EPISODE 1895
 
[EPISODE]
 
[0:00:12] GV: Hello, and welcome to SED News. I'm Gregor Vand.
 
[0:00:16] SF: And I'm Sean Falconer.
 
[0:00:18] GV: This is, as I'm sure many of you know by now, but just slightly different format of SE Daily, where we cover tech headlines. We have a main topic in the middle. Today's topic is tipping points of technologies. And then we are going to roll into Hacker News highlights, where we just take a few of the fun, weird, and interesting things that popped up on Hacker News that you may have missed, which is very easy these days, given how much lands on there.
 
As always, I mean, we're heading to the holidays. We're recording this the day before Thanksgiving. Certainly, I would love to celebrate Thanksgiving. It's a great holiday. Unfortunately, I tend to live in countries that it doesn't really get celebrated, but you're celebrating it, right, Sean?
 
[0:00:59] SF: Yes. As a Canadian that's now lived in the US for 15 years, I've migrated from the Canadian Thanksgiving, which is celebrated in October to the US Thanksgiving that's celebrated in November. I've done the full migration, essentially.
 
[0:01:14] GV: Awesome. Yeah, anything else that you've been up to, Sean, the last few weeks since we last recorded?
 
[0:01:20] SF: Oh, nothing specific. I mean, the big thing that's coming up for me is the week after Thanksgiving is AWS re:Invent. I'll be going, heading off to Vegas, very early Monday morning, to be there for the week, which is going to be a pretty busy week with 60,000, 70,000 various people in the technology industry. It should be fun. How about you?
 
[0:01:39] GV: Yeah, I've got a few colleagues heading to that. Yeah, just for those that are listening, I'm sure a lot of people have never been to a re:Invent, and maybe will never get the chance, like can you describe what is a re:Invent to someone that doesn't even -
 
[0:01:53] SF: Yeah. I mean, so this is AWS, Amazon's big marquee technology event. It's pretty massive. It's not the biggest tech conference I've ever been to. I think Mobile World Congress is, which might be the biggest tech conference in the world, whether that's 150,000 people. But I know 60,000 or so registrations, 70,000. It takes place in Las Vegas across multiple hotels. It's always the first week of December, probably because AWS got some deal there for a decade ago, even though it's inconvenient for people. There's just a ton of stuff going on. They make a lot of big announcements there. There's so many parallel tracks of talks. I'm giving a talk there.
 
On top of the registrations, there's also probably another 10,000, 20,000 people who are just there, who don't even register just for the side events, because they're just a continual thing going on. So many people that you would want to converse with in technology, or meet up, do business with are all there, all centralized around this. There's a lot of just networking and community stuff going on all the time.
 
[0:03:00] GV: Yeah, exactly. This wasn't re:Invent, but a startup. I wouldn't name the startup, but we did actually cover them on SE Daily. But they went to a conference of Vegas recently. Rather than pay the 20 grand for a booth, as a marketing thing instead, they said, "Hey, we're going to have 20,000 in credit on the blackjack table. You can come play your hands as long as you take a meeting with us," basically. Which I thought was a pretty cool way to leverage Vegas as a meeting point.
 
[0:03:32] SF: Yeah. I mean, Expo Hall is not - I mean, it's like you're in an airline hangar, or something like that. It's just so huge. It's pretty hard to stand out. I always felt like, because the booths are expensive, unless you have the means to pay for a gigantic booth where you can really stand out by paying for it, I think you just, if you want a booth, you go with a small one, because there's not that much, I think, to gain from having the slightly larger booth versus the small booth. The amount of money that you're spending on it, you could probably spend it on, I don't know, giving people credits, blackjack booth, or having some coffee, or drinks, or something like that in your booth.
[0:04:11] GV: Exactly. Yeah, on my side, just heads down work, unfortunately. I'm looking forward to the Christmas holiday season, which is coming up soon-ish. I'm going to head back to the UK next week, see some family. Shorter visit than usual, but it'll be nice to get some cold weather.
 
Right. Moving into the headlines. These are the main tech headlines. They might hit your normal mainstream news, things like Wall Street Journal, Financial Times, TechCrunch, this kind of thing. The first one, we've not heard about Bezos in a while, kind of, apart from things like weddings and frivolous things like that.
 
[0:04:46] SF: Yeah, mostly personal things.
 
[0:04:48] GV: Exactly. Yeah. But what is Bezos actually doing? Jeff Bezos of Amazon, actually doing from a work standpoint. He's not exactly unemployed. But, well, he's just announced that he is stepping back into the CEO role of something called Project Prometheus. They say that this is a company focusing on artificial intelligence for the engineering and manufacturing of computers, automobiles, and spacecraft, which is pretty broad brush these days. I guess there's something clear there, whereas AI, but for deep hardware, is what I took from that.
 
[0:05:24] SF: I did a little bit of research into this. There's not a ton about the company yet, but it sounds like it relates to something that we had talked about previously, which is AI applied to physical tasks, the intersection between AI and learning from the physical world, large language models were trained essentially on digitized, textual information. There's a bunch of companies now that are looking at how do we train models based on physical interactions, simulations with the physical environment for things like either robotics, sometimes scientific discovery, drug design, all kinds of different things. It'd be interesting to see which direction they go. They have 6.2 billion dollars in funding, so they have some runway.
 
[0:06:11] GV: That will cover Jeff's salary, hopefully. Yes.
 
[0:06:14] SF: Exactly. His co-founders was the founder of Verily, which was one of the - who's, like a health care spin off of some of Google's moonshot projects.
 
[0:06:24] GV: Ah, interesting. Okay. Yeah. I mean, I think this is - although he has Blue Origin, the space exploration company, he was never actually the CEO of that company. He was obviously a founder, but he did say at the time and has said again that Amazon was his focus. He's always trying to drive a wedge between him and Elon Musk, I think. Pointing out that he was actually being the CEO of Amazon and letting other people run his other companies, whereas Elon Musk, of course, tries to be the CEO of many companies at once. Some might say, he loses focus.
 
This is the first time that Bezos has stepped back into CEO after stepping down from that role at Amazon. Yeah, could be interesting. I think a quote from, what's the journal was just that it looks like Bezos is back to building and has a new list of ideas on his whiteboard. He's also known for being the prolific idea creation person. Too many, basically, is usually what the problem has been with Bezos. Too many ideas and his teams don't know what to do with all with all these ideas. Let's see if Prometheus can work with that.
 
[0:07:32] SF: Yeah. I mean, I think we're seeing a lot of well-known people in the tech world be drawn out of setting retirement, or whatever they doing by what's happening in AI right now.
 
[0:07:41] GV: Yeah, definitely.
 
[0:07:42] SF: Sergey Brin did his first pull request in however many years this year. There's a lot going on in space. It makes sense. It's the exciting space. People who've made their careers, perhaps, in other ways, there's probably enough of a draw to bring them back out of set a retirement to do something interesting.
 
[0:08:05] GV: Yeah. So, moving on, there's been a lot of headlines not exactly targeting one company specifically, but just a general AI. Is it actually going anywhere? Is it costing too much money? There was a article into the Wall Street Journal again, AI investors want more making it, and less faking it. Just this idea that companies are continuing to, in net terms, lose money on their AI gambit, but ultimately, are making it up on volume of could be revenue, or users, and so on and so forth. Yeah, what did you make of this one, Sean?
 
[0:08:44] SF: Yeah. I mean, there's always people say, losing money, but making it up in volume, kind of, joke in the in the valley and stuff. Overall, I don't know that I really agree with a lot of the points in the article. I think that assumes things like, inference costs being static. Ignores that, likely, compute costs will go down, just like the cost of transistors have gone down, compute has gone down over many decades now. As compute goes down, of course, with scale, then there's more opportunity to make money.
 
Also, one of the things the author talks about is he argues that investors may conclude that they don't want to pay these big costs to get to some uncertain point of whether AI is going to work or not. I would say, for the most part, the reality is a lot of these companies have no choice. If Google stopped spending tomorrow on AI infrastructure, but Microsoft continued, then Google risks, essentially, ceasing to exist at some point. Because, I think, spending right now in part is driven by this existential fear of becoming irrelevant. No one wants to become, I don't know, the Xerox of the emerging AI world.
 
There's also they talk about this concept of faking it until you make it. But I would say, AI isn't faking it, at least not in totality. There's certainly a certain amount of hype and things that aren't necessarily real, but AI is today at a point where it's writing quite a large percentage of global code. It's handling a lot of tier one customer support, generating media, and even outside of - we go outside of generative AI and search for large language models, like machine learning and AI has been used for very, very long time successfully in software. It's just, generative AI has really brought it to the forefront. Behind the scenes, Blackbox ML has been used in many, many industries for a very long time successfully. I think it's a little bit hyperbolic to say that it's all fakery, essentially.
 
[0:10:47] GV: Yeah, I agree with you generally. I think the part that is tricky to be emerging with that is there are unfortunately quite a few startups that do look quite snake oily at the moment. I mean, this always happens. You have this boom technology, everyone jumps on it, and just people still tagging just AI onto the end of a company name, instead of the dot.com boom bust, if you want to call it that. Everyone just said, "I'm dadadda.com." Now everyone's like, "I'm so and so AI."
 
[0:11:20] SF: I think it's consistent with any early thing that becomes hot. There was a period in the early days of the gig economy when Uber and Lyft and companies like that were taking off and suddenly, everything was Uber for nurses, right?
 
[0:11:33] GV: Right. Exactly.
 
[0:11:33] SF: Or if you remember the SOMO local, or whatever that social mobile local era as well, which is based on companies like Yelp.
 
[0:11:45] GV: Like Four Square and that kind of thing. Yeah.
 
[0:11:48] SF: Four Square being successful. Yeah. Then everything have a social, mobile, local component. When social, like Facebook and other social platforms, blew up, everything suddenly have some virality social component, whether it made sense or not. To me, if you've been in tech long enough, you've seen these cycles and the skills similar to me.
 
[0:12:12] GV: Yeah. We'll get onto that in the main topic where we look at tipping points, i.e. when and why do technologies end up getting the adoption and the respect, you could even say, that they deserve. We will get onto that. Yeah. Following on from last month's main topic, we were looking at the interconnected web of the AI players. OpenAI was the center of the web, if you want to call it that. Yeah, you turned up a pretty interesting article, Sean, called Is OpenAI Screwed? Just straight to the point. Yeah.
 
Although this wasn't main mainstream news, but this was a very well tons of comments and it was on Medium, like a lot of likes. You can almost say Medium is basically a semi-news outlet at this point. What was going on in this article?
 
[0:13:03] SF: Yeah. I mean, I think it's a very well-researched, well-written article that dives into this idea of investigating what's happening with OpenAI. What are the existential threats to OpenAI? Some of the moves that they're doing, they protect themselves against that. The author argues that they're doing a lot to make themselves too big to fail by intertwining them with a lot of other companies. We talked a little bit about this the last time as well. For a long time, I thought in a similar way, where OpenAI could be in trouble in the long run, versus hyper-scalers, because companies like Google have this very large existing business to fuel their AI investments. They have data centers, they have TPUs, they have the talent, they have billions of users across thousands of different products that they can immediately bring AI to.
 
That felt like an unfair advantage in comparison to being a dedicated model company. OpenAI has done a lot, at least from an outsider's perspective in the last year to try to, I think, protect themselves, essentially, against that to be too big to fail. I think some of the arguments in the article don't necessarily hold up. They try to make the case that OpenAI is burning so much money, 12-billion-dollar quarterly loss that it's proof that the business model is broken. If you spend, if that 10 billion dollars of that 12 billion was spent on Invidia H200, or something like that, that's not really lost money. You're essentially converting that money into compute, where that compute is tremendously valuable. You could even argue that that's part of their competitive moat, is to own all that compute and then turn it into money later. Yes, it is spending, but it's not necessarily like, "Hey, we just lost this money. Is it irrevocable?"
 
[0:14:52] GV: I think that's a good read on that. I think it's just the volumes. It's just the volumes known as disputing, that spending a lot of money to make it up later is that is tech often. It just seems like OpenAI, as you're saying, is almost making themselves too big to fail. The numbers just keep growing exponentially, without actual income dollars to back up the supposed spending. Again, when you say spending, we're talking about deals on paper, committing to things. Where is the money going to come from? That's like debt in a way. Call me old-fashioned, but that is debt where you're just saying like, "Hey, we promise to pay you if you give us this stuff, and we'll figure out how to pay the actual money later," kind of thing.
 
[0:15:35] SF: I think some of that, although the numbers are really big, and maybe that's why it's a little bit jarring, but if you look at Amazon lost money for a very long time, was in the red for essentially, eventually they had to get to the economies of scale to a crazy point in order to start making money. Then, Facebook had no monetization in it for years before, for it was all about gathering eyeballs. They were eating all that infrastructure costs, engineering costs to scale that platform before they turned it into this money-making machine, but it took quite a while to get there. I think you can make the same argument that OpenAI is on a similar trajectory. It's just the numbers are so astronomical that it's hard to - You're talking tens of billions of dollars.
 
[0:16:20] GV: The article also touches just, I guess, on Sam Altman himself.
 
[0:16:24] SF: Yeah. Obviously, I don't know the guy at all. I think if you look back at history, we're talking about Bezos, Bill Gates, Steve Jobs. All these people who've built these gigantic companies, these mega empires, become some of the richest people in the world. They're all notoriously difficult to deal with. I think that it probably takes a certain maniacally driven individual to build these monster companies. With that, probably comes a certain cost of that person sometimes being difficult to deal with.
 
[0:16:57] GV: Yeah, absolutely. Again, it's going to be interesting. Every week, something is popping up at the moment, which is touching on what is actually going on over there, OpenAI. Yeah. I guess it will remain to be seen. Let's see where we land on this in a month's time, because a month is six months of what used to be of pre-AI time. Yeah, so let's see where we land on that one.
 
Finally, just wrapping up on the headlines. Yeah, this was just TechCrunch touched on the fact that it looks like boom time in the Nordics, I would say, again, for tech. Nordics being Finland, Norway, Sweden, Denmark. Yeah, if I think pre-AI, the Nordics for me are very much like Spotify, Klarna, Skype, even, which I think was actually Estonian, that they do class themselves in the Nordics as well. It's been this interesting place outside of the Silicon Valley SF ecosystem that they've often produced some really, really strong startups. Not nearly as many, but they have produced some of the strong, like Spotify. Everyone knows Spotify. I can't say there's a single Spotify that came out of the UK, for example. I've always chalked it up to just, it's a very dark place half the year, and a lot of very smart, educated people, and they've got nothing else to do other than sit and program things.
 
[0:18:27] SF: Yeah.
 
[0:18:29] GV: This article also touches on the idea that, well, today we've got Lovable is one of the fastest growing startups of all time. That's from Sweden. It's definitely the epicenter of this Sweden, and Stockholm have become now this new second hub of AI, I would say, in AI startups.
 
[0:18:49] SF: Yeah, it's interesting. I mean, I think the Nordics hit rate on companies is pretty high, given their population, relative to say, like the US. I mean, I think there's less than 30 million people that live across all the Nordic countries. Finland in particular has a very strong education system. I also think that once a certain industry becomes successful in a country, or an area of the world, then the art of the possible has been demonstrated and people follow that. It's like, if you're going to be an actor, Los Angeles is the place to migrate to and everybody is in that industry. You create these bubbles of industry, of course, in the US. It's predominantly the Bay Area for technology and startups, and things like that. You're so surrounded by it, then it becomes something that, as a kid, you see like, "Okay. Well, I could do that. Why not?" Maybe they have something like that going on in those countries.
 
[0:19:43] GV: Yeah. I've actually spent quite a bit of time in Norway, so I can maybe relate to this. It touches on the article that the region's social safety that lets young people take risks, without fear of losing everything. I do agree with that. I would also take the other side of the coin, which is because it's so safe and salaries are pretty good in these countries, generally, I would say, it really - it's still 50-50 weather that incentivizes you to go off and start your own company.
 
Okay, strong, safety net. Equally, you can get paid quite well, quite frankly, on many, many jobs that in many other countries, like you just wouldn't maybe consider because the salaries just don't make sense. We're talking things like teachers and even working in a supermarket in Norway is not - You can make a completely okay life that way. That's a very nice way to run a country. Yeah, I think it's interesting looking at that side of things. I do agree to some respect. I would say that more so than say, Singapore, where people are quite afraid to fail. I think that does drive the how startups happen here. They tend to be often people that are, to be frank, coming from quite privileged backgrounds, because they can afford to try it. Whereas, a lot of people simply cannot afford to even try in places like this.
 
[0:21:01] SF: Yeah. I think there's a lot of things that go into that, because I don't think it's about necessarily, do you have a safety net, or do you have - is this your path to making money? Because if you're going into doing a startup for the money, you're going to be most of the time pretty disappointed.
 
[0:21:17] GV: Please, don't do that.
 
[0:21:18] SF: Yeah. There's a much easier path to financial security than trying to do your own startup. I think there's more of either a culture of innovation, and also, you had spoken to of, are you okay with the prospect of failing? Because there is risk that you take on in terms of like, "Hey, I'm going to go do this thing. I might not make any money from it. It might fail massively. I might sink multiple years in my life to try to make that happen, and it might not happen at all, and be okay with that."
 
[0:21:48] GV: Yeah. Just a sidebar on the Nordic side of things was yeah, Lovable just announced acquiring Molnett. This is a very young company, about two-years-old, but then so is Lovable, I think, or Lovable is even less. Yeah, Molnett, just three founders, it was aiming to be Europe's cloud provider, because I think there's been a strong leaning away from having to rely on US infrastructure, and around the data privacy that comes with that, especially if you don't live, or none of your users are based in the US. However, they were moving into hosted PostgreSQL. I think we've seen this movie before. Yeah. So, Lovable's just acquired them and announcing simply that the team and technology is being rolled into Lovable, and the team will continue to work with them on that one. That's interesting. Just another potential hosted PostgreSQL roll-up there.
 
Moving on to the main topic. We're calling it tech tipping points. Basically, how long does a far-out technology take to be adopted? There's many technologies we could touch on here. We're framing this, obviously, around AI. Why are we doing that? Because not just to talk about AI yet again, but people are asking, is there a bubble in AI? People come and ask me that. A bubble is a more financial question. We tend to look at it more from the technology side, so we're less interested about - In this case, we're less interested in the deals being done and valuations and all that. We're more interested in, sure, but if there is actually a technology behind it that is worth pursuing, at what point do people stop saying, "Oh, it's nonsense, and this will never work." At what point did we see the technology that actually get respected and adopted?
 
We'll look at things like the Internet is the obvious one that came. Internet obviously predates AI. How did that look? We'll also touch on things just like smartphones in general. We look at alternative services, like Uber. We've already touched on it slightly briefly in this episode. There's also these moonshot technologies that are starting to look less moonshotty now, like autonomous driving. I guess, if we look at the Internet to begin with, the Internet through the bubble years, people just couldn't really see what it was going to deliver for them for a long time. Even e-commerce took a long, long time to be taken seriously, quite frankly.
 
I think the probably running theme here is there have to be so many pieces of the puzzle that come together to make these technologies make sense. I'll take a slightly more nuanced example just to begin with, which is actually Internet on planes. If we take not just the whole Internet, but putting Internet on a plane, this has been around for 10 years now-ish. Airlines were starting to roll it out about then with these Panasonic boxes. Then it was Viasat, which was a slightly different way of looking at ground versus satellites.
 
Crucially, the speeds were terrible. Connection's very patchy. When you're flying, there's a map showing you, like, "Oh, you're going to fly over this country now, and it will drop out, and then it will come back again," which is just, doesn't - if you're actually expecting to work on a plane, that doesn't work.
 
[0:25:13] SF: Yeah. It's worse than the Internet in the 90s for the most part.
 
[0:25:16] GV: Right. Yeah.
 
[0:25:18] SF: I regret buying Internet access on planes at the time.
 
[0:25:23] GV: Yeah. Exactly. I actually only tried Internet on a plane if it's free. Again, I was on a, I think it was Japan Airlines flight and it's, "Oh, we've just installed Wi-Fi and please try it." I did and it didn't work. What's happened is Starlink. Starlink's come along. I'll say, Starlink is disrupting a lot of technologies and even countries quite frankly, because Starlink is suddenly breaking down barriers when it comes to censorship. As we just touched on in another episode with one of the other hosts, drone warfare has even changed because of Starlink.
 
In this case, Starlink on a plane giving actually good Internet speeds, actual reliability. It's less about, is Internet on a plane useful? It was about, is Internet on a plane reliable, fast? Is the Internet that we expect on the ground in the air? Qatar Airways have rolled out across a whole fleet of their planes, and the consensus is amazing. I'm going to be flying on Qatar next week. I hope at least one of the planes have Starlink. I'll report back and see if it has actually - I'm genuinely planning to work on that plane. For me, that's the tipping point. The tipping point of, can I actually work, say, "Hey, I'm going to work today on a plane," as opposed to, these are like, seven, eight-hour flights. Can I work on a plane versus just saying, "Nah, I've got to take the day off, because I can't guarantee I can do any meaningful work."
 
[0:26:47] SF: Yeah. I think Internet on a - that's a good example of something where everyone can agree that if you can make the technology work, there's value there. The number one risk is a technical risk. Is kind of things that like, the VC firm, engineering capital invests in, their number one thesis is they invest in companies that focus on solving some engineering challenge. If they could solve that engineering challenge, then it sells itself, essentially, because the key unlock is can you get the technology right? I think Wi-Fi on airplanes is a really good example of that.
 
[0:27:23] GV: Yeah. I mean, airlines could not have predicted Starlink, quite frankly. Starlink is very much linked to Elon Musk. I'm sure there's a ton of other big minds there that have made this possible. It's the thing where we've had all these companies, like Viasat, working on this stuff for years and years and years, and then Starlink just comes out of almost nowhere. Wasn't even designed specifically for this purpose. It was just, hey, if this is possible, this is going to be a game-changer. It was possible with a lot of investment, and here we are.
 
We'll move on to AI and Internet together, how do they compare and trust? If we just look at a couple of others before that, I guess, we could almost take these two together, I would say, like smartphones and things like alternative - when I say alternative services, either there were taxis, and then there was Uber, and then there was all the geographical derivatives of Uber. This was it. Again, people couldn't foresee how taxis could get disrupted, but it did rely on there being a device in your pocket that could do all that stuff, basically.
 
[0:28:30] SF: Yeah. I mean, the fact that you had a computer in everyone's pocket allowed you to get rid of the proxy service of the taxi dispatcher, and you just go point to point. Then essentially, the dispatcher is the software itself. Clearly, taxis have been around for a long time, didn't see smartphones coming and being that disruptor. I mean, I think that's probably a little bit more similar in some ways to the Internet on an airplane than I would say, the creation of the Internet, or what's happening in AI. Although here, you're disrupting an existing industry, so it's changing the pattern of how somebody uses a service. It's a little bit different. Again, it's some technology unlock that led to that disruption.
 
[0:29:17] GV: Yeah. As well, we get into, I think, the taxi dispatcher, that's interesting. Because that's a job role that effectively has mostly disappeared. I mean, of course, in I would say, probably in London, New York, there are still taxi dispatchers, but - and it's not like that was a massive job generally, but there were tons and tons of dispatchers. But it is a job that has effectively been made moot. As we get on to with AI, that's the big fear around people keep saying AI is nonsense, and it's going to take all our jobs.
 
[0:29:53] SF: I think, too, there was companies that tried to do the version of Uber, but work with the existing taxis, but they could never really make them work in a reliable way, because of essentially having this person in the middle of the communication. You just didn't get really up to date information on whether the car was coming. It was just ended up being extremely unreliable. The really disruptive thing, besides the fact that someone had these smartphones, was that companies like Uber, Lyft and others went and owned the end-to-end experience to make it possible.
 
[0:30:27] GV: Yeah. I think it's this all-or-nothing thing, where Uber and Uber-like services, they only work as well as they do. I mean, okay, they've all got their problems in specific areas, but let's just say, most people would agree that having these services is net positive now over just having taxis available. They only work because it was all or nothing. It wasn't, oh, let's try and adapt taxi services into this. It was, we're just going to start from scratch, going to take a ton of money, ton of investment. The tipping point, so to speak, was that so many people then had a smartphone with acceptable connection in their pocket, which means it's the same for the driver as it is for the person asking to have the car. The driver needs to have this connectivity and technology, as well as the person asking for it.
 
[0:31:17] SF: Yeah. I think people forget about, probably, all the money that Uber burned to get to the economies of scale. Because in the early days with companies like Uber and Lyft, they used to have to subsidize drivers to just have them stay up there, because they didn't have enough riders, essentially, to always be requesting. If you were in the early days of say, Uber, you download the app, you spin it up, and there's no cars available, you're gone. You'll never use that thing again. They had to essentially pay drivers just to be available, so some people could have a good experience, because they're building a two-sided marketplace. This is a classic chicken-and-egg problem. You have to subsidize one side of the market and lose money in order to build both sides of the equation and balance it eventually. But that takes a tremendous amount of investment and years of laying the foundations in order to get something where you have these network effects that starts to grow on its own and you start to basically print money.
 
[0:32:11] GV: Yeah. That maybe leads quite nicely into another type of technology bet, which is more like the moonshot stuff. When we say moonshot, we mean something that really does sound far out from a movie set 100 years in the future. When autonomous driving was starting to be looked into, that's what it really felt like. This is crazy having cars, or vehicles just generally on public roads that do not require a human at all. That just sounded crazy. Here we are, 2023, I think, was Waymo coming on the roads. I could be wrong about the year, but certainly in the last couple of years, I think is when it's actually publicly available that you can open up your phone and just call a Waymo and this car pitches up, which is pretty exceptional.
 
Again, I think now that people get to experience that technology, I think the consensus is, again, is net positive. Okay, there's a whole bunch of debate around the economics and removing human jobs of drivers and so on, but the net positive actually is around, people say, "I like to get in a car and not have to think who this driver as human is. I don't want to talk to this person. I don't want to hear their music. I just want to get in my car. Be driven somewhere as if it's my car and not have to worry about anything else." I can identify with that.
 
The state is somewhere where you get into an Uber and suddenly, you're being talked to a lot, so which is not what I'm used to when you're hearing about this person's political views and all sorts of things. Then they say, "Hey, buddy can I just put my earbuds and have a long flight?"
 
[0:33:53] SF: Yeah. I mean, I think that everybody who's taken any rideshare has had their fair share of probably uncomfortable situations in those ride shares. Then, I think, in particular I have kids, they're not at the age where I would shove them into Waymo, or something like that, but at some point, when they're older and they're involved in school activities and stuff, and I know parents that do this with some of their own, because, wait, if they did have to take a car somewhere, I feel a lot more comfortable putting them in a robot car, where no driver is going to put them in an uncomfortable situation. I could track them on my phone and I know it's locked. I know they've arrived there and all this type of stuff. That feels essentially a lot safer than having them with some stranger driving them around. I think there is a lot of net positive.
 
This is a good example of something that took decades of, essentially, investment. We're still not to massive, massive scale, but these are available in a number of cities here in the US now and people are experimenting in a lot of different places. The technology is there, and it's something where it has an extremely high bar when it comes to safety. There's a lot of regulations to navigate as well, which is probably in part, what it's taken as long as it has. That's a very, very complex issue.
 
I think when people are all debating, is AI real or not? It's like, well, walk around the streets of San Francisco and see a car driving itself and picking up a driver. Yes, that is amazing. Because it goes back to this argument we were saying earlier, or talking about earlier of like, fake it until you make it. Yes, there is some snake oil stuff out there, but there is some real value, 100% behind some of this technology.
 
[0:35:38] GV: Exactly. Thinking about what the actual tipping point with autonomous driving was, as you say, regulation was a big one. Technology was also a huge one. It wasn't that creating an autonomous car is straightforward. I mean, I think they quite quickly - I mean, there's been an autonomous car driving competition for years and years and years. That's actually, I believe, where the main person at Waymo, or there was the self-driving truck that had the slight legal issues with Google, the founder of that, he was often winning these competitions. But these are still in closed environments, closed situations. There's a whole different ballgame when you put an autonomous car amongst other non-autonomous vehicles and having to follow roads and so on and so forth.
 
Yeah, the tipping point seemed to be regulation, technology. Quite frankly, it does actually need people to want to use this thing. I think that's interesting that I think Waymo's done quite a good job of making the experience such that people do actually want to choose these over a traditional taxi, or ride hailing if it's available.
 
[0:36:49] SF: Yeah. I mean, I think they can really optimize for the rider experience. They can, essentially, train the vehicle to drive in a way that's comfortable for the rider. Whereas, if you're driving with an Uber driver, or whatever service that you're using, there's not necessarily, other than the star system, but there's not necessarily an incentive for the - or the driver might not even know that maybe they're just slamming on the brakes constantly and you're rocking around in the back, starting to get sick. It's not like they're doing that necessarily on purpose. That's just the way they drive. Well, the robot car doesn't need to operate that way if they're trying to optimize for comfort and having a good ride and the rider experience.
 
[0:37:29] GV: Yeah, the consistency of the service is huge. I agree. Getting in any ride share service, I'm just always dreading if it's one of these, yeah, stop-start drivers. Yeah. I mean, as we're figuring out here, tipping points have quite a different reason for many different technologies. I think this is where people maybe get a little bit confused as to why is there investment going in here and everywhere. I mean, it will never work. Well, there's just so many conditions under which a technology will make its case and people actually start adopting it. That brings us on to AI. I think looking at that versus the Internet is interesting. They both had quite similar, I guess, investment profiles, huge amounts of infrastructure investment was needed. Internet, it was actual laying of cables. For AI, it is data centers, production of chips, which there are shortages of. Yeah. I mean, I think you've got some quite nice ways to look at this, Sean, in terms of Internet versus AI.
 
[0:38:29] SF: Yeah. I mean, we touched on some of this stuff with some of these other technologies and tipping points, where I think there's just different risk factors. With the Internet, a lot of the risk was an economic risk. No one knew how to make money from it and whether it would even happen. Then they had to spend years laying some of the physical cable and also, wait for enough people to be on the Internet for it to have mass appeal.
 
I think in the late 90s, there was this risk of essentially, is this useful? With AI, I think the risk, or the question that people have is more about like, is it too expensive? Which, ideally, is a more -That's a better problem to have, because I think that with other technology, other types of investments, with economies of scale, the expense has typically gone down. The other thing with the Internet was there's this big question about the behavioral change. Would people actually stop going to the mall to buy things, versus buying those things online? I think mobile had a similar thing, where people thought, no one's ever going to book airline tickets from a phone.
 
Or with the company that I started and built, the big question there, no one believed that anyone would ever apply for a job on a mobile phone. We knew that that wasn't the case, because we saw people doing it in convoluted ways in the early days in mobile devices. Now, tons and tons of people do that. At the time, it was this unknown thing of like, is this behavioral change going to happen or not? I think with AI, there isn't this question of economic, and there isn't necessarily a question of behavioral change.
 
If you can make the technology work, if you can make AI work, I think there's clear economic value. If AI writes a code and answers support tickets and designs drugs, diagnoses diseases, that's not really a question of whether it's useful or not, like it was with the Internet.
 
[0:40:25] GV: I completely agree. These are so generally non-technology people, as I was saying at the beginning, are asking me, is there a bubble? I'm saying, well, that's the financial question. I just keep saying, I think there's just such a long way to go for a lot of, when I say non-tech people, I just mean people, yeah, not working really day-to-day in the weeds of tech, understanding how AI is going to help them. I gave to this person, who's a few years older than me, closer to my parents' age, perhaps, than me even. I did actually give the example of Claude has completely removed me from needing to be my parents' tech support, because I live quite far away from them, but I would still to this day get messages, "Hey, my MacBook has done this thing. What do I do?" Usually, I'd just look up Apple support, and then I would just ping the link on that.
 
Nowadays, I let you just say, Claude will have a better answer than me. Please, use Claude. It works. They're in their 70s and they are just amazed by this and to them. It's really incredible, once someone has shown the technology and how far it can take them. But I think there must be so many people in the world right now that just unfortunately, don't have access to this for many reasons, or they do have access, or could have access, but just it feels scary and they don't want to touch it. Yet, it could help them in so many ways.
 
[0:41:48] SF: Yeah. I think there's always more resistant to technology that feels somehow human, or it's mimicking something that's historically been associated as a uniquely human skill. I think driving is the same thing. We have these robot cars that feel threatening. If you actually have robots that look humanoid, I think that feels threatening. Then certainly, AI that can generate content, or speak, or create an image or something like that, historically, has been associated with human skills, also feels uniquely threatening.
 
[0:42:23] GV: Yeah. I guess, just to wrap up on what is a tipping point generally? I mean, infrastructure seems to underpin a lot of it. A technology can do better when the infrastructure is there. We saw that with the Internet. Once enough cables had been laid, enough routers around the world had been positioned, and everyone could talk to everybody, that's the tipping point when Internet gets its mass adoption, not just in the United States. It explodes everywhere. The same, when we looked at the autonomous driving and that, again, even if you look at infrastructure, well, okay, the roads were there, but the infrastructure within the regulation wasn't there, for example.
 
Technology, you could say, as infrastructure, the computing power needed to figure out how Waymo drives around. Yeah, wasn't there for a long time. Then especially, how do you put that technology inside a car, let alone - could sit inside a giant data center, maybe, but inside a car, okay, well, then you need really fast, or not inside the car, you need very fast speeds of data where we didn't have 4G, or 5G until very recently. Infrastructure seems to drive a lot of this. That's probably what we're seeing with AI as well.
 
[0:43:36] SF: Yeah. I've been saying that for a while, that whenever you have these technology shifts, the first couple of years is really laying the foundation of the infrastructure. Then it takes, once you've done that, then companies, or people figure out like, how do I create this uniquely mobile experience? It took years before we had Uber and Instagram and these mobile-first experiences that people started using and everyone's familiar with now. In the early days of mobile, it wasn't like that. It's just the same thing, where it took a decade of laying the foundations of the Internet before you had things like Google and mass economies being created through the Internet.
 
[0:44:16] GV: Yeah. Okay, so hopefully, that's been helpful, just hearing and thinking about tipping points. Maybe for something you're working on right now, you're thinking, this will work. This will never get adopted. Well, maybe just think about all the pieces needed to get there. Moving on to what we tend to think of as our favorite part of the show, Hacker News highlights. Sean and I just pull out anything that's maybe caught our eye in the last couple of weeks. Do you want to kick us off, Sean?
 
[0:44:44] SF: Yeah. Usually, I end up choosing some obscure, hacky project that someone did. This one's a little bit more mainstream, but I thought it was so interesting. I just couldn't help but call it out, which was this article about how scientists were able to put moss on the outside of the International Space Station and the moss survived for nine months, and then they kept it growing back on earth. The article goes into details about all the different types of moss they experimented with and how a lot of it continued to grow, essentially, in this vacuum of space, pretty unaffected. I think there was some stuff where if it was in direct sunlight for too long, then that was a problem. For the most part, moss can survive in space, which seems insane to me.
 
[0:45:31] GV: Yeah, that's pretty insane. Yeah, thanks. These are geox, I think it was, who posted that one. Yeah, I'm definitely taking, I guess, a holiday lighthearted approach to Hacker News highlights this month. Not going any super deep end developer stuff. I think it's nice just to look at something else. I've got a couple of more design related ones, actually. The first is it's called Fran Sans, a font inspired by San Francisco light rail displays. This was so interesting, basically, someone who designs fonts, but they wanted to emulate the font that the light rail in SF uses. These are tram-like trains. I believe, I've ridden them before. I'm just trying to think how to describe them to anyone that's not been in SF, but I think tram is a good description. It's the fact that these displays, they're not like LCD displays. They're like these big boxes that have these shapes that become the letters, but it is a font to itself. Then it's like the glow of the light that comes through the box that gives the font its personality.
 
The author of this article actually got to go and hang out where they make these boxes, and they got to play around with them. Then yeah, and then this font has now been produced and they showed how it's now been used on some quite cool theater posters for, I think, in for London, Shakespeare theater company, etc. I just love this stuff. I guess, I started off really as a frontend developer and fonts was always something I just loved spending far too much time on. Yeah, this caught my eye, just because I think it's sometimes nice to get lost in something that does apply to technology and stuff on, that you can download and use in your projects, but equally, just seeing the craft behind it was awesome.
 
[0:47:28] SF: Yeah, it's really cool. Then the other one I pulled was about - it caught my eye, but it partly caught my eye, because I have a lot of conversations with businesses in the industry. Of course, one of the topics of conversation is a lot of times security, privacy around AI. How are you as a company investing in this technology and how do you gain access to certain things? What are the principles that you create, just like you would for businesses that get vendor procurement and have certain security expectations when people are trying to adapt that to AI?
 
This article was the general principles of using AI as CERN. They lay out nine different principles of that. I just thought it was a useful reference. A couple of examples are like, every investment needs to address transparency and explainability, waffleness and conduct, sustainability, human oversight is a big part of it, data privacy, and so forth. I thought it was a good reference, something that comes up a lot, I think, and as a topic of conversation.
 
[0:48:31] GV: Yeah. No, it's good to see that kind of thing appearing on Hacker News as well. Yeah, things that actually drive change, but also drive responsibility. Yeah, like that. Yeah, I think the other one, this got voted up so far, further than I would have expected maybe for something like this, but it was called The Toy Story You Remember. This was basically just, I think, quite a lot of us maybe listening are of an age where Toy Story was, we were kids when that film came out. The TLDR is that Pixar's films, well, they used to literally be on 35 mil film. That's the difference between the look of the film, versus what - they've all been fully digitally converted. When you're watching it, you're not watching the 35 mil on a DVD, even it's all been digitally converted.
 
It's a really good article, because it's got so many comparison photos. It shows what Toy Story on 35 mil looked like, then now what it looks like on Disney Plus. Just the aesthetic that comes through, if I look at the - there's a top image of the 35 mil, that to me is Toy Story, as I think about it. There was just something about the colors and the slight softness to the whole thing, which you don't get with these high-definition digital equivalents.
 
[0:49:57] SF: Yeah. Some of these are pretty stark contrast. If you look at the Aladdin 1, 35 mil versus Blu-ray, or the Lion King is another one, where it's 35 mil versus Blu-ray.
 
[0:50:08] GV: Lion King, yeah, that really - that, I mean, that's again, a film that I watched as a very young child. Yeah, it's like, wow, it just, to me, the 35 mil version just looks so much nicer, because it's got so much more personality to it.
 
[0:50:21] SF: Yeah, absolutely.
 
[0:50:22] GV: Yeah, I thought that was super interesting.
 
[0:50:24] SF: Related to that, I saw something recently about, there's an article talking about why do movies feel less grounded in reality now? It was comparing how they would film people, say, from the 90s in the movie, versus now. Everything, like the backgrounds are always soft in order to put the actor in the foreground. The way you see in reality isn't like that. If you look at a person face-to-face in reality, it's not like their background is faded out and the face is prominent. By filming that way, they're creating this false reality that feels off, is essentially the argument of the article.
 
[0:51:03] GV: Yeah. I mean, as a lot of people have dabbled in photography, I've got a full frame camera that I love to pull out maybe once a year. Yeah. I felt there was just - there's been this phase towards using - It's called the F-stop, using very low F numbers, basically, which basically means, yeah, you focus only on what's in front of you. I think because these cameras can do film now, and they're - so many people will have access to them now, so much of what we watch these days, it just is a - I think it's a bit of a hack, is a bit of a hack to make a film look really cool and professional and nice is just dial down the F-stop. Suddenly, things are in focus and then they pass in and out of focus and that looks really cool. As what you say, Sean, it's it isn't reality, but it just looks and feels nice.
 
[0:51:50] SF: Mm-hmm. Yeah.
 
[0:51:50] GV: Yeah. Okay. Well, thank you again for tuning in to SED News. I hope that if you celebrate holidays at this time of year, everyone has a good, respective holiday. Sean's off to have his American Thanksgiving after this. Thanks for joining us just before that, Sean. Yeah, we'll probably catch everyone again, I guess, after Christmas time and try to wrap up what's been going on. I don't think predictions are even going to - who knows quite frankly what's going to happen across the holiday season, because things do dial down. Let's just come back and see almost like a present in itself. Let's just see what's happen when we come back.
 
[0:52:27] SF: Yeah, sounds good. Thanks, everyone.
 
[0:52:29] GV: Thanks a lot.
 
[END]