All right everybody, welcome back to Startup Basics. This week in startups.com/basics is the URL where you can find these videos and the series that we've been doing for over 5 years now. What do we do in Startup Basics? We look at things that founders, you know, need to get right and that they might make a mistake on sometimes, or they might not know about an opportunity. That's why we call it the basics. Legal with our friends over at Wilson Sonsini, we do accounting with our friends over at Kruze, and we're really excited because one of the basics you have to get right today is AI. Every startup I see, whether they're an AI startup or not, they're using AI to run their companies. And one of the topics we talk about here on This Week in Startups all the time is static team size. A lot of folks are sticking with 5 or 10 or 100 people at their startup, and instead of hiring people, just running up the headcount, they're deciding, hey, maybe I could automate stuff, maybe I can use AI to figure things out. And so it is now one of the best practices that you got to get right, just like you got to get your legal, just like you got to get your accounting right, you got to get your AI right inside your startups.
Don't I know it? When people email us their decks and they apply for funding from us, you know what we do? Zip, zip, zip. People didn't know this. I don't want to say it because I'm gatekeeping here. One of the things we do is we send all that information to Gemini. I don't know if you know Google's really amazing large language model and service, and then Gemini spits out this great report based on our criteria and this analysis of the startup, so we can just read that short summary based on all the information we have. And you know what? I have some researchers and analysts who write short summaries, and I get the Gemini one. I think the Gemini one's a little better, I'll be totally honest, than the humans. Then the humans can go do more important work. So there's like an example for you of how, you know, this stuff is impacting everything. And we're so excited, we have a partnership with Google Cloud for this series. They just published an amazing report. It's titled 'The Future of AI: Perspectives for Startups.' Hey, that is really on brand and on target for us. So what are we going to do here on this series? In other series, we would have one guest and maybe we do four or five topics. Here we have so many great opportunities to have a rotation of guests on who are building important tools in AI and, you know, who our friends at Google may or may not have partnerships with. And today we're very lucky to have Harrison Chase, the CEO of LangChain, on the program. They provide a framework for building and designing an LLM of your own, which sounds like something, Harrison, I need for maybe startups and pitch decks. Welcome to the program, Harrison.
Thanks for having me. It's great to be here.
Tell me a little bit about LangChain, and then let's get right into it. What do you think startup founders should be thinking about when it comes to using AI inside their companies to get an advantage, to save money, and to create better products?
Yeah, the types of applications that we see people building, they're starting to be ones that do the work of what humans would do in the past. So if they're kind of like functions inside a company that you would hire what I like to say a smart intern to do, those are now functions that can kind of be automated by some of these AI systems. So for example, inside LangChain, we have a few places we use this. I have an email assistant that helps respond to all my emails. We have a customer support bot that helps with some of the customer support issues. We have a marketing bot, and we have an SDR bot. And so all these are places where we'd hire maybe like in the past an entry-level intern, an entry-level person. And, you know, because we build tools, we like to dogfood them, and so we're dogfooding our own tools by trying to automate some of these processes away.
And the interesting thing about this dogfooding you're doing is the positions you talk about are not positions people want to stay in for a career. They're entry-level, they're the first rung of a career ladder. And you know what there used to be when I was coming up? I'm a Gen X, you're like a, I think you're Gen Z or Millennial. You're Gen Z, I think, right? Millennial. Millennial. You're Millennial, okay, great. You're team Millennial, no judgments there. You know, Gen X, we're like the last free-range generation. We're a little crazy on the margins. But when I was coming up, the reception desk, working in the mailroom, working in the typing pool, working as a runner, which is basically somebody who would run packages of paper around, those were the entry-level jobs. You know what happened to those jobs? Email, the internet. You didn't need a receptionist. You put technology in the front, people badged in, they pressed a number, and whatever, somebody came and got them. You didn't need all of humans doing those. And now we have another series of them that are entry-level jobs. SDR is a super fascinating one, sales development rep. A sales development rep, for folks who don't know, they find leads, they get those leads, they warm them up perhaps, and then they hand them off to an account executive, a salesperson. In plain English, tell us a little about the agent that you created for the SDR role. What do they do, and how well does it work, and how long have you been deploying your SDR agent?
That's probably one of the newer ones. Basically what it does is we get a lot of inbound leads, it does some research on who the people are, and it actually drafts an email to them if it thinks they're interesting. So it uses the reasoning of the models to determine whether it's kind of like an interesting prospect for us. It does some research on events that have happened to their company recently, and then it will draft an email. And notably, for all of these positions, you're absolutely right that they're kind of like entry-level positions, but I want to call out that we have a sales team, we have a head of customer support, we have a product marketer. It's not like we're eliminating these functions completely. It's rather like these are doing some of the parts of the job that people don't want to do. They're not the creative part, they're not the kind of like the value-add part. And then they're hooking in, they're communicating with the kind of like the experts when needed. So when it drafts an email, we have human in the loop that will go in and kind of like approve the email or something like that. So these, like, you know, we have a really good sales team. I think they can go in and basically talk to this junior intern and say, 'No, this is the wrong email, like don't send it to these types of people in the future.' So there's still this human in the loop component. I think that's really important for enabling a lot of these applications.
I think this is critically important at this stage in 2025 when we're recording this because we do see on the margins a hallucination here or there, and you know, you don't want to have a hallucinated mistake in an outbound email to a prospective customer, nor do you want it to make a mistake and say this person doesn't need the product, we're not going to email them. So I like this, you know, taking those emails that are outbound, maybe putting them in your drafts box, you take a look at those 10, you just read them. Okay, maybe we shouldn't talk about, I don't know, it pulls their high school or something and mentions their high school in the email. And that's like the super important part. Human in the loop reinforcement learning is a very important piece of this as well because over time, you know, these things could take on more and more work. Maybe, you know, you look up, hey, this person's company has 10 employees, this other person has 10,000. Maybe the one with 10,000 we should just book a Zoom. The person with 10 people, hey, maybe it's okay to send that one automated. You know, it could depend on what you're doing there. How hard is it to create these agents? And then are there situations where these agents have, you know, gotten a little bit out of control? Maybe they jumped the fence. How do you protect against that? Because that's everybody's concern, right? They may not say it to you, but people are like, oh my God, I don't want an agent to go wild. Just like back in the day, we wouldn't want somebody to spam, you know, and send a hundred accidental emails. Could be embarrassing, could be annoying to our partners and customers.
Well, that's exactly why the human in the loop stuff is so important. And I'll get to that after I answer your first question. I mean, we still see that it's still pretty hard to create these agents. So we build developer tooling to help people build these agents. We see that most of these agents are still being created by developers. There's a lot of integrations to figure out. There's a lot of what we call kind of like the cognitive architecture of the agent, like what information is it looking at, how is it processing that information. It's still a lot of work to get these agents to work. And the ones that we see work, some of the ones that have been built with our tools, Replit, LinkedIn, Uber, Clara, GitLab, these are like vertical agents. They're not like fully autonomous ones. They're vertical ones doing kind of like, you know, specific domain tasks. And then for the question around how do you keep these on the rails, this is why the human in the loop stuff is super important as well. And I think there's two big benefits to human in the loop. One is what you talked about, like it keeps them in check. It basically doesn't let them go off the rails. You have people not at every step. Like I think part of the benefit of having agents running in the background is you don't have to be involved at every step. You can be involved at the most important step. So for example, like you can be involved right before an email is sent because that's more important than before a Google search is done. Like, you know, it's kind of like a read versus write operation. So you put them in at kind of like the crucial steps where it actually could do things that would not be good. But the second underrated part of human in the loop is what you were talking a little bit about earlier, is basically aligning the agents with what you want them to do. So when they first start working, there's probably some prompt, and that prompt is, you know, like I think I'm relatively good at prompting. I wrote the prompt for my email assistant. I still forgot a ton of edge cases about who I would want to respond to or what emails I would want to ignore, just like didn't come to mind as I was writing that prompt. And I don't think it's realistic to ever write like a perfect prompt right off the go. And so this human in the loop helps you kind of like, if you set up the proper kind of like systems, it helps you update that prompt and update instructions and basically align these agents with what you actually want them to do. And so I think there's two really important benefits to human in the loop.
