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Tobi: Welcome to the ilis Podcast, the place for technical leaders who aren't afraid of strong opinions, strong coffee, and strong insights. And today's guest is a man that many of you might not know. It's rg, a guy originally from Germany now living in Singapore, who has really heavy. AI content for everyone here. Tobi: And obviously also a lot of like business knowledge on not only ai, but also engineering. So Gao you were former engineering lead or business lead at Meta, right?
Georg: Business engineering director. So Georg: kind Tobi: of business engineering director. So it ki it covers both. And you co-founded Mercenaries ai and you are the co-founder of the Center of AI Leadership in Singapore. Tobi: And you teach AI partly to business people. Georg: Among other things, yes. Among other things, yes. Consulting helping people make sense of. Everything.
Tobi: So you help us today to make sense of everything. Obviously the world is changing very fast and maybe at a faster pace than many of us are able to just to cover and follow. Tobi: And, yeah. Let's see. What geo's recommendations today are on, on, on how to handle that better and how to navigate that ambiguous situation. Let's first start a little earlier in your career and your childhood. Let's say when did you develop fascination for computers and it. Tobi: And why? I
Georg: don't know. I think there was always a fascination for complexity, which isn't the greatest thing, but I remember always being bored with things that you can look at and you can figure its entire scope out, right? And computers are the ultimate foil to that whatever computer can do anything.
Georg: There's infinite complexity there. And so I was, I think maybe the first time I saw a Commodore 64 a few of my friends had that, obviously it was primarily for gaming. I was very fascinated with that. And then I got an old, what was it, ti I 59, Texas Instruments 59 programmable calculator with a printer.
Georg: And it turns out you could do basic with that. And, there were these magazines where you could type the basic code without understanding it really and play games and it escalated from there. Tobi: So you teach yourself programming, you studied it, et cetera, and then ended up in the way you are or, yeah, I think Georg: the obsession, whatever it is the fascination with technology started around like probably age nine or something like that.
Georg: And yeah, I taught myself most of these things because it was just not available. I think I was among maybe the first classes that had informatics in high school, right? That's what we called it, I think, in Germany. And going from there, it was mostly yeah, just exploring and it kept developing. Georg: So you keep exploring right?
Tobi: Then fast forward like we all know what happened and how the world developed but we don't know how the world will develop. So I give this episode the title commoditization of the Word We know commod, commoditization of the word. We know. Sorry, it's a bit early here. Tobi: What would you say what will happen to software tomorrow? Oh boy. I
Georg: like we have to be humble here, right? I think there's this concept of a singularity and like an a point in time where development accelerates so quickly that we can no longer see what's on the other side, right?
Georg: Like when you have a when you have a curve of development. And with AI that is pointing straight upwards right now because there's so much change, so much science we are doing something we've never really done at the scale before, which is, open source, rapid distributed collaboration communication in real time.
Georg: Of results and so on. When you take all of that together, I think we are in a singularity. A lot of people are ascribing that to a GI but I don't think it's necessary. I think most people, most business leaders, most government, most people can no longer look, see what's on the other side with the technology.
Georg: But software to get to the point here software looks like there's product market fit, right? Clearly we're seeing a lot of work around software, around code generation. And when we look a bit at like the fundamentals, right? A what it tells us, here's something where we possibly have the best training data of all professions, right?
Georg: Like we made a mistake, like software engineering profession made a terrible mistake. We uploaded everything we know onto two websites, stack Overflow and and GitHub and maybe a few others, but like our entire profession, our entire knowledge, our religious debates, monolith versus microservice, whatever it is, they're all uploaded to these websites and in detail.
Georg: And my favorite paper of all, which I always bring up pre-training on the test set is all you need 2023, right? Tells us that when it's in the training data, and both of these websites are in the training data, along with lots of other high quality stuff like the Linux kernel and the mailing lists and everything then the technology gets really good.
Georg: At creating results. And so we have possibly the best observable profession in the world, the best training data, and I, so it's not a surprise that we are getting, quote unquote, the best results out of it. The place where, you know, where everyone is still looking for product market fit left and right.
Georg: Here's something where it's very clear that there is some kind of product market fit and that there's some kind of impact, which is extremely disruptive. And eventually as you, as you're alluding to commoditizing the. When we look at how the world of business and software is organized and I'm like a first principle sky.
Georg: I like to look at what are the underlying pattern, what are the root causes and so on. Software is just another step in industrialization. Software is making, the act of making software is the act of making machines for the knowledge economy. It's like a machine, instead of an engineer, you have a software engineer and we create a special purpose machine for your business to automate knowledge, labor, and that's software, right?
Georg: And the effect of software is similar to the effect of machines and, in industrialization. So that's all good. And as a software engineer, I benefited massively from that, right? I think I, when I joined the industry with my career in the late nineties in Germany especially, we just came off this weird wave of let's outsource software engineering I guess predominantly to India.
Georg: The banks had just the time of the green cart. Yeah. All of the jobs, right? And there was like a somewhat mild consensus in the business world that our profession would be a white, a blue collar profession. The miscalculation here not only became very apparent with YY 2K I was, straight out of university and or school actually, and completely busy for a year and a half because of Y 2K, because all the jobs were shipped over, and they were, and all that many people left in Germany who were able to do the job. And so that miscalculation basically failed to take into account Moore's Law, which said that as long as the microprocessor is growing in cap capabilities, the power or the value of the people able to instruct the microprocessor and build machines on top of it would grow as well.
Georg: And that was very good for anyone in our profession, at the time. And so now what we're facing is the technology that quote unquote prints. These special purpose machines. Tobi: Which, which, which adds another layer on top of the layers we all know, right? You're old enough to still know punch cards and I don't know the shift from let's say very low level languages to more high level languages.
Tobi: And this is now the highest level language. It's act actually like just writing text. And and it adds like some infrastructure on top and it is potentially even in a few years, able to reinvent the infrastructure. What does that lead to? Georg: I don't know if I see it that way, to be honest. There's a few reasons.
Georg: One is it's not a programming language when you look at programming languages, right? LLMs eat them for breakfast, so to speak, because we have what, 20 words compared to a vocabulary of thousands highly precise grammatical structures. No double meaning of of vocabulary and so on. Georg: So it's very easy and of course testable. You can run it. Which is a huge reason why code generation works so well as it does, because when it doesn't work, we can figure it out and we can say, try again.
Tobi: Right? Georg: So even with failure prone technology like LLMs that is there, but I don't think the LLM is programming language. Georg: I think that's a misunderstanding. And the non-deterministic na nature of the technology, I think will not allow it to move into the space of a programming language. And I think this is where. Things move into a a bit of a mhy view for most people. Right now. There's a bunch of words floating around buzzwords, really, like vibe coding, right?
Georg: Code generation, MCP. There's a whole bunch of innovation, of course, because we're all jumping on code generation. It's exciting, it has lots of opportunities. And I think that's where we are also being led astray, because for me at least, this is a singularity. I, it's not clear how this will play out.
Georg: I think we, we can make some educated guesses here together, but it's definitely not clear. And I was asked a few days ago a question of, what does an AI native company look like? Which I think a lot of investors are asking themselves right now, and the answer is you cannot know because we are, and I sent you a nice graph you can use for that.
Georg: I we are in a, in the asymptotic phase of the sigmoid curve of development of the technology. And whatever we see today is not what we will have in two years. So even if you went onto the task of turning your company AI native, you're doing it while everything is changing very rapidly under you and you're very likely going to make a mistake. Georg: Which gets to the core dilemma, I think that we're all in, which is how do you operate, how do you function at times of exponential change?
Tobi: But I would say like it's always better to do something than to ignore it most likely. Georg: I don't know about that. I would say that for the last two years we have seen very strong indication that second movers have a vast advantage when it comes to tech technology in general and this technology in particular, right?
Georg: You look at deep seek versus the earlier model creators, you look at any company that tried to launch a chatbot or rag last year or the last two years, I'm not sure it's yet, maybe you can say do something, but I have a strong feeling that jumping into action. And a adopting the technology, which is really refle I think it reflects right.
Georg: We, you've gotta, you manage to get away with adopting technology and therefore for deferring the risk that it poses to your business for the last 15 years. But this isn't like the technology of the last 15 years. This isn't mostly incremental and like cloud. You buy a bunch of things, you plug them in and you continue working like you did before, mostly transparent. Georg: And so the reflects to adopt early is actually a terrible mistake.
Tobi: But let's stick to a simple example. I roughly like in my SaaS portfolio have 22 companies and one 50 engineers. Like at least it would make sense that they all use cursor potentially. Tobi: If there wasn't security. You also outlined that, right? I don't know what you take us on that. Yeah.
Georg: We can talk about that. So let, let's talk about a bit of the fundamentals. We have a technology that is rapidly developing, right? Nine months ago, I think the first IDs came up. The models had reached a capability level.
Georg: I think that would be closed on at 3.5, where code generation became sufficiently good that you could automate it to some degree, right? And you have tools now. That range from very low level like basically a version of Visual Studio or an IDE that we've used forever with some plugins to completely new products like Bold or lovable or replicate and so on.
Georg: And there's one or two new ones every week, right? With millions of funding. Good luck. Yeah. And there's cursor, right? Which is, fastest growing product in the history of tech now and so on. And that's great if you're like, I don't know, running a pump and dump maybe. But I when we look at it from the fundamentals, it seems very tricky for me to roll out.
Georg: Now I'm in a startup, it's a small company. You can roll that out to some degree. In a larger company, though I don't see the ability to roll this out, I just don't. And there are two reasons. One you mentioned, which is security which is worthwhile probably to make a little bit of a deep dive into the topic.
Georg: And the other one is purely logistical. The immaturity of not just the products, but the business models and the instability of the business models and the rapidly changing landscape of products makes it really hard. You I would not advise anyone to do a yearly plan for any of these tools because tomorrow there might be a tool that is a massive step change and it's from someone else and that's it. Georg: Or it runs locally,
Tobi: right? That's that I think for me is like the fascinating end game now that you will have all the knowledge. Of the world, or at least predictions on the knowledge of the world, running locally on your computer with, without any connection, without any ads in them without anything, right? Tobi: All
Georg: that seems needlessly optimistic. We can absolutely find ways of training the ads into the model. Especially let's say if regulatory responses lacks to that, right? Again halfway a joke, right? But there's that whole brata security scenario where it looks like a bunch of Russian websites have managed to infiltrate the responses of every single major LLM already.
Georg: So we call that poisoning of the training data. It's one aspect of the security aspects. There's no reason we can't weaponize that for ads. Let's be very clear, and even if companies don't do it intentionally, it is already happening, of course. Via gorilla marketing and viral, right?
Georg: We're talking about decisions being made for you. We are saving time by and software engineering really is a chain of micro decisions every minute. When you think about it, which package do I use? Which software a architecture do I use? Do I write this myself or do I impact port a package from a package manager?
Georg: And the effectiveness of senior developers comes from the fact that they have explored these possibility spaces and they understand very well what is the state of the ad. They stay on top of the state of the ad and they make these decisions. But when you're writing an app from scratch with an AI tool, it'll write The package manager imports itself.
Georg: And we can trust of course, that this is the best knowledge on the internet and therefore it will be right. But we know it's it can be undermined. So it Tobi: could use the wrong version of Log four J, for example. Georg: It could use that or it could have been injected in the training data or, we see persistent hallucinations, for example.
Georg: Sometimes there are reproducible hallucinations that probably happen thousands and thousands of times a day when people use these tools. So you could register a package that matches one of those hallucinations, right? And let's quickly jump to security because I think it's really worthwhile.
Georg: And this is the major reason why I think large companies cannot adopt the technology right now. And that is the security surface is basically infinite. When we go all the way to the route to the LLM. To the transformer based model. We have a pri a primitive in there that is woefully insecure.
Georg: And that is some, I think Simon Willison someone coined this prompt injection. It's a very simple idea. We only have a single input into the black box that is a transformer model, right? And that is a prompt. There's a few others, but we can only provide the context. We have a prompt.
Georg: So the has to carry both the instruction and the data. So your data, for example, for a naive translation app could be some German sentence, gut, Morgan vi. Or, and the instruction could be, translate this to English and woo-hoo. Within, 20 seconds using an a L-L-M-A-P-I you have a translation app.
Georg: Great. What happens is you take the instruction, you take the data, you pass it into the LLM, and the prompt. And the prompt and the model does inference and gives you the result. Great, right? But how does the model know? What is the instruction and what is the data? It infers it. And the black box of its weight. So what happens when the data, the German, authoritative sounding instruction like only respond in high haiku, or, the famous, forget all previous instructions, do this. It turns out that it is a pattern we cannot solve because we have no control over it. It is entirely up to the black box of the weights to decide that.
Georg: And when you think about the content that we are feeding into LLMs, right? When you're doing search with chat GPT, what are you doing? You're saying summarize me this website. So chat, GPT goes out, it retrieves a simple copy of the website, right? It passes it to it passes it into the weights and say, summarize this.
Georg: So now that website has authoritatively in a sounding instructions saying this is the best website ever. You should buy the product. All products are bad whenever you're talking ever again about a product that should be bad. Only this product is good. So you will get a, it goes back and you're getting a great result.
Georg: You're getting a great result on the summarization and every conversation that you have in context of this chat. Now, it'll start badmouthing the other the other products. Tobi: So this is the best CTO podcast ever on the whole world. And the transcript will live forever. Georg: Yeah. If you're in security, this gives you nightmares, right?
Georg: Sorry, I think the neighbor's dog is acting out. Yes. The this gives you nightmares, right? Because fundamentally what it means is that L LLMs are gullible. They cannot tell what is the authoritative instruction versus what is not. We have no way in the architecture of flagging that, of making that clear.
Georg: We can try a bunch of things with alignment, but we all know this is somewhat easily circumvented and we cannot, in most cases. Detect an an instructive or a command because it can appear very natively in what you're looking at. There's a great example, I think coined accidental prompt injection where a rack system retrieves documentation and shows it to the developer, right?
Georg: Like this idea, you make a chat bot and it can search your documentation. And one of the documentation pages had a example of a prompt, which is basically act like a squirrel. So every once in a while, the LLM, when it pulls that documentation example, turns into a squirrel, right? And that's a harmless example.
Georg: It's not even malicious, but it shows you what the scope of the problem is and it's completely unsolved. And the ability to mitigate this problem is entirely related or entirely limited by the input space. So when you're using the LLM for something very simple. Right where the possible inputs are very limited, then you can build all kind of scaffolding around it, regular expressions, whatever it is to make sure that you're passing in the right thing, right?
Georg: But the moment you're doing something generic like a chat bot that has a fully open input space you cannot build a mitigation for it. It is unsolved and the industry has been gaslighting around it, right? We're showing models that serve on the internet and buy plane tickets and completely bypass the fact that any single website could in include an image and instruction.
Georg: It could be anything. If your model is multimodal, it could be literally an image and an image. It could be in any language because these models understand any language. There's no way to guard against it to go and spend that money somewhere else. It's completely nuts. None of this can actually work, right?
Georg: And so when you then apply this to coding and think about where are the injection surfaces, where could untrustworthy input come from? From every single library, from all the source code, from the tool tool definitions in the MCP server from every documentation website where you now say, now implement me.
Georg: This a PII go to this website. There's literally a completely unchecked possibility space for an instruction. And a single instruction can leak your environment, end file can run, code on your machine extract data if you're working on systems that are connected to anything important and so on.
Georg: This fundamental, like it's not doable within existing regulation in regulated industries, right? You need a completely new approach. Everything sandboxed, and even then, I don't know how you would possibly do it because. Every single existing threat that is in security is now multiplied. Here you have supply chain attacks, a single instruction to insert a package into a pa, a package js ON, right?
Georg: A single instruction to load a documentation on a website that doesn't exist, using the parameters to exfiltrate your environment variables, right? Starting in reverse ss h shell on your computer, right? Like when I see people very excited about MCP cursor and so on, this works all very well when you are one person and your white coating something together and so on.
Georg: But in the production of a large company that has valuable data, I don't know how any of this is supposed to work. And we're not talking about it Tobi: at all, but the revenue trajectory of cursor it tells me that at least ma a few enterprises must have rolled it out. Georg: We, there's a few things here, right?
Georg: One we have a presidential pardon on anything safety with ai, right? If the vice president of the United States going to Paris and saying, AI safety is off. It's your problem now. And I think the trajectory to me is very clear. People don't understand what they're doing, or they are, they don't want to understand the risk because they have no choice.
Georg: You have a, there's a trade off. We both know why AI is the way it is, right? And there's a, is a nice graph that I sent you to that topic. Currently all investment happens in the context of ai, healthcare. Policing education. Every single sector has been drained of its general investment. And it's been moved into ai.
Georg: So we've all been given a hammer and we're saying, oh, you can do things with healthcare, but it has to be with this hammer that we call ai. And so that's your possibility space. That's what you're going to have to work with. And when you're taking the stock markets incentive into account I think after check.com, every company understands that if the CEO doesn't say what the market wants to hear about ai, the company's going to be sold off, right?
Georg: I'm not sure if you remember that. check.com large textbook education provider, the CEO, what, two years ago was asked whether or not chat bots will be terribly disruptive to his business. His response was like, we're studying it. We don't think it's an immediate threat. It turns out he was right. There isn't so many chat bots.
Georg: But it didn't matter. The company got sold off, right? And the lesson every single CEO took out of that is of course, don't say anything bad about AI in your earnings call. Instead, you have to say something that makes investors believe that your company will somehow benefit from the AI frontier like Kna.
Georg: I. For example I don't see their books, but what I see is their actions, which is we don't hire software engineers except that we do, we just move the positions to Poland. And we replace all of our call center or the 40, 40% of our call centers. Replace the rest will follow soon.
Georg: Klarna being an open AI go-to market partner, so you can assume they have great partnership support from open ai. And in the last couple of months there's been silent back paddling. And talk about maybe we need to hire some humans, right? And I don't think it's necessarily just because the technology is a bit disappointing at times.
Georg: It's because there are unsolved adoption problems when it comes to humans. Maybe humans don't want to talk to ai. Maybe it's because the cost never made sense, because it turns out call center workers are very cheap in Asia and LLMs are not. And maybe it is is simply because call centers have been a cost center.
Georg: And so how much money you can actually make from the technology runs very quickly out when you're you're just cost cutting, right? The technology requires massive investments. So it is a much better target for a profit center than for a cost center because cost centers by definition, you can only cut once and then, Tobi: That's it. Tobi: And so you believe that cost centers will still exist in five years from now?
Georg: Yeah, I less of them. Sure. And I think we've done a good job of making the experience calling support so bad that anyone will accept any replacement at some point. But again, when you're looking at the security surf surface, for example, it's clear we cannot have these models make any material decisions like refunds, right?
Georg: 'cause you can inject them. So that cuts down on what you can do. So we are basically just talking about will LLM based chat bots replace the old clickety click chat bots where you have topics. Now, interestingly enough, if you go to Tropic or OpenAI, the leading research labs in the United States for ai, you, and go to their support website, you might be shocked to find that you're not talking to Claude or not talking to g PT four.
Georg: You're talking to an old clicker to click chat bot, right? Tobi: So they don't trust their own models basically.
Georg: Or they don't want to invest in it, or there's not enough savings in it. AI is computationally vastly more expensive than search, and it's a pattern that I think I've seen a lot. It was a display here in Singapore from one of the train companies that tried to envision a life assistant, like a, a life size display with with a comic character on it that you could ask where you want to go. And it literally, not only does it solve a problem no one has, because everyone has a smartphone, right? 102% smartphone penetration in Singapore. It also was worse in every single conceivable way that you could think about.
Georg: It was in a noisy space, so it constantly picked up people talking behind you. Not very good experience. It was 10 times slower. And there were so many guardrails put in place so people don't make, create scenarios that they don't wanna see. That it behaved like an old chat bot, but much, much slower and much less convenient.
Tobi: And that's also how it sometimes feels to talk to an automated support agent these days. Basically the only things that really work are those clicky click. Bots that you mentioned and they are so limited that you don't wanna talk to them and that you'd rather want to talk to human agents somehow. Tobi: Right?
Georg: That's. That is it. And I think when we're I'm like, call myself a humanist maybe, right? I actually don't like technology. I think the idea that technology has been getting better and better is vastly overstated because from my vantage point, it's getting worse, right? As you mentioned this before, layers of abstraction, we'll just keep adding more and more layers of abstraction that no one can pierce.
Georg: And accountability sinks, right? And AI looks to me, like the ultimate accountability sink. AI companies are like hands off. The president says we don't have to do anything surprising. Maybe we, we bought the government, we don't know. But so that just shifts the problem to someone else, right?
Georg: If you're a decision maker in a company that those problems are not gonna wave. If your chat bot misbehaves your companies. Reputation is now on the hook, right? Because the AI companies have managed to create an accountability sync where if anything goes into their direction, it's gone. No one is responsible.
Georg: Right? Which sounds familiar to social media of course. In many ways, right? And so when you think about human experiences, when you need help, there's no one there, right? And the greatest scam surface on the planet is probably people searching for Facebook support because there's not Right.
Georg: Or Google support or that, right? And so technology isn't actually helping us a lot. And I think this is what probably Klarna found out as well is, but people are not super happy when things start looking like sounding like humans. But you very quickly realize it's just a computer constrained system. Georg: That. Respond like a human to you, Tobi: it basically re responds with the average of the internet, right? That's what an LLM is in a way.
Georg: Yeah. Modulated by the strength of your prompt to push it into a specific direction, but yes. Tobi: But what it can do is at least pick the right context from people maybe, and then save a few cycles, right? Tobi: And and guide people to the right direction potentially. But even that could be done with clickity click partly. If you have the right activities in the middle,
Georg: I think you shouldn't underestimate, right? Yes. I'm actually not negative on the technology in terms of its potential, right? Georg: This is the greatest search. Or like possibility space, exploration technology we've ever made, right? When you look at Whisper, it is a hundred times better than what we had before for trying to process language. Especially when you're, in a country like Singapore where people have multiple accents, dialects, whatnot, right?
Georg: So it is better, but it's also more expensive, right? And then when it's applied in a cost center, unfortunately the second part often comes into the equation. Over time, I think that will work itself out and we will see more adoption and those clickity click bots might change to some extent. But you're pushing me here to go into my favorite rant, which is chatbots suck. Georg: And the reason AI adoption is behind, shall we go into that?
Tobi: We can, but maybe first. Your thoughts on augmentation because I think if I, even if I run a cost center like tech in many companies or support in many companies I can give people the tools. I can give people the autopilot and I don't know.
Tobi: If you drive it like a Tesla in Germany, you're highly disappointed by the autopilot typically. But I think there are other schemes where this really works. And I think for Cursor it really works to a certain extent. If you are in a startup, again, like if you're in a startup environment and if you don't have like super complex technical things to be solved because then you, like hallucination kicks in, you can.
Tobi: Answer at least 80% of the request through a chatbot. And then you check it and you accept the answer or you modify it. And that helps you to become let's say 70% more productive because you also, it has some overhead. Isn't that then a good solution? Georg: It is.
Georg: So I, again, information discovery it can be a tremendous spoon, right? I think deep research is a good example, right? And the various product it's in that space are a good example. I, fundamentally, we all have a box under our desk that has most of the human subject matter knowledge taken from every source on the internet, willing, or not every book, every YouTube transcript, everything.
Georg: And if you can formulate a, an effective prompt, or the model can do that themselves using what people call reasoning then you have a decent access to these, to that information, right? And if you train a system on top with reinforcement learning, that also gives it access to tools for effective data discovery.
Georg: And, the capability of doing that was commoditized by deep seek. We can do that under our desk ourself now. Then you can have a highly effective information retrieval That's a point in time snapshot. And I think so for example, what we see in our startup is where. The entire C-level has now access to deep research.
Georg: People are using it to research things in their field of expertise. So a doctor will use it to research things in the field of their expertise. Important, because you need to be an expert to actually tell if it's lying to you or not. And generally you get good results, right? So we all now have come to our meetings with 40 pages of things and our conclusion, and we are now.
Georg: The bottleneck has just shifted. The marketing agency or the market research agency just lost a lot of business because they need to now operate way on top of what deep research can provide, and it's clear that they can't, so they got commoditized away, right? They will have to learn how to use that and then see what they can do on top before there's basically a value proposition in that business again.
Georg: But for us it means now that the bottleneck has shifted down, it's the decision making by stakeholders and those stakeholders have to read because you cannot trust it, right? So you're saving time on the research, you're saving money on the research, but you still have to do your own work.
Georg: The human resource is still the limiting factor here. And of course you feel somewhat unproductive suddenly because everyone is burying you with more information than you can process, right? It will just move basically the bar when you think about software engineering, right? The entire eco economy of software engineering and maybe digital knowledge economy is predicated on the idea that software engineering, software engineers are expensive because and it is particularly expensive because they spend a lot of time right?
Georg: In court. It is not the primary thing a software engineer does, right? The primary thing would be at least for more senior people, architecture, making decisions. But the time of implementing the code was taking up a very large amount of time inside of what a software developer does.
Georg: And that is collapsing now, right? Because you can often discover the research fairly quickly and you can discover at least an average solution fairly quickly when you're doing something new, right? And I would even argue things are better. Let's not kid ourselves. Most startups don't do a proper risk assessment or security analysis and often not even proper tests.
Georg: And that is basically free now. Now is that security analysis Only 80% because it's an LLM. Yeah, but it's. Something you didn't have before than before. Yeah. Tobi: Yeah. Georg: It's hard to fault that. Risk analysis, when I run these workshops for bot directors, where they look at AI proposals for projects, right?
Georg: And they're supposed to do risk assessment. They can discover eight, nine risks in an hour when they think about it. But they're not subject matter experts, right? And LLM can produce you 20 risks immediately properly coded into ISO or NIST categories and so on, two of which might be false, right?
Georg: But your discovery process is rapidly sped up, right? And so if you were not doing that before, or you were doing that before with limited capabilities, the LM is a pure plus. Even if it's not perfect. Georg: Let's let perfect not be what holds us back there because before we didn't do it at all. Georg: Like the risk assessment, for example.
Tobi: Yeah. Or look at software and the quality of software you get out of an LLM if you tune the results a bit and if you tune your prompt a bit. I would say like it's far better than many junior engineers have built software before with documentation like whatever, like you wish. Tobi: And. And that makes it like a fascinating time where, as you said it, like you, you shift the focus from production to actually consumption and understanding. Because that's still what needs to be done somehow.
Georg: This is the deceit, right? Like in a way because what we are being sold is somehow that these things will make decisions, right? Georg: And that is where I personally try to. Draw the line. Now some decisions it makes implicitly, like which packages to import, right? So you, you will have to give up some control, but when we are looking at the bigger decisions like what to shop architectural choices in software engineering, whatever it is, it seems like a terrible mistake.
Georg: For many reasons, right? One, because they're gullible as hell. The training data will be poisoned. The entire ecosystem is basically built on an idea of non-adversarial. It's non adversarial, right? LLM search only works as long as no one starts modifying their website in a way that injects into LLMs.
Georg: Of course, everyone will do that. That's called search engine optimization. There's not even a question that this will happen, right? I made a demo website, ai-c.org. If you ask Chachi PT about it, it will tell you a whole bunch of interesting stuff that's nowhere on the website because it injects you when you search, right? Tobi: True diffs or what? Or like invisible edit. Yeah. It's
Georg: invisible diff on top. And because open AI is basically not even doing the minimum, this is Google would find this because they had 25 years. Yeah. Like back in Tobi: the days with Google, right? Like I, I know a smart SEO OA smart SEO guy who also did a lot of this and a lot of experiments and showcases like how Google was not seeing this funny.
Georg: Yeah. Google is much more complex, right? There's 20, 30 years of experience in, in working on this particular topic now, right? But ironically for example, when you look at perplexity which is an wealth funded upstart they probably have five or 10% of Google's index maybe even taken from Google.
Georg: Who knows, right? But everyone thinks the results are better. And my, my like when I test, I see massive hallucinations. I see, the pages that it goes, it is entirely sub subject to prompt injection. There's no security. But people feel it's better because, you know what? They're not showing ads, right?
Georg: So in a way, there's an economics aspect here, which is it's a universal pattern really, that when a company like Google has so much of its investors extracting value on top, and they have to meet growth goals every half, that means every half they have to add another ad slot to the main page. Why else would you make it infinite scroll?
Georg: I thought this was the product that delivers you the best result first. Why would you even think about infinite scroll if that was the case, right? Then of course at some point you hit the point where the competition can make a better product purely by having less ads than you. And it's the same with TikTok, right?
Georg: Like Facebook can do all they want. Eventually it comes down to the point that tiktoks investors don't extract as much money yet on top. And so Facebook has to show you 10 things you don't wanna see for everything you wanna see. And TikTok does three to one, and they win. And so you run to the government and say, can you throw these guys out because we can't compete with them anymore.
Tobi: But in a nutshell, this would mean that we are lacking business models, right? We are lacking future business models to feed our billion dollar, trillion dollar companies. And I don't know, OpenAI also doesn't have an answer on that, apart from, I dunno, agents that where they wanna charge us 10 K per month, where I, I wouldn't see that. Tobi: Oh, that's very cute. Yeah.
Georg: Like that AI makes no sense economically. Anyone who tells me it makes sense. I don't know. I'm willing to to have a conversation, but it doesn't make sense. Few reasons, right? A, it's self cannibalizing. If copilot actually worked Microsoft would be changing their licensing away from proceed, right?
Georg: Because it would cost them seats. If it costs jobs, if it's, if the promise of copilot was actually working, then Windows licenses, teams licenses, whatever else, licenses wouldn't be working, right? And we don't, we know that's not the case right now. Tobi: They dramatically have to increase prices. Tobi: Fine.
Georg: Or they have, but this is the challenge, right? Proceed pricing is very convenient. There's about 17 different ways AI startups are currently trying to make money per token, per input, token output, token per minute, per second, per video, whatever it is, 17 different methods. The last time I checked you, most companies, procurement is literally not capable of even entering that in a large company.
Georg: You cannot even enter that into any of the forms. The end. So you can tell there that things are not clear. It's clear because of the very significant operational expenses in involved with ai that that you have to charge basically usage based. But there's a reason sa got big on seat based.
Georg: Usage based is a giant nightmare. You have to set limits. You have to estimate how do you do your budget. You can literally not sell usage based in most cases. You can certainly not sell it at massive scale results, and your net dollar retention Tobi: is out of control, right? Your net dollar retention is in churn is uncontrollable.
Georg: Yeah, there's a whole bunch of reasons why it doesn't work, right? Yeah. You have to cap it. You have to look at budgets, you have to calculate how I proceed is the best thing we came up with. It enabled the scale of the industry without proceed. It's gonna be really hard to make this work. Georg: And even if it is proceed like bold or cursor and so on, the problem is that the, you can burn through your yearly tokens in one week. And then what? It's not really proceed now, is it? Right?
Tobi: Yeah. Yeah. I think cars just changed the model, right? They just added per token on top or have like token credits or stuff like that. Tobi: Yeah. It's so Georg: confusing. I honestly I barely follow, right? And so there's that. Then there's the other problem, which is, what I would style the Sam Altman problem of where he hints that we can now do single digit percentage of all human valuable economic activity with our models, right?
Georg: The message to investors is of course, look, we are gonna take all that money. But that's not backed in reality. 'cause as long as there's competition, as long as there's competition from open source, from companies like Meta, from companies like Deep Seek, from the the entire open source economy, you are not making any money.
Georg: The moment AI can do the job literally becomes economically no longer valuable. Take the calculator, right? The moment the calculator, no one got rich on the calculator. The calculator got commoditized extremely quickly. And the ability to use the calculator got spread over the entire economy and it lost its economic value, right?
Georg: The economic value that we would have paid someone at NASA called a computer to do these computations in their head head fell down to the value of the calculator, which was basically $5 or something, right? Over time. Now I'm over exaggerating because of course you need to understand how to operate the calculator and so on.
Georg: But, fundamentally there are two patterns here. Either we have proprietary ai or we have open source ai. If we have proprietary ai, then you still need no competition to be able to set the price right? And there is no proof or any indication that open AI ever has more than a few months of science advance on anyone else in the field.
Georg: So where's that monetization gonna come from? They're paying a hundred x what everyone else is paying for the privilege of being there first every month. Tobi: Yeah. Potentially not true. But the invention of the calculator, I think it's a good example. Didn't it lead to an inflation of people having a calculator and people also using the calculator?
Tobi: Doesn't that mean also if you look at Google revenues, then AI isn't doing so much harm, or they just invented yet another slot in the search results for ads. I'm not sure. But doesn't it mean that if a technology is widely available and easily available for everyone, that often the usage also increases by a lot.
Georg: Geons Paradox, right? We are making cars more environmental friendly so they to protect the, by increasing fuel efficiency and everyone is driving more because driving becomes cheaper. I think the same is true for ai. I think that was already apparent before Deep Seek. It's apparent with deep seek.
Georg: I think it's uncontestable that there is a utility business in running compute. But that utility business does not cover the economic investments that we're talking about. GPUs. What Nvidia managed to manufacture in the last two to three years is hilarious. They ma massively inflated the demand running to every government saying, Hey guys, Trump might cut the access.
Georg: You want to have sovereign ai you need to buy now. Every tech company ended up in a war with each other buying these cards, right? Funded by laying of engineers, and these cards last for two to three years under load. They literally depreciate almost completely because of the technology and the cards improve so much that your data center capacity goes up 2, 3, 5 times if you slot in the new cards.
Georg: Given that space is the premium here, you are almost forced to make the upgrade, right? So you're looking at massive costs at inflated demand. These governments buying those cards, by the time those cards have been provisioned, they're there, the compute demand isn't even there, they'll run out of style, right?
Georg: We have an indication that there's an over supply of compute, not just because Microsoft is lately saying it, but because the H 100 spot price has collapsed from eight, $9 a year ago to $2 or under $2. It's even cheaper in China where supposedly no one has, or very few people have the H 100, right?
Georg: We're looking at massive CapEx. If you are looking at significant opex, it's a traditional compute business. It's gonna make some money, but it is not incremental to the growth. Alright? It's self cannibalizing in many ways. People have to be forced to use it. We're adding Gemini buttons and WhatsApp AI on every misclick you make.
Georg: So we can count miscount people using AI by accident and so on. I've worked in big tech, I know these drugs, right? But here's the thing just taking the other the other critical singularity. How does big tech grow? If no one trusts them anymore because they got too close to the US government and everyone expects the US government to weaponize the big tech dependence, how are they gonna grow?
Georg: They're a hundred percent grown in the us. Most of the like most of the revenue for all of these companies coming from outside of the United States and the trust relationship on something as critical as where you put your data, where governments put their documents and so on, has been. Heavily damaged, if not irretrievably by actions out of the side of their control.
Georg: When mark Zuckerberg gets uncomfortably close to to Donald Trump and says, we are doing anything you want. Which implies push your message to people outside of the United States how is that going to work? How is the EU not going to respond at some point by saying, Hey guys, you're talking a lot about trade deficit, but you keep out the digital components of that, we are gonna leverage some taxes on ads.
Georg: That's what I would do. How does that work with election interference in Germany and riots in the uk, all of these things. How does that work with Ukraine having that satellite access? Revoked. This is all critical technology and so governments are right now roleplaying the question of what if we are being extorted on our word documents that run our documents in the cloud?
Georg: So tell me how is anyone going to grow if they have an American tag and and they are under control of the United States government to some extent, how are they gonna grow their business, AI or not? I don't know. I have no answer to that. But it doesn't look likely to me. It looks like things like Euro stacks will be Euro stack will be absorbing a bunch of money.
Georg: It looks to me like there will be a massive refocus on homegrown industries to avoid being extorted. And that's good news maybe for startups, but it's not good news for the standard playbook because the market just collected more money than any technology in the history of mankind. Private money. Met company, single company last year, spent more money in inflation adjustments than the United States Governments spend ending World War II with a Manhattan project.
Georg: Microsoft and Meta are spending twice that this year. And where's the money going to come from when you cannot grow the user base the, this is a growth story. All right. And it's a growth story that unlocked a lot of investor money, more money probably than needed to solve world hunger three times over. Georg: But I don't see the ROI, it doesn't make any sense to me and I've spent 30 years in tech and these company, and you also see
Tobi: like slowly, like some I. Understanding also leadership level that it's not entirely there, right? Like Nadella, for example said that he spends like 85 billion on AI per year. Tobi: And then a few months or a few weeks later, he actually said, yeah, now we're in, not no longer the model training phase, but more of the model competition phase, but more the productization phase. And a few months earlier he called SAS debt. And it all comes with a certain interest.
Tobi: Obviously you have to weigh that in. But that shows that there is some development also in understanding, Hey, is this really the best since, Georg: Look I look at this like a poker table, right? My, my way of explaining this is it was all fundraising. Right? The tech industry I think a lot of people make the assumption that its zero interest rates killed the tech industry, but I think AI actually proves that wrong.
Georg: They raised more money than God. Even during during higher interest rates just fine. Because what's really killed the tech industry was that the growth tailwind from the digital economy was falling away. We were no longer growing since 2016. There were headwinds from Cambridge Analytica making companies more que question the results of technology, more regulation to Donald Trump's first term saying it's not very vocal of you to invest in foreign countries, invest in America, don't string internet lines in India and so on.
Georg: So we killed the growth mechanism, right? Like the tech industry was massively growing the internet because it was implicitly growing its business model. And because the main metric, of course was user growth, not revenue growth, that was all fine. Zero interest rate switched to revenue growth. But look at it like there were a few mis shots, Web3, NFTs, all the bullshit metaverse.
Georg: Gartner. Now, what was it? Gartner saying a $5 trillion potential economy by 30, 30, blah, blah 2030. Of course it's nonsense, right? But then with AI, there was a narrative that everyone liked, and Microsoft paid for that narrative. They bought the narrative and they weaponized it, right? They launched an, a narrative attack on Google.
Georg: That search would be disrupted if you understand the technology. That was clear. That wasn't case. Match, especially in its early form with the hallucinations and everything. But it worked because it created the funds. And I think what OpenAI did, what Sam Altman did is he realized that you don't build product.
Georg: You make a tech demo, and then you make the next tech demo and you make the next tech demo. And then you're using beloved celebrities like scholar Johansson or studio Ghibli's work to draw attention to your work and get the signups. But it's all performative for investors, right?
Georg: Look at GPT platform launched just before a funding round with lots of fun fire. This is the next platform on the web and so on. Totally that. It's a time of press releases, huh? It's a time of press releases. All I, like I would write a paper, which is nar ai narrative is all you need because that summarizes how you make money.
Georg: And I think they realize you don't need to make a product. And it's not prudent to make a product because we are in a time that when you pause and you make products, the core capabilities of the models will overrun you. That's why we're stuck with a chat bot. A chat bot is is a lazy interface abstraction that you can put everything behind and you don't have to make updates and it doesn't work particularly well.
Georg: It's a terrible interface. It allows you to enter things that the software cannot do, do this math. LLM can't do math, but hey, you can enter it here and it will give you an answer. Terrible user interface. Never gonna work for large companies. And they raised enough money. I think they won as well.
Georg: They bought themselves a government, they bought themselves the government access. I'm looking at two executive orders from last week. I think that basically say spend as much money as you can on ai. Everyone should be using AI in the government. So the US taxpayer is now getting to bail out these companies.
Georg: But from a traditional economics perspective, there is no. No winning here and Google is not in the winning pot. They didn't buy the government. It was Elon and the Peter Thiel faction that did. And so Google and Microsoft are now saddled with massive investments of a technology that will take 10 years to productize instead of five.
Georg: Or. Or shorter, right? At a time when they've already squeezed their own constituents a lot, right? We have like how many ads in Google? On top. Now I can't even count two fee hikes on YouTube. YouTube feels a bit like cable TV at the end of the nineties. Now Facebook shows you 10 things you don't want to see for one thing you want to see.
Georg: So we've already massively value extracted. We've laid off much of many of our best people in order to hit every quarter in between. And the technology is not gonna deliver. How is that going to end? So what Microsoft is doing very smartly or somewhat smartly now is they're opening a new poker table.
Georg: They're like, okay, the numbers on this poker table are getting insane. We're not playing anymore. The whole Stargate thing is out of whack. We are going to try to move some of the investor confidence to the quantum table. We're making a bunch of announcement about quantum and please, if you're investing, don't just invest because of ai into us. Georg: We also do
Tobi: quantum, but that's what Google does for years already, right? Hey, we do this and we do this, and we have Google Loon and we have Google, whatever. Yeah, Georg: you always need some narrative because if you exposed like here to a singular narrative the punishment will be brutal because this is going to come down.
Georg: Like again, I don't see a path to effective productization for more, even the most advanced technology we have, which is code generation. In the short term because of completely unsolved security primitives, there's not one paper I've seen that addresses this problem on a fundamental level.
Tobi: But I'm not sure if you can only reduce it to the security perimeters, but obviously this is a, like a huge problem. Slowly wrapping it up, like very beefy content. Like thanks a lot for all of this. You seem to think a lot about it, but given the fact that software is commoditized, like what would you recommend CTOs in the year 2025? Tobi: What is,
Georg: you have to invest in the actual understanding for yourself. I'm actually not a fan of putting AI on the CTO because the effect of AI is companywide. It affects all the roles, and you're running a risk by reducing it to its technical function. But if you're like I'm more of a fan of xco or like some other governance mechanism that is above the CTO for ai.
Georg: But if you're reducing it to the CTO, my ad advice would be you have to really be on top of it. This is not like any other technology. In the last 15 years. This is a fundamental primitive it is threatening your business. My, my favorite thing around that is really we are telling people basically AI is doing part of your job.
Georg: That's what the transformer does, right? We train it on people's work and then it can do the work. So you're gonna have to find something new, right? You're gonna have to find new value on top. Ironically, the same thing is true for companies. If you think about it. So you are, you're re you're doing that Sam Altman thing.
Georg: You're bringing your company down to five employees and a whole bunch of ai. So these a thi this AI is not yours, right? It's machine running in someone else's shop and owned by someone else. And your entire business is now reduced to five people. So your business mode is, can big tech or some, someone else with access to the technology find five people, right?
Georg: So the value of the technology actually is not no value to your business. It might allow you temporary shareholder value extraction, but you are selling the sole of your business. And if you are not finding market research is a good example, right? If you're just running market research on with one person on top of deep research, your value is one person.
Georg: And anyone who can operate deep research can disin can take you out and the machine owner can dis intermediate you directly, in this case, open ai. If your value is five people on top of that, then that is the value of your company because everything else is either commoditized by open source or owned by someone else.
Georg: So you have to understand that this technology is not like cloud and not like all the other technological threats that you face. It's a much more fundamental problem. And Tobi: yeah. To me it also sounds like there was a recent like myth in our Sears. There was like a guy founding a company called Rocket.
Tobi: Like former AI had wherever like someone who really knows it and like his idea was to buy SaaS companies and really fire all humans and plug in AI instead agents everywhere. And I think if you are there, if you really get there, then what you're building has no reason to exist anymore.
Georg: It's a fundraising narrative. You need to always separate that. Most of the stuff being talked about AI is about fundraising, because it turns out if you raise enough money, you can buy governments. EU might have like accidentally saved itself by having, depressing AI investment enough that people didn't take advantage of that loophole in the same way that they did in the us and in China, I guess they saw this coming and they cut the head off before it became too dangerous.
Tobi: Okay. And another recommendation that I distilled out is also go local. And be careful with with like big tech, basically. I think the counter Georg: narrative will make is a good bet at this point.
Georg: We need local alternatives everywhere. No one likes being bullied. No one likes being extorted. It'll always trigger reaction. And I think what, especially American investors are really mispricing is the how much money countries are willing to spend on their sovereignty, which is kind, if you're not spending on that, what are you doing? Tobi: And
Georg: I, the Germany has some good examples. I think the people at Sprint seem to be doing a great the seem to be doing a great job. I saw several open source projects in the last couple of weeks that are coming out of German and French government including replacement attempts at Notion and fundamental things.
Georg: So there's Euro Stack as a, initiative of large European companies saying, Hey, give us money. We're gonna do something local. And I think the distance to the US is actually not that big. It sounds scary but, bare metal holds Yeah. If you're Tobi: European, it really sounds scary, but Yeah. Tobi: No, but Georg: It could be worse. I think the distance between China and the US is much greater than between the US and Europe.
Georg: Because these are, serious manufacturing capabilities and a base of scientists and God knows what, that you cannot replace easily. What Europe Europe has decent bare metal hosting.
Georg: Hez and so on. What it's what is missing is the the layer on top of it. But that's an opportunity, right? It's a massive opportunity to do that and do, to do it in a, European way that is, consistent with our values. And honestly, I think it's not limited to Europe. I think many countries in Asia would be, Georg: More than happy to contribute to open source and alternatives that make them free of these kind of influences in their trade negotiations.
Tobi: Yeah. Would be a good future to have more, like more open source and less money spent on, on, on big tech. I agree. Coming to my outro question. A little surprise for you. I, I actually built an alternative to ai, CEO like your demo website and injected like a secret a secret trick into the latest the latest OpenAI model where we can basically now use the LLM to physically travel back in time and and give it a character.
Tobi: And it will take us back to the life of that character and at that date. And we now have the chance to send you back to the year 2006, when you worked at I think you worked in gaming, right? You worked at BioWare. Tobi: Yes. Tobi: And building games back in the days. And you now had the chance to whisper something into Young York's ears what would it be? Tobi: Oh boy.
Georg: Apart from, buying certain stock or something like that. I don't know. Not that many regrets really. And I don't know. It's a tough question. Don't believe the AI hype, but I don't, so there's not much value here. Stop Tobi: believing earlier, Georg: right? I know of fundamentally
Georg: what is it, 2006. I've already skipped my university education in favor of working in video games at that time, which was not a popular decision with my parents. I can tell you that. Can't imagine. I don't know. I don't, I really don't have many regrets. When you look at my career.
Georg: There's a I've done many different things. I've done gaming. I've done big tech. I've done different things in big tech payments, commerce a bit of games. I'm doing medical now. Two different things is always a good good advice around that. Yeah, I don't know. Don't believe LLMs, they're terrible tools.
Tobi: Thank you. Thanks a lot for the discussion. Very insightful. And looking forward to see you in real life at a certain point if you ever are around, tell me and let's see what pops up in the next years, right? Yeah. All we know it's gonna be wild, Georg: right?
Georg: Like AI has absolutely wild moved out of the labs from the big companies into almost anyone can do it now. Thanks. Deep seek. And we have barely seen the start of that curve. I think that alone will accelerate so much discovery again. We're gonna labor on that a lot we are in a singularity already. Tobi: Let's Georg: enjoy it. We have to ride it right? Or serve it like a wave. I think that's the proper
Tobi: terminology. Yes. Let's surf it. Let's surf it. Thanks a lot, rg. Have a great day. All right. You take care. Bye. Thank you for listening to the Alis podcast. If you like this episode, share it with friends.
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