mode that was out like a month or two ago, but the one that if you have a chat GPT, I think maybe you need the plus or maybe I'm not sure actually if it's in the free plan, but the one where you can interrupt it,

1:34:39where you can just talk to it and you talk and it talks and you talk and you interrupt. Even just like playing with that for a minute on someone else's thing is like really drives home the power of this multimodality because previously,

1:34:50what would happen is people would talk and then it would transcribe it into text and it would send the text into a text model and then text would come out and then it would transcribe that into audio and then you would play you the audio and there would be this lag.

1:35:06Whereas now it's all in the same model. So A it's way faster, but then the thing that wasn't obvious to me until they demoed it is because now my voice is actually just going into the same model intermixed with the text and all the knowledge that this thing has all the way down to its ability to code.

1:35:21It can know if I'm annoyed. It hears me laugh and it has some that has a meaning to it's not just haha. It can tell maybe the difference between different kinds of laughs. I can ask it to respond to me in a Scottish accent.

1:35:31It can tell that maybe I'm putting on a Scottish accent and all that multiplied by every little nuance of human voice and that's just like scratching the surface of this multimodality thing.

1:35:41Right, and I don't want to get into the philosophical question of what is intelligence, but at least what we're mostly concerned about is our form of intelligence and human beings are so good at certain things that we just take for granted and you can be a dumb person.

1:36:02Somebody with a low significantly lower than average IQ and you still can pick up things like, oh, my dad's annoyed at me.

1:36:13Yeah, and just from the tone of voice, right? It's it's a really complex thing. If you think about how would you program a computer to tell if this man is annoyed at the moment.

1:36:24And there's a thousand little subtleties like in the advanced dingus voice on iOS, I've noticed there's more inflection going on. And so when I get a notification of that something was from Slack, I've noticed the voice at least the voice I have selected will tend to sort of sound like kind of like pleased and surprised that it's from Slack.

1:36:45Oh, new notification from Slack. There's just a little lilt in the thing that makes her sound like it's like, oh, hey, cool. It's that's what what the notification came from. And that is like in the model somewhere in our models to that we wouldn't be able to name it. We don't return for that. We don't have to teach it because it's totally implied in in the way we communicate.

1:37:05Right. And is it something that we literally like I said, like how we by nature, take for granted, fast paces of innovation after we're done being excited by them, then we just assume well, every six months, there's going to be a better blank. We just take it for granted.

1:37:21We've actually evolved over 10s of thousands of years to just take for granted that we pick up tone from other human beings, and amongst other things, facial expressions, right? I mean, especially when you get to know somebody like your spouse or your children or your parents, or your best friends or your boss or something you can just read so much on somebody's face sometimes.

1:37:44And yes, some people are better than that or care more about it. That's empathy. But it's like so much information and we just pick it up when you do. It's instantaneous and you just don't you don't think you did anything. You don't think your brain did any computation to pick up. My wife seems annoyed, but

1:38:03Yes. And there's a whole bunch of information there now that is like accessible or maybe like I'm over focusing on this AI thing, but it makes me think of when you train an AI model from a transcription of YouTube model, there is a lower ceiling of how useful that knowledge is than if you train an AI model that actually understands the nuances of the voice and maybe even the facial expressions of the person when they say something sarcastic, or they say something that they don't really see, feel like they believe.

1:38:32They're like, well, I guess AI is going to enslave us all, right? I'm obviously kind of making like a Simpsons reference or something. I don't really mean it when I say that or I'm not concerned about it. Whereas you just transcribe that text and then you ask, what is the sentiment of this? It's like extremely negative. This person is expecting to be enslaved and this is horrible. And so there's so much more bandwidth in that information.

1:38:55Are we ready to bring it to what we think of Apple intelligence?

1:39:00Yeah, well, I mean, it's fascinating for me as someone who's been spending all this time with these huge frontier models that have like the multimodality balloons the size of the model, of course. So it's hundreds of billions or maybe even trillions of parameters. Each parameter is maybe a bite. So we're like, maybe it's like a terabyte model somewhere in a cloud. They don't disclose how big the models are really in that order of magnitude. And then it's like, well, but what can I do with a model in my phone? How good can that be?

1:39:27And obviously Apple's been like hard at work trying to find the best and highest uses of a model like that, which are very different. The little model that's on our phones is not able to seamlessly jump in between our facial expressions and tokens and coding and stuff like that. It's way more specialized.

1:39:43What do you think about the just the basic two layer, some of its local on your device and more complex things will get formed out to private cloud compute architecture?

1:39:58It's so intuitively good strategy for Apple. Apple has the best fleet of devices in people's hands, billion-ish devices with the best processors for this type of work. And they're already in people's hands and having them use their devices locally, obviously, is cheaper for Apple and faster for the people and privacy preserving.

1:40:20So there's this huge strategy alignment of doing it the local inference, but obviously there's very harsh limits on what a model that fits on your phone can accomplish.

1:40:31And as these frontier models and the clouds and the G4Os just keep getting better, they're potentially probably expected, my expectation, they're going to keep getting better faster than the local models can get better also.

1:40:44Because we have 16 gigabytes of memory on our phones now and like maybe the next phone will have 24 gigabytes, but Apple does not tend to double its RAM very often.

1:40:55Meanwhile, these people who that's their entire job is hosting these things in the cloud, they're able to make them better yet faster and faster.

1:41:01And so the dual setup where Apple has, okay, we're going to process what we can locally, quickly and completely free for Apple and fast for you away, privacy preserving, great, makes total sense to use that for things that they can.

1:41:15And then this private cloud compute approach is also we maybe sometimes joke about, oh, only Apple could do this, only Apple to do that.

1:41:22And like, yeah, maybe Google could create like an equivalence of the secure enclave and secure chain of all of the way that Apple is doing.

1:41:31Like Google in theory could probably build something like private cloud compute, but it's extremely Apple-y thing and they're already ahead by having a lot of the pieces of that.

1:41:39And so, I mean, as a strategy, it makes perfect sense. The question is, does it play out in the product as we sort of maybe are seeing it come together in front of us and how will it play for them as the models keep getting bigger and user expectations?

1:41:53Back to those two things I started kind of thinking about, how do we think about Apple intelligence? You can evaluate as, is this useful?

1:41:59And then you can also separately ask, is this competitive in terms of capabilities? And those are like, I think they're two of the interesting ways to look at it.

1:42:09Right. And it's, again, I do feel patterns repeat themselves and it's where Apple by nature of the company finds itself, like that slogan from the original Macintosh, the computer for the rest of us, which wasn't turned out not to be true for the Macintosh until a lot longer.

1:42:29128K Mac. Yeah, just everyone was using one.

1:42:32You know, like if I were around back then and I got a job working under the pirate flag, whatever the bandy for.

1:42:39Bandly drive.

1:42:41Yeah. And suggested that instead of the computer for the rest of us, it was, I raised my hand and say, maybe let's tone it down a little and say the computer for more of us. I'd be kicked right out of there.

1:42:53But it was right. But it was like the computer for everybody didn't happen until the internet was everywhere because what the true killer app to make everybody want to have a computer, let alone a computer that they carry with them everywhere was communication because it's the one that's what we are.

1:43:11We're communicative animals, but it's funny to me that Apple, not funny, but it's like where Apple is, is when there's these breakthroughs, how do we make it useful to actual people?

1:43:25You know, and again, and the knock against Apple from people who don't like them is that everything happened somewhere else first.

1:43:32The graphical user interfaces with a mouse pointer came out of Xerox PARC. Apple didn't invent that.

1:43:39Our Android phone has had this feature for three generations.

1:43:42Right. There were multi-touch screens before the iPhone. The Microsoft Surface table was out before the iPhone, you know, and there were other people who had like academic things.

1:43:52But it's like, okay, multi-touch could be a thing that we bring to everybody. What would we do? That's what Apple does.

1:43:58And they're really just culturally really good at it. And they've been good at it now for close to soon 50 years because it's not the same people over 50 years, but because the culture is there that people who are drawn to approaching problems that way go to work at Apple.

1:44:19And that Apple has done a good job with steady, consistent, low churn leadership over the years of keeping the, you know, so what's their approach to these large language models?

1:44:32It's how do we make it useful to people, right? Of course it is.

1:44:35As opposed to Apple being the company at the forefront of creating the new ones on the frontier, it's, they're more focused on how do we bring it to people?

1:44:46And that's what they're branding as Apple intelligence. And in some ways, you know, I think that the sort of one word summary in the conventional wisdom is, ah, meh, right?

1:44:58Apple intelligence is, it's all hype. I think the truth is actually that it's pretty, I think they're on the right track. I think it's a pretty good debut feature.

1:45:11Are they over-marketing it right now? Maybe?

1:45:15I mean, they're marketing it, yeah. I mean, you have to get a little bit separated out. Are they over-marketing it from, is it useful, from, is it eventually going to be useful?

1:45:24Right.

1:45:24There's like three pretty, like the, I mean, they need to market something. They obviously are confident in it because they're very typically marketing a thing before it's fully there.

1:45:32They're obviously a little bit off their comfort zone, absolutely off their comfort zone in doing that, having national ad campaigns. The Siri glowing effect is launched before the product is even any different from the way it was before and stuff like that.

1:45:47Right.

1:45:48But I think the question of is this useful is hard because when you use it, obviously someone uses 18.1, it has a few features, 18.2 has more, there's going to be more all the way through all of the 18 and then it's going to get, we're going to obviously get yet more features and then also polish on these pretty, I don't want to say hastily done, but like obviously they built these Apple intelligence features rapidly and under an intensity where some of them are a little bit rough and obviously we're using big,

1:46:17we're using beta versions of even some of them and even some of the ones that it's not beta anymore, like use the writing tools or I don't know, what's your take? You're a writer and so, and I write often and so we have weird take on writing tools.

1:46:28Like what's your take on the UI for the writing tools?

1:46:32It's funny because I've, and I've written this and I do not mean to be arrogant about it, but I just don't feel like I need help with my writing at all and it's not that I can't go faster, I could definitely, you know, if there's any problem with my writing ability is that I'm very slow, but AI isn't going to help me go faster because the reason I'm slow is I don't even know what I want to say.

1:46:53It's the knowing what I want to say that takes me a long time to get out.

1:46:57It's not how fast my fingers move.

1:46:58And I've always explained my, I have a very, if you ever watched me touch type, I have some very idiosyncratic, not according to what's the,

1:47:07Not according to Hoyle typing.

1:47:09But what's the woman who was the, the, the

1:47:12Mavis Beacon?

1:47:13Mavis Beacon, right. Not according to, she's the Hoyle of touch typing.

1:47:16Yeah.

1:47:17And it's because I learned to type only fast enough to keep up with my thinking.

1:47:24Which is maybe not always the fastest.

1:47:27Right.

1:47:28And it's funny because my son is a significantly faster typist than me.

1:47:31And he introduced me, I guess, because he's into mechanical keyboards.

1:47:35Oh, yeah.

1:47:36And there's this, it's like a website or something, but it's a pretty cool interface for like trying out keyboards and you get like a words per minute and you can do it in different ways.

1:47:43And he blows me away.

1:47:45And I explained to him, I was like, well, I got to 45 or 50 words per minute and that's about how fast I think.

1:47:50And so I've never developed any faster skills.

1:47:52So I can't, I can't keep up with this.

1:47:54I just keep forgetting to use it. And then when it pops up in front of me, I'm like, wow, why are you popping this up?

1:48:00And, but I'm also, but for example, I keep getting the suggestions in messages, right?

1:48:06It's, it'll pre-fill a little suggestion at the bottom.

1:48:09And I'm kind of blown away by some of them.

1:48:12Oh.

1:48:13But they're not blown away by how they're not wrong, but they're not exactly what I'm saying.

1:48:21They're interesting but not useful.

1:48:22And they're not me, right? And I, my text message replies are, are in my voice.

1:48:28But they are.

1:48:29This is a hard, this is a hard thing for a local model, right?

1:48:32This is one of these big sort of things that Apple has taken on by saying, okay, we're going to try to build the most useful thing for a model that fits on your phone.

1:48:41And so it's like maybe like roughly the benchmark it compared to these like kind of three gigabyte ish, four gigabyte models maybe.

1:48:48And if you want to solve that problem, which you described is totally tractable with current models on the server, you say, oh, well, these little suggestions don't sound like John.

1:48:57The way you would solve that is you would use what we call in the programming jargon, RAG.

1:49:01But basically you say, you tell, hey, model, here's what you want to do.

1:49:04We want to create some suggestions.

1:49:05And here is just hundreds and hundreds of examples of the way that John writes in various contexts.

1:49:12And here's what context we think is in.

1:49:14And here's the whole conversation.

1:49:15And you can just send way more to it and do that and quickly and reasonably within the kind of arbitrary performance budget because you're in the cloud.

1:49:24You do it on your phone and you're more constrained doing that kind of thing.

1:49:28What does RAG stand for?

1:49:30It's jargon.

1:49:31Retrieval augmented generation is the buzzword that people in like the AI engineering world use to refer to this technique that was like completely transformative when people kind of discovered how useful it was, like, I don't know, 18 months ago or something like that.

1:49:47Where what people would try to do is you would try to get some behavior out of this model.

1:49:53You just keep prompting it.

1:49:54You say like, you know, all the jokes people say like, we're going to abduct your family unless you produce valid JSON.

1:50:00Or whatever.

1:50:01Or they would maybe give examples of like, okay, here's some examples of valid JSON to make it like that.

1:50:07But then there's been a huge now like literature and also just practical experience of developers finding that there's a lot of things that we can get the system to do, especially when it comes to like eliciting a certain style of behavior.

1:50:22Is by pulling relevant examples instead of just having boilerplate like the same examples every time.

1:50:27And so a great example, like Apple is using this for the feature where you can ask, hey, dingus, I'm having actually I had experience with this said, hey, dingus, how do I turn on double tap to evoke the typing interface rather than having to speak.

1:50:41And what they do under the hood is it's not that they've trained the model so it like inherently knows the answer to every single Apple support question.

1:50:48It has a little database and so it has a prompt that says, hey, the users asked a support question and then it pulls in using kind of traditional procedural code.

1:50:58Here is relevant support articles for them.

1:51:01Please explain to them using these relevant support articles, not just every single article, how to solve this problem.

1:51:07And then it is able to do it way more reliably and with less like hallucinations as they call it.

1:51:12And so in theory, they could and I'm sure in the near term probably will tweak this where like the suggested responses and messages could be informed by the entire archive of things I've typed in message before.

1:51:29And that's certainly the way you would want to solve this problem.

1:51:35If you were just given unbounded, hey, I want really good suggestions, that would be like the go to like absolutely you would want to do that.

1:51:41The thing that's not clear to me and maybe they have in the lab actually a solution to this that is good, but if not for this problem, there are pretty big large categories of problems that Apple hit with a local model.

1:51:52Just you can't give it as much stuff, right?

1:51:55It doesn't have as much world knowledge.

1:51:56It's harder with a certain amount of memory because one of the big problems is without getting super technical is what we call context length, which is like how much text you can send to the model that it can actually think out at once.

1:52:09Right.

1:52:10So, if you say here is the five words that John has typed so far, guess of six word, that's a very small amount of context.

1:52:18Any even a tiny, tiny model will be able to handle that.

1:52:20But if you say here's every text message John has ever sent Amy.

1:52:24Right.

1:52:25It's a lot, right?

1:52:26Right.

1:52:27And at some point, you end up needing more and more and more memory to be able to process all that and you need larger models and to be able to even also reason about all the subtleties of like, okay, well, what's an appropriate tone in response to this thing?

1:52:39Is this person joking or not is actually relevant, but like humor is a really complicated topic, which is part of the stereotype that we have that maybe people are funny or sometimes thought of as smart, which is like there's a little bit of evidence that like sometimes actually,

1:52:53humor is one of the more complicated things that we do.

1:52:55Right.

1:52:56And so you find like basic models and even the most advanced models struggle with that.

1:53:00But if I'm making a text message quip, like it needs to know I'm joking or they're joking.

1:53:04Right.

1:53:05And the example like that I'm thinking of, like in terms of picking up on one user's idiosyncratic style is imagine somebody who tends to overuse or just use which any use of it is overuse, bro.

1:53:21Right.

1:53:22Yeah.

1:53:23And it's just, you know, like you start sentences with bro, blah, blah, blah, or hey, you're supposed to start this meeting five minutes ago, bro.

1:53:31Yeah.

1:53:32Then, A, Apple intelligence doesn't pick up on that.

1:53:36So like if I type bro all the time, it's not going to suggest in my suggestions, tacking or prefixing bro.

1:53:43But then you could go even more complicated and maybe me and my pal Alan address each other as bro, just me and you.

1:53:54Yes.

1:53:55In a completely ironic way, knowing that we sound like assholes using bro in the end of our sentences.

1:54:00It's an in-joke in between the two of us.

1:54:01It's an in-joke just between me and you.

1:54:04I would never in a million years do it with anybody else, but I know me and you do it all the time.

1:54:08And when we see each other, we call each other bro.

1:54:10In an ironic, sarcastic, aren't the people who talk like that a bunch of jerks way.

1:54:16Yeah.

1:54:17There's no way Apple intelligence as it is today is going to pick up, oh, when John and Alan talk to each other, they're always calling each other bro for some reason.

1:54:26I mean, I don't want to be so confident and say it couldn't do that because you can imagine, especially if you're constantly saying bro, like it would be pretty straightforward to say, here, you're responding to John and here are the last 20 messages in between you and John.

1:54:41That's well within scope of the local model.

1:54:43Yeah, I think they will get there.

1:54:45But then the other thing is I don't even want them to, right?

1:54:48If you and I have a shared in-joke like that, it's because I like doing it myself.

1:54:52I don't want the computer to automate it, right?

1:54:55Well, I mean, now you're getting to, there's a whole philosophy and like Mike Harley and CGP Grey were talking about this on Cortex about how there's something fundamental about the act of writing to your friend, like actually choosing what words to say, that is so maybe sort of reinforcing.

1:55:12There's something kind of maybe philosophically different in between just selecting the suggested response versus actually going to the act of saying, either making the joke or if I don't type, I love you, it's just the well, yes, statistically, maybe I was likely to say I love you, so I guess I just tap it.

1:55:28And then if I tell the AI, oh, just send I love you when you feel like I would do that, like, I'm actually probably maybe hurting something in my brain.

1:55:37Yeah, it feels like I am.

1:55:39It feels, I feel like on the one hand, like I said to you, I'm looking, I've been looking at, especially on my Mac at these suggested replies in messages.

1:55:50And sometimes it's just thanks or exclamation mark or it's a real specific reference to the thing that person was talking about.

1:55:58I'm like, hey, that's pretty good or it's certainly apt.

1:56:00But at the moment, they're not like, yes, that's exactly what I want to say, click on it or down arrow to it and hit return.

1:56:07But if it gets a little better and it really is exactly what I want to say, that actually I'm going to be amazed for like a moment or two and then I'm going to turn the feature off.

1:56:18Because I don't want, you know, I don't want it.

1:56:22And here's the crazy feature.

1:56:23I don't know if I think it's the sort of thing.

1:56:26I don't even use Instagram itself very much at all anymore because I find it, I don't hate it.

1:56:32I just find it, it's transmogrified into something that doesn't resemble what it used to be at all and what it is.

1:56:38I don't really, it's not really part of my world, but like it might be unique to me because I do have a bizarre number of followers on Instagram compared to the average person.

1:56:50Just because of the nature of my work and my micro celebrity in this world.

1:56:56But they keep pitching me on enabling AI replies to my fans.

1:57:04Yeah, yeah.

1:57:05So that I can...

1:57:06Increase engagement.

1:57:07Right. And that people can send me DMs on Instagram and I don't have to look at them or answer them.

1:57:12Meta's AI will answer them for me.

1:57:15Yeah.

1:57:16And it's like to me that is insane.

1:57:18I don't, so there's this, I can link you.

1:57:21There's an interview in this, there's this podcast called Latent Space where they interview engineers who are building AI functionality and founders are building AI functionality.

1:57:30And there it's all over the map of different kind of business they have.

1:57:32But a few months ago they had on someone whose entire startup, and apparently it was flourishing, was what you just described, which is like AI generated responses to fans, but only for only fans creators.

1:57:44And so they, if they're successful, they have all these people sending them messages and asking for things and whatever, and they want to respond to all of them.

1:57:52But right now the solution to that, if you're very successful on that platform, which is I'm sure also true for celebrities that are really big on Instagram, is to have an offshore team doing it.

1:58:01But then it's hard to tell, like, are they doing what they want them to do? And how do you direct them? And are they doing the right things?

1:58:06And obviously it's also expensive and maybe slow, whereas this is one of those many, many, many AI startup shapes, which is automating these things.

1:58:14And yeah, obviously for you and I, we're turned off by the idea of an AI pretending to be us, but if that's your business model, then it happens.

1:58:22It just seems bizarre. It seems totally bananas to me, but simultaneously I'm like, but I can totally see why some people would say, oh yeah, this is amazing.

1:58:30Well, it makes sense that you would object to that because when you're on Instagram, you are you.

1:58:34Right.

1:58:35So it's not being a character.

1:58:36Right.

1:58:37Right.

1:58:38Whereas Chappell-Rone is not actually, like Chappell-Rone is a character that she's created and that if Chappell-Rone could have someone respond to their Instagram DMs, that probably actually would be great because it's overwhelming and there's so many of them.

1:58:50And also she's not being herself anyway.

1:58:52And so it's not like, but wouldn't want that to your friends, which is where this thing in messages, it's differently for me and I think for CGP Grey.

1:59:00Right.

1:59:01In a way that like being a modern, well, maybe being a celebrity has always been sort of like this, even before the modern social age, but certainly today's creator world, there is a blurring of line between being a person and being a character that the person plays.

1:59:21But if like, I was going to say Fox, but I guess Disney owns them now, but if Disney let you interact with Bart and Homer Simpson, there's no, maybe that's actually even available, but nobody is being fooled that they're talking to the real Bart Simpson because they know there is no real Bart Simpson.

1:59:40They know it's a fictional character.

1:59:42And there's a blurring of the line there. It just seems crazy that Instagram is offering it to me with, I don't know, 40,000 followers or whatever it is that I have on Instagram.

1:59:52I don't even know. But when I'm not even close to the Chappelle Rohn level of 40 million followers or whatever she has.

1:59:59Yeah.

2:00:00Yeah.

2:00:01And that's a whole, like a whole, there's obviously simultaneously what can be done, what should be done and what's going to get done with all of these technologies.

2:00:11Right. But it doesn't surprise me that Meta is leading the way. And I don't even mean that in the most pejorative sense.

2:00:18I, it just, there's a certain, it's not that I think Meta or Facebook is evil.

2:00:28I just feel that they are completely tone deaf in a certain way, culturally inside the company that that stems from Zuckerberg personally, in a way that a founder can infuse the company with a personality where they, I don't think the way that I think this feature sounds absurd.

2:00:47And I think a lot of people listening to this episode of the podcast think that that feature chat with Jon Gruber, but it's all AI is friggin ridiculous.

2:00:56Oh, unless you did it just for fun. Right. But as a serious feature on as part of my professional life, it seems insane. Whereas inside Meta, I'm sure that I don't think that that it really gave them second thought. If they can do it, they did it.

2:01:11You described Apple's mindset about how they approach products of like, how can we take this thing that's now been invented and create a really delightful, useful version of it.

2:01:19And they have crafted that to the nth degree of gotten really good at building products that way. Meta has this all, like you say, from Zuck on down through some of the world's most like brilliant people figuring out how do we engage people.

2:01:33How do we make numbers be statistically significantly more engaging. And sometimes that leads Meta to create things that you would never have thought that these incredible inventors like Ben Thompson talks about the newsfeed, which I remember as a user when they introduced that Facebook many years ago now,

2:01:48I was like, they're ruining Facebook. This was that was like one of the economic inventions of the century is what they that change that they made, because they think that way. But then it leads them sometimes into things like this AI feature you're talking about, or the way if you spend any time on threads, how they went way into a hole where the algorithm would promote literally anything that people clicked on, even if they just clicked on it in confusion, because just someone figured out, oh, this people tap on this tweet, that's our post, it's got cut off. And it's someone just being like, oh, I was in my kitchen.

2:02:17And then suddenly, I fell into a dot dot dot. And you're like, why is it? You fell into what? What did you fall into? And it's not it wasn't even like people were creating these tweets to be confusing. It's that it just if it just happened to truncate at a mysterious place, the algorithm would notice that, like accidentally and then promote it and then people would tap through and be like, oh, it wasn't actually interesting. But it is satisfying algorithm,

2:02:41or even just I guess I have to tap it to see where this goes. But it didn't even go anywhere because it was an accident. And yeah, everybody just but everybody tapped it. So the algorithm thinks Oh, everybody wants to see this happen on it.

2:02:53Half written more people.

2:02:54Yeah, everybody wants to see this half written piece of garbage.

2:02:57Yeah. So it's a blessing and of course, in the same way that Apple strategies is a blessing and of course, sometimes it makes it more difficult for them. And sometimes it makes them more able to do but

2:03:04So the writing tools to answer your question, I you'd think I would have the strongest opinions about them as a professional writer and I have the least opinions about that compared to the other features they have because I just don't

2:03:19You don't use a tool like Grammarly or anything?

2:03:22No, never have.

2:03:23And I do you think Grammarly?

2:03:24Nope.

2:03:25Do you know are you familiar with it?

2:03:27I'm very familiar with it. Well, I'm familiar with what it is. But

2:03:31Yeah, like it's a plugin that

2:03:33I guess I've tried it long ago.

2:03:35It gives you more advanced underlines of like, hey, this word is supposed to be written as two dashes or whatever. But you're very specific about your style of how you write things. So you probably find it even more annoying.

2:03:45Right, because I technically again, according to Hoyle break all sorts of grammar rules, or if it's not a rule, at least considered best practice all the time on purpose. And it's it. When I looked at it, it was green underscores everywhere. Yes.

2:04:03I do remember I when I wrote my iPhone review, I pumped the whole thing through the writing tools in beta. And it did find some typos and stuff that I didn't ask it to rewrite it. And I keep forgetting to try that again. And I thought about that last night when I was thinking about recording the show to you today. I was like, huh, I thought at the moment I was like, you know what, every time I write something of any, you know, like a page or longer, I should at least pump it through the proofreader.

2:04:28And I hit on something, I forget to do it.

2:04:31One of these kind of parts of wisdom that's seen as starting to kind of come about in the AI product world, which if you talk to enough people are trying to build AI products, which is that if you can build an AI feature, like our goal isn't to build AI features, right? Like users don't want AI, they want some problem solving, they want some. And so the ideal wonderful and I'm sure Apple thinks this way too, because it or at least they're I'm sure they're developing this mindset as well, because it fits with their ethos generally, you ideally want an AI feature, the users don't even think about it.

2:05:00It just makes things better automatically. And so the summary feature is that way where it's I get 20 WhatsApp messages and it's someone saying, oh, does anyone have an avocado that's ripe? Oh, actually, no, I don't. Oh, no, I actually, okay, it's fine. It's settled now. And that all gets summarized as to avocado needed and procured.

2:05:18And that's just, it works. I don't have to decide, oh, there's too many messages. Should I decide to summarize them? If they had built it that way, the feature like we would get 0.1% as much usage. It would just be kind of like a novelty, but you wouldn't think to evoke it, even if it is useful. And so that's the ideal workflow, but the writing tools one is not wired up that way, probably for cost and performance and battery reasons to be constantly analyzing.

2:05:37Although actually, well, so that goes into the whole thing about local versus server, right? Because the writing tools for long text, at least I think it's going to private cloud compute.

2:06:00Yeah, and, well, but they're so super cute. You'd, you'd, there, apparently you can go, I haven't done this either, but apparently if you go into settings, Apple intelligence, there's a way to see a log of,

2:06:15Oh, of what's going to PCC?

2:06:17Yeah, I don't know.

2:06:19So what I've been trying to in playing around with these tools I've been trying to notice, and I've actually experimented with like turn on airplane mode. That's the way I've been doing it. There's a lot of that would be better, but turn on airplane mode and be like, does this feature work? Does this feature work? And does this feature work?

2:06:32And almost all of the 8.2 features that I tried were still working. The few things that I noticed didn't work was the cleanup tool in images, which is going to the server. And then the some of the like writing tools for longer pros, like summarize this long article, key points, list table.

2:06:50But now that actually I say that I recall that the writing tool I was most interested in, which is the proofread one, not because I'm like you where I'm pretty opinionated, I do break some style things, but I do miss some. It caught a typo I had where I miss capitalized a letter in someone's name.

2:07:06It was a common name. And the thing that I found, two things I'm understanding. One is that it was very slow because it was doing it locally, whereas the amount of text and the amount of work it was doing could have been done faster on private cloud, but or certainly a big model.

2:07:20And the other thing that I found kind of infuriating was the UX of it was they defaulted to, and maybe this is better for the median person and we just shouldn't even be talking about this because we're two writers talking about something that's not meant for non writers.

2:07:32But it defaulted when you proofread to apply in all of its suggestions. So all at once, and I was like, horrified. Like, no, no, no, yeah.

2:07:40Let me pick the ones that I like. Don't just just rainbows in that cool animation. Now I've gone and made a bunch of mutations and you can like through them one by one and undo most of them because I'm like, no, no, no, that's just kind of a joke.

2:07:53Or I'm saying, like, you didn't like that I had YOLO lowercase instead of a free case. I would like to style it lowercase in this context.

2:08:02You're right. It's like you and me, people like us, we want to see like a diff interface, like a nice one. You know, it's Apple. So we make it like kaleidoscope, make it really pretty.

2:08:09But show me my original and yours and let me select which ones I want one by one with 100% clarity and certainty of which ones I'm getting. Don't just give me the magic apply it button.

2:08:23Yes. And that interface that you mentioned is exactly the interface that like pro, like the state of the art developer tools right now, like something like cursor, which you've probably heard of, which is like the huge buzzy editor that people are using that have AI or like even in Microsoft Co-pilot, but like cursors more advanced than it.

2:08:40It are like totally bought into that model of like, I want to know exactly what is happening to my code. I can't just trust an AI to change random stuff. But the AI is extremely useful to be like, Hey, how do I change this, whatever. And you can have a little conversation with it and you can click a button and it'll apply a diff and you can highlight exactly what it changed.

2:08:58And in these tools like cursor, you can save the file and then even check, does it still run? Does it run the way I wanted? And then if you come back and you're like, no, you can undo or you can keep editing until you're really like, it's so tentative about having the AI change your stuff.

2:09:13You can save the file without even like accepting the AI change.

2:09:17I want that, but I also don't blame Apple for not writing its writing tools that way. I do find of the features, I find that I've mentioned this analogy before, and I still stick by it. I find the notification summaries to be excellent. And I really like the feature, even though it is sometimes wrong and sometimes a little comically wrong.

2:09:39And my analogy is that it is like it's creating the equivalent of the subject line from an email. And in the same way that it's never been the case that you can skip the email just by reading the subject, because it's a really well written subject, which is its own argument of email style and etiquette.

2:10:01It just means what's in this email? Do I want to read it right now? Right? So if I got an email from you today and the subject was might need to postpone the recording, I would immediately open that email this morning and say, oh, what's up? I want to know what's going on.

2:10:16And if you wrote a subject that just said podcast, I would be like, knowing I was recording with you today, I'd probably still open it.

2:10:25But if I wrote an email saying like, funny article, I'll look at that when I look at it.

2:10:31Right. And then if you were, I showed up to record the show and I texted you and you're like, oh, I emailed you to tell you I couldn't do it at 3.30. I'd be like, what? That was what was in that email that said funny article?

2:10:41Yeah, exactly. Oh, by the way, I can't record.

2:10:44Right. And like, you know, Joanna was on last week and she was talking, you know, a bit like, it's really useful. I don't know, for the one to any specific use case, you didn't need LLM technology.

2:10:56But like if you have 20 notifications from your garage door, and garage doors open, garage doors closed, garage doors, but if you could just get them all summarized as a bunch of updates to the state of your garage door, but it's currently closed.

2:11:09Yeah, it's really,

2:11:10that's brilliant. And you think, well, you don't need to fancy LLM for that. But you do unless you want to hard code. Well, if it's garage door, you want to do this. And it's it's the old Siri, right?

2:11:20Boil the ocean. Exactly.

2:11:21Right. And it's like, okay, we will hook up to a database of sports scores. Okay, here's one that has the NBA. Here's one that has the NFL. Here's one that has, oh, what about college football? And then you're like, oh, well, college football, there's like 400 teams in all divisions. And it's like, oh, well, maybe we'll just stick

2:11:39Are you familiar with the bitter lesson?

2:11:41I don't think so. No.

2:11:43So this is like something out of the AI research world, where every AI agent, where you try to build an agent that could do chess or whatever it was that people were trying to build 10 or 20 years ago, would go to extreme lengths to do what you just described, which is create a database of all the things you might care about, and teach and like get expert, the world's best chess master. And what do you think about it's like, well, they need to think in terms of this and that and that.

2:12:07And the bitter lesson is what all the AI, especially the more senior AI researchers who have been doing that and slaving away for decades, felt when actually these fully generalized neural net approaches were at a certain scale with enough data, were just like, yeah, you just kind of like have it just play against itself, and you give it enough data and eventually just figures it out.

2:12:28Right.

2:12:29And all your hand tweaks were just all just trying to boil the ocean, you were never going to sit for past once there'd be we got enough data and enough compute. And it was all just a waste of time.

2:12:38I'm pretty sure I know the term I did too, but but I think I actually took an entire course to get my computer science degree called expert systems.

2:12:46Yeah.

2:12:47And it was exactly that. I don't remember this phrase, the bitter lesson, but

2:12:51The bitter lesson hadn't been learned yet.

2:12:53Yeah, I don't think

2:12:54We're taking our AI courses in

2:12:55Yeah, I don't think so. But I remember taking expert systems. And that was exactly I'm probably not very certain I took a course called expert systems. But that was basically it is you find these domains. And it was about like, how do you structure the data models for that particular problem? And can you apply it, you know, our sports scores similar to chess? Well, no, but you know, it's so you'd have to do it differently. And you just keep building up a list. And it's like, nope, it's never gonna work.

2:13:24Yeah, the sports score thing was somewhat useful before, but it's never going to be as useful as the fully generalized thing that is now inbound, where you can ask the sports scores and it says, oh, maybe it was eight to seven. And then you say, oh, what's their record playing against each other? And then you and then the answers and then you're like, how does that compare to this other thing? And you're just like following your curiosity asking questions that no one ever would, you could do 100 interviews, no one would ask that slice of that question. How does that interact with whatever other thing?

2:13:53So we have this amazing opportunity, but then on the nuts and bolts of building the product experience.

2:13:59What's your take on the notification summaries?

2:14:01I like I was saying, I love them for the group chats, like obviously, sometimes they're weird or a little bit wrong. Like I got one and like the types of errors that I see are the sort of errors I would sort of expect a smaller model maybe to struggle with where it says something statistically likely, oh, there was a fight and Mike Tyson was in it. So he won.

2:14:20That's like kind of a thing you might guess to be true.

2:14:23Yeah, I saw that one.

2:14:24The thing about Joanna Stern, it's like talking about, oh, there's an argument and then it's like it makes up a husband for her, which you do that does not exist because statistically, especially the smaller models will have these problems.

2:14:34I see those and certainly we need to take seriously like being trying to build systems that don't have some of these like cultural biases and stuff.

2:14:42But just in terms of usefulness for me personally, I find that the notifications, especially for noisy group chats are one of the more useful Apple tokens.

2:14:51Yeah, and I feel like the problem where it incorrectly and I, you know, I don't take it lightly because I'm on the assumptions are always going to be right. My spouse is a wife because I'm a man.

2:15:04And some of that, it's like just defaulting to calling your spouse or spouse, just using the word spouse, you'll never be wrong, you know.

2:15:13And it's totally fixable with what we call post training is like you train a model and then your web service behavior and then you add a few bit of different additional training data to be like, try to say spouse, try to, but you'll of course, it's tricky with cultural things like that.

2:15:26That's like one of the, yeah, easy to technically fix that you and I would not be fussed if it said something about our spouse, but maybe some Apple users would be fussed about in certain cultures and certain ages.

2:15:36And then indeed, ideally you would try to intuit that from the data, but then you're working with a small model.

2:15:41And so how tractable is that? And so I don't envy the teams that are needing to actually do that, but it is tractable.

2:15:46It seems like not a fundamental thing that it'll never get fixed with AI or even like local models.

2:15:51It seems like my instinct is a little bit to tighten some of that stuff up.

2:15:55You know, and again, the stakes are very different, but it's like you never ever want to insult somebody or just you don't want to call a woman's wife or her husband.

2:16:04I mean, it's just, it's a bad note, but it's the same thing where you can have an amazingly high accuracy rate of using the right word and getting the right thing.

2:16:14And one in a thousand error is still, it's tons of people if you're Apple, right?

2:16:20Yeah, this is a huge problem. This is a strategic challenge for Apple coming into a technology like this is fundamentally chaotic and uncertain and hard to keep on rails and hard to keep within.

2:16:34This is like the image playground that I've been joking around with and like playing with image playground and trying to get it to do things.

2:16:40And obviously, A, it's struggling a little bit because it's a local model, so it's small.

2:16:43And so it doesn't compare to like the greatest and best state Leonardo AI or stable diffusion or mid journey or whatever.

2:16:49But also they obviously Apple is especially concerned about people in even intentionally they call it a screenshot attack.

2:16:57Have you heard of this term?

2:16:58Maybe, but explain it.

2:17:00It's like when you have an AI system that especially like a high profile company like Google or Apple is published and then people purposely try to get it to misbehave so they can take a screenshot and get the likes or engagement online.

2:17:11Like, oh, look at what Apple intelligence and like even I was sort of stooping to that a little bit.

2:17:16At first I started to get an image of me and I'm like, I don't really like this.

2:17:19And I'm playing around with it and I switched to the illustration style.

2:17:21It's like horrifying.

2:17:22And then I was like asking it to do things like, oh, can you get me to be thoughtful and kind of be like thinking, hmm.

2:17:27And I was legitimately asking it to do that.

2:17:29But when it started giving me bad output, I purposely like kept scrolling and trying to then I was interested in what are the worst outputs?

2:17:36And of course, you can imagine this with inappropriate content.

2:17:38People would go even further trying to be like, oh, look, Apple did this thing.

2:17:41You'll have to send me a couple of those because I'll put them in the show notes.

2:17:45But there's some where it really made you look dim with it.

2:17:51I don't know how else to describe it.

2:17:54And it almost felt like something out of a like a Curb Your Enthusiasm episode.

2:18:01Imagine a caricaturist at a carnival or an amusement park and you come up and you give the guy 20 bucks and he draws a comical cartoon style picture of you.

2:18:12But the way that those caricature artists work is, yes, they're going to, like for me, they're going to exaggerate my nose because I have a large nose.

2:18:24So they'll make it even bigger because then it looks like me.

2:18:26But in a way that's in good fun, right?

2:18:29Like they're not going to make it look like I'm learning disabled.

2:18:32Well, I just texted you some of them and I have a whole bunch I can send you more after.

2:18:37But the thing that's really, it is very challenging.

2:18:40You can post, happy to briefly post those on the show notes.

2:18:43People don't know what I look like, so maybe it won't be as funny to them.

2:18:46But it's inspired by me, but not quite right.

2:18:49And this is, it's absolutely a hard problem to get an image, like a diffusion image model to make a likeness of a person that still evokes them.

2:18:58Like a caricaturist is like a professional human who is at the state, like at the limit of their craft at pulling out details and being like, look at this person is like, ah, you know,

2:19:08they're like the shape of my glasses, for example.

2:19:11Like I wear kind of distinctive glasses.

2:19:13And so 100% of caricaturists would have my glasses shape exactly identical.

2:19:18But that's like a complicated, subtle concept for like a small local image model to try to teach it.

2:19:23Like when you do glasses, always make them kind of exactly like the glasses.

2:19:28And like if they have a big nose, you might want to make it big, but they might be sensitive about that.

2:19:31And this is part of the risk to Apple where Apple has to be more careful as a extremely high profile coming to a billion users,

2:19:38whereas a company that their entire thing is like making a new generation playground stuff for fun,

2:19:43then they can go way further onto like, well, let's have a caricature model.

2:19:46And then some people are like, I don't like that.

2:19:48I'm upset that it made me look that way.

2:19:50But there's way less to lose.

2:19:52Again, like Larry David has a very signature eyeglass frame, right?

2:19:55These round frames that he's been wearing the same ones for 30 years, and he doesn't just wear glasses.

2:20:02He's got Larry David glasses.

2:20:03So any kind of picture of him needs to have those glasses.

2:20:06And I'm just thinking like with yours, and again, I remember we were in a private group where you first sent it.

2:20:14The one, your eyes are even slightly crossed.

2:20:16And you said that the crazy part is if you tell Apple's image playground app to make you with cross eyes, it won't do it.

2:20:24It won't do it.

2:20:25But I'm just imagining like the Curb Your Enthusiasm scenario is me and you and a couple of friends go up and we're like,

2:20:32ah, let's all get caricatures and mine looks very flattering and somebody else's looks flattering.

2:20:37And then you get this one where your eyes are crossed and you look like derpy.

2:20:41And then, you know, it's like that's the Curb Your Enthusiasm.

2:20:45Is that what it looked like? Is that what everyone thinks it looks like?

2:20:47And it turns out that like you stole the caricature artist's parking spot a week ago.

2:20:51Yeah, he has invented it against me.

2:20:53Well, I feel like Apple image playground. Well, I thought maybe Apple image playground had a vendetta against me, but Mike Hurley sent me his.

2:20:59And it's also, it's all just so, it's like a sad Mike.

2:21:03Like it's, yeah.

2:21:05So it's a hard problem.

2:21:08It is a very hard problem.

2:21:10And Apple has its own additional, the scale problem is enormous for Apple.

2:21:15The branding issue of not doing inappropriate things is unique to Apple, well not unique to Apple, but certainly they care more about it than anybody else.

2:21:25And it's kind of a neat place to see Apple.

2:21:29They're clearly, but they obviously think highly enough of the entire suite of features that they've made.

2:21:35Apple intelligence, a multi-zillion dollar ad campaign that shows no sign of abating.

2:21:42So it's fascinating to see Apple shipping something that is not in great state, that they are clearly internally from the highest level down under let's get this better as fast as we can.

2:21:55Let's, it's a very interesting way of seeing, let's see how fast Apple can move on this right now.

2:22:02Yes.

2:22:03And it, because that's what we're going to get.

2:22:05We're going to get an answer to how fast.

2:22:07And even in beta, like I said earlier, you can see it over the course of weeks, this feature is getting better.

2:22:13All of these features are.

2:22:15And I don't know where the limit is.

2:22:17I feel like the image playground one, there's two things simultaneously where there's like, okay, yes, it's a hard problem and they have a particularly difficult thing about these screenshot attacks and I'm making a worse by making sure I save the one where I'm cross-eyed or whatever.

2:22:28So there's that, that is hard.

2:22:29But also like it was a choice for them to say, okay, we have this new technology.

2:22:34You can generate images.

2:22:36What UI will we build?

2:22:37What will we encourage people to do with it?

2:22:39And in the keynote, they're like, we'll make an image of your friend and send it to them.

2:22:43And if somebody made this image of me, right, it was Apple's choice to be like, send your, if I'm going to send an image, a rendition of my friend, I want it to be the most flattering.

2:22:54Like it should, it totally evoke them, but like an idealized version of them, but still pleasant, but enough that it, it wouldn't be, this is not the choice that I would make on that dimension.

2:23:05But I think to some degree, maybe they were saying in June, like you had to make this decision in June, hey, in November, we're going to have December, we're going to have a thing that you're going to be able to send the image to your friends.

2:23:16And in June, they had to make a call, which is do we think this thing is going to be good enough by then?

2:23:22And maybe they were a little optimistic by how far they would be along by now to be like, yeah, people are going to love when you send these characters or maybe they think it's great.

2:23:31Maybe like renders Tim like really well.

2:23:33Yeah, I don't know.

2:23:34It's, it's hit or miss.

2:23:35Let's say the hits are really good.

2:23:37I made one of my son that really was sort of like an idealized, wow, this is like an idealized version of him with no, no buts.

2:23:46There's no, yeah, it's pretty good, but it's like, oh yeah, they got his curly hair, they got his face, they got his eyes.

2:23:52It's kind of got a look on his face.

2:23:54It's just flattering.

2:23:55And then I've, you know, I've made others where it's like, oh, I don't even know if I can show this to my wife.

2:24:00I mean, like a picture of her and it's like, I don't even want to show it to her because it's, yeah, I don't want to, I don't want to go there.

2:24:07I'm just going to delete this one.

2:24:09Yeah, it's a challenging place because really with the technology, especially with a local model, so it's not going to be like as powerful as the state of the art stuff.

2:24:18The real way to get good results from this and the results I posted some like good results from another tool.

2:24:24I spent a while with it and I played with a bunch of dials and I was like, oh, maybe I would have an anime style, how it looked like.

2:24:29What would this look like? What would that look like?

2:24:30And I tried variations. I'm like, oh, pin this, but then change this.

2:24:33Oh no, undo.

2:24:34And that is not the kind of tool Apple wants to build.

2:24:36They want to build a quick thing that's for everybody.

2:24:39Speaking of quick things, what do you think about Genmoji?

2:24:42I have to say so far I am unimpressed.

2:24:44I haven't made a good one yet.

2:24:47It's obviously the same or I'm assuming it seems like it's the same model as creates the animation style, Pixar style renderings for the image playground.

2:24:56I think I'm maybe well positioned in the target market of Genmoji because me and my wife have running jokes about emoji and misuse of emoji.

2:25:06And oh, there really should be emoji for this obscure thing.

2:25:08And so the first thing I did is I sent her the picture of a duck's butt because that's like just an ingest side joke.

2:25:16And she was like, what? There's a duck's butt?

2:25:17It's like, well, actually I had to like really fight Genmoji.

2:25:20It did not want to, none of these things want to make butts.

2:25:22So I had to be like, oh, like a picture of a duck from behind looking the other way or something eventually it's like, ah, okay, you can do this.

2:25:29And like duck's butts aren't very butt like it.

2:25:32You won't not do a pig butt, for example, I could not get it no matter what I tried.

2:25:35But she loved that.

2:25:36And then I also, I think I had one or two other ones where I was just like, oh, hey, that's fun.

2:25:40Like Apple doesn't need to make an emoji for this obscure thing, but it's kind of cute.

2:25:45I was able to make one.

2:25:46It's a toy.

2:25:47But the thing I found that's a little in its current state frustrating is like once I had a couple successes with it, sometimes I think, oh, I'd like to thumbs up or respond to this person with a Genmoji.

2:25:57But then it takes so long to like generate and then like you have to reject a few of them and you have to iterate your prompt a bit.

2:26:04And then by then the moment has passed me by.

2:26:07Maybe once I have it on the Mac, I might actually spend a little more time.

2:26:10But on my phone, it's like pretty quickly I'm like, ah, it's struggling a little bit to make a whatever my vision for this thing was.

2:26:17Yeah.

2:26:18The other thing I've noticed is that and I think, you know, it just speaks to the skill of icon artists to make something that scales to a relatively small size.

2:26:29But if you just send an emoji all by itself, Apple will send it big in iMessage.

2:26:35But if you're typing it in the middle of a sentence, you get the small text type version.

2:26:39And the Genmoji that I've tried so far at text height, in other words, the small size, they're like smudges because it's very hard to draw a distinctive whatever it is you're doing.

2:26:51And the funniest things I can think of are all they're not if they were super simple, I would just use the regular emoji.

2:26:57Yes.

2:26:58Well, I tried to send you you suggested the recording time and I tried to send you a head exploding double thumbs up emoji.

2:27:04Yes.

2:27:05Which sounded good, but then like even a professional illustrator would be like, all right, this is a real challenge.

2:27:10I think it's legible at the little like a response thumbs up the bubble.

2:27:13And the same thing.

2:27:14Right.

2:27:15Right.

2:27:16Because it's legible.

2:27:17Because the small size matters for the tap back feature where they've added at the same the same year that they finally added pick your own emoji.

2:27:24Now they're letting you generate your own emoji, but it has to fit in that tiny little size.

2:27:28And it's a lot of yeah, a head exploding with two thumbs up too much.

2:27:33Yeah. And that's a little bit of like, certainly they can make the model better over time and making them legible.

2:27:38Like it wasn't the world the most legible possible rendition of that.

2:27:41But also there's a little bit of I'm asking it for an impossible thing, which is like a really crisp, tiny two thumbs up head exploding emoji.

2:27:50So, yeah, curious on that.

2:27:53But I have fun playing with it.

2:27:55All right.

2:27:56Last topic after all of this.

2:27:57Let's bring it home to what you're up to.

2:28:00So you for a while were at Steam Clock.

2:28:03Yes, Steam Clock.

2:28:04Or it is a going concern.

2:28:06It is a like an agency for creating apps.

2:28:09Yeah.

2:28:10So product studio build apps for other startups and tech companies.

2:28:13Right.

2:28:14But you over the last year, you've struck out with a new startup called Forest Walk and Forest Walk is.

2:28:21Yeah.

2:28:22So it's a I'm not here to pitch it, but just to give for context.

2:28:26Because you are, as we mentioned, Canadian.

2:28:28Yeah.

2:28:29So already I'm like, oh, this is too much.

2:28:31But we're building developer tools for people who are building on AI.

2:28:34So this is obviously like my wheelhouse here is talking about what people can build on it.

2:28:39And we're building developer tools to make it easier to build great products faster.

2:28:43And in particular, so far, we've been focused on the tools that are required to test this stuff.

2:28:48Because as you can imagine, it's very easy to get a demo of the product that's built on these things.

2:28:54But then you start actually interfacing with customers and customers will put in unexpected things like duck butts and with dairy and fireball blog posts and all sorts of stuff that's outside of your guests of what you as a product team put through it under test.

2:29:07And so in order to kind of get these very capable but extremely unpredictable models consistently doing great things, there needs to be like a regular rigorous testing program on ideally automated high quality testing program.

2:29:21So helping teams build that.

2:29:23Because one of the fundamental, ha, that's not how I'm used to computers working thing about all of this is that the output is non deterministic.

2:29:32Like you, you have the same system with the same prompt, and you will get different answers.

2:29:39And it's even worse than that because often when we're doing something, we're using language models to generate an answer instead of reaching for some other more deterministic tool is because it's a subjective thing.

2:29:54It's not like there's always the exact right answer.

2:29:56It's like what is a good duck but thing?

2:29:58Right.

2:29:59Or what is a good summary?

2:30:00What is a good caricature?

2:30:01Right.

2:30:02What is a good caricature?

2:30:03It's subjective.

2:30:04And that makes it harder, the whole domain of how do you do testing like we've solved traditional software testing has become basically a solved problem for 15 years where you see write some unit tests, and you make sure it always does the same thing and it just checks that the answer is 42 and that always is 42 and then your automated thing on GitHub make sure that it's always 42.

2:30:21And that's just 100 times more complicated when we're building these tools, but also even more useful because if you can actually get some confidence that you're making some changes like you notice some problem about how spouses are referred to in your summarization thing.

2:30:36And so you add more either examples or change the prompt or whatever to try and make a deal with this, there is 999,000 other use cases where you may have made it worse, or maybe you made it better for this one test case that you're testing but you didn't fix all the various other problems.

2:30:53And so it's hard but also valuable.

2:30:55Right.

2:30:56And there is you have you have a very succinct five minute video right now on the forest walk website, where people can it's a great example of what you're talking about for anybody who's like, Hey, I think I might need that.

2:31:08And your example goes through a bunch of that stuff five quick minutes very, very easily understood, but you don't have but to make it clear, you don't have anything to sell or ship yet.

2:31:19It's

2:31:20Yeah, well, we're three months in, right. So it's the startup journey, which is many years since I founded Steam Clock. So it's fun to be back in it is every week we learn something that is both like amazing and terrible where it's like, Oh, this assumption that we had isn't true, but actually there's this other opportunity. So it's like building the airplane as it's moving time. So the fun and the joy of doing all that.

2:31:41Well, I wish you good luck at it. And I think you it's a super useful idea. Because there's always it's like the second step is, oh, here's this new technology. And then the second step is, oh, here's a new way to do developer tools on top of the technology.

2:31:59Yes, right. Yeah, one that's I mentioned a little bit cursor, which is this AI ID, it's moving so fast that if I had to use like the AI development tools, obviously, there's the testing set, we're working on, but even just like the developer, like ID that you're using to write your code and stuff like that, if I had to use a year ago version of that it would be so painful to have to use or like go back to Xcode or whatever. Oh, man.

2:32:25All right, moving. Well, I thank you. I will put link to that in the show notes. I have a whole bunch of notes I wrote down. So should be a busy show notes. I thank you so much for taking the time in this busy US Thanksgiving week. You didn't you and I guess I don't need to thank you since it's not really

2:32:43I'm happy to have had the shot. I had a lot of fun.

2:32:46And I will also thank work OS our premier sponsor for this episode.