The future of agents, AI energy consumption, Anthropic's computer use, and Google watermarking AI
Is Microsoft Copilot just like Clippy 2.0? Vyoma Gagyar is an AI technical solution architect. Vyoma, welcome to the show for the first time. Tell us what you think. Thank you. I do not think that it is Clippy 2.0. Microsoft Copilot has been one of
the, uh, pioneers in the field of code translation, extraction, coordination, Volkmar Uhlig is Vice President, AI Infrastructure Portfolio Lead. Volkmar, welcome to the show. Uh, what do you think? I think the judgment is out. I'll wait for 2.5. All that and more on today's Mixture of Experts.
I'm Tim Hwang and welcome to Mixture of Experts. Every week, we're going to bring you the world class analysis, debate, and thinking you need to navigate through the rapidly changing universe of artificial intelligence. We've got a discussion about nuclear power, AI using computers, but first we really want to talk about the rumble happening in the agent jungle. Um, the question is, uh, co pilot just like Clippy 2.0 was inspired by a spicy tweet from Mark Benioff.
Um, but I think more generally we want to focus here on a mixture of experts on kind of taking a look back over the last few months and the fact that, um, Salesforce has launched an agent platform. Microsoft has launched an agent platform. Really kind of 2025 is shaping up to be like a battle of competition over agents and specifically agents in their enterprise. And so I really want to spend a little bit of time talking about that and giving all you listeners out there an intuition of what to expect over the next 12 months or so. And maybe Volkmar, I'll turn to you first. You know, I think what's most interesting is that, you know, now there's going to be so many different agents.
platforms to choose from. Um, do you see different companies taking different approaches to kind of offering these technologies to the enterprise? What do you think are like the big kind of, you know, competitive dynamics that are playing out here? All companies are trying to experiment. Um, and we are, uh, in a, in a world where we are slowly moving from, you know, The training wheels on where the sys, where the systems, uh, get supervised by humans, uh, then, and right now the, the system is in the passenger seat.
That's why it's the co-pilot and not the pilot. Um, then at some point I think there will be a switch over that the system is, are more powerful, more trustworthy, and then the system becomes the pilot and the the users to co-pilot. And at some point we can get the Copilot out of the, out of the seat and the systems can be fully autonomous. So I think we, we are in a progression of. of how the technology is evolving. But I think at this point in time, human eyes are required on the systems.
And I think the big experimentation right now is how these user interfaces look like. We kind of know how the fully autonomous systems look like. You know, there's not even a screen.
Or, you know, in cars there's no driving, no steering wheel anymore. But in the systems today, we are experimenting. If you look at Microsoft, they integrated it sometimes as a chat agent.
Uh, on the side, sometimes directly in, in the applications. Um, Apple took a different approach. Salesforce is taking different approaches. So everybody's, is experimenting with the user experience at this point in time. But, you know, technology is not, you know, the, the.
Training wheels are still on. And so we are going through the training wheel phase. Yeah, for sure.
And I think it's so interesting is like how much some of the competition is just happening on the level of like the interface, right? It's just like we don't even know how to effectively interact with these agents. Um, I think you bring another angle to this question that I think is worth touching upon though, because You know, in some ways, right? Like I think for the kind of, you know, outside observer, they take a look at some of this stuff. And I think they occasionally are like, this is just Clippy 2.0, right? Like this is back in like the 90s or early 2000s. And you know, we're just talking to a paperclip on a word processor asking me whether or not I'm writing a letter. But it kind of sounds like one reason you think that this is a genuinely different thing, like what's happening in this market, is also that there's a lot of experimentation happening under the hood as well.
Is that right? That is correct. And all the information, the legacy information that has been gathered from Clippy, you see that Microsoft has been a great company, which has been operating seamlessly for years. Imagine the amount of data that it has gathered.
The Clippy data, as everyone's claiming that to be, there is so much other information around, um, the other platforms such as GitHub or anything, et cetera, as well. Um, Imagine feeding all of that information into a large language model and making your day to day life much better. So I feel that is what we are aiming at. There are just a couple of, uh, problems or a couple of solutions that we want to get from this.
And first being enhanced productivity. And I think Microsoft Copilot helps you do that. And it also gives us a lot of our free time back to do something more productive and creative.
Yeah, that's great. And I do think that, like, You know, particularly Volkmar, I know your background is working on autonomous vehicles, like this kind of model that like, the agents are sort of the less, next level of autonomy, but we're sort of getting people like to trust the technology enough to be able to take it to the next autonomous level. I think it's like a really interesting set of problems that we'll see, uh, kind of play out in the space. The nice thing here is your life doesn't depend on it.
Yeah, that's right. All that will happen if this technology fails is, you know, the code breaks, or you send a really awkward email to someone. So the stakes are a little bit lower. Well, perfect.
I think one of the topics I really wanted to touch on, uh, moving on to the next segment. Um, is on the topic of AI and energy. A few weeks ago, the news kind of leaked out that Microsoft was considering restarting the Three Mile Island nuclear power plant.
And, you know, all the current projections suggest that future models are going to need, you know, gigawatts of power in a data center to run. Um, and I think we've danced around this topic in previous episodes of Mixture of Experts. Um, but I think I wanted to kind of just tackle it head on is, How are we thinking about dealing with kind of the environmental impact of these models and how much energy is going to be required to kind of unlock all of their potential? You know, someone who's like very excited about the technology but also kind of concerned about climate change, you know, it's a topic that I think is like really sort of near and dear to my heart. And, and I am really sort of interested in, you know, the approaches that people are thinking about and, and trying. Um, I guess maybe Volkmar, do you want to, I'll start with you, I'm curious about like how IBM is thinking about it, but in general how you're seeing the space kind of evolve around this this tricky problem.
So I, in general at IBM, we are trying to look at being, you know, leaving a green thumb fingerprint on the planet when we are looking at tech. So you're trying to be conscious, you know, there, there is an environmental impact. The power consumption right now for data centers stands about 1.5 percent of total power
production in the United States. So it's, it's tiny, right? So, and, um, and then with the expected growth in AI and the projections are kind of not really friendly. Uh, assuming that, you know, H100s with, you know, seven, eight, nine hundred watts. And then the next ones AMD is producing, which is like 2000 watts.
I think we have not yet done the projections of technological improvements. And so I do not believe that we will, we will see these high powered cards. In the long run, I think it's just a moment in time.
But even if we stay on that projection, then the total power consumption we are going to have is an increase from 1.5 % to 4%. Okay? Well, take the population growth of the United States right now. Um, that's, that's, That's nothing, right? So it's just the population growth is already bigger than what we are adding here in total data center power consumption. So I think that the moment right now is that there is a concentrated interest in very rapid build out. And we are actually putting the discussion about what constitutes green energy and efficient energy back on the table.
And I do not think that has anything to do with AI, but it's actually a key moment of a tipping point where we can actually have a conversation about nuclear power in the United States, and I'm really excited about that because I, you know, this is one of the cleanest power sources and actually looking at it from tech companies trying to put on, you know, nuclear power and then, uh, actually doing that in a, Uh, careful, you know, orchestrated way is a good thing. And, you know, if the conclusion is then, oh, we should still not do it, then, you know, that's a, that's a consensus between the people who have their, these power plants in their backyard. But I think the discussion needs to be in a, in a rational way, and I think over the last 50 years it was irrational. Yeah, for sure.
And I think that'll be the most interesting thing. I mean, like, so often happens in AI. It's almost like the, the AI isn't the thing, but it is triggering the bigger discussion, which I think is fascinating.
I guess, Vyoma, you work a lot with customers and clients. Is, is the environmental discussion kind of popping up? Like, are clients raising it? Or, you know, people looking for solutions on the kinds of solutions that you work on saying, I want you to deliver this, but we have to make sure that the emissions are, are good, you know, on, on what you deliver? I'm just curious about what you're seeing kind of on the front lines there. Yeah, of course. Yeah, that's a good question. 2023, we were just getting up with this technology. People wanted to know more about it.
But in 2024, now we see that so many of our clients want to make it much more sustainable. As you see that these clients and companies such as Microsoft, Sam Altman also kind of is investing in a company called Ookla. Google has its own um, And Amazon has its own different ways in investing in some of these nuclear plants. But as you see, that they are trying to make this more sustainable. They're trying to avoid the lag.
Because if something like models or like AI runs on nuclear emissions, They run much more faster, seamlessly. There is very less chances of it being, um, a breaking in the middle so that you have to rerun those pipelines which take hours and hours of compute and resources. So that is something that clients, we are making them much more aware about it. I remember I was at a client location two weeks ago and I was telling them that right now, 15 to 20 percent of our electricity comes from nuclear plants. That's something that we have to look into. The government is also, uh, helping you with the inflation reduction, uh, reduction act, giving you more, uh, the tax credits for that as well because as mentioned, we have a much more better structure around it.
Um, technology has evolved trust, and we should be doing much fine. And one thing that I wanted to add here, but not everyone wants to be leveraging these large language models to do their jobs. People are pivoting towards having a smaller model which can do just the job right by techniques such as fine tuning or even prompt tuning.
So I feel that is also a caveat that I'm seeing nowadays. Yeah, for sure. And I think this you, I think you and Volkmar actually represent really two sides of a very interesting coin. I think, you the argument that you just made as well. Actually, customers are thinking about smaller models as a way of reducing their kind of like energy footprint for the deployments that they want to do.
And Volkmar also says, well, look, a lot of the projections are based on the idea that the chips that we're getting, the boards we're getting, like that energy consumption is just going to be the case forever. But it's actually likely that the next generation will actually consume a lot less energy as well. And so there's actually this really interesting interplay of basically like, Do, does the, does the model need to consume as much energy and does the, does the actual hardware need to consume as much energy and kind of like the efficiencies that you're gonna get accordingly? Um, like, I could see, basically, like, a world where I guess Volkmar what you're saying doesn't come to pass for some time. And so customers increasingly want smaller models to deal with this question.
I can also imagine a world where there's some breakthrough where the next generation of boards is like so energy efficient that people are like, let's just run the biggest model that we can because it costs a lot less energy than it used to. It's just way more efficient. Um, I think it'll be really interesting to see that play out, but I'm, I'm curious if either of you have kind of an impression on almost what's going to hit first, right, seems to be the question. The moment you have something which is so dominant in the market, uh, and costs so much money but has a huge upside potential, uh, innovation will take place, right? And so, and we are in a already, like, if you look at inferencing, we are on a perfect market, right? It's a commodity. Uh, you pay by, by tokens, um, and so you now have price competition, and so the race to the bottom is on, right, and so the race to the bottom is, uh, across different disciplines, so I can make smaller models, they run faster, I can make faster inference, um, or I can produce power more cheaply, right, and I'm what I'm expecting is that each participant in this market, because it's such a big market, right? If you consider something being, you know, 2, 3 percent of total power production or consumption in the United States, um, that is billions of dollars at stake.
And so I think each of them will innovate. You know, the model people will innovate on the models, the hardware people will innovate on the hardware, and the power plant people will innovate on the power plant. So I think overall we are better off. But, you know, because now there is a very specific problem which then radiates into the rest of the economy.
So if we can suddenly make power at half the cost, that's wonderful, right? It will make, you know, a model cheaper. Yeah, there's like other reasons why we want to do that. Right, exactly. Volkmar, this is the time you should get back to the Bay Area. Startup idea.
Yeah, exactly. Have you considered getting into Fusion? Yeah. There you go. I'm going to push this on to our next topic that I really wanted to talk about. Um, Anthropic just last week, uh, launched a new feature, uh, called computer use.
Um, and the basic premise of it is pretty simple and it's kind of a fun feature. It's basically the idea that, you know, ultimately, uh, Your AI, your agent, will be able to take over your mouse and pilot your cursor around and do things for you as if you were like a user on screen. Um, and uh, and this generated all sorts of really funny stories I want to talk about. You know, one of them is that they talked about during the testing how, You know, the computer use feature would occasionally get distracted. So, like, beyond the way of doing a task, and then it would take a pause to, like, look at photos of Yellowstone National Park for a while before going on to its next task, is that these models actually, like, have, like, these very funny kind of, like, simulations of actual human behavior.
But I think I want to just first start with, like, the business question. Um, which is, and you know, maybe I'll, I'll toss it over to you is why is Anthropic working on a feature like computer use? Like is it just a cool demo from a research lab or is it actually really connected to what they need to do as, as a business? Look at Anthropic, look at agents, everything that all these companies are trying to do is come up with some sort of a symbiotic relationship between humans and machines. And whatever use case that you take in this case, I think Anthropic is just trying to do that. I feel, um, with the.
Claude models that are coming into play, they are trying to help augment some of our behavior and help us make our lives better or help us be so much more productive. I was just speaking about this, uh, to my mother yesterday and she's like, I need to book this ticket. Help me.
And I'm like, I'm in the middle of a meeting. I don't have time for this. Just give me half an hour. Imagine if she had this, I was like, I was reading about it and I was like, imagine if she had it and she had the computer use model. It would help so many people in training, enablement, people with disabilities. It has a social impactful angle to it, which just goes unseen.
And I feel that are the things that the Um, market, the people in the market, the clients want something like this going in the future. So that's something that I feel has a great potential. Yeah, for sure. I think Volkmar, this is kind of fun because it does connect to what you're talking about earlier in terms of like innovation on the interface level. Um, you know, I, I think what's really funny is like we invented GUIs and the operating system in part just because like we needed an easier way for humans to interact with machines. But now we have this very funny thing where now the machine is taking over that interface to pilot the machine.
Um, and it's kind of like a very funny historical development that that ended up being the case, but kind of curious about how this fits into your earlier thoughts about, you know, all this innovation that we're seeing on the interface side. Yeah. So I think. When I looked at their video where they demoed it, um, it felt kind of useless at first.
tell us more. So why, why? And, um, but I think there is, um, like there is a certain level of smartness behind it. So. I believe that if a computer interfaces with a computer, there are many, there are much better ways how you can actually do that. You know, so, like, if you think about it, they use the browser.
So, like, there's a browser, there's an engine, that engine is JavaScript. I can just directly hook into a JavaScript engine. I don't need to render something into pixels. So that rendering effort, and then, I'm translating that rendering effort is just insane, right? So if, if computer to computer interaction happens, you do this to APIs. Now, I think we are seeing something very interesting emerging, which is the API to the computer becomes the English language.
That's effective with large language models too, right? So I talk to you in English and you're interfacing with. the outside world. And the outside world, like, you know, if you look at what ChatGPT is doing, is, you know, they're, they're creating a Python script, and they run the Python script for you to automate a task, and they pull data out of the internet, uh, and then, you know, they convert it into JSON, and then they give you an answer back.
So there, there are the translator in the middle. So I think there, the, the ability to actually interface with a human Uh, the alternate, the outer human perception, which is the visual and not the, you know, not text based, which is usually auditory or like, we are reading letters, but they're all letters, is, uh, is the visual domain. And so suddenly if I can understand the visual domain a human is consuming, now I can actually interface with that. So if I would be a business, uh, and I would do what Entropic is doing, my guess would be that they're probably looking at, uh, automating development processes and automating debugging.
Right? So, and so what the demo is effectively just showing is like, Hey, look, we can do this. But if you, if you convert this into something which has an economic value, it is probably in the testing quality control Q& A of software development, which is, you know, has millions of people employed today, automate. So this is, I think, from a, from a, you know, business value perspective, that's the direction I would take this. And so, and that's exactly then, it's not any more about, um, you know, replacing a machine, machine to machine interaction, but actually doing what the human is doing and saying, okay, are all my buttons correctly aligned? Is my text formatted correctly? And now then, then it makes sense, right? So it's effectively in that realm.
Quality control, potentially data generation, where you can actually visually then inspect whether your, your code generation was correct, if the webpage renders correctly in all browsers, et cetera. That's where I can see where you could take this. Yeah, that's really interesting. Yeah, it's kind of a debugging thing. I think it's, it's fascinating that kind of like their, their stated reason for releasing it is not really kind of like ultimately the business purpose.
Um, I mean one angle, which I don't know if you buy Volkmar is kind of like, you know, we don't live in a world with perfect APIs. Right. Um, and it is possible that you could imagine these kinds of models being helpful for, you know, facilitating interactions when, you know, there's no clean API for the system to talk to a system. I don't think you would do this to the visual domain, rendering something in a browser and having a laptop somewhere. I think it's still like a crazy way to do it. Yeah, it's just such an inefficient way.
How do I convert, you know, like 10 characters of JSON into a million pixels and then try to understand that? Um, and so the, I think there will be a different layer, but I think each of these layers has a value. And so, you know, you may either, you could also, I mean, if you want to make it efficient, have the code generated for the API by the large language model, right? But now you can go one layer up, and you say, okay, I run a JavaScript engine, and then the next layer up is, I run the, The output of the JavaScript engine in a web browser, and I'm reading the pixels of the screen, right? So there are, well, I read the DOM, you know, they could have just read the DOM out instead of actually converting the DOMs into pixels. But, you know, that's why I'm like, oh, this is my immediate reaction was like, oh, yeah, this is like kind of weird. I think from a quality control perspective, that's huge, right? And then now you can also say, okay, please judge me if this interface is better than that interface. So suddenly you can do experimentation. And I think that's where the true value comes.
If you can actually understand the screen. Well, we actually have like, I think a little bit of a difference of opinion between you and Vyoma. Cause I think Vyoma, you, you made an argument a little bit earlier, which is, this is like amazing as a way of interfacing with agents for like your mom, right? Like, I kind of curious, like, it seems like Volkmar is taking a very, technical approach, which I think is like very genuine, right, which is there's much more efficient ways of doing what computer use is doing. I think one of the things that you're making an argument for, though, is it like might help people understand and interface with these systems better, even though it's kind of like technically less efficient. I don't know if you would agree with that at all.
Um, yeah, there are two caveats to this. So right now we belong to the tech space. That's something that we do day in and day out.
When I go out and talk to clients, they have not even embarked on this journey of AI. They're still, um, working with traditional legacy models, legacy systems. Where they do not even know what AI does.
What, where do we go from here? So to like onboard these clients to onboard these use cases, I feel this is a great starting point to show them the value and then kind of get them excited about this. One of the use cases that I have seen in the past couple of days is, There are people who are retiring and who have a lot of information about COBOL or like legacy systems or network issues, et cetera. And where does all of this legacy system go, uh, information go now? So their companies are concerned that how do we reuse all of this information? And before someone retires, how can we? augment that information into new systems that we are kind of making. Imagine if you have something like a technical aspect like computer use which looks into that, okay, these are the logs or network issues that have been logged in for the past couple of years, and this is how we can embed it into our new software and help people understand through that process, uh, that this is.
Not something which is going to try to replace you, but this is going to make your life much more easier and bring in all the lost information. So, code translation, code understanding, et cetera, sure is a great use case. Validation, testing is a great use case.
And one of the other use cases that I feel in this entire process is, um, understanding. The language understanding the code, uh, code understanding would be one of the main use cases with computer use that I see going on that. Let's say someone built like a 70 year old COBOL language function.
It will tell you step by step or anyone that this is what is going on. This is how it's going to work. Go to the next step, et cetera. So it can be broken down into multiple, uh, cabinets. That's great.
Well, we'll have to see how this evolves, um, and I guess we'll have a long bet on whether or not this ends up being a debugging feature or a user facing feature. The final story I wanted to focus on today was, uh, a really interesting story that came out of Google. Um, they announced, uh, a kind of, sort of, advancement that they were working on called SynthID Text.
Um, and this is a, a, a thing that they've integrated into Gemini. Um, and the whole idea of SynthID Text is to help watermark generated AI generated text. And you know, if you're familiar with this space, traditionally, the problem is if you watermark this text in this way, you kind of have to force the model outputs into ways that are often not great for actually solving what you need to solve, right? Um, and their claim is that this methodology is better because you can do this watermarking.
That is to say you can identify what text is created by AIs, but you don't compromise quality, accuracy, creativity, or even speed of the text generation. Okay. Um, and so, um, Vyoma, maybe I'll kick it over to you first is, you know, why is something like this important? Like, do we need watermarking for text? Like, what's, what's this for even? What's it for? Let's, let me answer this question one by one. We do need watermarking for text.
And again, it is. quite controversial that I've said that. Google has been very bold to at least come up with this product and kind of be so vocal about it. There are companies who've been experimenting this, I know OpenAI has been experimenting this, but they've not brought it out in the public yet. Because, Some of these companies fear that people will stop using it because now there's a watermark uh angle to it or like oh this is something like writers etc they'll be like oh now I'll be caught or something like that that that that that really runs in the back of their mind but I feel watermarking is not there to kind of judge you or like oh give me this information but kind of creates some sort of an ethic standard this standardization around and that is something that Everyone is trying to move towards some sort of regulation that if X amount of tokens are generated by Y amount of models, then this is what we saw. This is how it should be watermarked.
There is some sort of logging that we are doing on top of it. And I feel that is what brings a lot of confidence in clients, a lot of confidence in people as well. That whatever model that I'm using or whatever text that has been generated, there are some, um, marks or metrics that have been attached to it and that is an angle that I I like to pick in this because I Work very heavily in AI ethics and standards and policies And this is the this this topic comes up every other day that how do I know this decision? It takes that has been generated as a right or wrong There are teachers who would come up to me and they're like, oh, I don't know if the student has copied this assignment It is kind of going to help Us, students, teachers, all of them create a more healthier environment to sustain AI. Yeah, no, I think it's great. Uh, Volkmar, I'm curious if you have any sort of thoughts on this. I mean, I think, um, you know, clearly this is not the kind of thing that's going to solve the use of these models for spreading fake information or something like that, right? But, you know, I guess I don't know if you agree that like these kinds of measures are really necessary to kind of like make this technology be used in an ethical manner.
So I'm at a total opposite side of Yeah, let's hear it. So I do Somehow I knew. I knew going into it. I have two school aged children and you know, the schools are trying to desperately prevent kids from using chat GPT to write their essays. And I believe they should just do everything in ChatGPT, Um, and the reason is that GPT does not substitute thinking, right? It just substitutes the process of content creation. And, or it enhances the content creation process.
So, what we are now arguing is I have a tool and I need to tag everything which has been done or produced with the tool, but I'm not tagging if, if I use a power drill and I'm not drilling the hole by hand, I'm not tagging every hole I'm drilling into a wall that's like, oh wow, I used, you know, a power drill to make this hole and therefore I need to tell you, whereas I, you know, I used um, and a tool which amplifies my. Personal capabilities, you know, and I'm not every time when I walk somewhere, I'm like, well, I drove here by car and I need a tag that I arrived by car and used, you know, energy, which came out of fossil fuels. And so therefore, you know, I need to announce it to the world. So I think we are in a, in a, in a world right now, which is bifurcated. And that's why, and, and we have, um, We have a society which is kind of split. There's the society which actively uses large language models and uses the power of them.
And we have a society which doesn't. Now the society which doesn't, and then we have the people who want to regulate everything and want to tell everybody how to live. Right? So we have.
Uh, we are now going, it's like, oh my God, we need to protect the people who are not using large language models. And the poor teachers, they need to change their way of educating the kids, and it will only take a hundred years until they are there. So let's give them tools in their hands so that they can do the useless teaching they've been doing for a hundred years, and then we can figure out if someone is actually using tools of the 21st century so that the teacher can punish them for it. So I was like, well, so I'm not, I need to walk to my school because, you know, my parents could drive me and I could save 20 minutes, right? So I think we are in a world right now, which is like still the split and we are in a breaking point how, you know, the technology is not yet widely adopted, but, you know, a good chunk of society, which are the early adults, in particular children, you know, because, I mean, ChatGPT probably grew like crazy when the first kid found out that it can write the essay with it.
And, you know, so, I think we need an education system which embraces it and we need to have a corporate system which embraces it. I think the second one is there's a certain arrogance by Google to say, Oh, look, um, we can watermark it. I was like, yeah, I use another chat, another chat agent, you know, which I can just download from the internet, which removes your watermark and you're done.
There is even thinking that a company has such a broad distribution that they can actually push watermarking into, into the world. It just tells you, it's like, okay, there will be models of different value. There's the Google model, which watermarks everything. And there's the non watermarking model, which is actually much more valuable because nobody can see that I use the tool. Right? And so, of course, you just create an economy of, of cheating, uh, because, you know, you are trying to tag everything, except you, you as being Google, you have to watermarking for your own purposes.
So, just the idea that you could actually do this is ridiculous from my perspective. We can agree to disagree on this. There are two caveats to this as what Volkmar mentioned. There are people who know AI, understand AI, and there are people who are scared to use it.
So I feel the merger point, a point where everyone's comfortable with it, comes at a point when all of these techniques and tools have been experimented for a while. I still feel we are a little fresh into this, like look at the internet revolution, and then look how the chat GPT or like now that we've, uh, I don't know. Uh, made the ChatGPT and agentic.
So after this whole large language model boom came in, what a short period of time it has been, there hasn't been enough, um, uh, to be completely honest, products or use cases that have gone into production, full fledged production also yet. So until and unless we reach a point where we see the. effects and the long term effects of all of these techniques that have been used. I feel, um, right now we can keep thinking about what are the best ways to come up and the best ways to regulate or not, but till then just keep experimenting, keep working on this, and I feel somewhere we'll all come to a merging point where everyone will be comfortable with.
I mean, but this is true for every technology. which has been invented by humanity and if something is like, you know, three years old, um, like, you know, we, we do not know. So let's experiment with it. The U.S. in general is always, you know, we first try and then we figure out what works and what doesn't work and then we regulate it and not let's first anticipate every bad problem that could occur and then regulate it before anything happened. So I think the U.S.will probably be, you know,
reactive in regulation. Typically regulators are 10 years behind. And so, like, let's, let's build something first which is valuable before we're trying to figure out how to, how to put guardrails around it.
We could go much longer on this. Uh, Vyoma, we'll have to have you back on the show. Thanks for coming on. Um, and, uh, Volkmar, it's a pleasure as always. Thanks for joining us.
If you enjoyed what you heard, you can get us on Apple Podcasts, platforms everywhere. And listeners, we'll see you next week.
2024-11-09 09:57
