[John Ling]: I think I just really enjoyed learning new things, doing more work was just like more opportunities to learn. Oh, there's like 50 problems. Like each problem you probably like learn a little bit more about something completely different. The way I would just think about it is I would just go try it. And if you fail, that's okay. I don't believe any person on the planet spent a thousand hours trying to build financial models with AI.

Okay, I'm going to do nothing except for like construct the AI and I'm going to try to build this like LBO model that I would otherwise have to do for work. If you think about it, the bankers are just like, "We're just going to do it by hand." And then if you don't know how to do it, you probably just don't know how to do it. I think that there's probably some kind of big decomposition that you can do where like models can do different parts of this workflow very very well, but you just don't know because you haven't really like spent the effort to do like the investigation. And I was like, we should go solve this problem.

[John Ling]: My name is John, co-founder and CEO of Meridian. We're essentially building AI for spreadsheets. We think about like Microsoft Excel as most distributed programming language in the world. And our goal really is to say, "Hey, how can we help all of the people that spend a lot of time in spreadsheet software today, just moved 20 times faster." Prior to that, I spent about a year and a half at Scale AI. Before that, I started a couple companies. We've raised slightly more than $15 million. Our seed round was led by Andreessen Horowitz and the general partnership. And that's kind of where we are relatively early, but hopefully we can continue to grow.

[John Ling]: I think I just really enjoyed learning new things. I think more than anything else, I felt like doing more work was just like more opportunities to learn. Oh, there's like 50 problems and each problem you probably like learn a little bit more about something completely different. And I think Scale was one of those places where if you wanted to learn about a different side of the business, you could go do that. It wasn't like, hey, your job is like X. You can only do X. It was like, your job is X, but like if you do X and you realize that like Y and Z and ABC could also be done. There was the opportunity to essentially say, hey, I'm going to go learn and like expand my personal sort of like knowledge space and like go do these things.

Being willing to sit down and like dig into research, for example, is extremely valuable. I think especially in AI, it becomes relatively easy to get lost in like the execution, meaning like, oh, okay, we're just going to do this because like we need to get this thing done. It's actually really valuable to take a step back. It's like, why are we doing this? And then the way you learn is like you probably go read all these research papers. Let's just for example take like quality of data. Like what does it mean for data to be high quality versus low quality? What do researchers care about? What specifically makes this data point valuable? Like I sat down and I read like I went through like so much of our data across so many domains and I think that's one way to learn.

[Visitor]: I met John through mutual friends at Scale where I had consistently heard that he was really a top 1% performer at Scale. I heard this across the board from many many people. He didn't allow the confines of Scale which was already a growth stage larger startup at that point in time confine like what he thought was right or not right to do in the business. And so he really took a very first principles approach in thinking about what would the right thing for Scale be and he was unafraid to voice those opinions to people and then actually move mountains to make them happen.

[John Ling]: Why go over to Scale? I do think like the biggest reason was definitely like I felt like it was a very unique place to observe AI develop. I think they were very convinced obviously that the next wave of like large language models are going to very dramatically change trajectory of what the world looks like. For myself, I think selfishly I've always wanted to start another company.

I think that not knowing what LLMs can do or like not really immersing yourself in sort of like this rapidly developing ecosystem or technology or however you want to think about it. It's like a mistake. I would be much better off spending like the next four years at least at that time I thought I was going to be at Scale for four years really like learning as much as I can about how large language models worked and how it was developing what was trajectory of technology and like how people are like implementing it etc. A lot of my job was making sure that like hey the data that Scale ultimately produced was valuable. Spent a lot of time thinking about like benchmarks and evaluations. also spent a lot of time thinking about like hey how can we internally like leverage LLMs to make our internal processes more efficient. Um so I think that for me was like really really really interesting.

[John Ling]: I started using Cursor a lot over the last couple months or like you know the last generation of models where like hey coding like really felt very real 0 to one actually went from 2 weeks to like 30 minutes or like half a day. I had a moment where I was just like, "Wow, this thing is like magical." And I want like everyone to like go use it, you know? I was just like, "Everyone on this team must vibe code." And if you don't know how to vibe code, I feel like you're just going to be lost or you be left behind. But like ultimately, I think it was just, hey, there's like a new calculator, but it's like not it's like a super super powerful calculator.

But I think like more tangibly cuz I live in New York, a lot of my friends work in finance. And I think that like the energy is just like completely not the same, right? where like you're in San Francisco, everyone is like super super excited about like okay here's like the latest vibe coding like unlock right where like oh you have all these like skills that you can leverage for like Claude for example or like here's how you can do these like crazy architectures it feels like the ground or the the the number of tools sort of like is increasing like exponentially and then like you come back to New York and like that's just like not true when I talk to like our team when I talk to like candidates or even like investors.

[John Ling]: I think I think a lot about the idea that I don't believe any person on the planet spent a thousand hours trying to build financial models with AI. I don't think anyone has been sitting down and be like, "Okay, I'm going to do nothing except for like construct the AI and I'm going to try to build this like LBO model that I would otherwise have to do for work." If you think about the bankers, they're just like, "We're just going to do it by hand." And then if you don't know how to do it, you probably just don't know how to do it.

But I think that there's probably some kind of like decomposition that you can do where like models can do different parts of this workflow very very well, but you just don't know because you haven't really like spent the effort to do like the investigation. In contrast to that, when you think about code, I think that a lot of these coding tools are built by the people who use them. So they have a much clearer idea of like what the success look like, what are the different use cases that I care about. I can very clearly articulate where the model is failing. But I do think when you take that and you apply it to a domain where you're like not really an expert, it's it's pretty easy to say like this model is wrong, but it's pretty difficult to really identify exactly why like the number is not the number that you would expect it to be. But yeah, that's kind of how I thought about it and I was like we should go solve this problem.

[John Ling]: I think if I look back my first job out of college, I think that most sales people can probably also tell you this, right? Is like if you don't try to talk to someone like you will never know. And I think that's something that I've like always done. I would say like don't be scared to reach out to people. Don't think that like hey Satya Nadella will never respond to your email. I mean if you think that way he's obviously never going to respond to your email but if you reach out you might be surprised. Maybe he'll respond. That's like something that you know that I thought was really really interesting.

It's really easy to fall into this narrative that like oh these things are like impossible but you actually don't know and I think like you know most entrepreneurs sort of just have that belief. I think it requires like an enormous amount of like suspension of disbelief right where you can where most people would just be like you're crazy but you can actually go in and just be like I don't know what they're talking about. sounds totally doable, right? And then you would go try to do it. You also learn by like trying things that you've never tried before. And like if you up, you up. It's okay. Nothing wrong with that. But at least you know, right? And you can build reps internally. You know, our company as a whole actually promotes and allows people to like try to solve things their own way. And if you fail, it's okay. You just go support them, right? You're like, "Hey, you tried this thing. maybe we need to push back the deadline by a few days and then we'll like find other people to support you, right? Everyone in the company will come support you. And I think you have to build this like environment where it's okay for people to like experiment and not succeed.

[John Ling]: I mean I think like obviously you always want to build something that is like like a masterpiece, right? Like I think our goal for like starting a company obviously is to like build something that we can be really really proud of that we think is going to transform a lot of people's lives that is going to be like hey here's a company that we can look back on in like 5 years and it has like dramatically impacted the lives of like a lot of people as we think about how knowledge work is going to change with AI. There's almost no bigger category of knowledge work than the spreadsheet and Excel worker. And as someone who worked in spreadsheets and Excel as a banker for a brief period of time and then as a consultant, um I could just viscerally understand one like why this was an enormous market um and probably in some sense like one of the largest uh software markets out there and two why AI was going to fundamentally change how we did work on spreadsheets. And so I think that uh Meridian's vision to really augment this form of knowledge workers similar to how a lot of the the coding companies have augmented the work of the developer. I think there's just so much potential here to actually be able to infuse the work done in spreadsheets with meaningful intelligent and automation.

[John Ling]: The more time you spend with the technology, the easier it is for you to have an intuition around like what is possible today. And if you do this over like a very sustained period of time, you also build an intuition of what is going to be possible in like 3 months or what is going to be possible in like 6 months or a year, right? And I think like that in of itself is extremely valuable. I would just spend as much time as you can playing with it, right? like I think it will be advantageous to be one of the people that have spent let's say you're interested in finance right that have spent you know like 10,000 hours trying to do finance with AI I think that prompting is still a very very valuable skill like when you apply to like Y combinator they actually tell you that like doing the application in and of itself is super valuable because it forces you to sit down and think through these aspects of your business that maybe is not as well articulated in your head as it is until you write it down. I think that when you explain a task to a large language model in a similar vein where you learn how to be relatively specific about your ask, you learn a lot from that process, right? like trying to explain to NLM like what you really wanted to do actually gives yourself a lot of clarity around what you really want to do and I think that part of it is actually very valuable by itself.