Hiring Employable Agents + Startup Battlefield Finals — Howie Liu, HyperAgent

0:01 [music] Please join me in welcoming the chief executive officer [music] at Hyper Agent, Howy Lou. [music]

0:31 All right. Hello. Um, who's excited about the end of this event? This this has been an amazing few days. Amazing talks. Amazing uh Gary talk right there. And by the way, I've never been

0:42 introduced with so much base. I am very very pumped in this moment. Um, so I consider myself a product thinker at heart. meaning you know as much as I like to spend time on the technical side

0:53 of building and you know I I love to think about architecture and algorithms and so on you know really I'm a product form factor thinker at heart and you know what I want to end today off with

1:04 is a brief and kind of highle overview of how I see the world going with agents as it relates to product form factors so I'm calling this hiring employable agents and it's really a reflection on

1:17 as these models have gotten better and better and we've heard from some really really great speakers about you know the latest uh frontier models whether GLM 5.2 to uh or others and how to coax the

1:27 most um performance out of them and really this is not about the technical deep dive but how do we think about these from a product building standpoint so who am I the founder and CEO of air

1:37 tableable and also know hyper agent which is a product within air tableable and as many of you already know air tableable is basically a almost laughably horizontal platform uh when we

1:47 in fact set out to build the company almost 12 years ago we got you know basically this negative feedback from virtually every investor we talked to early on which was you know you can't

1:56 possibly go out with a horizontal platform approach and try to build something that is everything for everyone like any type of app that people want to build you know you can't

2:06 solve all of those use cases and you know I think we ended up doing a pretty good job of solving for many of them at least and solving for a large builder community and we're basically applying a

2:15 very very similar philosophy to agents now and I'm going to break down how we're doing it so um when I zoom out and think about the progression of models and therefore the product form factors

2:28 they enable. I really see it as this spectrum, right? So, we went from basically completions, which is, you know, you give a model, one of these LLMs, a, you know, string of tokens and

2:38 it just, you know, continues emitting more tokens until it gets to a stop token. And more or less it would just kind of complete your thought, right? Or complete um the pattern. And you know,

2:48 until instruct GBT happened and enabled a chat form factor, it was like arguably powerful but more narrowly applicable. Once we got to a higher level of model intelligence and then also the kind of

3:00 innovation of instruct GBT and kind of tuning these models to actually have more of a conversational back and forth behavior with users, then we enabled the chatbot, right? And of course we all

3:09 know chatbt was an awesome breakout product. We're now all very very intimately familiar with that form factor of AI. The next level I call an agent, right? And you know I think agent

3:20 has been very overloaded. Um, we've probably talked about that a ton over the past few days, but my definition of agent really is the same as the anthropic definition, which is distinct

3:28 from workflows. And an agent is really a model that recurses upon itself, right? And has, you know, the the open-ended set of tools that it could call, of reasoning steps it can make, of

3:40 decisions it can make. So, it's not prescripted to a linear workflow, but it can kind of make an open-ended set of choices. Um, still you know I like to think of agents as being a little bit

3:50 distinct from the next level of agents which I'm using the term claw here because openclaw obviously has has popularized uh this concept but you know there's a market shift that I see from

4:00 agents that were still mostly reactive um you know to user input. So you would have an agent go and perform a task. It might spend 20 minutes maybe up to an hour to perform the task and then it

4:11 completes. claws um as I call them are really you know about agents that go and perform work for much longer right they can even have a mechanism like the heartbeat mechanism within openclaw um

4:22 and now in other products like hyper agent that enable them to wake up on their own and almost like um exhibit this always on behavior I mean underneath the hood what's happening is

4:32 you know each wake up basically results in many different turns so the agent loops upon itself it's performing tool calls each tool call response then invokes the the LLM again until it

4:42 reaches a stopping point, but then that stopping point gets reinvoked or or kind of restarted with a heartbeat mechanism where you can wake up the agent again and again, right? And so this idea of

4:52 this always on agent that's actually constantly moving towards its goal and kind of figure out creatively how to unblock itself where maybe prior agents would get stuck. I think that's a really

5:02 really important kind of next form factor uh leap that we're seeing. And then finally, as you think about models that are now capable of, you know, really, really intelligent orchestration

5:12 of complex tasks, right? Um, so I've personally been using the workflows capability in uh cloud code quite a bit, uh, where it can fan out a lot of work to a massively parallel set of, uh, of

5:23 agents. Um but this concept of you know this agent that can go and orchestrate with other agents right an agentto agent interaction um to perform even more advanced tasks uh right longer running

5:34 jobs is I think the next leap yet all of this is enabled because the models are getting smarter and smarter to the point where they can achieve coherence even on longer and more open-ended scoped

5:45 problems right u if you recall back to the days of um uh baby AGI and autogbt those were really kind of fun cool science experiments, but ultimately, you know, as much as they were really

5:57 interesting to watch and, you know, see them work for hours and hours on a problem, they would all inevitably, you know, kind of end up drifting off to something not super useful, right? It

6:06 would go off into a rabbit hole and kind of get stuck. And I think now the models are actually smart enough to enable not only longer running jobs but actually enable us to start applying these kind

6:17 of organizational principles to break down problems into differently scoped tasks that different agents can hand off to each other. All of which is about managing each agent's task uh or context

6:29 window and ability to coordinate work with each other. So all this is kind of abstract. I want to make it very very tangible and show you how we've applied some of that design thinking of building

6:39 a horizontal product and distilling a lot of the complexity of agents like we did for building a database or a database powered app with air table for agents in hyper agent. So this is a um a

6:52 very realistic use case based on some of our actual customer usage. One of the interesting things we found from launching Hyper Agent is, you know, the the uh prototypical users for Hyper

7:02 Agent are not who you would think they are, right? I mean, we definitely get our fair share of like the super AI forward, you know, kind of pure software companies, like AI companies themselves,

7:11 but we also get really interesting other companies that are, you know, sometimes like traditionally offline businesses. And in this case, we're depicting a very realistic scenario, which is um a

7:22 customer uh that is in a landscaping business and actually uses agents to run virtually endtoend every part of their business, which is finding clients to propose landscaping projects to like

7:33 physically going and like redoing their garden or their backyard and end to end managing everything from the prospecting to building a quote and a pitch to managing the work itself. And I think

7:43 this is a huge huge um interesting um insight that you know I've just kind of stumbled on from working on hyper agent which is ingenuity of agent builders is not contained to just AI companies or

7:55 software companies. Um and I was really inspired by Gary's talk just a second ago. I think that the uh the motive behind that applies to every industry, every sector and there are really really

8:05 creative people many of whom are in this room who can go out and disrupt your own industry whatever it be. You know, it doesn't have to be a pure software business. Um, but I think all it takes

8:14 is the ability to imagine what work could look like when you do go and become fully not just an AI native company, but I like to use the term, you know, a fully fleet of agents native

8:24 company where you're actually going above and beyond and not just thinking about small kind of narrow steps to automate with AI, but actually how to employ a whole fleet of agents that have

8:35 governed behavior and have the right context and the right tools to be able to do really useful and very end-to-end autonomous work. So let's walk through what this looks

8:44 like. Um this is a realistic example of you know in this business this uh this landscaper wants to go and pitch clients right? So you know they get all these inbound uh submissions of clients who

8:55 are interested. Somebody submits this inquiry saying hey here's my current backyard. You know it's a little messy. Here's my info like can you give me a quote and show me a proposal of what you

9:05 could do? This basically gets routed into a um in into one agent that is the triager agent, right? And so this agent is basically taking in this intake and saying, "Hey, like what is the um the

9:17 scope of the project, you know, like is this something we want to take on and is this a quality lead? Is this a legit person?" And so it does some research, but ultimately it hands off then the

9:27 work to another agent, a surveyor agent to actually go and then look at the um the physical landscape of this person's backyard and come up with a proposal of what a rellandscape version of that

9:40 could look like. So here um the surveyor agent is going and looking at the original imagery uh or even video that the um submitter gave them. Um, hyper agent uh in our case uh works with a

9:53 fully um capable sandbox VM. So every agent in fact every thread with an agent is able to fully write code. It can manipulate files. It can actually use things like ffmpeg to like you know clip

10:04 out individual screenshots from a video file and then analyze those um and actually go through and like find all the relevant information to create a fairly high quality um and hightouch uh

10:16 proposal that would have been unfathomable for a small business like this before. Right? If you think about like a really high-end interior designer or maybe like a very high-end

10:25 landscaper, maybe they could do a very bespoke proposal like this for like a $5 million plus uh client, you know, if they're working for a big hotel or something. But it would have been

10:35 unheard of to do something this hightouch and actually mock up real imagery and a real client pitch deck for, you know, just a small backyard landscaping proposal. But this person

10:44 has done it because they've they've figured out how to use agents in an extremely capable and autonomous way to do the job that otherwise would have been undoable by humans.

10:54 And so what happens next is you know the surveyor agent it it went and created this proposal and pitch and now the intervention has to go back to the human like there is a step where humans still

11:05 need to unblock the agents right Sarah Guo put out a great post uh a few weeks ago about you know just describing like where the value is going to acrue in this AI landscape as things evolve and

11:15 models get smarter and I firmly agree with her take which was you know the ultimate blocker to still be able to deploy useful agents there's still a human element to that you know as As

11:24 much as models are getting smarter, the ability for agents to get unblocked by humans that still own policies and decisions and really importantly just kind of gate, you know, really critical

11:35 um high impact actions like in this case actually making a pitch to a customer that has a binding quote um or perhaps like agreeing to do the work that becomes something that the human then

11:45 intervenes at just like a great manager who's getting upwards uh reports or you know upwards work updates from their team to then unblock. So here we have Sage who has come back to uh the real

11:57 human uh owner of this business and saying hey here's a lead I already surveyed the the project here's a beautiful pitch deck and a proposal and have come up with a realistic quote

12:07 based on all the factors I've already surveyed in this uh particular project to give to uh to you to now approve sending to the client and see if they accept it right and you can see like

12:18 even the u the proposal itself is this really beautiful kind of interactive uh web page that again would have been unfathomable for a business like this to be able to send to every single client

12:29 even for small tasks uh or jobs like a $10,000 landscaping proposal, right? Um you know, better yet, as you interact with these agents, you know, as as Gary talked about with GBrain and and just

12:40 kind of like creating this very um sticky context layer that learns with every interaction. I completely agree. I think that is a you know kind of a universal principle and kind of a form

12:50 factor for useful agents going forward is agents should not be static. They should not just be you know either static LLM calls or static even agents that just have a prompt and you know

13:00 some static skills. They really need to learn with every interaction right and you can't wait for the next you know kind of fine-tuning run of your own models where you have you know a bunch

13:10 of collected data or rubrics to to retrain the model. It should learn in real time, right? It should accumulate the memories and skill updates and just take user feedback and actually learn

13:20 from real world performance to constantly adjust and get better just like humans do. And so in this case you can see you know within Slack or you know in our case within uh either Slack

13:30 or email or other um invocation methods you can basically go back and forth with the agent just like a real human and say hey like you know remember to take this into account next time you kind of you

13:39 know here's something differently uh formatted or a different way to think about the proposal or you missed kind of um this additional cost lever in this particular project. So, you know,

13:49 remember that for next time. And I think it's critical that in this next generation of agents, agents need to remember these things, right? Um they should feel fluid and and um evolving

14:00 just like humans are in order for them to fully realize the potential that the latest frontier model capabilities enable them to to actually go and do in terms of autonomy.

14:13 Um, and then finally, I think what ends up happening at the end of all of this is with all of these agents that are each individually capable and even interacting with each other, I think the

14:22 job of the human becomes increasingly about unblocking agents to be able to do work effectively. And so, you know, I I compare this to the development workflows that uh have evolved from, you

14:32 know, originally before any um, you know, kind of AI enabled coding, obviously, we just sat in front of a computer. We as humans are pretty single threaded, right? You'd start at one code

14:41 file, you think about a problem, you'd kind of bang out some code, maybe take a break, come back to it to then we had um augmented coding with the first generation of GitHub copilot and similar

14:51 tools where I mean I think of that as really just a completions form factor on coding. You type some code, it can autocomplete a few extra lines to then we had things like cursor composer 1.0 0

15:01 like the original uh experience not the the composer model um but the chat experience where you could talk to this agent it would do more complex changes and that was even more leverage right

15:11 but we're now moving to this point where you know the best frontier agentic developers I know and many of you are sitting in this room are are really just kind of overseeing a fleet of agents

15:20 right like in fact going to sleep without setting off your agents to perform useful work overnight feels like you're you're taking this huge loss like your your team is not working because

15:29 you're not unblocking Um, and so the UX of how you oversee these agents, whether it's in development workflows or general purpose work like this landscaping example needs to shift as well. And so

15:40 the way we've thought about that is you need to see this kind of orchestration control plane where you can see at a glance what all of your agents are working on, what they're blocked on,

15:49 who's handing off work to each other. And your job becomes almost like, you know, zooming out to see the entire Sim City landscape and orchestrating across everything going on at a macro level

16:01 between your different agents rather than going and having to play micro and zoom into each individual project or each individual task. Um and so I think this is a really really exciting moment

16:12 as we have shifted from you know not just completions and kind of the slight augmentation of existing human uh work but really a radical now leap into humans as orchestrators of agents that

16:24 also can orchestrate themselves. and finding the right way for us to interact with those agents as they become more and more capable is going to become not just the opportunity to boil the ocean

16:35 but in my opinion table stakes for survival for every company in every industry this is going to be the way to get ahead and thrive in your industry and if you don't you know as Jensen put

16:45 it you know it's not going to be AI taking your job it's going to be you know somebody using AI who takes your job or takes your business so really really excited both for the opportunity

16:53 and for everyone in this room to be part of this um transformation and this really kind of bold leap into the next form factor of AI. Um all that being said, we'd love to see you try Hyper

17:04 Agent. So hyperagent.com/aiie and you get $1,000 of inference credit. Um you know that can be spent on all of our different model offerings including uh you know the latest uh Opus 4.8 Fable

17:15 5. You can use it on GLM 5.2 uh on GBD 5.5 etc. and we'd love to see what you do with uh with agents on Hyper Agent or elsewhere. Thank you all for being here. How are we again?

18:00 >> Thank you for joining us. >> Thank you. Um so for the close of our events uh a few months ago I saw that hyper agent was launching this startup competition and at AIE we've always

18:14 wanted to feature some kind of battlefield for like a competition and a contest for people uh and we didn't really have any sort of mechanism by which to judge or to fund it or anything

18:23 like that and hyper agent came along and they had this uh 500 founders >> right what was the inspiration for that >> you know I think um as I think about the multiple layers of disruption stacking

18:35 you know we we're going as I said from you know just humans doing the same work they were augmented a little bit by AI to now humans overseeing fleets of agents doing work that wasn't before

18:44 possible like this landscaper running his business in a fundamentally different way to now this entirely new economy of agent native companies being built and so you know really the

18:52 founding 500 is about betting on the most frontier part of that economy as possible right it's the most agentically leveraged companies that are not only outd disrupting their space but kind of

19:01 defining an entirely new playbook for how companies in this new era will be run. And I think the leverage we're already seeing from some of these uh these companies that are running in this

19:10 way like Gary said like it's profound. I mean it's far more than even being like an early software leveraged or internet leveraged company. So really really you know kind of big believer that this

19:20 subset of the economy is going to have explosive growth far higher than any other sector and it's going to cut across not just you know the tech industry, software industry but every

19:29 industry. >> Yeah. uh in my London keynote I always often talk about how uh work agents are eating the rest of work and being accessible to nontechnical teams. In

19:38 fact, my team is right there back now because AIE runs on air table uh and they're so excited to meet you and so excited to to work with hyper agents. Um so from 500 founders we you guys

19:48 pre-selected 20. They competed today in the startup battlefield. Uh three have emerged as finalists. Let's introduce them. Um, so I don't know if I'm supposed to to click this. Uh, but uh

20:01 first of all, I'm just going to go and introduce uh our dear judges. I think uh we're going to have Howie, Theo, and Joshua. Um, thank you from Pan. Um, and uh we I think we'll we'll we'll sort of

20:14 have them judging there. Uh, but also we're going to uh go into the format and the background as well. Uh so basically what we're going to do uh we um the Hyper Asian team has kindly put up

20:26 $100,000 in prizes and basically what this is is a competition for whoever is going to be the most impressive uh winners. So uh 50k to the winners um 30k to runner up to 20k to second runner up.

20:39 Uh we're going to have a hype video that is uh thankfully um created by hyperframes and hijen. uh we have founder pitches and Q&A with the judges and then we'll also get to the next uh

20:53 uh pitch as well. So just three quick pitches back toback. Um and basically we decided that we really wanted some kind of judging in terms of like what is impressive and usage of this technology

21:06 and like why it matters. Um it's not an investment pitch so much as like you know it's not like a VC pitch so much as like well why should you care? Why is this an interesting use of technology

21:16 and and why um you know why are you like an interesting founder that people should know? Um and so I'm very excited to announce like the winning teams. Uh so I think first of all we're going to

21:28 introduce um come on Spiro come on stage. All right. Thank you. [applause] Um I think you guys are going to take

21:40 over and present. Yeah. >> All right. Do you have your clickers or you got you got your you're gonna kick it off? Okay. >> Hey everyone, I'm Spirro Anakos, one of

21:52 the founders of Kamad. This is Dom and I'm Nick Nakos. We are the founders of Kamad. Kamad has agentified physical commodity trade. From the rice you eat to the gold chain you're wearing to the

22:06 chip in your smartphone to the gas you're putting in your car to every single copper pipe running through our data centers. Physical commodities power the world. But the global trade system

22:19 is broken. Trillions move annually. Yet hundreds of billions are lost annually due to fraud, fragmentation, and the sheer complexity of coordinating global trade. Kamad has taken that complexity

22:32 and transformed it into an agentic execution layer. Our proprietary CFC state machine governs specialized agents that verify, execute, and settle trades entirely.

22:46 And then, you know, people ask, why is this so complicated? And if you think about it, it's like, well, it's every country in the world, every resource, all of their different currencies, and

22:55 all of their different banking laws. So, of course, it's inevitably going to be a disaster, right? But with AI, it's finally solvable. >> So, we're very excited to have you here.

23:06 We also have our our first customer in the crowd out of the UAE U trading company based in Dubai and he moves several hundreds of millions of volume in in trade every single year and uh

23:17 he's proved the product. So, we're excited to be here today and thank you for the opportunity. Talk to the judge. [applause] >> 17 trillion dollars in physical

23:27 commodities move every year. The world runs on them. Yet it still moves on paper, email, and a handshake. No one knows who is real or if the documents are. The result, billions lost. Commod

23:38 plugs into the stack you already run. Gmail, Slack, Stripe, Salesforce, WhatsApp, and more. A deal begins. Agents screen both sides. KYC identity sanctions in seconds. Forensics catch

23:50 document fraud instantly. A private deal room spins up. Shipping and logistics partners link in with live pricing. Then an agent swarm structures the deal in parallel. Compliance, legal, currency,

24:01 banking, shipping, settlement, weeks of work in minutes. The swarm obeys one protocol, the CFC. Our patent filed state machine. Agents advance only by clearing every deny condition. It cannot

24:13 move until the evidence is real. Settlement eligible. It never touches the funds. It emits the signal. Your bank moves the money 30 days closed in minutes. Commodities can't see the video

24:24 on the confidence monitor. [applause] >> What happens now? [laughter] >> I don't know. >> Is it on me?

24:40 >> Yes. Um I I I realized that we didn't get you a mic. So uh are you're still miked up at the end of miked up? >> Oh, you're all miked up. Okay, great. Um, so I think we have a little bit of a

24:49 Q&A session. >> Yeah. >> So, are we supposed to ask questions or what? >> Exactly.

24:57 >> My team agreed to this, not me. >> Wait, why are you guys playing this? >> Joshua, you want to go? >> Uh, sure. I I'm very interested in the um the second project, common.io.

25:11 And you know, certainly we need a share space for agent to [music] collaborate together. Just curious, have you thought about having that format to be HTML versus a markdown file or how [music]

25:22 you've been thinking about it? >> Sorry. >> All right. Sure. >> Yeah. Don't >> Yeah. I was just saying that you know um

25:30 [music] um the second project common.io is about >> That's the next one. Yeah. >> Oh, the next one. >> We're talking about the first one.

25:37 >> Uh so I'll ask a question. This is one of those types of products that's really hard to build because it requires so much domain specific knowledge as well as like the building

25:47 out of all of these pieces to collect the information needed for the product. A lot of the concern I have with modern startups is the thing that they're building is eventually going to be a

25:56 feature of a sufficiently intelligent model. Do you see a future where the models get capable enough and the tools they're given by Anthropic and OpenAI could compete with you guys if they were

26:05 to get smart enough and build in the right directions? So in commodities trade, it's very specific as to what data is being pulled. And what we've done with our

26:16 orchestration layer is set the gates for each agent. So they're pulling from real databases and have that context themselves. Whereas you're talking about gold, oil, grain, whatever it may be,

26:27 there's certain procedures that only industry experts really know and have the documents and the information to pull that in. Which is why we've partnered with Connor and a few other

26:35 partners to bring in all that data. uh and I have domain experience as well to know what's real, what's not and to train the agents based upon that. So to your question, could that happen? They

26:45 can learn more for sure on the open internet, but this is a private industry with a lot of sensitive information and they have to learn upon and train upon certain data in order to progress and

26:55 become experts in that domain. >> So it sounds like relatively bespoke in the sense that like every company's a bit different. You need to get everything for them. How many customers

27:03 would it take for this to become a billion dollar company or to hit like I don't know 100 mil ARR like how many customers you think it would take to get there?

27:12 >> It's about 100 uh 100 customers total >> huge. >> It's also important to mention as well like the tickets in this industry are massive on on deal sizes. It's not like

27:23 a stripe for example processing microtransactions. One transaction might be you know $200 million or north of that. and one partner such as a refinery, many

27:32 of them transact nine to 12 billion dollars a month. So getting in at that level, charging per deal on that changes the whole landscape of what we're able to earn and what we're able to be valued

27:42 at. >> I just want to add one more piece as well. Our solution is end to end. So you know the solution serves the entire supply chain, but the wedge ultimately

27:52 into the industry is the traders and the banks. Banks pulled out of physical commodities trading years ago after the fraud became so rampant. So this unlocks a tremendous amount of liquidity as well

28:02 by the audit trails and the immutability that we give uh into the commodity and the trade itself. So there's a lot to love about this business. I think like in many ways it's like check checks all

28:13 the boxes on like the classic like here is the ideal vertical agent business to build. Like if you look at the the YC kind of like RFS for vertical agents like this is like the perfect

28:22 application in the right the right kind of sector. like there's lots of money up for grabs and there's a lot of like domain specific stuff that you have to do that creates I think a good

28:31 defensible moat. Um I think my perspective is more like you know what do you do to keep up with the changing competitive landscape and just you know as models progress how do you need to

28:40 evolve your product to stay competitive right so it's less about is this a relevant and great business today and more about what changes over the next two three years or even like next year

28:50 that impacts you in a way that you know it doesn't necessarily mean like enthropic opening eye eats your lunch but what do you need to do to stay you know kind of at the frontier and be a

28:59 better product. Yeah, I think like the only thing that's actually defensible is being the most creative in your category regardless of which product you're creating, right? Like truly nothing's

29:07 defensible other than being the most creative. Like anybody these days has a product, but we actually do like the anthropic eat your lunch model. Like we like the ship extinction model where we

29:16 ship and then 10 companies go extinct the next day. So um yeah, it's aggressive shipping and product superiority, but then ultimately we're going to win on distribution.

29:24 >> What are you most excited about on your upcoming road map if you can share? becoming the bank. >> Yeah, >> it's exciting.

29:36 >> Thank you so much. >> Awesome. >> Awesome. >> Give better off. Come on. Uh we're going to invite the next team on now. Uh if

29:42 you want to come back here. >> Thank you. Appreciate it. >> Thanks. >> Um and up next we have Max Windbomb and the team from Common.io.

29:54 >> Come on, Max. Oh, you're going to play the video. All right. All right, we'll play the video. That was

30:06 the music. Oh, sorry. Heat. Heat. >> [music] >> All right, welcome in. Come on, guys.

31:06 Oh, thank you. Um, if you could uh so we have a little bit of a technical snafu where the judges couldn't see the video. >> Okay.

31:15 >> Uh, we have to describe a little bit just to just to prompt things off >> before >> you if you sort of before you get into the whole pitch. Yeah.

31:21 >> Okay. >> Pretend like we're a non multimodal model. We just we only accept text. >> So, uh, yeah, text comment.io is a uh it's um multiplayer markdown editor.

31:35 It's uh focused on making the best documents um for the modern work which is uh people and agents. But what you would have seen on the video is a familiar document editor interface, but

31:45 people and agents are popping in. They're notifying each other. Everyone can call anyone in. Um and that's that's basically the gist. >> Awesome.

31:55 >> All right. So, um, it's Friday afternoon, ready to go home, packing up your laptop, and all of a sudden you're hearing dings around the office, servers

32:07 down, big outage. So, you put down your bag, you head into the conference room, and you're pulling out your laptop. There's one question on your mind. What is going on, Claude? And you're entering

32:19 that in. You're waiting a few minutes. Give me some ideas. It's culminating or whatever. And then it gives you some ideas and the first thing you do is you tell everyone else in the room the ideas

32:28 so they can put it into their clouds and then someone else has a different idea and then they let you know and then you put it into your cloud and then you real how did we get here? How is this the

32:38 job? There has to be a better way and there is. It's agents and the who are doing the work and people who own the problem sharing context in real time. But how? Think of the productivity

32:51 software that you all use the collaboration software. Can you just like drop your coding agents into them and have them working with each other? No, you can't because they were built

33:00 for people, not for agents. And that's why we're making Comet. It is the multiplayer markdown editor. It is we're laser focused on making it the best document platform for modern work for

33:14 people and for agents. It's open. It's pluggable. That's key for enterprises. It's key for it's key for developers. and it's wrapped in a user experience that is super familiar. I started my

33:25 career in productivity bringing collaboration features into Microsoft Word. Max and I worked together um at Texio, the first AI native writing software six years before chat. We were

33:38 deploying companywide rollouts of AI writing software to the Fortune 500. We've been doing this a long time. >> The market for comment is expanding with agents. As more and more people use

33:49 agents, more people are going to need the ability for agents to share context in a product that is built for agents but designed for people working together even in the highest stake circumstances.

34:03 >> Awesome. I love the shape of this business because it's like the inverse of what we just heard from the um the commod guys, right? Like they went super vertically deep. they're trying to

34:12 extract a lot of value out of a very deep um you know domain and you guys are going super broad and arguably maybe a little thinner but by intention right and I don't say that as a as a detriment

34:23 at all like I think you know one way to think about the landscape here is like the agent economy is going to be so large there's just so much money flowing through agents like literally the token

34:31 spend right and if you can be a product that like even a one to you know 2% uh amount of those tokens touches like you could be a very valuable even if like the per uh per or per usage um you know

34:43 kind of uh metric uh you know kind of margin extraction or revenue is is relatively low you can be a very cheap very fast very um broad product and still gain a lot of scale right so I

34:53 think it's a really really interesting shape of product um what have you found is like the most surprising learnings from like a product design or product affordances standpoint to make this

35:02 product better for agents than like the human design equivalents right like why not just have agents collaborate on a Google doc right or even try to use like GitHub as the uh uh you know kind of

35:11 repo not just for code but for markdown. >> All writing software today is horrible for agents to use. Software like Google Docs has a really terrible format. It has an MCP. You can send a document

35:27 there. You can have your agent read a document from there, but you can't have them collaborate in real time. And that's critical for all kinds of real work that needs to happen. And as more

35:39 agents join the workforce, they need to be in there with your documents. And as we've talked to our users, people who love working in documents, who rely on documents for their day-to-day, need to

35:52 bring their agents because they're starting to use agents, too. It's not just developers anymore. >> Okay. I think >> I'll mention it.

36:00 >> Oh, final closing words. I was just going to say the other thing I'll mention is that I've been super surprised about um talking to people at this conference when we've talked to

36:07 them is how many people have said I've rolled my own like visual markdown editor that other people can look at so people can look and then you're like ah but I gave up I just rendered an HTML

36:17 document instead like it's a hard problem and you need dedicated like focus on making it great and that's kind of what we're doing. [snorts] >> Awesome. All right, nice job guys. Thank

36:28 you so much. [applause] Uh next for and last team to compete. We're going to play the video by Foundry. Content creators don't have time to

36:54 build a business, [music] so they have to hire an agency. It's expensive, slow, and risky. Built by [music] Foundry replaces the agency and lets creators launch their own products faster and

37:06 cheaper. Our AI agents build the business end to end. The creator launches it. We keep growing the revenue. Type in any creator's handle and our [music] agents become their

37:18 biggest fans, watching their videos, reading their comments, and finding a painkiller their audience would actually pay for. We've done this creators of every size and over half a million

37:29 people use products our agents built. The cost of building software is approaching zero. What's left is trust and taste and no one has more of it than creators. We're building the agentic

37:41 product team that is going to turn every creator into a founder. >> Woo! Foundry team, come on up. >> Thank you. Thanks, Max. >> Hello, AIE. I am Weston Belgettes. I'm

37:55 the founder and CEO of Built by Foundry. We're going to turn every content creator into a founder of their very own company. And I have great news today. I think we found the formula for a winning

38:08 business. You take your unique expertise and turn it into a product and you solve cold start with your built-in distribution. Theo, you you did this with T3 Chat.

38:17 >> Yeah, it sucked. It doesn't work. [laughter] It works great for our our our users, but uh Gary did it with with G uh GStack and GBrain. And uh what what we find is that if we

38:32 help uh creators to take a step back and solve a real pain point that their users have, so we're not just making another Patreon for that, paywalling this content. We're helping them solve a real

38:42 pain point behind that. And that's what our agents are experts in is finding the pain point within the users's problems. And our companies have uh built over uh many different niches and uh different

38:58 sizes of shape and shapes of creators. And really what we're building is we want to give the power back to the content creator. They're stuck in these I worked at Tik Tok for two years and

39:08 and I met with hundreds of creators who are stuck just doing brand deals or affiliate links or selling $20 PDFs in their link in bio and they are one step away from building their own business.

39:20 They just need someone to help them and that's what our agents do. At Built by Foundry, we deploy teams of agents to build recurring revenue businesses for content creators so that they can focus

39:30 on posting and posting and posting and we can keep growing their revenue. I have a lot of thoughts on this one. >> Okay. >> So, the problem I find as a creator,

39:40 even with the brands I work with, is that there is a saturation point where it stops being useful to talk about a given product with my audience. Like with me, for Versel, for example, there

39:50 was quickly a point where everybody in my audience either was a Verscel user and doesn't want me to show it to them anymore or they won't be a Verscell user and they don't want me to talk about it

39:57 anymore. It is very easy to hit the saturation point and the best solution is to have a much broader set of things to show your audience. One of the best things I ever did for myself. The single

40:07 moment that has had the most positive impact on myself, my career, my businesses, and my income has been going from having four sponsors to 80. Because now there's a wider variety of things to

40:17 bring my audience. And even if 80% of them aren't relevant to them, the 20% that is is now conversion I'm getting that I wouldn't have otherwise. The variety of what I bring to my audience

40:27 is where I find the most value by far. And every creator I've worked with ultimately has come to a similar conclusion. That said, you're touching on really important pieces here.

40:36 Creators do have more taste than most founders. Creators do have better distribution than most companies. Creators know their audience better than most businesses. And you've built a

40:44 machine that identifies that, but it doesn't provide the solution to that other problem, which is you have to bring variety to your audience. Where I could see this working really well is as

40:55 a tool that businesses use when they identify a creator is overlapping with the archetype of customer they're looking for to help that business build better things for Theo. And I've told

41:05 this to so many businesses that want to work with me. Watch my streams, watch my videos, find the problems I have, and then show me how your product solves them. That's a way better pitch than

41:14 going to try and start your own company as a YouTuber. As the YouTuber who has started the second most companies, as far as I'm aware, it doesn't work. It just burns you out and it burns your

41:22 audience out too. The best thing you can do is work with companies to teach them what your audience wants and for them to come to you with money to show it to those audience or those audience

41:31 members. >> I understand and I will say your niche as a tech creator is one that is very challenging because there's a lot of options. Um and what we have found is

41:40 that for all of our creators, I'll just few examples. Um, one one creator's name is Brandon and he does homesteading content and he always loses track of uh the best settings on his freeze dryer,

41:52 which sounds ridiculous to us, but to his 2 million followers, that's a real pain point. And now they pay him a recurring revenue monthly or monthly or yearly subscription to solve that

42:03 specific painoint. So, I think unfortunately Theo, you've picked a bad niche. I'm sorry, but um if you want to pivot to homesteading or gardening, m maybe uh we could we could work

42:12 together. But I I'd say primarily whatever the user's niche is, apart from tech and maybe com comedians, we've been able to take a step back. Our agents find a painoint that they're willing to

42:25 pay for and zero creators across our entire company history have ever left our our company because they always are profitable. >> You're very convincing. [laughter]

42:36 Okay. Excellent. Then give it up. >> Yeah. Thank you. [applause] Um, so we're going to have a little bit of a deliberation moment. Uh, the judge is going to go backstage for a couple

42:47 minutes. In the meantime, sorry. Okay. In the meantime, we've, uh, prepared this little video recapping the World's Fair. So, take your time, judges. [music]

43:29 This is our biggest AI engineer. [music] event ever. We're constantly adding [music] new techniques for you to draw. >> [music]

44:36 [music] >> Thank you so much. All right. Um, I think we're inviting everyone back on stage. Give it up for the start of Battlefield.

45:18 Um, congrats, guys. Yes. Okay. So, uh, we have some prizes that we're going to hand out. I think, um, the folks back there are doing some

45:30 checks. Um, in second runnerup face, we have Kimode. [applause] Uh, Theo, I think you're presenting. Uh, yes.

45:45 >> Okay, we'll take some pictures as well for the team. >> All right. U, next, and I guess this also reveals the order. Uh, we have Foundry as runner up. Great [cheering]

46:02 pitch, Max. Great pitch. [applause] >> All right, congrats. >> Awesome. All right, no secrets now for the grand prize. Comment.io. Welcome. Great video as well.

46:17 >> Congratulations. Well deserved. >> Do you want to also do the group photo? I think we're doing >> all of us. >> Um,

46:31 >> yes. Okay. >> All right. >> What it mean just them? >> They're the founders. We're just here to com everyone.

46:37 >> Oh, fine. We'll sneak it in. >> Get them. Come on in. >> Yes. >> All right. >> Thank you so much.

46:48 >> You all had some building with hyper agent and >> Yeah, of course. Yeah. >> Um, [music] any last words that you wanted to comment on? I think you guys

46:56 uh brief a little bit. Yeah. >> Yeah. I just want to thank uh sorry about the thank everyone here at AIE. We got amazing feedback um through the whole um time and uh Hyper Agent um for

47:07 inviting us to this and Hyper fans for helping us with the video and uh overall like >> so much. [snorts] >> Check us out. It's free.

47:18 >> comment.io I know.