灯塔客户还是圈地销售：AI 创业公司如何选销售策略

There's a moment right now to go sell big software again. We're now looking at a different way of doing business entirely.

What are the lighthouse and land grab sales playbooks?

Here's the framework for evaluating which playbook should you be following. There's this very obvious one. Go after the very obvious companies here in San Francisco that probably have some sort of proof or social value associated with them. Or like go out and sell in Ohio, find people who need your solution. If you think about sort of the enterprise networking world in 2009, people thought we were crazy. Like we had no chance of getting into the largest corporations in the world, Lighthouse, because Cisco and HP had them all tied up.

But what we could do is we could say, listen, we can configure, we can deploy faster, we're simpler to use. And that was very much a land grab strategy. Too few people are willing to pick up the phone and willing to get on the plane and willing to get, you know, in front of those customers right now because they feel like it sounds way more sexy to sell [music] to JP Morgan Chase than to Morgan Chase. [laughter] I think the biggest mistake that I see [music] founders make at an early stage, honestly, is just

Welcome back to the A6Z podcast. Today, we're getting into the single most expensive question an AI founder makes, how you sell. Joe Schmidt just wrote a piece called Lighthouse or Land Grab, which gets into the two dominant playbooks he's observed among enterprise AI startups. Joe, tell us about the piece in your own words. What are the Lighthouse and Land Grab sales playbooks? Yeah. And and this this piece actually all stemmed back from an observation uh that I had actually driving up the 101 freeway.

Uh maybe this is like three, four, five months ago. I can't remember. And you just realize that you have the same kind of like two competing companies like one's on one side of the freeway and the other's on the other side of the freeway and they're selling the exact same piece of software. And for some reason they all have decided that the only relevant companies for this piece of software are in San Francisco and driving on the 101 freeway.

And then this has gotten even more ridiculous. It's obviously every bus has been wrapped and every you know it's like planes are flying up above and they're now towing you know towing startups and so I think it's it's all very clever but it's all targeting the same kind of sales motion reality is

you don't always have to do that like you don't always have to sell to the same companies in San Francisco and so what I wanted to try to do is tell founders like hey here's the framework for evaluating which playbook should you be following there's this very obvious one which is like go after the very obvious companies here in San Francisco in New York City in a major metro that probably have some sort of proof or social value associated with them or like go out and sell in Ohio, go out and sell in Chicago, go out and sell in St.

Louis, find people who who need your solution. Um, and so that was the whole point of the piece is like you don't always have to go sell these notable logos. And um, we'll see how that plays out, but that's why. And and just to get a little bit deeper, like when does it make sense for a a founder to, you know, go and buy a giant billboard that you see when you're driving from SFO into the city? When does it make sense for you to kind of do a more targeted sales activity or like motion elsewhere?

Yeah. And I think the way that we tried to make this make sense, of course, we did very uh consulting style with a 2x2 matrix. Um I did never work at a consulting firm but um I'll do my best. Uh and so we really were thinking about okay what are the the axes that we should be kind of mapping opportunities against. And so we decided upon like the y-axis is really what we called like a the buyer's exposure. Um and it's intentionally called exposure because there's the exposure of you know making a mistake with the solution that you buy.

There's also the sol the exposure of the solution inside of your company like does this does this product that I'm selling to my customer end up you know being sh is it shown to their end customers right which is actually an important distinction um and really just kind of the overall risk associated with buying this piece of software so that was like kind of the y- axis and it goes from like high to low um and then the the x-axis is whether or not proof travels in any given market and so you know if you think about like the top right would be proof travels in this market um and it's it's high buyer exposure ure and high, you know, high buyer risk.

Um, and the bottom left would and and that's a lighthouse market, right? Just to be clear. Um, and the bottom left would be, you know, low low, uh, you know, low proof traveling, but also low buyer exposure. Uh, and that would be a land grab market. Um, and so I think those are actually quite different. And if you think about the the, you know, the standard markets that fit into the lighthouse model, it's, you know, regulated industries.

Oftent times there's a a more constricted number of logos. Um, if you're wrong in the industry, like if the buyer buys the wrong piece of software and it ends up doing the wrong thing, uh, it can lead to of, you know, very bad things happening for your firm, uh, including, you know, potentially getting in trouble with the regulator, you know, even going, uh, and doing something illegal. That's very bad. Uh, of course.

Um, and then, uh, you know, on the flip side, when you, uh, when you're looking at the more of a land grab market, there's an established budget. People have been very, you know, used to and accustomed to paying for a type of service. and you can kind of come in there and show like the end buyer the math of like hey my solution is better than whatever solution you're you're using today whether that's you know a software driven solution or a human driven solution.

So you know the the the kind of distinction that we drew was between like proof on the the top right of the of the of the quadrant and math on the bottom left of the of the quadrant. Um and that's how we kind of thought about the framework.

Yeah. And Andy, we'll get into your background uh in a little bit, but I think first maybe it's it's good to kind of categorize some of these modernday AI startups within these two frameworks. And I know you both work with a lot of these companies. So, so Joe, I'm not sure if you want to call out specific examples or Andy, if you want to call out specific examples and really just kind of like talk through the the sales strategies that you're seeing.

Yeah. I mean, well, you're like, you know, the the the land grab uh you know, mastermind. So, maybe you want to talk about some of the stuff that you've seen.

Yeah. Well, I I think you you framed it up really well in in your in your article, but um you I think that concept of like the the lighthouse being more in industries that require, you know, regulation, a lot of social proof and so forth. And those tend to be companies that are going after like a category creation, right? It it doesn't exist today. So, therefore, you got to go out and you got to kind of prove yourself with the big the big names.

And obviously we have a bunch of portfolio companies out there. There's a there's a bunch of companies I think you mentioned a couple in your article that are doing that. And then on the other side the the the sort of land grab uh you know less social proof but those tend to be especially in the AI world today. Those are companies that are maybe replacing or improving workflows that already exist and there's existing budget and again we have a whole bunch of portfolio companies you named a couple in the article um that are doing that today.

They're inserting themselves in saying hey we built a better w