Making $$ with AI Agents

Can AI agents become a real source of income? In this episode, we explore how entrepreneurs, freelancers, and creators are using AI agents to build businesses, automate services, and unlock new revenue opportunities.
Learn practical ways AI agents can help with content creation, customer support, research, marketing, lead generation, and workflow automation. We also discuss the tools, strategies, and business models that are shaping the future of AI-powered entrepreneurship.
Whether you're looking for a side hustle or planning to scale an existing business, this episode offers actionable insights to help you understand how AI agents can create value—and potentially generate income.

Transcript

SPEAKER_01 0:00

Howie Lu is an absolute legend. I mean, this guy started Airtable. Half a billion in revenue, a billion dollars in a bank, growing quarter after quarter. So he's one of those people that when I want to know where is the world going, I call Howie. This episode is structured into two parts. First, where is the opportunity when it comes to AI agents? I think that there's a trillion dollars up for grabs in AI agents. Does he think there's more? Does he think there's less? Spoiler alert, he thinks there's way more, and we get into it. The second part of the episode is where he reveals hyperagent.com. Now, hyperagent is an AI agent builder that allows you to build digital employees, allows you to build apps on different ideas, and I don't know why more people aren't talking about it. So I had him just give us the tips and tricks for how to use hyperagent so that you can outperform 99.9% of people. I got good news. How is he going to give you $1,000 of hyper agent credit, no strings attached? You just log into the account, there's gonna be a thousand bucks right there to go and build the business of your dreams. The catch is first a thousand people do it, get the thousand dollars, he's committing a million dollars. How crazy is that? Just writing a million dollar check of tokens to you, to the Startup Ideas Podcast community, play with hyper agent, to automate some stuff, to do some research, to build their business. So thanks, Howie. You know, all I ask is you like and comment on this video, show some love for Howie for doing such a cool thing. We need more entrepreneurs, more builders, and it's I'm stoked to see him support you all. Thank you to Airtable for sponsoring this episode. You guys are legends. Enjoy the episode and have a creative day. He's the co-founder and CEO of Airtable, and today we're gonna talk about agents. He's gonna do a little show and tell of his new product that I've been using for the last few weeks. Um, but first, Howie. I have been I haven't been sleeping very much, to be honest. Yeah, exactly. And I've got I I just need your reaction to some to just some things I've been thinking about. Yeah. So this chart over here, it's by Sequoia. In what domains are AI AI agents deployed? You can see software engineering is at almost 50%, back off is at 9%, marketing and copywriting 4%, sales and CRM for 4.3%, and down. When you see this, like what's your reaction?

SPEAKER_00 2:39

I mean, I think two things. One is I think it absolutely reflects the underpenetration of AI in industries that clearly could already be disrupted or benefit with even today's AI capabilities, right? If you took like frontier agents today and deployed them into every one of these categories, you should get to 100%. And then two, I think even the higher numbers, like software engineering, is actually kind of an overestimate. Meaning, you know, like as I think frontier developers and companies applying frontier agentic development practices are finding, like, you know, the new model of software development is not even just like every engineer using AI autocomplete, like tab autocomplete, which like we all figured out like three years ago, right? Uh with even GitHub Copilot. But it's now like you don't even need the IDE, right? Like the way I develop on hyper agent is I have like 30 different Claud Code instances running in parallel, and each one is coupled up to like a browser, fully autonomous. It can go and like get other agents to comment on any uh PRs it creates. And so like this modality shift of like, you know, no AI to like kind of what I would call Gen 1 AI, which is like basically like AI augmentation for still like very human-driven development workflows. Andre Karpathi talked about like you know, in October, November is when he completely inverted from like mostly still human written code with AI augmentation to completely the opposite, right? And that's what we've seen like the frontier companies leap into. Like I think even the 50% is an underestimate because the number of companies and even people who have switched into that new frontier mode is actually like you know, definitely less than 50% of software engineering today, right? So I think what we're actually seeing is like the frontier is advancing so quickly, and many companies and many industries and many functions are barely catching up to like the three-year-ago state of the art, let alone like you know, disrupting themselves and their comp you know and their industry with the new state of the art.

SPEAKER_01 4:42

Right. Well, I mean, another way to think about it is like there's co-pilot territory. These these charts are from Sequoia, right? There's co-pilot territory, there's autopilot territory. Like, how do you see, you know, you look at this, right? Uh this, you know, this is what Sequoia says. There's a there's a trillion dollars up for grabs within agents. Yeah. Um, but they're very different. What's your reaction to this?

SPEAKER_00 5:04

I mean, look, I I think um to me, it's like these agents really reached a breakthrough, really, you know, call it like four or five months ago, right? And um, I think developers felt this with Opus. Uh, you know, Opus 4.5 just kind of set a new high water mark of like, whoa, this thing for the first time, like really feels like a true software engineer that's able to work like on a task that would have taken a real human engineer like maybe many hours, if not days. It can go do it completely autonomously, and it ships me a perfect clean PR that I can just review like a, you know, like a reviewer would, right? And I think that that experience is going to be unlocked and already is unlockable across every single other domain, right? Because we've kind of just reached this point where like the models are more than smart enough, right? Like you talk to these models even in like a more synchronous like chat interaction, not like an autonomous agent interaction, and you like you can ask it the most advanced things, give it like really complicated subject matter content, right? Like management consulting, you give it like you know, kind of some some really hard media problems in the context thereof, and it gives you really smart answers that truly are like expert level. And so it's clear that the model intelligence is there. The models are smart enough also to kind of coherently execute across multiple terms with lots of tools and context. And so I think it's more of just a matter of how and how quickly we can deploy agents into every role in industry before we can like truly just almost do anything that humans could do in each of these functions with agents. And I mean, the TAM for that is like not even a trillion, it's like probably like the whole GDP of like all white-collar labor, which is like obviously many tens of trillions, right? Like in in even like the Western hemisphere alone.

SPEAKER_01 6:51

Right, which is sort of like I don't understand how you're not how people aren't motivated to create startups right now in that sense. Like the person listening to this is like, yes, yes, Howie, you know. Um, but it just feels like you know, I can't think of a better time to be creating a startup than now.

SPEAKER_00 7:08

Totally. Right? I think like, I mean, yeah, I think the weird thing is like it's almost like using is believing, right? Like it's really hard to fully grok the power here if you haven't actually gone and hands-on spent like at least a full weekend playing with agents, right? Like, and that means more than just a superficial like you did like some naive, like one-shot thing, like, hey, like, you know, who's gonna win the next presidential election? Like, kind of question that you could have asked a chatbot. Like, I think people are not actually coming in and when they're doing light experimentation, they're not actually putting in an ambitious enough prompt or task in front of the frontier agents, and they're still kind of using it like they use Gen 1 chatbots. And like until you actually experience the full power and autonomy of these frontier agents, um, you know, I think it's hard to fully extrapolate like what types of companies can be built now that were possible for structurally. How could you build like a multi-billion revenue business with one human and like hundreds of agents, right? Like you have to use it to get it.

SPEAKER_01 8:13

Also, you know, this is another chart I can't stop thinking about, which is the unique economics just absolutely crushed. When you look at a human a human person versus an AI agent and what a cost, like you can create some serious gross margin businesses on top of this.

SPEAKER_00 8:29

100%. And this is the funny one because you know, I've seen kind of you know, a lot of people like complain about the the cost per token of the frontier models, right? So like Opus 4.6, now 7, clearly the most expensive model, right? Um, you know, and then like GPT 5.4, very good, still kind of expensive, even open source, like you know, like it's cheaper, but like it's not free, right? And I think like people, you know, are some people are struggling, I've seen, to like, you know, adopt to this mental model of like, you know, in the old days of software, like a lot of stuff was free. Like you could get like, I mean, even ChatGPT has a free version, right? That you just use however much you want, you get a cheap, dumb model, but like you're not expending that many tokens because it's not actually doing like autonomous multi-turn work and expending like a billion tokens like every few days, right? Like it's much more token cheap or token token lean. And I think that like we have to get over this hump of like, you know, anchoring our price expectations for AI on like traditional subscription software where it's like, oh my God, I have to pay like 20 bucks for like Netflix per month now instead of like whatever it was 1299 before. And instead think of this as like, yeah, like to your point, like how much would it have cost a human to do the thing, right? Like, you know, if I wanted to go and like create an entire marketing campaign, um, or actually in my you know, CEO, CEO role, like it's funny, like one of our recent uh board memos that I wrote uh and sent out to our entire board and kind of major investor lists, like, you know, a lot of it was researched and crafted by hyperagent, right? Obviously, with like my you know kind of instincts and context and whatever imbued into the agent. Um and of course I oversee it at the end, but like I got feedback that that was the best memo from some of our best investors that I had ever written. And I'm like, yeah, like, you know, because an agent did it. And by the way, I got to do it in like 10 times less time. And so, like, even if it cost me, let's call it like $150 of tokens to generate that output, like, think about the opportunity it costs my time. And so I think that is a real reframe moment that's needed. Um, is let's think of this as like, what is the human equivalent time cost versus wow, $150, that sounds really expensive, versus like a $10 per month sub.

SPEAKER_01 10:46

100%. Yeah, I think uh the way I always think about it is like I anchor it around value, right? What's the value I'm getting out of that? I mean, the truth is with your boy, you know, your board deck or whatever, like it probably was the best. You know, it probably was the best because you had you had so much research support.

SPEAKER_00 11:04

Yeah, totally.

SPEAKER_01 11:05

Um two more quick graphs, and then I want to get into uh hyper agent. Um percent of enterprise apps with embedded AI agents. Um, this is the fastest adoption curve in enterprise history, right? So like when you see this, you know, how do you react?

SPEAKER_00 11:26

I am not surprised. And I think even this reflects the pace at which like incumbents can even like integrate AI into their products, right? And I think even that is like stimmied by like just incumbency and like you know, kind of how how seriously did enterprises, um, you know, uh enterprise apps or enterprise app makers or internal app teams like take this. I think the real show of how profound this growth curve is is like if you take the aggregate revenue created from zero of all the leading AI companies, right? Or companies like doing AI things, like take OpenAI and Anthropic alone, right? Let's just say they have a combined revenue probably of like 80 million uh plus, right? Or 80 billion, sorry, plus right now, up from like basically zero a few years ago. Like, what in in the history of software, like has there ever been an industry where like any company, let alone like, or even in aggregate, like you know, across all the companies, you got a category that went from zero to like you know 80 billion plus, right? And that's not even including like all of the other AI providers, inference uh inference providers and like you know, tooling, et cetera, like out there. Like the the revenue of like I think the AI category is an even sharper curve. And I think that really reflects like just how profound this lightning in a bottle is.

SPEAKER_01 12:50

Totally. And just from an opportunity perspective, it's like you know, selling to these enterprises and helping them figure it out and and and just you know, helping them transform is just you know a huge, a huge uh opportunity.

SPEAKER_00 13:04

I think it's like probably the one like one of the bigger cash grabs in like business history is you know, there's kind of two angles I think that you know to create uh a very valuable business right now with with AI as a wedge, right? One is PLG, and obviously we see a lot of these like PLG products. I kind of put OpenClaw itself in this category because even though it's like not actually like a monetized business, like it is getting this massive amount of adoption, right? And and uh, you know, just the raw token consumption through OpenClaw is, I'm sure, in the many hundreds of millions, if not billions already, right? Um, and and likewise other other products in the PLG genre. So that's one way, it's just like let people use the AI thing that actually works, and you're gonna get profound growth. But the other is like to come in top down, Palantir style. This is why OpenAI and Anthropic and like you know, the the big guys are also doing it. There's new companies as well going after this opportunity, which is go pitch to every enterprise board and CEO, like, we will fix your AI problem, pay us a massive check. Like, give us a hundred million dollar plus check, and we will purportedly solve your problems for you. Like, that is an existential like risk mitigation that like every large company incumbent should be willing to pay. Because frankly, like the CEO's choice is like either I pay it and I risk wasting $100 million and maybe getting fired over it, or like I don't do anything with AI and I'm definitely getting fired over it. So on a game theory level, it's like everybody's gonna pay it, right? Now, whether that actually results in like long-term substantial structural like you know, kind of transformation to the business that probably could be run now with like five people, maybe instead of like 50,000, right? In some cases, uh, that's a bigger question.

SPEAKER_01 14:48

Yeah, and and this this is uh you know, it sort of speaks to my my last point too, which is like if you can help a company uh, you know, run a fleet of 20 agents doing customer intel, content production, competitive research, lead enrichment, like all these different things. Like this is the future of work, like in one image, right? An agent command center, right?

SPEAKER_00 15:08

Yeah.

SPEAKER_01 15:08

So when you see this, your reaction.

SPEAKER_00 15:11

I mean, look, that literally is a view in hyperagent. Uh I look, I feel like I'm looking at a hyper agent, and I think this is the future, right? Like we are building towards a world where, you know, it may not be that every company is like literally one person, right? And we have a lot of like one person companies, you know, but I do think like every company will have a fleet of agents. And, you know, what's interesting to me is actually that like, you know, agents are converging on like these purposeful, like they almost map two job roles that humans were playing, right? And you know, maybe it's a little bit like why are why are robots like hardware robots converging on a humanoid form factor? And part of it is like, well, like a lot of the infrastructure of everything we have in our homes, in construction sites, in in factories are built for human ergonomics. So for the robot to effectively, you know, kind of um just kind of insert themselves seamlessly with the current infrastructure, they have to kind of have human scale, you know, kind of capabilities, right? And so I think there's a kind of very similar phenomenon happening with agents, which is it's not like, I guess like five years ago when people talked about superintelligence, I always imagined like there's gonna be just like like the single omnipotent like AI that just like figures everything out and looks at everything all at once, like everything, everything everywhere all at once, right? And I think now like I'm more and more of the belief that like there are gonna be fundamental and and always you know kind of present limitations on like context windows, for instance, right? I just don't think we'll ever we're ever gonna get to a point to where like a an AI model can like have infinite context window, right? And I think there's like a physics to that, right? Like you can just literally only have so much attention and like so much you know, context at once. And you know, I think what that means is that like for the same reason why we partition humans into different roles and org structures so that not everyone in a company has to know everything and work on everything all at once. Like, I think the same is true for agents. And so hence, like you get this like overview of agents that actually maps like to kind of intuitive human-played roles really well. And that's the really kind of interesting emergent phenomenon for me. You know, I just uh recently like spent um some time playing around with paperclip, which is kind of fun, uh, because it literally creates the org chart metaphor. Um, but I think this is really exciting, right? Where it's in a way, it's it's both familiar because we're not like just completely upending like everything we knew about like job functions and like roles in the old world to the AI world, and yet like there is a rethink and reapplication of like, okay, how do I play that content production role with an agent?

SPEAKER_01 18:02

Right. Well, I think we should get into hyper agent. Now is the time, right? So you know, for the listener, like what is hyper agent? Why are you building it? And this is a show and tell podcast. So, you know, by the end of this part of you know, by the end of this episode, like, you know, can you commit to you know giving all the sauce around how to use hyper agent to sort of build a business?

SPEAKER_00 18:26

Sure. Yeah, let's let's go for it. So this is hyper agent. I'm currently in a thread. I'll zoom out in a second and kind of um show you what like the uh entry point looks like. But you know, think of hyperagent as like if all of these other agent products out there like OpenClaw, et cetera, are kind of more like Linux, like hyperagent is our take on like the Mac version of it. Like we want it to just work, to be secure, it's cloud native, like you know, you don't have to run a Mac Mini. And and perhaps most importantly, like, you know, hyperagent is like applying a lot of the same design philosophy and like obsession with great UX that we applied to the no-code app category 10 years ago, but now to agents, right? Meaning like apps are kind of complicated, right? Like, you know, if you're a developer, even at that time you could build a Rails app, you had like a data layer, a logic layer, a view layer, but like it was kind of technical, right? And or very technical. And the whole idea of Airtable was to distill that into a really intuitive experience. In fact, we were very inspired by like the Macintosh, the GUI, like taking terminal-based command line computing and making it into something that like people could just grok immediately. And so, you know, hyperagent is really intended to be like a very intuitive and like visual uh way of using agent. So this is actually a um a task thread that I ran a little bit earlier. And uh this is actually uh one of your startup ideas, Greg, uh, that we had a hyperagent work on. Um and basically the pitch was hyperlocal uh market reports for real estate agents generated from public data, right? And um, and so basically, this agent went around and did research on the landscape of the market. Um it ran a bunch of like uh analysis, it's got full coding capability, it's got a full sandbox environment. So it is running a full computer. It's just one of the cloud, not like you know, kind of your own computer. And you can connect it to all your accounts if you want, like it can access your Slack and granola and email, it can send stuff if you want it to on your behalf, or just pre-draft emails. Um, you know, it's got uh already pre-configured ability to do things like pull from Twitter, um, use uh advanced tools like generate imagery or use Google Maps, et cetera. But basically what happened was it went around, it did all of this, it researched the opportunity, right? And then created this research brief. Um and let me just uh show you what this one looks like. Um this is kind of the business case for uh for the idea you pitched, right? Uh I kind of love it because like I actually think um, you know, these what I would call like medium-sized markets, like it's not like a hundred billion dollar market, which is gonna be super competitive and there's gonna be massive incumbents going after it. But I really love this idea of like the kind of like maybe it's not micro, it's more like me mini or medium market, like a couple billion TAM large, which is to say you can build a very lucrative business, even capturing like a double digit percent chunk of this, like you can make a few hundred million per year, and yet like it's small enough to where really big guys are not coming after it, right? So um, you know, this this uh this agent created kind of a business case for it. It found some really cool um like user validation of the problem. So it saw like you know, looked up Reddit, like, you know, and found like some real real estate people who are actually saying, like, I need this product, right? So it's kind of validating the market need. Here's actually the current problem. Uh I didn't even know about this, but like apparently I guess there was some like legal thing that um you know kind of uh changed uh you know kind of the dynamic of the market. Um people don't want more software, like you know, another tool with an interface, and did like some competitive analysis. Here's who who else is out there, um, and then kind of just put together the case for this, right? But then, you know, better yet, like you don't just have to stop there, right? You can go and like actually tell it to go and just build a v1 of the product. So in this case, because hyperagent has full coding capability, it just went ahead and like created a V1 of this product, right? Which uh I think this will actually work. Like, where do you farm? Like um here's uh my report style.

SPEAKER_01 22:35

It also looks really clean.

SPEAKER_00 22:37

What's that? Yeah, I mean, and like honestly, a lot of this is just like if you have a good frontier agent running a frontier model, i.e. like Opus 4.7 or GPD 5.4, like it just does a lot of this really well out of the box. So any frontier agent powered by frontier model should be able to create an app of this quality. What's unique about Hyper Agent is that it can do that perfectly well, but then kind of do that in the in the uh workflow of like it's not just an app builder. App building is just a feature now, it's a commoditized feature. And what it can actually do is like go and research the end-to-end of like, here's actually the business context of what I'm trying to do, and then build the app informed by it, right? So it's more like hyper agent is the founder in this case. It's not just the developer, it's the founder. Um, one other cool thing I like about uh hyperagent is like it just comes out of the box with like really powerful tools. So it has like, you know, Google Maps as a tool, and it can actually go and like let's say, I think I already did this, but um like I wanted it to go and actually find like real Street View imagery of billboard locations so it knows how to use Street View to like find actual points of interest, and then to take that image and use that as a reference seed image uh for like a uh AI image generation or video generation, right? So, like, I mean, another cool thing you can do with hyperagent uh is you could tell it like take this house and like I want you to redesign the house using interior photos from Zillow or like the exterior shots, and it will do that like really, really well, right? So that's hyper agent in a in a nutshell. Uh, can walk through some of the other stuff here. Um, you know, once you actually build like a lot of agents, then you get like this this ability to start looking at like, well, what if I wanted to see, you know, um not