Episode summary
What Happens When AI Replaces Investment Analysts?
How I Invest With David Weisburd
· · 29 min · Hosts: David Weisburd · Guests: Chaz Englander
Summary
Chaz Englander argues that Model ML was born from a family office need: consolidating messy investment updates, reporting, and monitoring across many formats into something usable. He says the core opportunity in private markets is still workflow automation, not chat, because firms need systems that can ingest documents, emails, calls, filings, and spreadsheets and turn them into repeatable outputs.
He draws a line between today’s AI productivity tools and the next phase, which he thinks will deliver real investment insight. Chaz says 2025 is the productivity year and 2026 is when systems start producing opinions that matter, such as AI-generated views inside IC memos. He cites a middle-market private equity client that has automated 80% of its investment committee memos, with a few pages reserved for AI-generated analysis.
He also argues that firms should start capturing more data now, because future models will be limited by what they can learn from calls, meetings, emails, and internal documents. For adoption, he says top-down buy-in, structural change, and forward-deployed engineers matter more than generic AI enthusiasm, and that customers are really betting on a team’s ability to keep shipping fast.
Key takeaways
- Chaz Englander says Model ML began as an internal family-office tool to clean up reporting, monitoring, and investment workflows.
- Chaz argues chat interfaces hit a ceiling on complex finance work, while workflow automation is easier to buy, measure, and deploy.
- Chaz expects 2025 to be the productivity year for AI and 2026 to be the year firms start getting investment insight from AI.
- Chaz says one European middle-market private equity client has automated 80% of its IC memos, including AI-generated opinion pages.
- Chaz says firms should capture more calls, meetings, emails, and files now because future AI advantage depends on having more usable internal data.
- Chaz says the best adopters have CEO- or CIO-level buy-in and reorganize junior staff around AI workflows.
Notable quotes
“We launched the end of 2024 and we raised about 100 million across a couple of rounds in our first 12ish months”
“We're really looking at, you know, in the hundreds of seats at the minimum, if not the thousands, right”
“We think 2025 is the productivity year. 2026 is the year where we're actually going to start to see these systems deliver insight that wasn't possible prei”
“They've pretty much automated 80% of their IC memos”
“We do an entirely free you don't have to sign up discovery phase”
How the episode opens
You built model ML to solve your own problems within your family office. What were those problems?
So when an I, who's my brother, we sold the second company, we were still pretty young. Ans was I, I don't know, 21, 22. I was maybe like 26, turning, 27.
We made a bit of money and we decided that we wanted to invest our own money for a while. So we decided on a few kind of asset classes, strategies. We hired a few people to predominantly do the investing.
And then we spend a lot of our day writing software to make that process better. Before we knew it, that was maybe 21 coming into 2022, you know, 2023 coming in 2024 as LLM started to really be useful in these sort of environments. Frankly, the product just got better and better and better.
Uh, and before we knew, we just had too many people asking us whether they could use it, and they're willing to pay for it. Um, so the story goes that we called my mom, asked my mom if we could build a third startup, and she said no. And so we started it the next day.
give me a specific lowh hanging fruit that investors are using model ML in order to solve their their everyday problems.
A classic is just like um reporting and monitoring in general, right? It's the classic problem. You know, you're you know, even when I think about this back at the family office, you know, we were making, you know, on the venture startup size maybe like 25 investments a year, right?
And we would receive updates in like every single format. You know, sometimes even now it's like a website link, you It's like you got a website link, you got a notion page, you've got just a bunch of documents, you've got it in the body of the email, you've got it in an Excel file with multiple tabs, etc.
From the episode’s captions: roughly the first two minutes, lightly cleaned. Keep watching on YouTube →
Topics
- agentic ai
- investment analyst automation
- private markets workflows
- ai productivity gains
- investment insight generation
Companies and tickers mentioned
- Model ML
- AlphaSense
Watch the episode
Watch “What Happens When AI Replaces Investment Analysts?” on YouTube
Summary, takeaways and quote selection by Octagon. Quotes are short excerpts; the full conversation is in the episode.
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How I Invest With David Weisburd, this episode and its artwork belong to David Weisburd. Octagon is not affiliated with the show; this page is an independent summary.
