AUTOMATE DILIGENCE

“I think that as a VC, having the ability to tailor the output of a model to meet you where you're at from an understanding perspective is one of the most useful aspects. Opening up a 10, 15 page long academic paper full of citations is extremely intimidating. The AI can remove some of that barrier.”

“When I was at Spark... I started realizing that very few investors in the private markets really had access to great software. It was this cottage industry that had suddenly grown a lot and become very competitive and very global, but software hadn't yet caught up. If people had access to better data, they could make better decisions.”

“We're redesigning kind of a core part of our app. And one of our designers built a fully functioning prototype using, cloud code. It's amazing. And and it's not just like a a mock that you can look at. It's actually something you can play with and play with. You can start to identify edge cases and corner cases that aren't being met necessarily. So that's changed a lot. See velocity has increased overall and also the ability for people to be creative and contribute to areas outside of their traditional core domain has dramatically changed.”

“There is that metric, that sort of yeah. The it's like what there's there's a task, that has a 50% chance of successful completion. How long does that task look like for a given model? I think Claude is now at, like, twenty hours or something like that, and it was at, like, you know, one hour, a a a year ago. It's pretty remarkable.”

“They're using tools like Cloud and Chatch GPT to poke holes in them. What am I missing? Where's my argument weakest? Investors for decades have not had access to the kinds of tools that they need to really operate their businesses in a data-driven way.”

“I think that the SaaS pocalypse likes most things. There is there's a there's a lot about it that's correct, and there's certain things that are probably overblown. Historically, the number one, you know, competitive barrier to entry, for software companies has been switching costs. Because of AI, that's changing. It turns out though that there are other competitive barriers other than switching costs. So for example, for our business, one of the major competitive barriers that we have is around network effects. You can't, like, vibe code a network. You can't vibe code a new data set.”

“On the diligence side as we discussed before, you know, AI as a technical copilot is super powerful. Another thing we're seeing is that firms are writing investment memos, and then they're using tools like Claude and and ChatGPT to poke holes in them. What am I missing? Where where is my argument weakest? And then using that to help to guide the diligence process and then feeding that back in again. So you start to get into an iterative mode.”

“It feels like the opportunity cost of not working is so large right now though because there's so much happening. With a little bit of time, you can create so much. Which actually is my life. I'm obviously working really hard because every little bit of time I could do something to help cure a disease or help fix this thing that's broken over here. And so every little bit of time work, you're kind of compelled to work more because you get so much done.”

“We use software that helps us. It runs hundreds of prompts per day across four or five kind of major LLMs. It helps us to identify exactly where standard metrics is getting mentioned, which pieces of content that are linked to standard metrics the LLM is referring to, and helps us identify gaps to improve the rate at which we're getting. We're creating content specifically for LLMs that's also human readable and useful for humans, but it's really it's really engineered to make sure the LLMs understand what we do.”

“AI as a technical co pilot for diligence is actually one of the most interesting use cases for AI as an investor right now. I don't think it's a replacement for human experts necessarily but it certainly is a huge help. If you're if you come across a really interesting company in a field that you know a little bit about but you're not an expert in, you can get really really deep within the matter of hours and then pull in kind of human experts to help you to go the last mile. Whereas, you know, five years ago, it would have been this kind of mad scramble kind of calling people, trying to assemble folks that might have taken days or weeks.”

“We had this hypothesis that we could do a bunch of really crazy interesting new workflows. One of them was, okay, what if we hook up with Notion, calendar, and StandardMetrics? And then you ask a question, what are all of my upcoming board meetings over the next month? Create a Notion page for each of them. Go into standard metrics, pull all the latest data and build me a, like, quick summary and an agenda for each of those board meetings. And it'll go and it'll understand what a board meeting is. It'll go into calendar. It'll look at your calendar. It'll pick out, okay, these are the six board meetings coming up over the next month. It'll go create those Notion pages.”

“AI as a technical co-pilot for diligence is actually one of the most interesting use cases for AI as an investor right now. If you come across a really interesting company in a field that you know a little bit about but you're not an expert in, you can get really, really deep within the matter of hours, and then pull in human experts to help you to go the last mile. Whereas five years ago, it would have been this mad scramble, calling people, trying to assemble folks that might have taken days or weeks.”

“You can go into Excel, hook it up with Cloud, hook up Cloud with Standard Metrics, and then ask it to do a discounted cash flow. It will just build it for you. It's amazing. People are going to be able to spend more time on what they're truly passionate about.”