Pod Insights Y Combinator Podcast AI Apps Are Broken — Here's How To Fix Them About this episode In this episode of The Breakdown, Tom and Dave are joined by fellow YC General Partner Pete Koomen to lay out a new vision for how AI should actually work: not as a chatbot bolted onto legacy software, but as a customizable tool that helps people offload the work they don't want to do.
From editable system prompts to agents that act more like collaborators, they dig into what it means to build AI-native software—and why the future belongs to products that let users teach machines how to think.
Transcript 0:00 Speaker 00 We're using old software development techniques to build these features, and we're not actually taking full advantage of what AI can do. I think the promise of AI for many of us is that it allows us to build software that the user can program to do whatever they want using only natural language.
0:17 Speaker 01 Steve Jobs describes software as a sort of, as a bicycle for the mind. This feels like, I don't know, like a rocket ship for the mind.
0:30 Speaker 02 Welcome to another episode of The Breakdown. Today, we're lucky to have Pete Cuman, our partner here at YC. Pete was the founder of Optimizely, which built software to help companies A/B test. Pete, welcome.
0:41 Speaker 00 Thank you. Happy to be here.
0:42 Speaker 01 So Pete, you recently wrote an essay which caused a bit of a stir and basically told everyone on the internet who's building AI agents that they were doing it completely wrong. So tell us more about that.
0:52 Speaker 00 Sure. When I use AI in my day to day life, I've kind of had two radically different experiences, right? On one hand, when I use tools like Cursor and Windsurf to build software with AI, it feels like the most powerful tool I've ever used, right? It's this feeling of being able to create anything I want, anything I can picture in my head, you know, will materialize in front of me with these tools. And it makes me so excited about AI. And then I've had a lot of other experiences. A lot of times when I'm using existing apps that I'm used to that have incorporated AI into them, where it doesn't feel that way at all. It actually feels like more of a chore, right? Like using the AI actually creates more work for
1:36 Speaker 00 me than just doing whatever it was myself. The example that I chose in my essay was to pick on the integration that the Gmail team built between their AI model Gemini and Gmail's interface. That this is a little draft writing agent that can create email drafts for me, given some instructions.
1:59 Speaker 01 Yeah. And the underlying Gemini model is pretty phenomenal, right?
2:01 Speaker 00 Gemini is amazing. The model itself is absolutely incredible. We've been using it a lot at YC to automate our own work. I'm so impressed with What they built. But a lot of that power, I think, is hidden behind a UI that makes it really frustrating to use. And that was really the focus of my essay. The feature that I chose as my example was this little draft writing agent that shows up in the Gmail interface. And what it does is I can put in some instructions. So in this case, let my boss Gary know that my daughter woke up with the flu this morning, and I won't be able to come into the office today. And it will spit out an email draft for me.
2:39 Speaker 02 To make your life easier.
2:40 Speaker 00 To make my life easier, exactly. So here is the email draft that I got when I put that prompt into the UI. Dear Gary, I'm writing to inform you that my daughter woke up with the flu this morning. As a result, I won't be able to come into the office today. Thank you for understanding. Best regards, Pete.
2:58 Speaker 01 And if I got this, I'd be like, Pete has been, has his brain taken over by like.
3:01 Speaker 02 Or he's been phished. And I need to report phishing, like something's going horribly wrong.
3:05 Speaker 00 Pete's accountant hacked, great. There's two big problems with this. The first problem is, like you're pointing out, that doesn't sound anything like me, right? If you got this email, you wouldn't, you would assume somebody else wrote this. My account got hacked, I got phished. And then the second problem is that the draft, or rather, the prompt that I used to explain what I wanted in the draft is roughly as long as the draft itself. That was my contention here, right? On one side, you have, using AI makes you feel like a superhuman with unlimited powers. And on the other hand, using AI is frustrating and adds more work.
3:44 Speaker 01 Maybe we're jumping ahead a little bit, but the ideal experience, I think, would be something like
3:49 Speaker 01 telling the AI, hey, my daughter's ill, like, figure out my calendar today. And then it go through your actual calendar and then figure out each person and then write the appropriate email in the appropriate tone, right? That's, perhaps we're skipping all the way to the end, but.
3:59 Speaker 00 I, I think that's exactly right. These AI models are now capable of doing things like that, where it's actually anticipating all of the things that will need to happen as a result of my daughter waking up with the flu and helping me do those things. But that's not what we've built, right? That's not what, what the team has built.
4:17 Speaker 02 So Pete, like, the people who work on Gmail, I know a lot of them, they're not dunces, right?
4:21 Speaker 00 Not at all, yeah.
4:23 Speaker 02 They probably don't use the feature themselves, is my guess, because I don't use that feature. So how did we get here? Like, how did this happen?
4:29 Speaker 00 I know many people who work for Google, they're incredibly smart. Why did they ship a feature that ostensibly nobody, including probably them, actually uses? I don't know. Part of the problem is that we're using old software development mentality, old software development techniques to build these features. And we're not actually taking full advantage of what AI can do. And so I'll explain what I mean by that, right? And I'll go back to these two problems. Let's start with the tone problem, right? This doesn't actually sound like me. Why is that? Well, what's actually happening under the hood? When I ask this Gmail agent for a draft is that this Gmail agent, this little UI,
5:11 Speaker 00 combines my prompt, which is the one we just talked about, with what's called a system prompt. And the system prompt is the text that explains to the AI who it is and what its job is. And this gets reused every single time. And in the Gmail case, I don't actually know what that system prompt says. This, it's been hidden from the user. I'm not allowed to see it, let alone edit it myself. But we can kind of guess what it says, right? You know, and I've got a little caricatured version of this in the essay. The system prompt here is something like, "You're a helpful email writing assistant responsible for writing emails on behalf of a Gmail user.
5:54 Speaker 00 Follow the user's instructions and use a formal, businessy tone and correct punctuation so that it's obvious the user is really smart and serious." I actually built a little demo so that you can see the impact of a system prompt on the email draft, right? And so we combine our sort of supposed Gmail system prompt with my user prompt here, asking for the draft. And we generate the draft. And we get something like this, right? Again, it's an email that seems perfectly reasonable. It does what I've asked, but it doesn't sound like me.
6:25 Speaker 02 So just digging in, like, we are guessing what the system prompt might be in this case.
6:30 Speaker 00 Totally.
6:30 Speaker 02 But it seems we've guessed close because the output is basically what you get when you use Gmail.
6:35 Speaker 00 Relatively similar, yeah.
6:36 Speaker 02 And so like, just to point out, the system prompt that we have created here is one that is generic. It would apply to all users in exactly the same way. It's one that's like, safe. You've explicitly told it, use a businessy tone, look smart and serious. Don't say anything that might make the parent company of this product look bad.
6:56 Speaker 01 And that's got to be the overriding concern for a lot of these companies, right? We don't want the AI to say anything that's going to embarrass Google. I remember it was either Google or Facebook released like a scientific model like pretty early on and then very, very quickly pulled it back because it was saying some hallucinating sometimes. Yes. And so I think like Google were really on the cutting edge of this transformer technology and were like leapfrogged by OpenAI just because I was too cautious to put stuff out there. Yes.
7:20 Speaker 00 Yeah, I think that's probably a big part of this, which is that, you know, my, my little supposed version here is probably incomplete. There's probably a whole bunch of additional text in the real system prompt about not embarrassing Google and all of the different things it should avoid doing.
7:33 Speaker 01 If you say anything bad about Sundar, you're fired.
7:36 Speaker 02 And we've seen this recently as the system prompts kind of get leaked at times. And it's a big deal because everybody's, oh, we see the system prompt now for this AI assistant or this one. And if you read those system prompts, yeah, they're quite elaborate and they have direct instructions to avoid certain problems.
7:54 Speaker 01 You can see the mark of the HR team in that system prompt.
7:57 Speaker 00 I think there's a deeper issue here, which is that we're designing features like this in a way that looks a lot like software that we've been building for decades. Right? And so just to illustrate what's possible, let's imagine that Gmail allowed me to not only see, but edit this system prompt for myself. I might write a version that instead of saying you are a generic Gmail writing, you know, email writing agent, my version might just say you're Pete, right? You're a 43 year old husband, you're a father, you're a wicy partner, you're busy and so is everyone you correspond with. And so you do your best to keep emails as short as possible, right? What I've done here is I just take the little program in my brain that is used for writing emails and I've done my best to explain to Gemini how I do that.
8:42 Speaker 00 And if you just take that same user prompt asking for a draft of an email, explaining that my daughter's sick and you use my system prompt rather than the generic one size fits all. Gmail system prompt, the draft we get. Hi Gary, my daughter's sick with the flu, so I can't come in today. Thanks. That sounds like an email that I would have written.
9:02 Speaker 02 Totally.
9:03 Speaker 00 By editing this system prompt, I'm able to explain to the AI model how I write emails in general, so that I don't have to do it every single time. And so, going back to your question, why did the Gmail team decide to hide this system prompt away?
9:19 Speaker 02 Yeah.
9:20 Speaker 00 My contention is that a lot of AI app developers, including the Gmail team in this case, are treating the system prompt the same way they've been treating code for decades. For, you know, as long as we've had a software industry, there's been a division of labor between me, the user,
9:37 Speaker 02 Yeah.
9:38 Speaker 00 and you, the developer.
9:39 Speaker 02 The user does not see the code.
9:40 Speaker 00 User doesn't see the code. The developer's the one who is responsible for building the system, defining the system. It's all hidden away and it's abstracted behind an interface that I can point and click around to do something. It's more or less one-size-fits-all software. That's the only way we've been able to build software up until now, is one-size-fits-all, right? If you're building an online bank software or a piece of A/B testing software, your job as a developer is to go and talk to hundreds or thousands of users and synthesize all of their needs together into one common set of features that you're going to build in your one-size-fits-all piece of software. Sort of lowest common denominator software. Exactly. Exactly. And that's, I think, the Gmail system prompt in this case.
10:24 Speaker 00 You can think of it as the lowest common denominator email writer. It's the safe, anyone could use this thing, but what it results in is emails that no individual would actually write. Yeah, no one's getting fired, but this is not the software that's going to take over the world. That's exactly right.
10:39 Speaker 02 You think that developers today are using AI in the previous generation of software development that they've been used to?
10:48 Speaker 00 Yes.
10:48 Speaker 02 And so you use this phrase, the AI horseless carriage.
10:51 Speaker 00 Yes.
10:51 Speaker 02 Maybe tell us what you mean by that.
10:52 Speaker 00 This is a reference to early automobile designs that looked a lot like carriages, with the horse replaced with an engine, right? And there were- All sorts of problems with that design. You know, for example, there's less suspension on carriages, which didn't work running at high speeds with a vibrating motor, right? That the center of gravity was higher, which made turns harder at high speeds. Basically, inventing the motor was only a small part of what was needed to produce a vehicle that could take advantage of the motor's power.
11:25 Speaker 02 It only became useful once you redesigned the entire.
11:28 Speaker 00 That's exactly right, right?
11:29 Speaker 02 Yeah, and this phenomenon we see over and over and over again in technology. Like some ones that I have lived through. When the internet came about, a lot of the first search engines were literally just like digitized Yellow Pages. It was just a directory of listings. And now, of course, we think like, that's silly.
11:46 Speaker 00 Kind of like Craigslist, right?
11:47 Speaker 02 Yeah, that's what it started with. When mobile came out, the first mobile apps or a lot of the first mobile apps were basically just websites wrapped in a native app wrapper. But they didn't take advantage of any of the new technologies available on the mobile phone, like GPS, like multi-touch. And it takes a few years typically to get to the useful bit of the new technology. And I guess what you're claiming, Pete, is we are like not there yet.
12:12 Speaker 00 I think the deepest problem here is that when the Gmail team set out to build this, they kind of asked, how can we slot AI into the Gmail application?
12:24 Speaker 02 How do we replace the horse and put an engine in?
12:26 Speaker 00 That's exactly right. And the problem with that is that Gmail is an application designed for humans to do work in, right? The real promise of AI, I think for many of us, is using AI to automate repetitive busy work, right? And a lot of the time I spend on email is repetitive busy work, right? It's work that doesn't really need my full brain power, but because we haven't had the technology, requires it, right? And so I give a little example in my essay of just, you know, using these simple techniques, what you could do with an email inbox. And here's that example. So this is an example of an email inbox. It's got a bunch of messages on the right-hand side.
13:11 Speaker 00 There's an agent operating in this inbox. And instead of an email writing agent, it's an email reading agent. On the left-hand side here, you can see this is a system prompt that I wrote that just tells this agent what to do with each email. And the agent can assign a label to an incoming email. It can archive it. It can put a color on that label. And it can write a draft, right? Those are the things this agent can do. And my instructions are just what to do for each email. So if the email is from my wife, draft a reply and label it personal. If it's from my boss, draft a reply and make it priority one, right? Which is just one notch lower than emails from my wife.
13:53 Speaker 00 And for anyone else at YC, where I work, a YC label and give it priority two, right? If it's from a founder who needs help, put a founder's label on it. This is my sort of internal, again, mental model for how I organize my email. And I'm just explaining to Gemini how I do this work so that it can do it on my behalf.
14:11 Speaker 02 One thing I find interesting about this is this is really the code. This is the programming you are doing for this agent. But if you read it, it's pretty accessible, right? It says, if it's a tech-related email, label it tech. If it's somebody trying to sell me something, archive it. And this is like a great example of how the LLM technology is actually good enough to let non-programmers program these apps.
14:34 Speaker 00 You know, the way that I think of these models, a super smart, fresh grad, right out of college, that can do anything pretty well, but has no idea what to do. And the missing step is giving me the ability to teach it how to do work that I don't want to do, which is what I've done here. And you're right, it's completely accessible. And one of the sort of pieces of pushback I got when I started talking about letting users just write their own system prompts was like, well, most people aren't technical. They don't know how to do that, which I think is true. This isn't a skill we're born with. But in doing this myself, I've found it really intuitive. It's basically like thinking of the way that I make a decision and then trying to explain to this AI model how to do it.
15:15 Speaker 00 And as long as you can kind of watch it do the job and give it feedback by adjusting this prompt, it's pretty intuitive. And so, for example, here, here's what happens when I apply this little agent to each one of the emails in this inbox.
15:31 Speaker 02 Amazing.
15:32 Speaker 01 I love this. This is an essay, but it's not static. I love the fact that you've built this little widget so people can actually go in. They can edit the system prompt themselves to kind of reformat it the way they want and rerun it. I think it's such a smart way to convey this kind of idea.
15:47 Speaker 00 One of my favorite things is building demos. And this felt like an intuitive way to start talking about some of these concepts. And this is just another example of how powerful AI makes you feel when you're building with it, when you're just interacting with these models.
16:01 Speaker 01 How did you build this, by the way?
16:02 Speaker 00 I vibe coded the whole thing. So I wrote, you know, this, I mean, ironically enough, these AI models are not actually that useful to help you write things from scratch, right? The part where I used AI was describing a demo that I thought would help communicate the point I was trying to make and then watching it appear, right? Like, it's absolutely magical.
16:23 Speaker 01 It's astonishing how good these, especially the coding agents. I mean, I rebuilt my entire, I was on Tumblr and I migrated my blog to custom software. I wrote in one hour on a train journey. The coding agents especially just seem so far ahead of all the other general purpose agents right now.
16:40 Speaker 00 Yes. And to me, like, we will have caught up when everybody has the same experience that we have when we're using these coding agents in their particular domain, right? And so when accountants can build accounting agents that do just all of the repetitive workflows on their behalf, when lawyers can be lawyering agents that do the repetitive workflows on their behalf. It's basically every profession, I think, will have its cursor moment or its windsurf moment.
17:07 Speaker 01 Why is cursor, windsurf, or Claude Code so far ahead of, you know, lawyer agents or accounting agents?
17:13 Speaker 00 I think there's two reasons. The first is that these AI models are incredibly good at processing text, right? So if I can write a good description of a thing I want, they can process that description, that prompt, and turn it into a bunch of code, right? This is going back to why it was so annoying to write that original email prompt is like, these agents aren't good at writing from scratch. They're good at processing instructions and doing and turning them into some text output, right? For code, that's actually really useful, right? If I can describe in English what I'm looking for. An agent can output code that does this and it's remarkably effective. This is just a domain where these agents are really powerful.
17:56 Speaker 00 I think the other reason, though, is that developer tools are power tools, right? Developer tools, by definition, allow you to get to the bare metal, right? To get under the hood of whatever it is you're working with. And so the teams that built these tools allowed me full access to have this agent do whatever I wanted it to do. They didn't spend a lot of time making sure that I didn't do anything that was embarrassing to Winserve or embarrassing to anyone. I'm allowed to just interact directly with the model and use the full power. Whereas I think a lot of other domains were still using this sort of like kid glove mentality of like, oh, don't let them use the full power of these models. I hope we're able to move beyond this moment in time
18:40 Speaker 00 where there's so much assumed liability on the part of the application developers for what these models do.
18:46 Speaker 02 Well, I think the interesting point is if you do change the model where the user is in charge or at least has access to the system prompt, then the repercussions of that are on the user and not on the company that built the tool.
19:00 Speaker 01 Yeah, it's like Google put Gmail out and if you write an email full of profanity, that's on you, right? Like exactly the same way. Like if you change the system prompt to act like, you know, a violent jerk, like that's on you. That's not on Google.
19:13 Speaker 00 Yeah, it's a shift in mentality, giving AI to the user as a tool and they can use it for whatever their purposes are versus feeling like you as a developer have to be responsible for everything these models output. And therefore you nerf it and it's not useful.
19:28 Speaker 01 So I broadly agree with everything you've said so far, but I had some questions. And the first one is, do you think the vast majority of people are able to write these kind of system prompts?
19:40 Speaker 00 I think the answer today is no. But I think the answer to that in the near future will be yes.
19:47 Speaker 01 Why is that?
19:48 Speaker 00 When I was growing up, computers were still seen as a power tool that only nerds really knew how to use, right? And today, that's just no longer the case. All of us use computers all the time. It's no longer like remarkable or interesting for somebody to use, to be able to use a computer, right? And so what happened is we all just sort of figured out how to use these things and the interfaces got better, right? It's the tech, the tech got better and we learned how to use it in time and so it is unremarkable, right? And I think the same thing is going to happen with prompting. So I think this is actually going to happen a lot faster with prompting because writing a prompt is a lot more accessible than, you know, operating your file system manager on, on your computer.
20:31 Speaker 00 You don't need to understand much, except to be able to explain yourself in English or whatever language you speak. Having done this several times myself now, it's surprisingly intuitive, right? Thinking about, okay, how do I write an email, right? It's, it's a bit of a toy example, but it's kind of fun. You can write your little internal algorithm for it and then you can watch. And if it gets the tone right, your system prompt is, is right.
20:56 Speaker 01 Yeah, and I agree with you on that. I think basically everyone will be able to do this. I'm not sure everyone will want to or to have the initiative or age. Like we're three founders sitting here, we love to tinker with this stuff. Like you're telling me about your mom using.
21:10 Speaker 00 Yeah, like my mom uses Gmail every day, but is she gonna write a system prompt? Like probably not.
21:15 Speaker 01 Probably not, yeah, maybe.
21:17 Speaker 00 So I don't know, I've, I've learned not to be pessimistic about what, what users can learn. But I agree with, with the point, Rich, is that I don't think I'd want to write my own email writing system prompt from scratch. I'd like to have that option, right? But I've been using Gmail for 20 years, right? And there are 20 years of my email history that a good product could use to, to create a draft prompt for me, right?
21:42 Speaker 01 The idea that, take a human analogy, you hire and you employ, you hire an assistant. The idea that you're going to sit down and write out 30 pages of instructions of how you do every single thing in your life is possible, but none of. Others actually do it. You sort of, you give this gradual training. And if they were able to go through and read all your previous emails, they kind of learn. And then you, you kind of interact with them step by step and they write a few emails and you check the drafts. And when you're comfortable, you say, I didn't want to check the drafts anymore, send it. And then if you spot something that's not quite right, you say, oh, no, no, no, I would have phrased that this way or this other way. And the human would take that feedback and edit their own system prompt.
22:18 Speaker 02 So Pete, you've been actually doing this at Y C, right? You've built some internal tools that our finance team or our legal team uses. And it feels like the interaction has been exactly what Tom just described, where you literally go sit next to a finance person. And you ask them like, hey, tell me how you do this, this workflow, like, show me what you do. And then you write some system prompt for them, they try it, it doesn't do it quite right, they edit the system prompt with you. So it's kind of this like back and forth iterative model that you're suggesting.
22:47 Speaker 01 Yeah, and I'd go further than that. I think there's like a missing tool in A I app development, which is like, not everyone has a piece sitting next to them. But you could have an A I system prompt writer sitting next to you to take. You say, no, this wasn't right, or I would have rewritten it like this, and it will translate that back into a system prompt and kind of self-edit or auto-update.
23:06 Speaker 00 Absolutely, right? And, and these are great examples of UI conventions that we just haven't figured out yet, right? And, and you can see people are experimenting with different models here. Some of the foundation model labs have started building memory into their, into their chatbots, right? Which is, that's one mechanism for taking context that I've shared over time and storing it forever, right? I haven't had great experiences with that because again, it's, it's sort of, it's treated like a black box. Where I can't actually see what it has internalized and what it's stored. My big contention here is that because this is all just English, we no longer have to treat these things like black boxes.
23:46 Speaker 01 The system prompt is almost like this, this document that you don't have to edit if you don't want to. But in the, at the limit, if there's something really goes wrong, you can go in and tinker with it if you want. But probably for most people, the A I will auto generate it. It will look through your previous work to customize it to you and take your feedback on an ongoing basis to kind of edit the system prompt. And, you know, the kind of break glass cases, you go and edit the file and be like, no, no, I really don't want this to happen, but. I'd suggest, I think, in five years, for 99% of people, they're not touching their system prompt, but the system prompt is custom to them.
24:19 Speaker 00 I think that's possible. It'll be interesting to see how this plays out. Some of the work that we've done at YC, building agents to automate some of the repetitive work that we and our teammates do on a regular basis, actually suggests to me that writing prompts is going to be part of the day-to-day flow for a lot of people.
24:38 Speaker 01 I agree in the short term, but I think my contention is like, there's a higher level of abstraction on top of system prompt writing, that you shouldn't actually have to go and edit the system prompt, that you're able to like nudge or like say, no, no, that, okay, here's a new term sheet, that's a new term we've never seen before, and here's how I would think about it. And the AI goes, thank you very much, I will take that, distill it down into system prompt and auto-edit the system prompt, rather than Alex having to look up this like 50-page document and go like, I'm going to change that specific line. You know what I mean? It's like a level of abstraction on top of raw, kind of raw-dogging the system prompt.
25:11 Speaker 00 I think we'll probably have tools that make it easier to teach an AI over time.
25:17 Speaker 01 Yes.
25:17 Speaker 00 Rather than going up and editing, you know, a 10 or 50-page prompt every single time you want to do this. And I hope that developers stop treating these prompts like black boxes.
25:28 Speaker 01 I agree.
25:29 Speaker 00 Being able to look at the ground truth of what this agent is being instructed to do on my behalf is incredibly valuable, and it seems to be totally missing from a lot of applications.
25:39 Speaker 01 100% agree with that.
25:40 Speaker 02 So we've been talking a lot about the prompting.
25:42 Speaker 00 Yes.
25:42 Speaker 02 Right? And Tom is kind of saying like, there's a, going to be a bunch of innovation on how we write system prompts, or how we edit them, or iterate on them. In the essay, you also talk about tooling, which I think is a really important and interesting topic as well. Talk to us about that.
25:57 Speaker 00 Yeah, sure. So if developers aren't the ones who have to write all these system prompts from scratch, what are they going to focus on? I think the promise of AI for many of us is that it gives us software, or it allows us to build software, that the user can program to do whatever they want, using only natural language. The thing that these agents need in order to be able to do anything useful We call tools. If we go back to my little email reading agent here, the tools that this agent has access to are a tool for labeling an email, a tool for archiving an email, and a tool for writing a draft. You could imagine many other tools that would make this agent more powerful and more able to do a lot of the busy work that I have to spend my own time on right now, right?
26:40 Speaker 00 And so a lot of the emails I get are not emails that take a lot of my brain power to handle, but they just need to get done, paying bills, right? Doing introductions and handoffs, right? The number of times that I've had to create a draft and put this, you know, a certain person in BCC and like, it's maddening to have to do this stuff over and over again. The point is, is that an email in a lot of ways is like my inbox, you know, it's my to-do list or a whole bunch of chores in life, right? The very few emails that I write are really heartfelt, original thinking. A lot of it is sort of transactional, you know? And with tools, this email reading agent could handle a lot of that.
27:19 Speaker 01 Especially across things like Slack and your calendar and your Notion or your Jira or your Linear, whatever.
27:26 Speaker 00 Yes.
27:26 Speaker 01 There's a company in the current patch called Den, basically building Cursor for knowledge work that's trying to chain together all of these different MCP servers, which basically a way to call tools for agents. So that if your boss, you know, your bossy sends you a message on Slack and says, hey, review these terms and conditions, you pull it from Google Docs, you review it. Maybe the AI sends it via email to your legal team. They review it and then you publish it via GitHub. And all of this is controlled via one place. We're kind of agents calling these tools from different places. And you just sort of sit back. It's like a, Steve Jobs described software as a sort of, as a bicycle for the mind. This feels like, I don't know, like a rocket ship for the mind.
28:03 Speaker 00 It's incredibly powerful, right? And we built a, we built an early version of this inside of YC and we're already seeing employees of YC automate parts of their job that are easy to automate. And, and the difference that these tools provide is it takes an agent, which previously could only be basically like a Q and A, right? A question and answer thing into something that can go off and accomplish things in the world on your behalf.
28:25 Speaker 01 Yeah, I, I kind of hate the, the kind of chatbot paradigm. Like it was the easiest, it was the way to bring the most basic LLM experience to the whole world. You know, we had GPT before ChatGPT and ChatGPT brought it to the mainstream. But now every product is just embedding this chat agent in it. It's like, that's not the way this stuff should be used in almost every case.
28:44 Speaker 00 I totally agree. Because we started with chatbots, a lot of us developers and users are sort of anchored in this place where the thing that LLMs are great at is producing text. Where I think what we're trying to argue here is these LLMs are capable of automating work on our behalf, accomplishing things in the world on our behalf so that we don't have to, and that's the promise.
29:07 Speaker 01 And I think that's an amazing future. I look forward to it.
29:09 Speaker 00 Me too.
29:10 Speaker 01 This has been so interesting, Pete. How do you think founders should be thinking about this when building their companies?
29:15 Speaker 00 That's a great question. I think this is one of the most exciting times to be a founder because almost every tool that we've been using for decades can be rethought from the ground up with AI. And I think the AI native version of a lot of tools will look different from the versions we were used to using, right? What I showed here looks pretty different from an email client that I've been using for most of my life.
29:40 Speaker 01 Yeah, so they've got to go beyond simply like embedding a chatbot inside their existing product.
29:43 Speaker 00 Yes. Instead of asking, how do I insert AI into my tool? It's how would I design this tool from scratch to offload as much repetitive work from the user as possible so they can focus on what's important?
29:56 Speaker 02 All right, Pete, thanks for joining us today.
29:57 Speaker 00 Thank you for having me.
29:58 Speaker 02 See you on the next episode.
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