Uncorrected Scribe v2 machine transcript. Neutral speaker labels are specific to this recording. Review flags: 5.
TXT · SRT · VTT · Untouched JSON · Review flags
Source: interviews/107-unraveling-the-future-of-ai-the-developer-s-perspective-with-steven-schrembeck.mp3
SHA-256: dafaf5befbdae16c8396a4be98ce485f00e8bfd41d00c935bcc745908cd673c0
00:00:00.060 Speaker 1 [upbeat music]
00:00:10.080 Speaker 1 Hello, and welcome to a new episode of "The CTO Show with Mehmet." My name is
00:00:13.820 Speaker 1 Mehmet, and as you know, in each episode I discuss different topics about emerging
00:00:17.880 Speaker 1 technologies from AI, digital transformation, cybersecurity. And also I
00:00:23.500 Speaker 1 sometimes have guests with me who are subject matter experts in one of the domains I
00:00:28.140 Speaker 1 usually talk about. And today I'm very pleased to have with me Steven Schrembeck,
00:00:32.420 Speaker 1 who's joining me from the United States, from Georgia. Steven, thank you very much
00:00:37.640 Speaker 1 for being on the show today. I will keep it to you to introduce yourself, what you
00:00:41.800 Speaker 1 do, and what you are up to.
00:00:45.420 Speaker 2 Hey, Mehmet. So thanks for having me on. So I do three things that are of relevance.
00:00:51.300 Speaker 2 One, so I'm the founder of a startup called Impossible Labs, so it builds AI stuff.
00:00:57.240 Speaker 2 It sounds very fancy, but I'm basically a solopreneur plus AI, [chuckles] plus
00:01:02.000 Speaker 2 contractors, and I, I really like it that way. Uh, beyond that, I make videos on a
00:01:07.800 Speaker 2 channel called Seeking Minima, so that's what brought us here today. That channel
00:01:12.380 Speaker 2 has one purpose, and that is to help people use technology instead of being used by
00:01:17.640 Speaker 2 it, and that's sort of my goal. And then the, the last part is I write stories,
00:01:22.820 Speaker 2 science fiction and fantasy.
00:01:24.680 Speaker 1 Oh, wow. I love this combination, really.
00:01:28.160 Speaker 2 Mm-hmm.
00:01:28.380 Speaker 1 Now, uh, I know Steven also writing like you, you do software development as well,
00:01:34.180 Speaker 1 right?
00:01:35.680 Speaker 2 That's right. Yeah. I didn't mention the one thing that like pays the bills, right?
00:01:39.520 Speaker 2 Um, so I guess AI does pay the bills. Um, but yeah, so I've been a software
00:01:44.600 Speaker 2 developer for 12 years, something like that. Um, big corporate software developer.
00:01:50.500 Speaker 2 Uh, I made the rounds. I worked at Amazon for a while, uh-
00:01:53.460 Speaker 1 Okay
00:01:53.460 Speaker 2 ... worked at smaller companies. So it's been good to me, uh, but it's not,
00:01:59.760 Speaker 2 it's not as exciting as, um, cutting edge tech, which is always where my heart has
00:02:03.940 Speaker 2 been.
00:02:05.820 Speaker 1 Uh, that's great. So Steven, just wh- out of curiosity, like you have a mix of,
00:02:12.100 Speaker 1 you know, multiple things at the same time. So what I would say
00:02:18.220 Speaker 1 drove you to choose this path, uh, for your career?
00:02:25.180 Speaker 2 So
00:02:27.680 Speaker 2 I've moved through different kinds of software development and first of all, I can't
00:02:31.920 Speaker 2 help myself. That's the easy answer, right? [chuckles] It's, I can't help but be
00:02:35.600 Speaker 2 interested in things. Writing is something I haven't been able to put down for a
00:02:39.140 Speaker 2 long time, so that's just like something that won't leave me alone. Um, and same
00:02:43.640 Speaker 2 with cutting edge tech, right? Uh, I moved into software development because I liked
00:02:49.440 Speaker 2 building things. I'm not one of those people that loves code for code's sake. I, I
00:02:54.800 Speaker 2 like it as a tool, and while I am fascinated with building things and understanding
00:02:59.220 Speaker 2 how they work, I'm more interested in the opportunity to solve cool problems and
00:03:05.240 Speaker 2 help people. And software was just the easiest way to do that 'cause as you know,
00:03:09.120 Speaker 2 it's easy to set up, tear down, like there's, there's no sandbox like software. Um,
00:03:13.360 Speaker 2 so I think that's really what drew me in. And then AI is just... Honestly, it felt
00:03:18.100 Speaker 2 like modern day magic. I started building models in 2016, uh, when deep learning was
00:03:23.700 Speaker 2 having a first or second renaissance, depending on how you look at it. And I knew
00:03:28.420 Speaker 2 that this was computer magic [chuckles] and I wanted to know how to do it. Uh, so
00:03:33.520 Speaker 2 that's what drew me into AI.
00:03:36.500 Speaker 1 That's very, very cool I would say. Now to- l- I know this is the hot topic. It's
00:03:42.480 Speaker 1 on everyone's mind, right? So it's AI. Um, how do you
00:03:48.140 Speaker 1 perceive it from not only a developer perspective, a solopreneur perspective,
00:03:54.400 Speaker 1 how do you look to AI? Do you see, do you see it as, you know, the technology that
00:03:59.540 Speaker 1 will, you know, make really people's life easy or do you see it
00:04:05.680 Speaker 1 more as a, just a cool technology that we can do cool stuff with it?
00:04:12.480 Speaker 1 How, how do you perceive really the technology? Because there are a lot-- And the
00:04:16.019 Speaker 1 reason I'm asking you, Steven, this question, there are a lot of debates recently
00:04:20.740 Speaker 1 with all the hype that happened after ChatGPT and all these technologies. What's
00:04:26.160 Speaker 1 next? What's going to happen in the world? So from a developer perspective, a
00:04:30.960 Speaker 1 solopreneur perspective, what you can tell us about this?
00:04:37.940 Speaker 2 So a lot of the time the hype isn't real. So I also, you know, I'm very interested
00:04:42.980 Speaker 2 in Web3 mostly again for like coordination technologies. I'm terrible at, uh,
00:04:47.400 Speaker 2 [chuckles] I'm terrible at riding the hype wave. I'm always interested in the tech
00:04:51.240 Speaker 2 and the values. Um, so for AI, this time the hype is kind of real, and it was last
00:04:56.980 Speaker 2 time too. Uh, if you'll recall, I don't know what, four or five years ago, the last
00:05:01.200 Speaker 2 time deep learning really took off it was for vision networks, and this is really
00:05:04.960 Speaker 2 where the killer use case was. And the hype was real, it just wasn't evenly
00:05:09.560 Speaker 2 distributed. Now, as, as you know, diffusion networks for generative AI and large
00:05:14.560 Speaker 2 language models, uh, are the latest hotness and the hype really is real again. Um,
00:05:21.180 Speaker 2 I think the reason that we might not see another AI winter between here and
00:05:26.980 Speaker 2 AI really doing very impactful things to society is because
00:05:32.900 Speaker 2 we have a lot of Lego blocks that we've built up. If you followed AI development for
00:05:37.640 Speaker 2 the past, you know, decade or so, it keeps increasing. But what has happened is that
00:05:43.100 Speaker 2 everyone was increasing in their own silos. So, you know, nat- natural language
00:05:47.200 Speaker 2 processing was getting better. Um, time series analysis was getting better. Just
00:05:52.500 Speaker 2 transformer networks, all, all the vision networks, all of these things were
00:05:56.120 Speaker 2 improving. Diffusion, autoencoders, all these things were improving separately. And
00:06:01.240 Speaker 2 what happened is people from one domain connected a piece to another domain. So in
00:06:06.620 Speaker 2 this case, it was reinforcement learning plus natural language processing. Um, and
00:06:11.620 Speaker 2 plus-- And that was basically you connect two pieces from two verticals that have
00:06:15.700 Speaker 2 been advancing for a long time, and the result was exponential increase. It could do
00:06:20.760 Speaker 2 things. The difference was we have generative networks. We've had GANs for a long
00:06:25.200 Speaker 2 time, but they're sort of obtuse and really hard to train. The difference is we
00:06:30.080 Speaker 2 needed, we needed an interface that understood what we meant. That was the big,
00:06:36.060 Speaker 2 like, unlock here, is that we could always sort of get networks to do really good
00:06:41.420 Speaker 2 things narrowly, but we couldn't interface with them in a, in a very good way. If
00:06:45.480 Speaker 2 they know something like, um, like a large language model does, how do you get the
00:06:50.780 Speaker 2 information out of it? And I think that natural language processing was an interface
00:06:55.660 Speaker 2 moment. So the combination of all these individual advancements is
00:07:01.500 Speaker 2 really why you're seeing huge changes now, and the amount of individual advancement
00:07:06.740 Speaker 2 has not stopped. So I would be very surprised if there were not more verticals to
00:07:10.920 Speaker 2 combine and again, create another ridiculous pivotal moment every six
00:07:16.800 Speaker 2 months or so. But you might see lulls in the meantime. So I'd say the hype is real,
00:07:22.080 Speaker 2 sort of, right? [chuckles] People always tend to overhype no matter what you do.
00:07:27.740 Speaker 1 Um, actually, Steven, you said something important which I discussed couple of, uh,
00:07:33.500 Speaker 1 episodes back. I, I go solo sometime and I said like I'm, I'm an old, not very old
00:07:39.120 Speaker 1 guy, but I'm an old guy enough to remember all the hypes in history and I don't
00:07:44.500 Speaker 1 remember a moment, you know, similar to what we are seeing today. Because I was like
00:07:51.120 Speaker 1 maybe 10, 11 years old when the first time I heard about the internet. You know, I
00:07:55.940 Speaker 1 was 14 years old when I tried the internet and then, you know, later on the
00:08:00.620 Speaker 1 smartphone, the Web2. Um, yeah, like there was
00:08:06.280 Speaker 1 sometimes exaggeration, sometimes, you know, things going, but you know, the, the
00:08:10.920 Speaker 1 cycle used to take very long until we see the next thing. But with AI, I mean
00:08:16.440 Speaker 1 generative AI and the NLPs and, you know, these all models that we are seeing today,
00:08:21.240 Speaker 1 I think we, we, you know, as you said, I like the word you said, an AI we get-- we
00:08:25.600 Speaker 1 will not see an AI winter. I love this expression and I think we are going very
00:08:31.560 Speaker 1 fast. Now from a, I would say developer perspective, right? What do you think would
00:08:37.360 Speaker 1 be the best applications that let me make it very general
00:08:43.280 Speaker 1 that everyone can benefit on other than, you know, the,
00:08:47.700 Speaker 1 I would say the sometimes very simple things we see, write me an email, write me
00:08:53.680 Speaker 1 this. So I believe like from your perspective, Steven, you see a bigger picture. Can
00:08:58.580 Speaker 1 you share that with us from your perspective?
00:09:02.300 Speaker 2 Yeah.
00:09:04.160 Speaker 2 I think another reason why this time it's different is we have a very general model,
00:09:09.880 Speaker 2 a shockingly general model, meaning it can, it can reason about things, right?
00:09:14.460 Speaker 2 Whether it doesn't really matter what's happening under the hood, all that matters
00:09:17.900 Speaker 2 is the input output results in something that looks like commodified intelligence.
00:09:21.940 Speaker 2 It's a brain in a box. You can spin it up. Now it's not super smart. It's like maybe
00:09:26.840 Speaker 2 average adult smart, and then in a couple domains it's exceptionally smart, like law
00:09:31.480 Speaker 2 or some solving SAT problems. But what matters is that you have common
00:09:37.560 Speaker 2 sense reasoning in a box. That's the kind of thing that honestly I expected to take
00:09:42.180 Speaker 2 a lot longer and I think a lot of people did. I did not see this one coming next.
00:09:46.040 Speaker 2 There was a number of hurdles to artificial general intelligence and I didn't think
00:09:49.560 Speaker 2 this one was gonna get knocked over first or next anyway. So really when you think
00:09:55.540 Speaker 2 about how can I use this, what could you ask somebody that you could pay minimum
00:10:00.720 Speaker 2 wage or just somebody with no training of average intelligence to do for you on a
00:10:05.000 Speaker 2 computer? Now you can do that for instead of $15 an hour, depending on where
00:10:11.020 Speaker 2 you live, you can do that for 20 cents and you can do it a thousand times
00:10:16.900 Speaker 2 faster and you can have 100,000 of them working. So what can you do with that?
00:10:23.060 Speaker 2 Well, any information processing is if your job is information processing,
00:10:30.200 Speaker 2 that's AI job now. Um, and you're gonna do something else. So it's kind of hollowing
00:10:34.140 Speaker 2 out the middle. It's hollowing out people who work on-- If your job is to turn one
00:10:39.400 Speaker 2 piece of information into another piece of information, if there is a broad skill
00:10:44.420 Speaker 2 set and data set for what you do, you're gonna be replaced pretty quickly, um,
00:10:49.600 Speaker 2 depending on the economic output of what it's worth to replace you. Except if you're
00:10:55.380 Speaker 2 a specialist. There is, is very hard to replace specialists. So I know a person
00:11:00.620 Speaker 2 whose job is to audit, uh, oil and gas refinery, uh, machinery for safety.
00:11:06.840 Speaker 2 And even though his job is basically to analyze data and spit out, okay, safe, not
00:11:11.660 Speaker 2 safe, do maintenance, don't do maintenance, replace, he's not gonna be replaced for
00:11:16.560 Speaker 2 a very long time because it's such a specialized knowledge set that he's fine. So
00:11:22.940 Speaker 2 if your job is pretty generic junior coder with no specialization or pretty
00:11:28.860 Speaker 2 generic junior designer who just does websites,
00:11:34.000 Speaker 2 you should consider using other things. Now I think your question was to ask how can
00:11:38.580 Speaker 2 people use this? That was sort of like the negative side-
00:11:41.540 Speaker 1 Yeah
00:11:41.760 Speaker 2 ... which gets a lot of attention. But the positive side is true too. This is
00:11:46.440 Speaker 2 commodified intelligence in a box. If something, if there's enough data, you can
00:11:51.500 Speaker 2 generate it. But that's the very general thing.
00:11:56.160 Speaker 2 I can tell you that what I'm using it for is to remove, is to really take
00:12:01.740 Speaker 2 control of my sort of- To fight back against the attention economy, I guess is the
00:12:07.672 Speaker 2 right way of putting it. So just for instance, yesterday-- Actually this morning and
00:12:11.912 Speaker 2 last night, I wrote a tool that uses OpenAI's Whisper to transcribe podcasts. So
00:12:17.912 Speaker 2 it takes from audio to text to give you a transcript, including speaker attribution,
00:12:21.892 Speaker 2 so who's talking. And then it takes that, pumps it into ChatGPT-4 through the API,
00:12:26.912 Speaker 2 [chuckles] and then I run it through a prompt where I tell it basically, "All right.
00:12:32.392 Speaker 2 Here's the questions I'm gonna ask. Here are the things I need to know about this
00:12:35.732 Speaker 2 podcast. Here are the viewpoints I wanna see." And then it just curates this report.
00:12:40.212 Speaker 2 So there are certain podcasts, let's say like macro investing, right? I don't really
00:12:43.572 Speaker 2 care about this too much. I wanna know roughly, you know, where the markets are. I
00:12:47.552 Speaker 2 don't wanna pay attention to it. News is-- makes you feel bad anyway. But now, with
00:12:52.272 Speaker 2 the push of a button, here's everything I need to know distilled down into a s- tiny
00:12:56.592 Speaker 2 amount, and I can curate that for many podcast feeds. And this is just stuff I'm
00:13:00.332 Speaker 2 making. So I am using it to sort of
00:13:04.932 Speaker 2 get more information and learn more than I could before, and to underst- to learn
00:13:10.772 Speaker 2 quickly and to synthesize information. I'm using it like intelligence in a box.
00:13:15.892 Speaker 2 "Hey, read this for me."
00:13:18.152 Speaker 1 Mm-hmm.
00:13:18.532 Speaker 2 "Hey, distill this down and..." But only in the places where my goal is just
00:13:23.412 Speaker 2 information. So if you wanna be matrix mainlined, just plug me in. Like, I just
00:13:27.772 Speaker 2 wanna know how to do this. This is a, a killer use case right now for anybody. You
00:13:32.852 Speaker 2 don't have to know how to program. Just interfacing with the, the chat clients is
00:13:37.012 Speaker 2 enough. Uh, you could be learning really, really quickly right now. And so this is
00:13:42.712 Speaker 2 the sort of-- This is the trade.
00:13:45.592 Speaker 1 Yeah.
00:13:45.652 Speaker 2 So you have an opportunity where AI is really, really useful at helping you learn
00:13:49.812 Speaker 2 just about anything that makes you very marketable right now. Or you can ignore it
00:13:54.572 Speaker 2 [chuckles] and sit around and wait for it to-- your job to be slowly eaten away. Um,
00:14:00.052 Speaker 2 so it's like you can ha- you can really stand out right now if you're willing to do
00:14:04.972 Speaker 2 the work and learn and try these things out, or you can, you know, wait on the
00:14:08.792 Speaker 2 sidelines.
00:14:11.012 Speaker 1 Wow. You know, like what you are trying to build is, uh, something similar. Uh, I
00:14:16.552 Speaker 1 mean, not exactly the same idea, but I wanted to... I rely on
00:14:22.552 Speaker 1 preparing this podcast on some news feeds. So, and one of the use cases I thought is
00:14:29.292 Speaker 1 let ChatGPT or whatever API read these feeds and just select which ones are
00:14:34.712 Speaker 1 important so I can choose a topic, for example. Now, you mentioned
00:14:40.732 Speaker 1 the word prompt several times, and there's a hype, if we can call it, about prompt
00:14:46.532 Speaker 1 engineers, right? So prompt engineers and, you know, from your perspective, and
00:14:51.892 Speaker 1 let's discuss it both, you know, from a real software development perspective and
00:14:56.832 Speaker 1 plus, you know, a realistic use case perspective. How important is the prompt
00:15:02.812 Speaker 1 when interacting with models like ChatGPT?
00:15:08.732 Speaker 2 Ideally, over time, the prompt becomes less important. If you have a good natural
00:15:13.652 Speaker 2 language model, the goal is for it to be a better interface. So I'll, I'll just take
00:15:18.332 Speaker 2 a step back and do a little technical explanation that I-
00:15:21.012 Speaker 1 Yes, please
00:15:21.352 Speaker 2 ... hopefully doesn't put people to sleep, but I think is very useful for
00:15:24.572 Speaker 2 understanding how these things work. So at the center of
00:15:30.872 Speaker 2 these large language models, and even stuff like diffusion models, um, is something
00:15:36.012 Speaker 2 that researchers call latent space, which in my opinion, they just use a technical
00:15:40.932 Speaker 2 sounding term to describe something that's very complicated. I like to think of it
00:15:44.992 Speaker 2 as just this big information soup. It's encoded in a sort of machine language. It's
00:15:49.952 Speaker 2 just a representation that the model has learned about the wor- the world in the
00:15:54.032 Speaker 2 most dense format, right? This is the center of everything it knows. This is where
00:15:58.352 Speaker 2 it's recorded all the information it knows, all the patterns it needs to find. You
00:16:03.712 Speaker 2 can think of it, it's like its repository of knowledge, but it's not in words. It's
00:16:08.832 Speaker 2 in, technically in embeddings. Uh, it's just hyper-compressed information.
00:16:14.992 Speaker 2 So it knows a lot of stuff about a lot of stuff. You know, the, the OpenAI models
00:16:20.752 Speaker 2 have read a bulk of the public internet, so they know a lot of
00:16:26.472 Speaker 2 things. In fact, they know a lot more than we can pull out of them. And so a lot of
00:16:30.932 Speaker 2 the trick is how do we get out-- I know you can do this. How do I get you to know
00:16:36.332 Speaker 2 what I mean and so that I can synthesize, either do these operations, write this
00:16:40.692 Speaker 2 code, perform these tasks, help me-
00:16:42.752 Speaker 1 Mm-hmm
00:16:42.952 Speaker 2 ... read this thing, understand what I'm trying to get? So that interface gap
00:16:48.332 Speaker 2 between I know you have this information in your latent space, in this ocean of
00:16:52.512 Speaker 2 information, I know you can do this. I'm trying to get you to focus only on what I
00:16:57.772 Speaker 2 want you to do, and there's some misalignment there. So there's a gap in the
00:17:02.092 Speaker 2 interface, which is do you know what I mean, and can you-
00:17:05.692 Speaker 1 Mm-hmm
00:17:05.712 Speaker 2 ... sort of pull it out of what you actually know how to do? So that's what prompt
00:17:10.172 Speaker 2 engineering right now solves for, is can you shrink that gap to be better at
00:17:15.792 Speaker 2 synthesizing what you want from these large language models? So right now it is
00:17:21.032 Speaker 2 useful. I-- It will probably be useful for a while, but in theory, as
00:17:27.052 Speaker 2 things get better, that interface should get better, and the models will get better
00:17:31.452 Speaker 2 and better at understanding what you want so that you don't have to use all these
00:17:35.732 Speaker 2 tricks of the trade. So I wouldn't say there's a super long shelf life on this
00:17:39.452 Speaker 2 skill, but I could be wrong.
00:17:43.472 Speaker 1 And that could be [chuckles] -- You know, like, because yesterday, when actually in
00:17:48.372 Speaker 1 the, during the weekend, one of the things I was trying to do is to let the
00:17:54.212 Speaker 1 model tells me how best it prefers to be interacted with.
00:18:00.572 Speaker 1 And-
00:18:01.052 Speaker 2 Got it
00:18:01.392 Speaker 1 ... uh, I spent some time until, and actually you, people might laugh, but
00:18:08.032 Speaker 1 I told ChatGPT after I get the answer I wanted, I said, "Okay." Can we write an
00:18:13.328 Speaker 1 e-book about the... And put some examples, and it did. So as per ChatGPT,
00:18:19.708 Speaker 1 it prefers to have the following: a context, specificity,
00:18:24.988 Speaker 1 instruction, purpose or goal, and limitation. So this is what I was told by
00:18:31.028 Speaker 1 ChatGPT, and it gives me an example, by the way. If you ask me this way, exactly as
00:18:35.888 Speaker 1 you said, Steven, I know that I should search exactly in this. You know, I don't
00:18:40.548 Speaker 1 have to go search the whole language now model, because I know exactly what I have
00:18:44.528 Speaker 1 to go. So this is maybe where the prompt engineering, but I believe, you know, I
00:18:48.868 Speaker 1 have a little bit of technical background and I know a little bit how these models
00:18:52.528 Speaker 1 work. I think the more they are trained, the better they get, so even you will not
00:18:56.728 Speaker 1 need even, you know, an exact prompt to get the information out of it, right? So
00:19:03.188 Speaker 1 that's, that's very, very cool, I would say. Now, here I want to ask you, now we
00:19:08.528 Speaker 1 have this moment, I would say, what do you think other technologies will get
00:19:14.248 Speaker 1 combined or let's say, you know, used together with the, you know, all this AI,
00:19:20.528 Speaker 1 um, technologies? Like for example, personally, I think automation with AI will play
00:19:25.788 Speaker 1 a huge role. I started to see, you know, someone talked about autonomous bots that
00:19:31.288 Speaker 1 they can interact, you know, with each other. What's your view on this?
00:19:37.408 Speaker 2 All right. So the use... Just that's like three good questions.
00:19:40.308 Speaker 1 [chuckles]
00:19:41.088 Speaker 2 Um,
00:19:43.048 Speaker 2 so
00:19:45.388 Speaker 2 I think that there's a lot of-- It will be combined with everything. It's
00:19:49.028 Speaker 2 intelligence, uh, so it, it gets combined with everything. But I think the ones that
00:19:53.268 Speaker 2 are sort of juicy and next, I actually don't think it's automation. Now, I could be
00:19:57.768 Speaker 2 wrong. There is a lot of value there. So I think it's more like narrowly there are
00:20:03.748 Speaker 2 new kinds of automation that were just unlocked and those, yeah, that's greenfield.
00:20:07.408 Speaker 2 That'll be anything that required like common sense reasoning. I think that those
00:20:11.548 Speaker 2 things... There is-- Okay, automation will move forward. The reason I don't think
00:20:16.308 Speaker 2 that this is the automation moment, I mean physical movement and manufacturing,
00:20:21.408 Speaker 2 stuff in the real world, is because physical stuff is hard. [chuckles] It's hard and
00:20:27.348 Speaker 2 expensive and physics is finicky, and moving stuff around in the real world requires
00:20:33.248 Speaker 2 precision and rules and money and a lot of testing that is a lot
00:20:38.968 Speaker 2 slower than doing stuff in the digital world. And this is something that surprised
00:20:42.228 Speaker 2 me, but I, as soon as I saw diffusion, I don't know, what is that? A year ago,
00:20:46.548 Speaker 2 something like that. When stable diffusion first came out, I knew immediately that,
00:20:51.528 Speaker 2 oh, I was wrong. It's not automation. It's, it's coming for knowledge workers
00:20:57.308 Speaker 2 first. It's coming for everything. So that sounds very sinister, but like, because
00:21:02.548 Speaker 2 that's where the data is. Not only is that where the da- the data is, but the people
00:21:06.308 Speaker 2 building these things,
00:21:08.688 Speaker 2 this is what they understand, right? They understand how to use computers, how to do
00:21:13.608 Speaker 2 information jobs, how to program, how to do design. So not only do they have the
00:21:19.168 Speaker 2 data sets, but this is a dom- set of domains they understand. So knowledge work will
00:21:23.428 Speaker 2 be automated actually before robots. So hilariously, it's the plumbers and the
00:21:28.828 Speaker 2 mechanics and even the factory workers of the world that will probably, uh, be
00:21:33.368 Speaker 2 gainfully employed, you know, longer than the sort of Silicon Valley types, which is
00:21:38.328 Speaker 2 im- highly amusing. Um, but again, it, that sounds sort of dark.
00:21:44.648 Speaker 2 I think the other ways I would combine this, I, I can just tell you what I'm doing.
00:21:49.228 Speaker 2 One of the products I'm most excited about is called Intent. Um, so I'm trying to
00:21:53.688 Speaker 2 build a system that literally watches your screen. So this is a combination with
00:21:59.628 Speaker 2 other data streams. So it's, it's using vision nets to watch your screen and it
00:22:04.628 Speaker 2 knows what you're doing, uh, all the time. It's private, so it runs on just your
00:22:09.148 Speaker 2 computer.
00:22:10.208 Speaker 1 Mm-hmm.
00:22:10.428 Speaker 2 And it just creates a stream of-- It answers the question, what did I do all day,
00:22:14.588 Speaker 2 right? It literally just keeps a log because it can understand what you're up to.
00:22:19.088 Speaker 2 Uh, right now you're doing an interview and it can log moment by moment. So at face
00:22:23.448 Speaker 2 value it's just analytics, but the real goal is to do executive control software. So
00:22:28.667 Speaker 2 sort of you tell it what you wanna do and it sort of keeps you in line. It's like-
00:22:34.128 Speaker 1 Mm
00:22:34.488 Speaker 2 ... "You shouldn't be doing that. Why are you up at 2:00 A.M. watching anime? You
00:22:37.328 Speaker 2 shouldn't be doing that. You said you didn't want to." So it's sort of like
00:22:40.688 Speaker 2 outsourcing your higher mind to keep your lower minds in check.
00:22:45.808 Speaker 1 Mm-hmm.
00:22:46.108 Speaker 2 And, but ultimately I wanna combine information from other things. So wearables
00:22:52.668 Speaker 2 is a big one. Uh, anything that... AI just wants data. So anytime-- So I have an
00:22:58.628 Speaker 2 Oura Ring, right? It has all my sleep data as well as continuous like biometrics.
00:23:03.008 Speaker 2 Wow, that would be great to incorporate into the thing that knows everything about
00:23:06.968 Speaker 2 my goals and what I'm doing all the time, and these things just stack on top of each
00:23:11.168 Speaker 2 other. So I would say anything that has a consistent high quality amount of data or
00:23:16.868 Speaker 2 data in large volumes is going to be very useful.
00:23:22.888 Speaker 2 If I had to bet on a dark horse interaction with AI that most people don't see
00:23:27.008 Speaker 2 coming, I'd say it's probably brain computer interfaces. Um, most likely in terms of
00:23:32.828 Speaker 2 consumer EEG.
00:23:35.568 Speaker 1 Mm-hmm.
00:23:35.828 Speaker 2 Um, so I think Meta and Apple both have EEG coming out incorporated into wearables.
00:23:40.548 Speaker 2 Um, it can do more than you would expect. Um, so that's probably...
00:23:46.548 Speaker 2 Plus informa- brain computer interface information in, you can do that through
00:23:51.688 Speaker 2 sensory augmentation. Won't go into it, but turns out the brain is pretty good at
00:23:55.948 Speaker 2 getting information into it if you train it. Um, that one is also pretty
00:24:00.668 Speaker 2 interesting. So I think people are gonna start to chafe at how slow information goes
00:24:05.728 Speaker 2 out and into their brains and they're going to be interested in more or less hooking
00:24:11.328 Speaker 2 up to these systems all the time, and these are the tools that enable that. You
00:24:17.296 Speaker 2 may have asked another question, but those, those are the answers I got for you.
00:24:20.676 Speaker 1 No, no, that's, that's completely fine. And, uh, it, it made sense because, you
00:24:25.256 Speaker 1 know, anything which is, as you said, generating data, actually these language
00:24:30.216 Speaker 1 models lives on data, right? So they, they, they need to suck data to give us back
00:24:34.816 Speaker 1 what we want. And I think maybe something related to, to, uh, if we think
00:24:40.656 Speaker 1 consumer perspective sensors, you know, anything that comes from cameras and this
00:24:46.776 Speaker 1 side. In industrial, uh, vertical, I can think also about sensors and this kind of
00:24:52.716 Speaker 1 thing. So yeah. Now a, a question that came to my mind just out of curiosity,
00:24:58.156 Speaker 1 honestly, like of course I'm too much into the, uh, you know, anything similar to
00:25:03.896 Speaker 1 OpenAI and like, and there are a couple of others, but when it comes to, you know,
00:25:09.316 Speaker 1 something like Stable Diffusion, like, uh, Mindjourney, like, uh, Da- uh, DALL-E 2,
00:25:16.796 Speaker 1 I want to understand personally. So, so consider me a, a guy that, that he's not
00:25:22.336 Speaker 1 from a technical background now. So for me, what I see is that these are algorithms
00:25:28.196 Speaker 1 or whatever you want to call them to generate for you medias, right? So whether it's
00:25:33.036 Speaker 1 say photos or, you know, kind of paintings, but
00:25:39.236 Speaker 1 what are really the real uses or real world use cases that we can get out
00:25:45.296 Speaker 1 of, of these like, uh, Stable Diffusion or, uh, uh,
00:25:50.096 Speaker 1 Mindjourney?
00:25:52.316 Speaker 2 I think people would probably be shocked about what is currently in development. Um,
00:25:57.696 Speaker 2 it takes a long time to build production systems for really-- You get this Cambrian
00:26:03.556 Speaker 2 explosion of like people eat up the easy use cases first. Just slap a user interface
00:26:08.936 Speaker 2 on ChatGPT or, um, Stable Diffusion. Cool, there you go. Now you can edit photos.
00:26:13.236 Speaker 2 That's great. [chuckles] Cool. You can make people disappear in photos. All right.
00:26:17.876 Speaker 2 Now what? You can generate art. Who cares? Um, but you should really consider this
00:26:23.256 Speaker 2 more like...
00:26:25.395 Speaker 2 By the way, there are other really good generative networks. Transformer networks
00:26:29.176 Speaker 2 are competing against, uh, Diffusion now, and I think it's only a matter of time
00:26:33.136 Speaker 2 until they end up working together. They have different strengths. So generative
00:26:37.196 Speaker 2 information is really, it's, it's not slowing down either. I think you should look
00:26:42.236 Speaker 2 at this not as generating media, although it does do that, and that will have a
00:26:46.136 Speaker 2 profound impact, uh, particularly in terms of needing identity solutions, um,
00:26:52.056 Speaker 2 and probably the death of the public anonymous internet in a way. Um, but that's
00:26:57.236 Speaker 2 okay. Uh, it will bring other good things too, but it's more like generative
00:27:02.276 Speaker 2 information. So here's an example that I, I like to give. You should think of
00:27:08.176 Speaker 2 these things as being
00:27:11.396 Speaker 2 really good at generating things to about 80% or 90% fidelity. Uh, they're not so
00:27:16.796 Speaker 2 good at generating it to 100%. So let's say you're, I don't know, you're an aircraft
00:27:21.636 Speaker 2 designer at Boeing, or you design cars. Your, your job is gonna look more like
00:27:26.436 Speaker 2 sitting back, crossing your arms and saying, "Okay, I want something that is sleek,
00:27:32.056 Speaker 2 powerful, uses this last year's model. Show me 40 different combinations." Whoop,
00:27:37.016 Speaker 2 here they are. That one. [chuckles] Okay. Now given this one, make four different
00:27:41.976 Speaker 2 variations of this. Run it through the, you know, the stream or the, um, the tests
00:27:47.036 Speaker 2 for wind resistance and all this stuff. Great. I'll come back after lunch. Okay,
00:27:51.156 Speaker 2 here it is. Here's your model. That would've taken you how long with clay models or
00:27:55.536 Speaker 2 even just like going in with computer models?
00:27:58.456 Speaker 1 Wow.
00:27:58.556 Speaker 2 It's un- unbelievable. The amount of testing an idea, I- the
00:28:04.616 Speaker 2 i- the cost of testing out ideas is going to zero, and that's gonna have a very
00:28:10.536 Speaker 2 big impact on innovation, which is not really well captured right now or understood.
00:28:16.256 Speaker 2 When it costs you $100 or $1,000 or even $10 to do something and it takes
00:28:22.216 Speaker 2 you 10, 10 hours, 20 hours to test it, you don't test that many things.
00:28:28.296 Speaker 2 You have to go with, by necessity, the things that look like they're gonna have ROI.
00:28:32.296 Speaker 2 But when the cost of testing a thousand things is basically zero and you can test a
00:28:36.576 Speaker 2 thousand things in a minute, things change dramatically. You, you can explore
00:28:42.256 Speaker 2 horizontally a lot more than you could before. You can test out really dumb ideas,
00:28:46.676 Speaker 2 and sometimes those dumb ideas turn out to be really interesting, useful ideas, and
00:28:51.456 Speaker 2 that's gonna happen everywhere. So it's not just generating media, it's generating
00:28:55.816 Speaker 2 information of any kind. Uh, but it will be-- The reason we see media is because we
00:29:01.376 Speaker 2 have a public open internet and everyone dumps all their data online, so guess what
00:29:06.096 Speaker 2 they trained on, is the stuff that there was a lot of data for. But there is data
00:29:10.696 Speaker 2 for other stuff. It's a little harder to get and it's a little less lucrative
00:29:15.476 Speaker 2 upfront and a little harder to build, but it's coming. So, uh, any
00:29:21.156 Speaker 2 information fundamentally, whether it's science experiments, you know, generating
00:29:26.836 Speaker 2 novel drug compounds, all of this stuff is just information. Genetics.
00:29:32.976 Speaker 2 So if you want to test something out to just 80% or 90% fidelity really, really,
00:29:37.516 Speaker 2 really fast, and then that last 10%, 20% is still done with humans and maybe regular
00:29:42.476 Speaker 2 computers, that's a big change. It's a couple orders of magnitude, I think, in terms
00:29:47.696 Speaker 2 of like speed of innovation. So that's, that's a huge use case for the generative
00:29:51.096 Speaker 2 networks.
00:29:53.196 Speaker 1 That's, uh, you know, like it, it's, it's like very enlightening, I would say,
00:29:58.736 Speaker 1 thinking about doing simulations using these technologies because they can generate
00:30:03.336 Speaker 1 different, let's say, prototype for you. But there are some people that
00:30:09.196 Speaker 1 they are arguing that, um, we might be too
00:30:15.096 Speaker 1 much depending on the existing knowledge that already these- ... models they have,
00:30:21.060 Speaker 1 so it will be lazy to generate new content for the language models to use. What do
00:30:26.260 Speaker 1 you think about that?
00:30:29.580 Speaker 2 I think
00:30:31.860 Speaker 2 how much does it cost to get the best aircraft modeler or
00:30:37.900 Speaker 2 the best car modeler to do something? A lot of money. How much does it cost to get
00:30:43.680 Speaker 2 80 to 90% [chuckles] of the best? Still probably quite a bit. So the difference is,
00:30:50.160 Speaker 2 okay, yes, if even if we-- let's say I'll, I'll buy that argument, and we can only
00:30:55.640 Speaker 2 get to even 80% of the average of any skill, given it has enough training
00:31:01.420 Speaker 2 data. Who cares, right? All it's doing is repeating existing patterns. Well, what
00:31:05.960 Speaker 2 matters is that I don't have to learn a skill. I can have 0% car modeling knowledge,
00:31:11.660 Speaker 2 and now I can model a car. That's a very big deal. Uh, just in the practical
00:31:16.400 Speaker 2 perspective, like for my stories, right? I can do the cover art. I can't do cover
00:31:21.440 Speaker 2 art. Before this, I spent $250 or $500 for like incredible concept art, and it
00:31:27.260 Speaker 2 took weeks of back and forth, and it was a huge pain, and half the time it doesn't
00:31:32.160 Speaker 2 end up looking right. Now I can just, just, I don't know, I'll just throw stuff at
00:31:36.180 Speaker 2 the wall. I'll just do it for four hours, and I've generated a couple hundred
00:31:39.440 Speaker 2 pictures, and it costs me basically nothing. You can imagine that for any field.
00:31:45.020 Speaker 2 Like, you don't have to learn a skill to do it at 80% capacity of
00:31:50.800 Speaker 2 average-
00:31:51.520 Speaker 1 Mm-hmm
00:31:51.520 Speaker 2 ... like the average professional. That's a very big deal. And so you don't even
00:31:56.720 Speaker 2 have to advance state-of-the-art in order for it to matter significantly. It's about
00:32:01.080 Speaker 2 democratization of skill set.
00:32:05.560 Speaker 1 Well, that, that's another point of view, I would say, which is also valid. Uh,
00:32:11.060 Speaker 1 Stephen, can you tell me, because I know you, you write sci-fi also as well, right?
00:32:15.060 Speaker 1 So, uh, can you tell us a little bit more about this side? Uh, I, I'm sure that I
00:32:20.620 Speaker 1 have a lot of my audience who would be interested to, to know more about it.
00:32:24.600 Speaker 2 Sure. Yeah, it's just for fun. Um, but I do occasionally try to write--
00:32:30.560 Speaker 2 Sometimes there's a combination of these. So it's sci-fi and fantasy, so the fantasy
00:32:34.480 Speaker 2 is mostly for fun. I always ha- try to have a lesson. Um, so I've written a story
00:32:40.080 Speaker 2 recently called "Reverie." That is a story about I think that humanity doesn't have
00:32:46.120 Speaker 2 a lot of great things to run towards, like collective goals. I think we have a lot
00:32:50.900 Speaker 2 of things we're trying to avoid, but that's not super motivating. Who gets up every
00:32:55.120 Speaker 2 day saying, "Oh, I hope I don't-- Hope I'm not in pain today"? That's not super
00:32:58.980 Speaker 2 motivating. Um, so I, I tried to come up with here's where I think we should try to
00:33:04.120 Speaker 2 go as a species. Um, I'm writing a story now called "Hellmakers." This is
00:33:09.780 Speaker 2 about... This is why I smiled when you mentioned, um, s- I think you mentioned
00:33:14.860 Speaker 2 something about, um, bots, automated bots.
00:33:18.920 Speaker 1 Yeah.
00:33:18.980 Speaker 2 So already I've seen people talk about somebody has already had the bright idea. You
00:33:24.340 Speaker 2 know what would be great? We could-- We need to open source these models because,
00:33:28.460 Speaker 2 you know, people probably don't want us to have access to this. It sh- everything
00:33:31.500 Speaker 2 should be free and open. You know what else we should do? We should put this on
00:33:35.720 Speaker 2 decentralized computing platforms. This is a good idea. And I'm just like, this is
00:33:40.260 Speaker 2 the worst possible idea.
00:33:41.720 Speaker 1 Wow.
00:33:41.760 Speaker 2 Let me write a story to tell you why. So it's a, it's basically a story about the
00:33:46.760 Speaker 2 founder of a decentralized digital autonomous worker protocol who they just start
00:33:52.320 Speaker 2 like a decentralized computing, uh, blockchain protocol, right? Um, the goal
00:33:58.340 Speaker 2 is that it uses a consensus mechanism, so you can't turn it off easily unless you
00:34:02.480 Speaker 2 can turn off all the nodes on the network.
00:34:05.000 Speaker 1 Oh.
00:34:05.600 Speaker 2 You can't turn it off. And it's permissionless, so anyone can insert money, and you
00:34:10.300 Speaker 2 insert a prompt, pull a model off the shelf, and you go tell it to do something, and
00:34:14.000 Speaker 2 it will go do whatever until it runs out of money. Uh, and you can't turn it off. So
00:34:19.580 Speaker 2 I show this is probably not a good idea. So the punchline of the story is that
00:34:25.620 Speaker 2 the, the, uh, creator of the protocol ends up using this to get back at their
00:34:30.340 Speaker 2 co-founders who cuts them out of a deal and, as the name implies, creates a
00:34:36.340 Speaker 2 bot that literally just publicly tries to ruin this other person in
00:34:41.780 Speaker 2 perpetuity and cannot be turned off. So this is not good. We should think very
00:34:46.800 Speaker 2 carefully about not having an off switch for our machines. It just seemed like a
00:34:51.020 Speaker 2 very dumb idea to put them on a box that you can't turn off frankly.
00:34:56.180 Speaker 1 [clears throat] Um, a question that I ask now, you-- it, it became
00:35:02.000 Speaker 1 a, um, traditional question for me. Like, um, do you think that
00:35:08.180 Speaker 1 AI is allowing all of us, humanity, to reach, to reach
00:35:13.900 Speaker 1 singularity?
00:35:20.500 Speaker 2 Maybe. Um-
00:35:22.660 Speaker 1 That's a, that's a philosophical question, I know, so [chuckles]
00:35:26.240 Speaker 2 The key is in what you said at the end, which is all of us and humanity to reach the
00:35:31.500 Speaker 2 singularity. Yes. I actually think that positive outcomes are the most likely
00:35:36.200 Speaker 2 scenario in the long term, and there's a lot we can do about it, and a lot of things
00:35:41.180 Speaker 2 that we could be doing about it that are actually quite feasible that are probably
00:35:44.660 Speaker 2 not well known, so it's something I'm focused on. But yes, I think technically
00:35:50.640 Speaker 2 speaking, yes, this is the way you would need scaled intelligence. And I think the
00:35:56.080 Speaker 2 most important part of AI as moving us towards singularity is that
00:36:02.060 Speaker 2 maybe is also underappreciated, is that it doesn't have cognitive biases. Uh, unlike
00:36:06.220 Speaker 2 our own brains, we can reprogram it, and we can program away the blind spots that we
00:36:11.740 Speaker 2 know we have. It doesn't matter if you know about anchoring bias or recency bias,
00:36:17.260 Speaker 2 um, if you still d- you experience them whether you want them or not. You still make
00:36:21.940 Speaker 2 dumb choices all the-- If you're tired- And hungry, you're gonna make bad choices.
00:36:26.832 Speaker 2 Like, this is just how the human brain works. You can't program around it. But
00:36:31.512 Speaker 2 that's not the same for our machines, which means that we should be able to program
00:36:35.372 Speaker 2 them to our ideals. And as long as we can keep advancing them, I don't see why, you
00:36:40.752 Speaker 2 know, you can't have wonderful, incredible outcomes from that. So in short, yes, I--
00:36:45.612 Speaker 2 but I think it's neutral. It depends on what we do.
00:36:49.072 Speaker 1 That's, that's fair enough, I would say. Um, be- before I will, you know, keep it
00:36:54.852 Speaker 1 for you to do a final conclusion, not related to AI, related because you are a
00:36:59.372 Speaker 1 solopreneur, right? So how, how,
00:37:05.072 Speaker 1 how do you, do you see it? Like, what's your experience being a solopreneur? Because
00:37:08.772 Speaker 1 again, this is the, this is the second, uh, common question I'm asking to my guests
00:37:13.452 Speaker 1 if they are solopreneurs, like why you opted to be a solopreneur, like why you're
00:37:18.612 Speaker 1 not part of-- I, I know that you, you worked for some companies before, and I did by
00:37:23.112 Speaker 1 the way as well. I'm a solopreneur kind of now. So tell me your experience as a
00:37:29.072 Speaker 1 solopreneur and why we are seeing more solopreneurs, uh, in, in the field.
00:37:36.952 Speaker 2 So I'll answer the last one first 'cause it's, it's fairly simple. I think we're
00:37:40.432 Speaker 2 seeing more-- people are more isolated in general, but I think we're also more
00:37:44.292 Speaker 2 empowered by technology in general. Like I mentioned, like I, I can do a lot of
00:37:48.432 Speaker 2 things I don't know how to do thanks to advances in, in AI and other software,
00:37:53.312 Speaker 2 right?
00:37:54.192 Speaker 1 Yeah.
00:37:54.292 Speaker 2 Um, I don't know how to compute compound interest. I mean, I could do it by hand if
00:37:58.872 Speaker 2 you gave me the formula and a lot of time, but a spreadsheet can do it. Uh, so I'm
00:38:03.652 Speaker 2 enabled to do a lot by myself, and because I'm working in software and AI, which is
00:38:08.452 Speaker 2 like software for software, it's-- I can-- I have a lot of leverage. Um,
00:38:14.772 Speaker 2 so I am enabled to do it. Um, but also at the same time, it's often
00:38:20.612 Speaker 2 easier just to pay contractors, and I find that there is a sort of cultural
00:38:24.352 Speaker 2 zeitgeist movement towards,
00:38:28.012 Speaker 2 uh, people who want their own autonomy, and they wanna interface with other people
00:38:32.372 Speaker 2 as people, and they don't want to be an employee. So I found it very easy to just,
00:38:37.932 Speaker 2 "Hey, what's your rate? Like, let's figure out a deal, let's figure out a
00:38:41.052 Speaker 2 partnership." And that has been the easier way to go. So that's the answer to the
00:38:45.872 Speaker 2 second part. So the first part, my experience as a solopreneur, I think that
00:38:51.752 Speaker 2 the challenge, especially with AI, right? So selling software development is fairly
00:38:56.312 Speaker 2 straightforward because people understand kind of how to, how to value it.
00:39:02.272 Speaker 2 The biggest challenge of getting paid to build stuff with AI is how to help
00:39:08.032 Speaker 2 people understand the opportunities that they have and how to help them value it.
00:39:13.872 Speaker 2 It's-- There's a lot of work that has to be done. You don't have to do a lot of work
00:39:18.152 Speaker 2 to tell people about regular software systems. They already know how to reason about
00:39:22.632 Speaker 2 it. There's a lot of competitors they know what to compare it to. It's very simple.
00:39:27.492 Speaker 2 But for, for AI, there's a long conversation of education. Okay, here's what is
00:39:33.372 Speaker 2 possible. Okay, now I need to fully understand your business so that I can analyze,
00:39:39.232 Speaker 2 okay, where's the opportunity? And then from there, I have to help you understand
00:39:43.772 Speaker 2 what the value is, that we can justify the cost, which is higher than regular
00:39:47.112 Speaker 2 software. There's a lot of steps. So-
00:39:49.832 Speaker 1 Yeah
00:39:49.972 Speaker 2 ... it's a very high trust thing. Also, I noticed that
00:39:55.272 Speaker 2 most businesses-- So again, this is just money from consulting, not building my own
00:39:59.572 Speaker 2 products. Most businesses would benefit... They're not even fully utilizing
00:40:04.172 Speaker 2 software. They're n- definitely not even fully using non-software solutions. And so
00:40:09.712 Speaker 2 AI is often like, why would you bring this really complex solution in? 'Cause it's
00:40:14.572 Speaker 2 sexy, it's got buzzwords. But I've found so far, for the most part,
00:40:20.732 Speaker 2 price optimization, retention prediction, uh, collaborative filtering, just--
00:40:27.012 Speaker 2 even just regression, like linear s- um, logistic regression, just statistical
00:40:32.512 Speaker 2 models. These are often like 80/20, so most people don't need the big guns.
00:40:38.872 Speaker 2 That is the cool stuff for sure. I'd say generative AI or the large language models
00:40:43.911 Speaker 2 changes things a little bit. But by and large, you don't need AI for most solutions.
00:40:50.152 Speaker 2 So a lot of the challenge is finding the people who really do have a slam dunk use
00:40:54.852 Speaker 2 case and building the trust, telling the story. So yeah,
00:41:01.292 Speaker 2 that's, that's what it's like, is that there's a lot of legwork. That's sort of the
00:41:05.112 Speaker 2 AI side.
00:41:07.932 Speaker 1 Yeah. That's fair enough, I would say, and, uh, I have to agree with a lot of the
00:41:12.072 Speaker 1 points that you mentioned, Steven. Steven, any final thing you-- maybe something I
00:41:16.852 Speaker 1 didn't ask you, you want to, to, to say or share before we, we close?
00:41:23.212 Speaker 2 Um, well, my producer and, um, the social media growth manager would
00:41:29.292 Speaker 2 probably hit me over the head if I didn't say go to the YouTube channel.
00:41:33.732 Speaker 1 Yes, please.
00:41:34.032 Speaker 2 Seeking Minima. [chuckles]
00:41:36.052 Speaker 1 Yes.
00:41:36.672 Speaker 2 Um, but-- So I mean, do that if you want to hear more words like this.
00:41:43.032 Speaker 2 But-
00:41:43.092 Speaker 1 Actually, I wanted to ask where people can find more about you. [laughs]
00:41:46.892 Speaker 2 Ah, yeah.
00:41:47.032 Speaker 1 Like this one. Yeah.
00:41:48.432 Speaker 2 I, I, I'll get better at this, guys. Um, but so what I wanted to say is, uh,
00:41:54.492 Speaker 2 I wanna-- the whole purpose of Seeking Minima in general is this is not a for-profit
00:41:58.732 Speaker 2 thing. Like, I can just build software. Like, there's no point recording videos or
00:42:03.172 Speaker 2 talking to people like you for, um, for a living. That's not what I wanna do. I am
00:42:08.672 Speaker 2 trying to help people use technology and to create better outcomes for everybody,
00:42:15.212 Speaker 2 and I think that we are at a fairly pivotable, pivotal moment, and I see a lot of,
00:42:20.892 Speaker 2 despite some of the
00:42:23.392 Speaker 2 pessimistic takes so far, that's not really how I look at the world. I think that we
00:42:28.372 Speaker 2 have a lot of opportunity- To make a big difference right now. And I want to help
00:42:33.800 Speaker 2 people realize that they don't need to stand here and look scared. Um, there's a lot
00:42:38.660 Speaker 2 we can do in terms of, uh, AI alignment, um, wealth inequality. We
00:42:44.620 Speaker 2 have a lot of tools at our disposal, and we're at the really sweet spot where
00:42:48.560 Speaker 2 there's... Most of this stuff is greenfield. It's brand new. We have a lot of
00:42:52.580 Speaker 2 opportunity to use it well, to put common sense rules in place, and to really set it
00:42:57.260 Speaker 2 up to work for us instead of against us or for a select few peopl- few people.
00:43:03.360 Speaker 2 And I think that, yeah, I'm gonna continue exploring those things. I have a lot of
00:43:07.440 Speaker 2 concrete ideas, but also I just like to synthesize other people's ideas. There's no
00:43:12.180 Speaker 2 need to be terrified of the future because, uh, we have a lot of control over it,
00:43:17.800 Speaker 2 and we're the ones who control it. I don't know. People seem to forget that. As if
00:43:20.660 Speaker 2 by the way, the future, what future happens is based on what we do. So
00:43:26.660 Speaker 2 if you don't like it, you should do something about it, and that's, that's what I'm
00:43:30.740 Speaker 2 doing. If you're interested in that too, then yeah, I'd love to keep talking with
00:43:35.500 Speaker 2 you.
00:43:36.720 Speaker 1 That's really great, and I think we are on a same mission, I would say, Steven.
00:43:41.440 Speaker 1 Because one of the reasons I'm doing all, you know, this podcast, I'm, you know,
00:43:47.600 Speaker 1 creating a lot of content is to raise awareness. This is first. And tell people, you
00:43:53.060 Speaker 1 know, like, hey, uh, you know, it's not like only the scary stuff you see in the
00:43:58.080 Speaker 1 media. You know, like people gonna lose their jobs. I don't know what. You know, all
00:44:02.580 Speaker 1 this negativity. So I'm trying to put some light on the positive side of the
00:44:08.540 Speaker 1 technology, and not just AI by the way, any technology. Uh-
00:44:12.220 Speaker 2 Sure
00:44:12.300 Speaker 1 ... 'cause I, I, I love technology myself, like since I was a child. And I always
00:44:17.040 Speaker 1 believe that technology's main goal is to take us forward, make
00:44:23.060 Speaker 1 our lives easy. From business perspective, it's always, I'm repeating myself on
00:44:28.360 Speaker 1 multiple episodes, increase revenues, increase customer base, decrease
00:44:34.260 Speaker 1 churn, all these nice things that, uh, business people like to, to, to, to hear. And
00:44:39.780 Speaker 1 AI is no different than this. So AI will make, you know, um, your business thrive
00:44:45.440 Speaker 1 and, uh, better. And for individuals, I'm saying exactly what you mentioned at the
00:44:49.260 Speaker 1 beginning also as well. Guys, like if you are scared of something, go study it. Like
00:44:54.280 Speaker 1 this is the best thing you can do. Because if you just keep watching it and do
00:44:58.300 Speaker 1 nothing, yeah, you're gonna be scared. I, sorry, I cannot help. This is why I'm
00:45:02.760 Speaker 1 trying to help you in generating this content as much as I can, of course. Um,
00:45:08.660 Speaker 1 so yeah, this is why we are aligned, and this is actually why I wanted to interview
00:45:12.900 Speaker 1 you, Steven, because when I read, uh, your bio, this what attracted me. And by the
00:45:17.840 Speaker 1 way, this is I do with all my guests. I will share, you know, the YouTube channel,
00:45:22.280 Speaker 1 like anything else I can find if you want to share that with me also as well. So
00:45:26.080 Speaker 1 that would go into the description of the episode and desc- the description on the
00:45:30.800 Speaker 1 YouTube also as well. Well, we, we came to an end, Steven. Thank you very much for
00:45:35.960 Speaker 1 being my guest today. I really appreciate the time. And-
00:45:39.420 Speaker 2 My pleasure
00:45:39.900 Speaker 1 ... hope... Thank you very much. And for my audience, whether you are watching this
00:45:44.220 Speaker 1 on YouTube or you are listening using your favorite podcasting platform,
00:45:50.340 Speaker 1 don't forget to subscribe. And as usual, I'm telling you guys, I would love
00:45:56.300 Speaker 1 to hear your feedback about this episode or about the show in general. If you are
00:46:00.740 Speaker 1 interested to be a guest like Steven today, like don't be shy. Please come up with
00:46:06.580 Speaker 1 the idea that you want to discuss, and I will be more than happy to have you as
00:46:10.380 Speaker 1 guest with me. And as usual, until we meet next time, thank you very much, and we
00:46:15.820 Speaker 1 will see you soon.
00:46:16.300 Speaker 1 [outro music]