The Future of AI: Personal Agents, Taste & Private Data | Lin Qiao & Demi Guo | E9

18:28Demi Guo:Absolutely, yes. So one thing that's clear to us is we have seen a boom of ideation to production. Never have been faster. In the past, a prototype that can reach into many hands of the people to test product market fit, it will take multiple quarters. And now just take multiple days. And that empowers a lot of people to have great design taste. And the great ideas, they can access so many tools for them to realize their dream quickly without a large team, without creating, without having the organizational skill set to hire people, to kind of organize them, to deliver tasks together. Because now you can orchestrate a fleet agent to solve those problems collaboratively.

19:20Demi Guo:So I think the creativity is off the chart right now. And we do see a lot of a ton of startup with brilliant ideas. So the most fun operating in our space is we are the AI index. We see a lot of great use cases, crazy ideas emerge on top of, build on top of fireworks and a lot of experimentation. And a lot of them just take off. like, you know, it did just escape the gravity and reach a lot of people quickly. So that's the fun part. With that said, our company, we only have 150 people. And like you mentioned earlier, we're tens of trillion tokens a day. We process that extremely high traffic. Just as a reference, based on our understanding, we may be mistaken.

20:12Demi Guo:then that traffic is bigger than OpenAI's API traffic. So we reach that as a reflection of the direction we're heading towards is we happily operate on customized model that is not off-the-shelf model inference at all. It is using private data to tune the model and then bring the best quality, customized focus for your application and speed and cost to your application. So that's what we see. Obviously, that is what we see on the startup land. We have many enterprise customers, especially digital natives. Interestingly, those digital natives, they were startup decades ago. One decade, two decades ago.

20:56Demi Guo:And they are the survivors. They are the winners winning over the competition. They have a huge amount of traffic. And they have a lot of people. and they're rethinking how to convert themselves into an AI-native company again, how to reinvent themselves. So as part of process of this reinvention, it's not just a product reinvention. It's also organizational reinvention because in order to survive this wave of heavy competition, they have to revive their velocity. And that velocity got buried through layers and layers of hierarchy in the organization. And many companies start to kind of reduce that layer, especially mid-management, where, for example, from my experience, in the past, there were like hierarchical binary tree organization.

21:53Demi Guo:That kind of probably doesn't make any sense these days. And now we're talking about a manager not just be responsible for seven to 10 people, in a layer. Probably they can handle 20 to 50 people.

22:06Lin Qiao:So this is, I think, such a key point, Lynn, is if these tools are so great, one manager can manage two, three times as many people because all of the check-ins, all of the knowledge is already surfaced by AI. And you don't have to be a warden or a babysitter of employees, which is what, let's face it, middle management was in a lot of these organizations. You probably saw that acutely, Lin at Facebook. Yeah, there was a middle management layer that was coordinating and writing notes and doing meetings and doing standups. In your experience, now that's all automated, correct, by AI?

22:43Demi Guo:Yeah. So I think by and large, it can be automated because, as you said, information is much more discoverable. And because of that, so imagine a manager's job in the past was collecting information and relay that and make sure everyone's aligned. But these can be much more efficiently done. For example, at Fireworks, we don't do a lot of one-on-ones because a lot of context is shared with a group of people. If we make a decision, we just quickly make a decision by checking and discuss that and done. Because we are very chatty on Slack. A lot of information is on Slack and people, we all share similar context and also we can summarize what's happening on Slack per individuals.

23:30Demi Guo:needs. So that is extremely streamlined. That helped us stay on high-velocity decision-making execution. And we're a very flat organization as a startup. But interestingly, I also saw many large public companies start to transition their organization structure. They're flattening out because that smooths out the information flow up and down. And the reason they can do that is the information discovery, data discovery is much easier. It's not just limiting to individuals' work and people management. It's also about data scientists. For example, product analytics, right? So how do you reach a product decision?

24:15Demi Guo:And you just understand a lot of data. And in the past, we have large layers and layers of people trying to do that. But if you MCQify your data access, and then you can write an agent, you can build an agent to be able to extract that information and summarize it and synchronize across different fronts. And they get very precise analysis, assessment of the health of the business.

24:42Lin Qiao:And interestingly, Lynn, I don't know if you saw the report, but Rulof, Bofa, and Jack did a podcast this week. And they did a blog post two weeks ago, Rulof from Sequoia. And Jack Dorsey is basically saying he wants all 6 ,000 block employees to report directly to him and that with AI he can manage that. That's a fantastical vision, but it's not ridiculous. Ridiculous. If you're a hardworking CEO and you work 12 hours a day and every 30 minutes you have 24 segments a day to work, 24 segments of 20 workers, you start doing the math on that 400 workers per segment information coming in, you could actually make it work.

25:26Lin Qiao:You could actually get through in 10 days, 15 days, every single worker's output. Demi, what's your take on the combination of new structures and then what's happening in old organizations and then maybe even how you're running your organization as an AI-first organization?

25:44Demi Guo:What I really believe, the human value in the future will come from the judgment and taste or personality or unique identity of the human. uh i think it's i actually feel like it's ultimately not not even like um necessarily the most most productive thing but but but like what it really value for humans like for example like um you you um like you you the only thing that human matter is you have your own judgment right like because it's because it's your company for example or like you know you what you're doing And then you can just have one AI agent that really reflects your judgment, your taste, your thinking style, and your decision-making style or whatever, your personality.

26:29Demi Guo:And then that agent can just do everything, execute everything for you, right? Whether through orchestra or a lot of agents together or through just working on it's all. So I think in the future, that's kind of what Jack Dorsey is doing. I think he's trying to really amplify his own judgment and taste for the company. And I think in the future, we'll see more than that. And the value is more about there will be more people who are going to create their own unique agent that really reflects their taste and judgment. And then there will be more freelancers in the future.

27:06Lin Qiao:Yeah, many more freelancers who can just come in and be that human in the loop and maybe even work with an agent. Here's a clip of Peter from OpenClaw talking about you need to have a human right now in the loop with agents. That's definitely been my first inexperience because it doesn't have taste perfectly yet, but here it is.

27:28Demi Guo:It can create code and run all night and then you have like the ultimate slop. Because what those agents don't really do yet is have taste. They are spiky smart and they're really good at things. But if you don't navigate them well, if you don't have a vision of what you're going to build, it's still going to be slop. If you don't ask the right questions, it's still going to be slop. When I start a project, I have this very rough idea what it could be. and as I built it and as I play with it and as I dare as I feel it I my vision gets more clear and like I I get like I try out things some things don't work and I evolve my idea into into what it will become and that's that's like my next prompt depends on what I see and feel and think about the current state of the project yeah but if you if you try to put everything into a spec up front you miss this kind of like human machine loop.

28:27Demi Guo:And then I don't know how something good can come out without having feelings in the loop almost, like taste.

28:37Lin Qiao:All right, so Demi, I guess that is a critically important thing for when you're running your company and people are creating agents. Doing everything upfront seems like the right idea. Hey, I want to create this website. I want to create this piece of content. I need it to be entertaining. I want it to have these themes. I want it to accomplish these tasks. But you can only kind of front load so much in the prompt or so much in the skill. And it's really about, I think, iterating on the skill over and over and over again and pointing the agent in the right direction so that it does the task for you.

29:11Lin Qiao:Is that your experience as well, Demi? Yeah.

29:14Demi Guo:The reason we transition from like a web interface or prompting or mobile to agent is because we realize in the future, what matters is you train your agent to have your own taste, basically. So by using your agent or like iterating with your agent, by giving feedback to your agent, you're gradually making your agent to have your own unique taste. And your agent can do a lot more things than you. So it's almost like the goal is not about like, okay, you're creating individual website. but actually about like you're using your agent maybe whether it's just telling the agent what the taste should be or just throughout like using your agent to create website you're like training the agent to understand your taste and then your agent will in the future when your agent have your taste it can just create infinite websites right so i think what matters is really just to um that's why we transition from the just like prompt interface to more like a interface lynn this

30:07Lin Qiao:seems to be, I think, a hard fought lesson. I thought setting up my open claw and then saying, I want to solve this specific task on reporting was a one time skill. I create it. I run the cron job. I never have to touch it again. It turns out that's wrong. You have to be a bit more interactive. And a lot of times I'm finding these agents drift from what I told them explicitly to do. And because they're so sycophantic or they're very inconsistent, I don't find they have the consistency. So maybe some thoughts on when these will anticipate a little bit better and when they'll be more consistent, Lynn, in your life.

30:46Demi Guo:So I have a lot of conversation with my daughter. She's in high school about AI. So it's very interesting. I'm surprised. I feel like her generation will be AI native. They grow up with AI around them, all the tools. They will just kind of be very deeply embedded in using those tools natively. But I'm surprised her reaction is they're like Gen Zs are cynical about AI because they don't feel it's authentic. They don't feel it's creative. They feel those are all the repetitive, mundane things. If you use AI, it's not being thought highly off. For example, they have this school magazine. And if you use it to generate pictures, they're like, it's better if you draw it yourself and it shows authenticity.

31:47So I feel like AI is getting really, really good, actually.

31:53Demi Guo:If we look at genuine images, it's actually really good because it's trained using the human intelligence and kind of is able to simulate that. But I feel like we as a human, as part of our soul, we need the creativity as a satisfaction fundamentally. mentally. I'm wondering if there's a future that we can integrate deeply, always have human in the loop to kind of, to be part of the soul of, of this new world we're building. I think that would be fascinating. Uh, and I liked the demo from, uh, from Demi, the new, um, Pika agents. I feel like if I can embed my, uh, part of the creativity into this humanoid agent that can represent me and give a little bit of surprise here and there because there's a little bit of impromptu of how we react and so on.

32:57Demi Guo:And give it that. It would be really fun.

33:00Lin Qiao:Yeah. Yeah, go ahead.

33:04Demi Guo:Oh, yeah, I really agree with this thing about the creativity and also the human in the loop perspective. and that's kind of what we're really like leaning towards because I really think right now the reason people people are really treating I think we need to change our mental model about what AI is I think a lot of people in Silicon Valley are really treating agents as a tool and as a productivity tool and to people choose who they work with not for only for productivity but also for you know they just enjoy working with this person so that's why we really feel like the mental model of the agent should not be a tool but actually just be another human and then you should really treat like having your own agent as having your own child so it's a constant feedback and iteration so humans should always be in a loop it's not like you one click everything done it's more about okay you're raising your child you're like constantly teaching it and gradually you will go up and you will you're like can do things for you um and it's more this like like iteration process with our children.

34:06Demi Guo:And it's also this emotional attachment beyond productivity.

34:09Lin Qiao:Yeah. And if we just recap this previous segment, Lynn, the take on how organizations are changing, well, even if people get laid off and organizations become smaller and flatter, there's been this Cambrian explosion, I guess, which happened hundreds of millions of years ago, where life just suddenly emerged and in a very violent many people competing because of oxygen and this perfect ecological soup that occurred. I think that's kind of what's happening now with startups or just individuals being able to say, hey, I could just make a startup in a weekend, test it next week. As you were pointing out, it used to be a two or three quarter journey to get your product out and tested and beta testers and then closed beta and then open beta, et cetera.

35:01Lin Qiao:Now you're talking about doing that in three or four days, potentially, with just one or two people. That means many more ideas. And we could see instead of 10 ,000, 20 ,000 startups getting funded every year, you could maybe have 100 ,000, 200 ,000, or a million or 2 million created. And the startup then doesn't have to be venture-backable, Lynn. It could just be enough to pay somebody's salary or maybe even half of their Facebook salary. If they were making $300 ,000 at Facebook, they might be completely happy to make$150 ,000, but live at a ski resort half the year and live by the beach half a year.

35:40Lin Qiao:Check out a bit. Yeah, Lynn?

35:42Demi Guo:Yeah. So that's what I mean is I feel ultimately I see if this continues to play out, I'm actually pretty optimistic about the future is we human will focus on the most creative part. Because that's how we evolve over thousands, over so many tens of thousands of years, is we find creative way to form new structure, new invention, and new technology, new economics, all the time. And every time there's some wave powering it for us to leap forward. but we always resolve back to being creative. We never stand still and circling on the same spot. I feel like AI is actually, if we do it right, it's going to power the next level of creativity.

36:41Demi Guo:I couldn't imagine what would come out of it. We may be able to do space exploration much faster. We may be able to reach the planet light years away much sooner. and it's just kind of I think our limitations, our imagination is our limitation so I'm very optimistic towards that direction but

37:06Lin Qiao:in the face of doing that

37:10Demi Guo:even step by step before we were like hobbyist is hobbyist they tinker just as a hobby But now we're like hobbyists could be the next phase of inventors because a weekend project could really hit something fundamentally deep and hit our biggest pain point. Because everyone is now empowered to create, to think, to imagine with their tools. So that's the part I really believe is the combination of human creativity. I do not think, I do not believe that we replaced. And our brain is only a few percentage activated. Also, at the same time, there's a lot more we can derive out of it. So we actually, as a platform, well, Frontier Inference platform, as a platform, we pay a lot more attention to hobbyists these days.

38:15Demi Guo:because we give them the tools for them to test, invent, experiment. And once they hit something interesting, they can quickly scale and they do not need to worry about scaling because scaling is a complex system problem, especially scaling your deployment of your tuned model. First of all, how you tune a model involves a large amount of GPU fleet or a lot of kind of data tinkling, a lot of parameters to set and experiment. And once we want to scale, you want to scale globally across many regions with low latency, high reliability, robustness, and a lot of people using it and so on. So how are those open source models doing on a, you know, how many months behind in human years,

39:04Lin Qiao:not dog years or AI years, just actual human years? Because everybody knows dog years are seven years to a human year, I would say a month is seven months. Each month is a year in AI time right now. So are they six months behind or three months behind, nine months behind, opus 4.6, whatever?

39:29Demi Guo:Yeah. So it's a very interesting race that's happening right now. But I want to put them against each other. I just, to us, we saw like every two weeks or sometime every week, there's a new model released, open, close, doesn't matter. And they are oscillating with each other on the leaderboard. Whenever things are oscillating, it's a clear signal they are converging. So last year, I think like one big sticker shock is last year's DeepSeek V3 release. And that's the first time open model got very close to Frontier Labs model. And since then, Frontier Labs continue to leap forward and then open model catching up.

40:10Demi Guo:So it's kind of, but overall, we see the quality gap start to converge. And that's great news for applications who has a lot of data. That means they tune. If they can activate their private data, they can actually leap forward and really get to the next level of intelligence, specific design for the application. and we see since last year we start to see a lot of adoption of this customization especially led by more frontier thinkers more application they are pushing the boundaries so they're getting closer

40:48Lin Qiao:but still frontier models are ahead so how far ahead I guess is the question I'm asking and still people so I get it's oscillating There is definitely convergence, but one group seems to be leading. That's the frontier proprietary models. How far behind are the open source models in your estimation? And then depending on how many months or years you think they're behind, what is the roadmap to this convergence?

41:15Demi Guo:For complex tasks, for example, most complicated, hey, if you want to generate the most intelligent agent to build a distributed system, P2P system for eventual consistency or whatever, that is really hard. So I think Frontier model is absolutely leading. They're probably six months ahead, six months to one year ahead. But for many day-to-day tasks, for example, managing spreadsheet, managing calendar, having a router or classifier of doing some kind of routing logic or doing writing improvement or doing some good, very good kind of document processing and so on. There are many tasks varying with less complexity but actually cover a lot of our day-to-day.

42:18Demi Guo:They're very close, I would say, even on par.

42:22Lin Qiao:Okay, so simple tasks, couple of months on par, complex tasks, six to 12 months behind, I think would be what I'm interpreting from your comments there, which I think is super helpful for folks because you do have this issue, Demi, with the cost of these models. And people with agents seem to very quickly, and I don't know if this is your lived experience right now.

42:45Demi Guo:I would say, but this is off the shelf model quality. But with our private data, we have so many cases after tuning. on this complex task, it can be unported or even better than Frontier Labs.

42:58Lin Qiao:Demi, what's your experience with your customers when they start embracing this technology? What is their usage profile in terms of token usage and how does it change when somebody goes from using a search engine or a researcher and just asking one question versus, hey, I'm going to give you a complex task. I'm going to give you an agent, agentic kind of existence, dare I use the word existence, but we're going to will this agent, this replicant to exist, and I'm going to interact with it every day. How does that change? How does that token usage change on a multiple?

43:36Demi Guo:For sure. I think we do see that it really depends on what kind of modules we're using. We're trying to maybe get more user, more customization, because it will really value a lot based on like to link's point like if it's open open source like much cheaper versus like the more the frontier lab it's more expensive like it could be up to like 10k we have like user who have

43:56Lin Qiao:like 10k per month or something 120k a year to empower their agent and do they make back 120k a year or are they just they're so their business is so great they don't mind losing 120 to be on

44:11Demi Guo:the cutting edge i think the the reason is we should really not compete like really like treat obviously they're the agent when the model is better where you always like open source or like was tuning what link says you will probably be cheaper over time but but also just generally i feel like we should not really treat agent as a tool that okay you cannot pay like 10k per month for a tool but you should really treat agent as a human so whether it's your child your race or or your employee, right? Your assistant. It's like, it's like whether you hire a junior creative assistant or you use your agent or like, you know, either you raise your own pet or you use agent.

44:47Demi Guo:So it's like, that should be the comparison. Like people spend a lot, like it costs a lot to have a junior creative assistant or it might cost a lot. People are willing to pay a lot for their pet. So it's not really a fair comparison. Your comparison with agent was like a tool, but we should really think about agent. as like a human or like a life form. And you're like, you know, you're like keeping it, right? Whether it's you're hiring or like raising it.

45:17Lin Qiao:In technology, there are some folks who were early on in the 70s and 80s who very much looked at what are the hobbyists doing? Because the hobbyists quickly become, if they figure something out, the entrepreneurs. and then they would look for the toys, the tinkering and the toys, and those would become the tools and the services in the future. And so a lot of what we're working on right now feels like toys. It feels like tinkering. It feels like a hobbyist and creatives and agents certainly fills that kind of description, but then they become actual real tools and services in the future. And it's a great way to either build a company or invest money in companies is to just watch, what are people tinkering with?

46:04Lin Qiao:What are the tools? And that's why I became obsessed with OpenClaw the moment I saw it. That's why I became obsessed with Tau and the BitTensor subnets, or even app development and content in the form of apps back during that revolution, Lynn, when you were at Facebook, it just was like, oh, the flashlight, oh, a little flappy birds became like, you know, a critical economy very quickly in the future. Lin, question for you. I believe you went to school in Shanghai, yeah? Yeah. Wudan. Yeah. So that's the second, I think, city in terms of the density of large language models. Beijing is number one still in terms of where all the computer scientists are working on large language, or then maybe Beijing and Shenzhen, I guess, because of the hardware footprint.

46:57Lin Qiao:What is the movement there like? I'm sure you have friends and family and colleagues that you went to school with. What is their perception of the race in terms of open source frontier models, and just overall, what this technology will do for society?

47:14Demi Guo:My understanding is I think because of population, consumer-facing products are much more popular and much more polished. So many of the models have been heavily used to power newer consumer-facing experiences. for example I heard OpenCloud at China is becoming very popular and the various different companies offer OpenCloud as a hosted managed service and get a lot of traction so that's kind of I think I think it's just because of the population advantage and the focus is more like consumer facing product and how to bake those new way of surfacing user experiences through LM and more like multimodal JNI models, not just LMs, are the kind of primary focus.

48:17Lin Qiao:Interesting. And what is the general perception of AI? We just saw some studies come out. People in America obviously have a very negative view of AI. We had this New Yorker story come out last week about Sam Altman. It was quite negative. and then we had these horrible attacks on his home, a Molotov cocktail, and then somebody shot it. This is absolutely terrible. But there's an incredibly negative perception of AI in America today. And then in China, my understanding is it's extremely positive. People think that this is going to be amazing for society. Is that true and why in your estimation, Lin?

48:55Demi Guo:I do not get, I guess I'm living in the Silicon Valley bubble. I feel extreme optimism here about AI. But my observation is I feel like China is probably more advanced on the robotic side because it's day to day. It's in like delivery, full delivery, all robots. And people can order ice cream. It got delivered in a couple of minutes via robots. And that's kind of the limit test of how fast things are. Is your ice cream? It's still frozen. Is your ice cream melting or not? It's the ice cream test. Right. So that's my sense. Obviously, the robotics habit depends on AI. And it's just one reflection of the consumer facing focus.

49:48Demi Guo:and yeah again on the AI sentiment I shared earlier even from my daughter she told me their Gen Z is cynical I mean she also grew up in Silicon Valley maybe the Silicon Valley teenagers are cynical about AI and they want to preserve authenticity of creativity from their heart as their identity I do think that's a human need win to address. We shouldn't lose that along the way. So I think it's more a societal homework for us to figure out together is, you know, as we co-evolve with this new technology and how to focus on maximize our fundamental needs as a human to express ourselves over time. So I think that's just, we're at such an early stage of this revolution and we're going to figure it out.

50:51Demi Guo:I'm very optimistic about it. I want to go down to what I linked to because that's actually something that we're trying to really solve. It's really like what we really believe is, I think Silicon Valley is really crazy about how productive the agents are and how it is like replacing work, blah, blah, blah. But something we really care about is like, I think probably people, a lot of people are very scared that, okay, the agent are replacing my job, blah, blah, blah. and something that we really care about is enable people to create their own unique agent like their own agent that they can customize and agents should not feel like a cold hearted thing, the productivity thing, it should really feel like a child you raise and it should really feel like you own it and it's your own agent and it should really be working for you and it should really be something beyond just productivity and there's also the emotional connection because like you know it's like there's like identity creation of it and it's i think it's very important actually beyond the productivity value of the agent obviously all your agent will be productive has utility but like the personality or the human perspective and identity creation agent i think is also very important and i think it's almost a sense of it's almost a form of self-expression in some sense where you could imagine like, okay, like now Picasso's art really inspired us, but maybe a hundred years later, like your own agent, which was unique personality and unique voice and face, really inspired people, like really touched people's heart a hundred years later and like among other corporate agents.

52:29Lin Qiao:Yeah, I don't know if you guys saw this. This was one of the, you know, Shenzhen comes out with all these incredible toys. And again, back to toys becoming tools, this one is called Bubble Pal. And I don't know if you've seen it, but it kind of went viral last year. I just gave the link to the team. They'll pull it up here. Essentially, you put this device, it's like a little Alexa sort of digital assistant, but you put it on any toy, and then the toy gets a persona, and it gets a personality. And then here's a quick video. We'll play with a little bit of sound. But it's definitely having a lot more playfulness, and then beauty filters and AI filters and AI chatbots and making short videos with AI becoming incredibly popular in China with consumers.

53:19Lin Qiao:So here it is. You pick any... Here's a little girl getting left out of playing soccer with her friends and then a little girl who wants to be an artist. And you bring your toy, whatever toy your kid has. Like I can give this to one of my daughters and their teddy bear. And then I just strap this on and it creates a persona for the toy and it becomes like a best friend, an imaginary best friend. But it actually does talk back to you. I'm not sure if this is dystopian or just incredibly engaging. Your thoughts, Lynn?

53:56Demi Guo:I think we all we all when we were little, we all have an imaginary friend somewhere. I think it's fulfilling our need of having this imaginary friend in some form. I could see my, you know, if it goes back 10, 15 years, my kids would love playing with it.

54:17Lin Qiao:Pretty interesting stuff. I think it relates a lot to what you're doing at Pika, right? Is this concept of creating some kind of relationship with digital beings. Yeah.

54:30Demi Guo:Yeah, I think for sure. Like, obviously, I mean, agent, it's not just a toy in a sense that like everyone will have agent in the future, right? It's we all like kind of feel like agent is not only the next interface for web and mobile, but actually replacing next computer because agent like has storage and compute and everything, right? So everyone have agent, but it's really about like, okay, do you want a generic agent or do you want your own agent that has more human-like and more emotion, has more emotional beyond just productivity? Yeah.

54:58Lin Qiao:And here's, just to put a final end cap on it, here's the U.S. perceptions of AI societal impact. These are the general public versus experts. In other words, the bubble you responded, you referenced, Lynn, we're living in the AI bubble. We see all these incredible things as possible. Here's U.S. adults versus AI experts. Here's the chart. And if we look at medical care, if you're an expert, if you're in the industry, you think it's 84 % is your percentage that AI will have a positive impact over the next 20 years. The public, 44%. K-12 education, elementary school, 61%. People in our industry think it's going to have a great impact.

55:42Lin Qiao:24 % of people who are not in the industry, just U.S. adults, helping people do their jobs. This one is the one we all see in the economy. 73 % and 69 % of people in the industry think it'll help people do their jobs and will help the economy. But 23 % and 21 % for the public and for personal relationships. People do not think this is going to help personal relationships at all. 22 % of AI experts, one in five of us believe this will help your relationship. 7 % of the public think. So we have some alignment here for the elections and for news and relationships. Pretty good consensus. It's not going to go well.

56:27Lin Qiao:But for everything else, we do have this massive swing. Your thoughts, Lynn, just generally looking at this. Maybe how do we change the perception, I guess, is the bigger issue if we accept that this is the case.

56:39Demi Guo:So for example, in medical care, right? So we have many customers from medical care. They are doing amazing things. I now see doctors and by default, they will ask me if they got my permission to turn on recording because they can use Medical Scriber to automatically take notes. we power another medical company that they are doing building preventive care software as in as i going to you know see my doctor and then they quickly can pull out the software can pull out my medical history and suggest to the doctor what kind of preventive check i need to have and then they will give me a list i will work on that the idea is great because um they putting a more preventive either exercise or checks or exams and whatnot is going to help make us healthier and reduce also the medical bill.

57:37Demi Guo:So they built their business out of that. But the barrier is actually software integration with hundreds of patients' record systems where there's no standard how to integrate. And they want to roll out this great preventive care idea. they are going to hit the system record barrier. However, AI is here to the rescue because there is one universal interface, that is screen. Screen is one universal interface. So it's very interesting, right? In the past few decades, we have made the perfect interaction between human and computer, HCI, through various different kinds of software development. In order for us to kind of make the long-horizon human-driven workflow more automated, then the next phase will be automate HCI.

58:37Demi Guo:So that has already started piloting through authentic creativity from people in your chart, the first row of AI expert working in medical care system. That's why I think that makes sense. They are very optimistic about the impact of AI. I just feel the more people they see the outcome of AI, the more we share the stories, share day-to-day stories. What are the benefits? And we can get out of the end product. Yeah, I think our industry is doing a terrible job of this.

59:14Lin Qiao:We have to show all the wins for people, yeah, Demi? And maybe a little less of the fear that everybody's losing their jobs and it's the end of the world. There's so much joy and fun and cost savings and life extension that could come from this. So where do you think the industry is getting it right? Where do you think the industry needs to improve, Demi?

59:36Demi Guo:My feeling is there is like really a differentiation between like pure, like maybe like productivity AI and versus like some AI that's more like diverse or like more uniquely yours in some sense, which is like maybe not even have to be the most productive, but it's uniquely your own agent and maybe like more humor, more creative or emotional, whatever it is. I think what Silicon Valley is really cared about is like okay is the agent productive is it smart enough or is it going to solve this productive issue the enterprise work or developer developer issue and I think something people like and then like that really the narrative really scares like regular people because it feels like okay like you know like okay like human like agents are really like AI are replacing my job and I think something that is really missing is like on how do we like use ai to enable people to create their own ai and to empower themselves instead of you know use it like how the angle of like okay using your ai to replace versus like empower yourself uh instead of like you know like age like you know ai is like replace like for productivity and really um like you know really um like really good and i actually think what what is like to your question about it is like i feel like there will be two type agent like like i mentioned one is more like your own agent which is more diverse it's really for diversity like really really unique your unique taste and judgment even though it might not be the smartest judgment and taste and personality and there's another agent which is like purely for productivity and actually for productive agent i actually think it should have no personality it should be um maybe you should not have human judgment because humans are not the most productive people i think because human has flaws like human has emotions people has like strong personality and human are has ego or whatever right like human and i think like so there is this two kind of ai in my opinion like the the to have to achieve the best productivity you should have like no human perspective like no human component to it um and then there's like the other ai which is more human and more personality, like more personality, it's, it's really like, you know, like for diversity is really important for human because we, you know, it's like, it's, there is value for human that we, we, we might not just want like the most productive thing.

1:01:59Demi Guo:We might want something to tell us and make us feel good. So something I think we're like kind of ignoring right now.

1:02:06Lin Qiao:Perfect segue. Meta introduced just last week, MuseSpark, M-U-S-E, MSL's first model, purpose-built to prioritize people. So this is, I guess, the first language model that Facebook has produced that's closed sourced. This is, I guess, their frontier model. Interestingly, when you look at the specific examples they gave, and I had a perplexity here, do a little summary of this incredibly long announcement. They gave a bunch of what I would call domestic Mr. Mom or mom focused benefits. So planning a family trip to Florida. One agent drafts the itinerary. Another compares Orlando versus the Florida Keys.

1:03:00Lin Qiao:A third finds kid-friendly activities all in parallel. So they're not doing, here's the enterprise and how we kill jobs. They're doing, here's how we make whoever the homemaker is, mom or dad. Here's how they're going to do, ask health questions and take a picture of all the snacks. They keep going to travel, all the snacks at an airport shelf and say which one has the most protein in it. Or shopping mode, get ideas of what to wear, how to style a room, or what to buy for somebody, drawing from inspiration across Meta's apps. And this is using all of the information on Instagram, WhatsApp, and Facebook.

1:03:40Lin Qiao:Really interesting, I think, very human-forward approach. So your thoughts, Lynn, on two things. One, meta going from open source to closed source here. Disappointing, interesting. What are your thoughts and how good were their open source models? And then two, their human first approach and what you think the public reaction to it should be.

1:04:09Demi Guo:Yeah, we're hoping meta will open source their avocado model for sure. but I also understand like their main business is a consumer product and this next generation model need to power their consumer product and Mata is really good at product model co-design very very good at that they have been doing that for a decade and I'm pretty sure this model powering WhatsApp and Messenger and all this consumer facing product is not a static model It's going to evolve over time. It's going to be smarter and learn from meta's private data and so on. So that's kind of the typical pattern, I think, all application developers in the future, all AI-native application developers should be able to adopt.

1:04:58Demi Guo:And today there's not such kind of tool. It's a vacuum in the space. It should be as easy as product analytics. It should be as easy as everyone's doing A-B testing. And our dream is kind of give this tool to everyone's hand and it should be turned on by default. So on my side, I really hope there's a lot more US-based open models. We work very closely with NVIDIA. NVIDIA is putting a lot of focus on driving the NemoTron model, very, very bullish about that. And I think they're constantly improving the model quality. And there are many other providers on the US soil, on the real-time soil.

1:05:39Lin Qiao:Tell me about Neotron. It's Nemotron, N-E-M-O-T-R-O-N. Not a great name. Nemo is a Pixar movie, or Disney movie, I'm not sure. And Tron is a Disney movie as well. Nemotron, two Disney movies put together. Very strange name. But how good is it? And are people using it yet, Lin?

1:06:05Demi Guo:So I think it's getting better and better. So one thing about model training is you cannot jump ahead of time. You have to go through steps. You have to train from a small-sized model, generate synthetic data, train a medium-sized model, use the synthetic data, blend with whatever data you have, and then generate more synthetic data. And so there are a kind of process you have to go through. and then during that process and with very complex mid-training, post-training of SFT, ERL and then the model becomes a lot better. So imagine kind of the process is like first thing to build a base IQ. Like we human, when we're born, we come with IQ that doesn't change over time.

1:06:53Demi Guo:But that IQ accumulation takes time of learning basics. and then once you have base iq then we human take years to be trained as a domain expert right whether we are a dentist or a heart surgeon or a lawyer across the board it's kind of we take years to get to certain specialty and that's rl training and rl training is basically narrow down the focus um and and kind of really specialize in certain areas solving certain kind of problem really well. So that just takes time. And I think all these models, as long as there is substantial amount of research effort behind that, a lot of GPUs, it's a matter of time they will get there.

1:07:42Demi Guo:So we really -

1:07:44Lin Qiao:And what do you think about the data drought, Lynn? I'm curious, and then we'll go to you, Damien, just on where you're sourcing data to make better models.

1:07:51Demi Guo:There will be a combination of everyone sourcing from public internet. There's no secret sauce everyone's kind of saying. And there's nothing left, right?

1:07:58Lin Qiao:There's nothing left.

1:08:00Demi Guo:And again, a lot of data is locked inside of vertical private applications. You just don't have access to. And only those data owner has access to. And then there's labeling companies, label data, almost all companies, all foundation, frontier labs use the same labeling companies. So then what is, could be differentiating is how they generate inside the data. and the mixture, a blend of the data could be differentiation. And even the algorithm of training, pre-training, mid-training, post-training are converging. I think there's a less, I think we are overdue across the industry, across the research community to have a new model architecture.

1:08:42Transformer is way overdue.

1:08:45Demi Guo:Usually every three years, there's a leap of new model architecture. I think this is probably seven years in the making more than seven years so there could be likely that there is a leap forward new model architecture it will learn knowledge in a completely different way then this phase of convergence model convergence we may have a breakthrough so far we haven't seen that yet so if we stay in current course I think whoever owns data, owns a unique amount of data, is going to bring up a new level of intelligence.

1:09:26Lin Qiao:Demi, your thoughts on where people are getting data from? And has that reached your customers yet where they say, hey, I want to bring some data or I need you to go find me this data or I want to hire a data labeling, data sourcing company? These dark pools of data seem to be the next frontier, seem to be the next opportunity. We have an investment in Micro One. We have Ali on this week in AI in one of the first pilot episodes. So maybe you could talk a little bit about where you think the next data is coming from.

1:09:55Demi Guo:Yeah, I actually feel like kind of what we really believe in the future is people own their, like, potentially maybe they own their own data in some sense, which is, like, the one they're using agent is technically, like, they're, like, kind of feeding their data to their own agent. and and i think and then like they have the ownership of their own agent right because what's important in the future to differentiate different people is actually your own taste and judgment right so and then you want to use ai to really amplify your judgment taste to maximize your value so it's almost like you're trying to train your ai with your own taste which can be feedback iteration or it can be data and to train your ai to be like to the the taste whatever judgment you have right that's like kind of a good data feeding process and then like that it could be like okay i'm i'm a ceo that's kind of what mark zuckerberg or like a rey dally or a lot people are doing which is like they train agent ai that's like agent that's like themselves and then like they use it to talk to like reject doors you kind of also want to do it right like to use your own judgment and to really amplify by like managing the company right so and then like probably they want to own their own data and then because that's like the value accrued in the future so like people will probably like that might like in the future when we like don't even like work everyone just gonna train their own agent and to inject your own data or case and judgment to your agent and then like the agent will help you to do stuff so in some sense that like that kind of is also like a maybe in the future like people are like kind of owning their private data in some sense from that perspective.

1:11:31Lin Qiao:Okay. I think that's a great place to start. Two amazing guests this week on the program. I know you're both, or I'm assuming you're both hiring and growing quickly. So Lynn, tell us a little bit about what positions you're hiring for and how folks can get in touch with you directly if they're a genius or where they can go to join the fireworks team.

1:11:53Demi Guo:Yeah, we are into a kind of exceptionally fast growth phase. So we're hiring across the board and from product engineering to marketing, sales, and GNA across the board. Even recruiting team we're hiring.

1:12:14Lin Qiao:So recruiters are back to work. We have this whole recruiter apocalypse where everybody thought they didn't need recruiters and now it's back.

1:12:23Demi Guo:Yeah, but we love people who love using AI tools because that does bring us extra amount of productivity. And we aspire to be the smallest, biggest company in the future. And it's possible. And if you are dreaming big and you want to drive the maximum amount of creativity, it doesn't matter which domain you work on, please talk to us and we'd love to have a conversation with you.

1:12:51Lin Qiao:Awesome. Okay. okay uh demi who are you hiring for who do you need how do they get in touch yeah for sure we're

1:12:57Demi Guo:we're also like uh we're still a very small team we're hiring like a i would say designer like engineers and researchers um and yeah so we're we're trying to like you know really like really build like a plat like for people to create their own unique agent that's more humanized like the multi-model like like research we're doing and and yeah so we're like we're hiring Like more designer who is really interested in the vision or like to help people to build agent that has unique tastes, not like a dry agent. We are hiring like engineers who are interested in like the backend problem and more agent problem.

1:13:36Demi Guo:And we're hiring researcher who are interested in multi-model research. Yeah.

1:13:39Lin Qiao:Great. Awesome. This has been another amazing episode of This Week in AI. We drop every Wednesday. We record on Tuesday. We drop on Wednesday. please go ahead and visit us this week in ai.ai and sign up with your email and you'll get our research we're doing proprietary research on the ai space and who's getting funded and who are the next unicorns in the space on the show you get to meet the companies that have already reached that unicorn status and have vibrant businesses in the research department that we've created this week in ai.ai go sign up for the email and you'll find about about the next cohort of companies that are just three four or five people and that are growing and building interesting things we'll see you all next time.

1:14:17Lin Qiao:Bye-bye.