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

AI Engineer · 47 min · 568 sentences · from YouTube's caption track

Each timecode opens YouTube at the start of that sentence. Line anchors (#s42) are the cue ids in the WebVTT, and every line carries its start and end seconds. All transcripts has every talk, and the whole corpus as one file.

00:01[music]
00:12Please join me in welcoming the chief executive officer
00:15[music]
00:15at Hyper Agent, Howy Lou.
00:20[music]
00:31All right.
00:32Hello.
00:33Um, who's excited about the end of this event?
00:36This this has been an amazing few days.
00:38Amazing talks.
00:39Amazing uh Gary talk right there.
00:41And by the way, I've never been introduced with so much base.
00:43I am very very pumped in this moment.
00:46Um, so I consider myself a product thinker at heart.
00:49meaning you know as much as I like to spend time on the technical side 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 is a brief and kind of
01:05highle 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 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
01:25uh or others and how to coax the 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 tableable and also know hyper agent which is a product within air tableable and
01:41as many of you already know air tableable is basically a almost laughably horizontal platform uh when we 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 possibly go out with a horizontal platform
01:59approach 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 solve all of those use cases
02:07and 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 very very similar philosophy
02:17to 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 they enable.
02:29I really see it as this spectrum, right?
02:31So, we went from basically completions, which is, you know, you give a model, one of these LLMs, a, you know, string of tokens and it just, you know, continues
02:40emitting more tokens until it gets to a stop token.
02:43And more or less it would just kind of complete your thought, right?
02:45Or complete um the pattern.
02:47And you know, until instruct GBT happened and enabled a chat form factor, it was like arguably powerful but more narrowly applicable.
02:56Once we got to a higher level of model intelligence and then also the kind of innovation of instruct GBT and kind of tuning these models to actually have more of a conversational back and forth behavior
03:06with users, then we enabled the chatbot, right?
03:08And of course we all know chatbt was an awesome breakout product.
03:12We're now all very very intimately familiar with that form factor of AI.
03:16The next level I call an agent, right?
03:18And you know I think agent has been very overloaded.
03:21Um, 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 from workflows.
03:30And an agent is really a model that recurses upon itself, right?
03:34And has, you know, the the open-ended set of tools that it could call, of reasoning steps it can make, of decisions it can make.
03:40So, it's not prescripted to a linear workflow, but it can kind of make an open-ended set of choices.
03:47Um, still you know I like to think of agents as being a little bit distinct from the next level of agents which I'm using the term claw here because openclaw obviously has has popularized
03:56uh this concept but you know there's a market shift that I see from agents that were still mostly reactive um you know to user input.
04:05So you would have an agent go and perform a task.
04:07It might spend 20 minutes maybe up to an hour to perform the task and then it completes.
04:12claws 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
04:22um 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 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
04:40then invokes the the LLM again until it 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?
04:51And so this idea of 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.
05:01I think that's a really really important kind of next form factor uh leap that we're seeing.
05:06And then finally, as you think about models that are now capable of, you know, really, really intelligent orchestration of complex tasks, right?
05:14Um, 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 agents.
05:23Um 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 jobs is I think the next leap yet
05:37all 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 problems right u if you recall back to the days of um
05:49uh baby AGI and autogbt those were really kind of fun cool science experiments, but ultimately, you know, as much as they were really interesting to watch and, you know, see them work for hours and hours on a problem, they would all inevitably,
06:02you know, kind of end up drifting off to something not super useful, right?
06:06It would go off into a rabbit hole and kind of get stuck.
06:09And 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 of organizational principles to break down problems
06:20into differently scoped tasks that different agents can hand off to each other.
06:24All of which is about managing each agent's task uh or context window and ability to coordinate work with each other.
06:33So all this is kind of abstract.
06:34I want to make it very very tangible and show you how we've applied some of that design thinking of building a horizontal product and distilling a lot of the complexity of agents
06:44like we did for building a database or a database powered app with air table for agents in hyper agent.
06:51So this is a um a very realistic use case based on some of our actual customer usage.
06:56One of the interesting things we found from launching Hyper Agent is, you know, the the uh prototypical users for Hyper Agent are not who you would think they are, right?
07:04I mean, we definitely get our fair share of like the super AI forward, you know, kind of pure software companies, like AI companies themselves, but we also get really interesting other companies that are, you know, sometimes like traditionally offline businesses.
07:18And in this case, we're depicting a very realistic scenario, which is um a customer uh that is in a landscaping business and actually uses agents to run virtually endtoend
07:27every part of their business, which is finding clients to propose landscaping projects to like physically going and like redoing their garden or their backyard and end to end managing everything from the prospecting
07:39to building a quote and a pitch to managing the work itself.
07:42And I think 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 software companies.
07:56Um and I was really inspired by Gary's talk just a second ago.
07:58I think that the uh the motive behind that applies to every industry, every sector and there are really really creative people many of whom are in this room who can go out and disrupt your own industry whatever it be.
08:11You know, it doesn't have to be a pure software business.
08:13Um, but I think all it takes 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
08:23fleet of agents native 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
08:32a whole fleet of agents that have 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.
08:43So let's walk through what this looks like.
08:45Um this is a realistic example of you know in this business this uh this landscaper wants to go and pitch clients right?
08:51So you know they get all these inbound uh submissions of clients who are interested.
08:56Somebody submits this inquiry saying hey here's my current backyard.
08:59You know it's a little messy.
09:01Here's my info like can you give me a quote and show me a proposal of what you could do?
09:06This basically gets routed into a um in into one agent that is the triager agent, right?
09:12And so this agent is basically taking in this intake and saying, "Hey, like what is the um the scope of the project, you know, like is this something we want to take on
09:21and is this a quality lead?
09:23Is this a legit person?"
09:24And so it does some research, but ultimately it hands off then the work to another agent, a surveyor agent to actually go and then look at the um the physical landscape of this person's backyard
09:36and come up with a proposal of what a rellandscape version of that could look like.
09:41So here um the surveyor agent is going and looking at the original imagery uh or even video that the um submitter gave them.
09:49Um, hyper agent uh in our case uh works with a fully um capable sandbox VM.
09:55So every agent in fact every thread with an agent is able to fully write code.
10:00It can manipulate files.
10:01It can actually use things like ffmpeg to like you know clip 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
10:15um and hightouch uh proposal that would have been unfathomable for a small business like this before.
10:20Right?
10:21If you think about like a really high-end interior designer or maybe like a very high-end landscaper, maybe they could do a very bespoke proposal like this for like a $5 million plus
10:31uh client, you know, if they're working for a big hotel or something.
10:34But it would have been 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.
10:44But this person 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.
10:54And 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 need to unblock the agents right Sarah Guo put out a great post uh a few weeks ago about you know
11:09just describing like where the value is going to acrue in this AI landscape as things evolve and 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
11:22there's still a human element to that you know as As much as models are getting smarter, the ability for agents to get unblocked by humans that still own policies and decisions
11:32and really importantly just kind of gate, you know, really critical um high impact actions like in this case actually making a pitch to a customer that has a binding quote
11:41um or perhaps like agreeing to do the work that becomes something that the human then 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.
11:53So here we have Sage who has come back to uh the real 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
12:05and have come up with a realistic quote 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 even the u the proposal itself is this really beautiful kind of interactive
12:22uh web page that again would have been unfathomable for a business like this to be able to send to every single client even for small tasks uh or jobs like a $10,000 landscaping proposal, right?
12:33Um you know, better yet, as you interact with these agents, you know, as as Gary talked about with GBrain and and just kind of like creating this very um sticky context layer that learns with every interaction.
12:45I completely agree.
12:46I think that is a you know kind of a universal principle and kind of a form factor for useful agents going forward is agents should not be static.
12:55They should not just be you know either static LLM calls or static even agents that just have a prompt and you know some static skills.
13:02They 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 of collected data or rubrics to to retrain the model.
13:13It should learn in real time, right?
13:15It should accumulate the memories and skill updates and just take user feedback and actually learn from real world performance to constantly adjust and get better just like humans do.
13:24And so in this case you can see you know within Slack or you know in our case within uh either Slack 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
13:39of you 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.
13:48So, you know, remember that for next time.
13:50And I think it's critical that in this next generation of agents, agents need to remember these things, right?
13:56Um they should feel fluid and and um evolving 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.
14:13Um, 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 job of the human becomes increasingly about unblocking
14:25agents to be able to do work effectively.
14:27And so, you know, I I compare this to the development workflows that uh have evolved from, you know, originally before any um, you know, kind of AI enabled coding, obviously, we just sat in front of a computer.
14:38We as humans are pretty single threaded, right?
14:40You'd start at one code 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 tools where I mean I think of that as really just a completions form factor on coding.
14:56You type some code, it can autocomplete a few extra lines to then we had things like cursor composer 1.0 0 like the original uh experience not the the composer model
15:04um but the chat experience where you could talk to this agent it would do more complex changes and that was even more leverage right 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
15:17are are really just kind of overseeing a fleet of agents 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 you're not unblocking Um, and so the UX
15:32of how you oversee these agents, whether it's in development workflows or general purpose work like this landscaping example needs to shift as well.
15:40And so 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, who's handing off work to each other.
15:52And 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 between your different agents
16:03rather than going and having to play micro and zoom into each individual project or each individual task.
16:09Um and so I think this is a really really exciting moment as we have shifted from you know not just completions and kind of the slight augmentation of existing human
16:18uh work but really a radical now leap into humans as orchestrators of agents that also can orchestrate themselves.
16:25and 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 but in my opinion table stakes for survival for every company in every industry
16:40this is going to be the way to get ahead and thrive in your industry and if you don't you know as Jensen put 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
16:53opportunity 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.
17:01Um all that being said, we'd love to see you try Hyper Agent.
17:04So hyperagent.com/aiie and you get $1,000 of inference credit.
17:08Um you know that can be spent on all of our different model offerings including uh you know the latest uh Opus 4.8 Fable 5. You can use it on GLM 5.2
17:17uh on GBD 5.5 etc. and we'd love to see what you do with uh with agents on Hyper Agent or elsewhere.
17:25Thank you all for being here.
17:57How are we again?
18:00Thank you for joining us.
18:01Thank you.
18:02Um 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 wanted to feature some kind of battlefield
18:16for 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 like that and hyper agent came along and they had this uh 500 founders
18:29right what was the inspiration for that
18:31you know I think um as I think about the multiple layers of disruption stacking 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 possible like this landscaper
18:45running 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 founding 500 is about betting on the most frontier
18:55part of that economy as possible right it's the most agentically leveraged companies that are not only outd disrupting their space but kind of defining an entirely new playbook for how companies in this new era will be run.
19:06And I think the leverage we're already seeing from some of these uh these companies that are running in this way like Gary said like it's profound.
19:12I mean it's far more than even being like an early software leveraged or internet leveraged company.
19:17So really really you know kind of big believer that this 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 industry.
19:29Yeah.
19:30uh 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.
19:38In 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.
19:46Um so from 500 founders we you guys pre-selected 20.
19:50They competed today in the startup battlefield.
19:52Uh three have emerged as finalists.
19:55Let's introduce them.
19:56Um, so I don't know if I'm supposed to to click this.
20:00Uh, but uh first of all, I'm just going to go and introduce uh our dear judges.
20:03I think uh we're going to have Howie, Theo, and Joshua.
20:08Um, thank you from Pan.
20:11Um, and uh we I think we'll we'll we'll sort of have them judging there.
20:16Uh, but also we're going to uh go into the format and the background as well.
20:20Uh so basically what we're going to do uh we um the Hyper Asian team has kindly put up $100,000 in prizes and basically what this is is a competition
20:30for whoever is going to be the most impressive uh winners.
20:33So uh 50k to the winners um 30k to runner up to 20k to second runner up.
20:39Uh we're going to have a hype video that is uh thankfully um created by hyperframes and hijen.
20:47uh we have founder pitches and Q&A with the judges and then we'll also get to the next uh uh pitch as well.
20:54So just three quick pitches back toback.
20:57Um and basically we decided that we really wanted some kind of judging in terms of like what is impressive and usage of this technology and like why it matters.
21:07Um 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?
21:14Why is this an interesting use of technology and and why um you know why are you like an interesting founder that people should know?
21:22Um and so I'm very excited to announce like the winning teams.
21:26Uh so I think first of all we're going to introduce um come on Spiro come on stage.
21:33All right.
21:33Thank you.
21:36[applause]
21:38Um I think you guys are going to take over and present.
21:41Yeah.
21:43All right.
21:45Do you have your clickers or you got you got your you're gonna kick it off?
21:48Okay.
21:50Hey everyone, I'm Spirro Anakos, one of the founders of Kamad.
21:54This is Dom and I'm Nick Nakos.
21:58We are the founders of Kamad.
22:00Kamad has agentified physical commodity trade.
22:04From the rice you eat to the gold chain you're wearing to the chip in your smartphone to the gas you're putting in your car to every single copper pipe
22:12running through our data centers.
22:14Physical commodities power the world.
22:17But the global trade system is broken.
22:20Trillions move annually.
22:21Yet hundreds of billions are lost annually due to fraud, fragmentation, and the sheer complexity of coordinating global trade.
22:30Kamad has taken that complexity and transformed it into an agentic execution layer.
22:36Our proprietary CFC state machine governs specialized agents that verify, execute, and settle trades entirely.
22:46And then, you know, people ask, why is this so complicated?
22:49And if you think about it, it's like, well, it's every country in the world, every resource, all of their different currencies, and all of their different banking laws.
22:57So, of course, it's inevitably going to be a disaster, right?
23:00But with AI, it's finally solvable.
23:05So, we're very excited to have you here.
23:06We 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 he's proved the product.
23:18So, we're excited to be here today and thank you for the opportunity.
23:21Talk to the judge.
23:23[applause]
23:2517 trillion dollars in physical commodities move every year.
23:28The world runs on them.
23:30Yet it still moves on paper, email, and a handshake.
23:33No one knows who is real or if the documents are.
23:35The result, billions lost.
23:38Commod plugs into the stack you already run.
23:40Gmail, Slack, Stripe, Salesforce, WhatsApp, and more.
23:44A deal begins.
23:45Agents screen both sides.
23:47KYC identity sanctions in seconds.
23:50Forensics catch document fraud instantly.
23:52A private deal room spins up.
23:54Shipping and logistics partners link in with live pricing.
23:57Then an agent swarm structures the deal in parallel.
23:59Compliance, legal, currency, banking, shipping, settlement, weeks of work in minutes.
24:05The swarm obeys one protocol, the CFC.
24:08Our patent filed state machine.
24:10Agents advance only by clearing every deny condition.
24:13It cannot move until the evidence is real.
24:15Settlement eligible.
24:16It never touches the funds.
24:18It emits the signal.
24:19Your bank moves the money 30 days closed in minutes.
24:22Commodities can't see the video on the confidence monitor.
24:29[applause]
24:36What happens now?
24:37[laughter]
24:37I don't know.
24:39Is it on me?
24:40Yes.
24:40Um I I I realized that we didn't get you a mic.
24:43So uh are you're still miked up at the end of miked up?
24:46Oh, you're all miked up.
24:47Okay, great.
24:47Um, so I think we have a little bit of a Q&A session.
24:50Yeah.
24:53So, are we supposed to ask questions or what?
24:55Exactly.
24:57My team agreed to this, not me.
25:01Wait, why are you guys playing this?
25:03Joshua, you want to go?
25:05Uh, sure.
25:07I I'm very interested in the um the second project, common.io.
25:11And you know, certainly we need a share space for agent to
25:15[music]
25:15collaborate together.
25:16Just curious, have you thought about having that format to be HTML versus a markdown file or how
25:22[music]
25:22you've been thinking about it?
25:24Sorry.
25:26All right.
25:26Sure.
25:27Yeah.
25:28Don't
25:28Yeah.
25:28I was just saying that you know um
25:30[music]
25:31um the second project common.io is about
25:34That's the next one.
25:35Yeah.
25:35Oh, the next one.
25:35We're talking about the first one.
25:37Uh so I'll ask a question.
25:40This 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 out of all of these pieces to collect the information needed for the product.
25:51A lot of the concern I have with modern startups is the thing that they're building is eventually going to be a feature of a sufficiently intelligent model.
25:58Do 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 to get smart enough and build in the right directions?
26:11So in commodities trade, it's very specific as to what data is being pulled.
26:15And what we've done with our orchestration layer is set the gates for each agent.
26:19So they're pulling from real databases and have that context themselves.
26:23Whereas you're talking about gold, oil, grain, whatever it may be, there's certain procedures that only industry experts really know and have the documents and the information to pull that in.
26:33Which is why we've partnered with Connor and a few other partners to bring in all that data.
26:37uh and I have domain experience as well to know what's real, what's not and to train the agents based upon that.
26:43So to your question, could that happen?
26:45They 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 become experts in that domain.
26:57So it sounds like relatively bespoke in the sense that like every company's a bit different.
27:00You need to get everything for them.
27:02How many customers 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?
27:12It's about 100 uh 100 customers total
27:15huge.
27:17It's also important to mention as well like the tickets in this industry are massive on on deal sizes.
27:22It's not like a stripe for example processing microtransactions.
27:25One transaction might be you know $200 million or north of that.
27:30and one partner such as a refinery, many of them transact nine to 12 billion dollars a month.
27:36So 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 at.
27:44I just want to add one more piece as well.
27:46Our solution is end to end.
27:48So you know the solution serves the entire supply chain, but the wedge ultimately into the industry is the traders and the banks.
27:55Banks pulled out of physical commodities trading years ago after the fraud became so rampant.
28:00So this unlocks a tremendous amount of liquidity as well by the audit trails and the immutability that we give uh into the commodity and the trade itself.
28:08So there's a lot to love about this business.
28:10I think like in many ways it's like check checks all the boxes on like the classic like here is the ideal vertical agent business to build.
28:17Like if you look at the the YC kind of like RFS for vertical agents like this is like the perfect application in the right the right kind of sector.
28:24like 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 defensible moat.
28:32Um 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 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
28:48next two three years or even like next year 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 better product.
29:00Yeah, 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?
29:06Like truly nothing's defensible other than being the most creative.
29:09Like anybody these days has a product, but we actually do like the anthropic eat your lunch model.
29:14Like we like the ship extinction model where we ship and then 10 companies go extinct the next day.
29:18So um yeah, it's aggressive shipping and product superiority, but then ultimately we're going to win on distribution.
29:24What are you most excited about on your upcoming road map if you can share?
29:30becoming the bank.
29:32Yeah,
29:33it's exciting.
29:36Thank you so much.
29:36Awesome.
29:38Give better off.
29:38Come on.
29:39Uh we're going to invite the next team on now.
29:41Uh if you want to come back here.
29:43Thank you.
29:44Appreciate it.
29:46Thanks.
29:47Um and up next we have Max Windbomb and the team from Common.io.
29:54Come on, Max.
29:56Oh, you're going to play the video.
29:57All right.
30:00All right, we'll play the video.
30:04That was the music.
30:06Oh, sorry.
30:22Heat.
30:22Heat.
31:00[music]
31:03All right, welcome in.
31:04Come on, guys.
31:06Oh, thank you.
31:09Um, if you could uh so we have a little bit of a technical snafu where the judges couldn't see the video.
31:14Okay.
31:15Uh, we have to describe a little bit just to just to prompt things off
31:18before
31:18you if you sort of before you get into the whole pitch.
31:20Yeah.
31:21Okay.
31:22Pretend like we're a non multimodal model.
31:24We just we only accept text.
31:27So, uh, yeah, text comment.io is a uh it's um multiplayer markdown editor.
31:35It's uh focused on making the best documents um for the modern work which is uh people and agents.
31:40But what you would have seen on the video is a familiar document editor interface, but people and agents are popping in.
31:47They're notifying each other.
31:48Everyone can call anyone in.
31:50Um and that's that's basically the gist.
31:54Awesome.
31:55All right.
31:55So, 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 down, big outage.
32:08So, you put down your bag, you head into the conference room, and you're pulling out your laptop.
32:13There's one question on your mind.
32:15What is going on, Claude?
32:18And you're entering that in.
32:20You're waiting a few minutes.
32:21Give me some ideas.
32:22It's culminating or whatever.
32:24And then it gives you some ideas and the first thing you do is you tell everyone else in the room the ideas 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
32:34and then you real how did we get here?
32:37How is this the job?
32:39There has to be a better way and there is.
32:41It's agents and the who are doing the work and people who own the problem sharing context in real time.
32:48But how?
32:50Think of the productivity software that you all use the collaboration software.
32:53Can you just like drop your coding agents into them and have them working with each other?
32:58No, you can't because they were built for people, not for agents.
33:03And that's why we're making Comet.
33:05It is the multiplayer markdown editor.
33:08It is we're laser focused on making it the best document platform for modern work for people and for agents.
33:15It's open.
33:15It's pluggable.
33:16That's key for enterprises.
33:17It's key for it's key for developers.
33:20and it's wrapped in a user experience that is super familiar.
33:24I started my career in productivity bringing collaboration features into Microsoft Word.
33:29Max and I worked together um at Texio, the first AI native writing software six years before chat.
33:37We were deploying companywide rollouts of AI writing software to the Fortune 500.
33:42We've been doing this a long time.
33:44The market for comment is expanding with agents.
33:48As more and more people use 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
33:59even in the highest stake circumstances.
34:03Awesome.
34:04I love the shape of this business because it's like the inverse of what we just heard from the um the commod guys, right?
34:10Like they went super vertically deep.
34:12they're trying to 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 at all like I think you know one way to think about the landscape here is
34:26like the agent economy is going to be so large there's just so much money flowing through agents like literally the token spend right and if you can be a product that like even a one to you know 2%
34:36uh 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 kind of uh metric
34:45uh 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 think it's a really really interesting shape of product
34:56um what have you found is like the most surprising learnings from like a product design or product affordances standpoint to make this product better for agents than like the human design equivalents right like why not just have agents
35:07collaborate on a Google doc right or even try to use like GitHub as the uh uh you know kind of repo not just for code but for markdown.
35:14All writing software today is horrible for agents to use.
35:20Software like Google Docs has a really terrible format.
35:24It has an MCP.
35:25You can send a document there.
35:27You can have your agent read a document from there, but you can't have them collaborate in real time.
35:33And that's critical for all kinds of real work that needs to happen.
35:38And as more agents join the workforce, they need to be in there with your documents.
35:43And as we've talked to our users, people who love working in documents, who rely on documents for their day-to-day, need to bring their agents because they're starting to use agents, too.
35:55It's not just developers anymore.
35:58Okay.
35:58I think
35:59I'll mention it.
36:00Oh, final closing words.
36:01I 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 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
36:15then you're like ah but I gave up I just rendered an HTML 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.
36:25[snorts]
36:25Awesome.
36:26All right, nice job guys.
36:28Thank you so much.
36:30[applause]
36:31Uh next for and last team to compete.
36:34We're going to play the video by Foundry.
36:52Content creators don't have time to build a business,
36:55[music]
36:55so they have to hire an agency.
36:57It's expensive, slow, and risky.
37:00Built by
37:01[music]
37:01Foundry replaces the agency and lets creators launch their own products faster and cheaper.
37:07Our AI agents build the business end to end.
37:10The creator launches it.
37:12We keep growing the revenue.
37:14Type in any creator's handle and our
37:16[music]
37:16agents become their biggest fans, watching their videos, reading their comments, and finding a painkiller their audience would actually pay for.
37:26We've done this creators of every size and over half a million people use products our agents built.
37:32The cost of building software is approaching zero.
37:35What's left is trust and taste and no one has more of it than creators.
37:40We're building the agentic product team that is going to turn every creator into a founder.
37:45Woo!
37:46Foundry team, come on up.
37:51Thank you.
37:51Thanks, Max.
37:52Hello, AIE.
37:54I am Weston Belgettes.
37:55I'm the founder and CEO of Built by Foundry.
37:58We're going to turn every content creator into a founder of their very own company.
38:03And I have great news today.
38:05I think we found the formula for a winning business.
38:09You take your unique expertise and turn it into a product and you solve cold start with your built-in distribution.
38:15Theo, you you did this with T3 Chat.
38:17Yeah, it sucked.
38:19It doesn't work.
38:19[laughter]
38:20It works great for our our our users, but uh Gary did it with with G uh GStack and GBrain.
38:27And uh what what we find is that if we 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.
38:41We're helping them solve a real pain point behind that.
38:43And that's what our agents are experts in is finding the pain point within the users's problems.
38:50And our companies have uh built over uh many different niches and uh different sizes of shape and shapes of creators.
39:00And really what we're building is we want to give the power back to the content creator.
39:06They're stuck in these I worked at Tik Tok for two years and 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
39:19their own business.
39:20They just need someone to help them and that's what our agents do.
39:23At Built by Foundry, we deploy teams of agents to build recurring revenue businesses for content creators so that they can focus on posting and posting and posting and we can keep growing their revenue.
39:35I have a lot of thoughts on this one.
39:37Okay.
39:37So, the problem I find as a creator, 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.
39:47Like with me, for Versel, for example, there 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 anymore.
39:58It 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.
40:05One of the best things I ever did for myself.
40:06The single 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.
40:14Because now there's a wider variety of things to bring my audience.
40:18And 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.
40:24The variety of what I bring to my audience is where I find the most value by far.
40:29And every creator I've worked with ultimately has come to a similar conclusion.
40:33That said, you're touching on really important pieces here.
40:36Creators do have more taste than most founders.
40:39Creators do have better distribution than most companies.
40:41Creators know their audience better than most businesses.
40:44And you've built a 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.
40:52Where I could see this working really well is as 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.
41:04And I've told this to so many businesses that want to work with me.
41:08Watch my streams, watch my videos, find the problems I have, and then show me how your product solves them.
41:13That's a way better pitch than going to try and start your own company as a YouTuber.
41:16As the YouTuber who has started the second most companies, as far as I'm aware, it doesn't work.
41:20It just burns you out and it burns your audience out too.
41:23The 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 members.
41:32I understand and I will say your niche as a tech creator is one that is very challenging because there's a lot of options.
41:38Um and what we have found is that for all of our creators, I'll just few examples.
41:44Um, 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, which sounds ridiculous to us, but to his 2 million followers, that's a real pain point.
41:56And now they pay him a recurring revenue monthly or monthly or yearly subscription to solve that specific painoint.
42:04So, I think unfortunately Theo, you've picked a bad niche.
42:07I'm sorry, but um if you want to pivot to homesteading or gardening, m maybe uh we could we could work together.
42:13But 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.
42:23Our agents find a painoint that they're willing to pay for and zero creators across our entire company history have ever left our our company because they always are profitable.
42:34You're very convincing.
42:35[laughter]
42:36Okay.
42:36Excellent.
42:37Then give it up.
42:40Yeah.
42:40Thank you.
42:41[applause]
42:42Um, so we're going to have a little bit of a deliberation moment.
42:45Uh, the judge is going to go backstage for a couple minutes.
42:48In the meantime, sorry.
42:51Okay.
42:51In the meantime, we've, uh, prepared this little video recapping the World's Fair.
42:55So, take your time, judges.
43:00[music]
43:29This is our biggest AI engineer.
43:30[music]
43:31event ever.
43:40We're constantly adding
43:41[music]
43:41new techniques for you to draw.
43:55[music]
44:36[music]
45:08Thank you so much.
45:10All right.
45:11Um, I think we're inviting everyone back on stage.
45:15Give it up for the start of Battlefield.
45:18Um, congrats, guys.
45:22Yes.
45:23Okay.
45:24So, uh, we have some prizes that we're going to hand out.
45:27I think, um, the folks back there are doing some checks.
45:30Um, in second runnerup face, we have Kimode.
45:38[applause]
45:40Uh, Theo, I think you're presenting.
45:42Uh, yes.
45:45Okay, we'll take some pictures as well for the team.
45:53All right.
45:54U, next, and I guess this also reveals the order.
45:58Uh, we have Foundry as runner up.
46:02Great
46:02[cheering]
46:02pitch, Max.
46:04Great pitch.
46:05[applause]
46:09All right, congrats.
46:10Awesome.
46:11All right, no secrets now for the grand prize.
46:14Comment.io.
46:15Welcome.
46:16Great video as well.
46:17Congratulations.
46:18Well deserved.
46:24Do you want to also do the group photo?
46:26I think we're doing
46:27all of us.
46:28Um,
46:31yes.
46:32Okay.
46:32All right.
46:33What it mean just them?
46:34They're the founders.
46:35We're just here to com everyone.
46:37Oh, fine.
46:38We'll sneak it in.
46:40Get them.
46:41Come on in.
46:42Yes.
46:43All right.
46:46Thank you so much.
46:48You all had some building with hyper agent and
46:50Yeah, of course.
46:51Yeah.
46:53Um,
46:53[music]
46:53any last words that you wanted to comment on?
46:55I think you guys uh brief a little bit.
46:57Yeah.
46:58Yeah.
46:58I just want to thank uh sorry about the thank everyone here at AIE.
47:02We got amazing feedback um through the whole um time and uh Hyper Agent um for inviting us to this and Hyper fans for helping us with the video and uh
47:12overall like
47:13so much.
47:14[snorts]
47:15Check us out.
47:16It's free.
47:18comment.io I know.