Podcast Summary: How I Invest with David Weisburd

Episode E326: What Happens When AI Starts Replacing Analysts?

Date: March 17, 2026
Guest: Chaz, Co-founder of Model ML

Overview

In this episode, host David Weisburd sits down with Chaz, the co-founder of Model ML, to explore how agentic AI is transforming the world of investing, specifically by automating workflows that have historically depended on human analysts, and what it means for asset managers, GPs, and LPs. The discussion covers the origin of Model ML, concrete use cases for AI automation, cultural shifts within organizations, productivity gains, challenges of scaling, and lessons drawn from legal tech. Along the way, practical advice is shared for investors looking to harness these new technologies, and the two conduct a brief live “discovery” of business use cases.

Key Discussion Points & Insights

1. Genesis of Model ML and the Problem it Solves

[00:00-02:06]

• Model ML was born out of the founders’ frustrations in their own family office with manual, inconsistent reporting and data monitoring after experiencing the explosion of unstructured data formats from portfolio companies.
• Chaz:

  "It's somewhat baffling to me that a lot of these tasks are still being done manually. Frankly. That is a classic example of something that should be automated." [01:32]

• The decision to pivot Model ML from in-house tool to startup was driven by external demand and rapid LLM advancements.

2. Agentic AI: Workflow Automation vs. Chatbot Paradigm

[02:06–03:53]

• Chaz distinguishes between chat-based AI and agentic, workflow-oriented AI:

  “If you think of any relatively complex workflow…are you going to be able to solve that as of today in a chat interface? The answer is almost certainly no.” [02:48]

• Model ML offers workflow automation rather than purely chat interfaces, which makes adoption and ROI measurements easier for enterprises.

3. Productivity vs. Insight: The Coming Evolution

[03:59–07:42]

• 2025 is predicted to be the year of productivity; 2026 the year of true, AI-generated investment insight.
• Chaz provides a clear example:

  “One of our middle market private equity clients…they've pretty much automated 80% of their IC memos…there's two or three pages in that now that are not just generated by AI, but it's… the AI's opinion…” [04:47]

• Over time, the reliance on and trust in AI-generated insights during investment committee discussions is expected to increase; firms need to embed these systems now to capture future benefits.

4. Data Capture as Strategic Edge

[06:24–10:16]

• The number one “preparation” for the AI future: collect more, better data now — from calls, meetings, emails, docs.

  “The more data that they have, particularly now…you want to try and capture as much data as you possibly can as part of the investing process.” [06:47]

• David:

  “If it's truly oil, why aren't you capturing more of it and why aren't you creating the schema, the processes and the cultural aspects of becoming a data driven organization?” [09:19]

• Up to 70% of vital decision-making info lives in calls and meetings.

5. Where AI Delivers Productivity Gains

[10:16–13:06]

• Chaz expects 50–55% productivity uplift for organizations in the next 12–18 months, especially through automating junior analyst workflows.
• The bottleneck is not technology, but organizational and cultural adoption.

  “We don't really look at workflows unless it's a 60 plus percent efficiency gain on a single workflow level.” [11:53]

6. How Model ML Works — Under the Hood

[12:19–13:46]

• Users interact via an Excel-like interface; documents are uploaded, broken into components, data is sourced from internal and external databases, and users confirm or alter workflows before deployment.
• On competition with open source:

  “The entire product…bottom of the agency system through to the UI is designed with the customer in mind. That last mile delivery…is where the impact is.” [13:16]

7. Lessons from Legal AI and Importance of 'Last Mile'

[13:54–20:27]

• Legal tech (Harvey, LEGO) is 12 months ahead of finance in AI agent deployment, showing the advantage of “forward deployed engineers” to drive customer feedback tightly into the product cycle.
• Chaz emphasizes speed of learning and product improvement as the core business moat:

  “…someone will be at a customer's office, they will receive product feedback in real time…write oral to the code base…in production within half an hour.” [19:38]

8. Scaling Versus Customer Delight

[20:27–21:47]

• Model ML pursues a “fewer, happier customers” approach—prioritizing trust, delivery, and sticky revenue over aggressive short-term sales.
• The company has raised $100M, scaling team from 20 to 100 and aiming to double soon.

9. Adoption Patterns Among GPs and LPs

[21:47–24:35]

• Key traits of early adopters: top-down buy-in, willingness to alter organizational structure, and proactive reallocation of analyst time into AI roles.

  “…your job is to either assess AI tools or build AI workflows in a product like Model ML. They're the ones that I think are doing incredibly well.” [22:54]

• Chaz’s team assists in identifying high-impact automation targets during the “free discovery phase.”

10. Pricing, Scale, and Target Customer

[24:35–25:27]

• Currently, Model ML works with firms in the hundreds to thousands of seats, charging $100–$300/seat/month, but plans to target smaller firms soon.

11. Live Discovery Roleplay: Mapping Use Cases

[25:27–30:45]

• Chaz's process starts with understanding the individual’s “AI journey” and pain points. David cites challenges in video editing and guest/outreach mapping.
• The conversation highlights potential for AI in categorizing content feedback, mapping relationship graphs for outreach and deal sourcing:

  “That relationship instantly…if there's a mutual connect with management team…that's very much untapped.” [30:14]

12. Timeless Advice for Builders

[31:24–33:52]

• Chaz credits perseverance—“not blind perseverance, but perseverance”—as the key trait for founders to weather cycles and convince investors.

  “If something makes sense to you and you are passionate about it…it makes sense in itself, you should probably continue doing that thing and persevere at all costs.” [31:37]

• David closes with broader reflections on compounding value and contrarian conviction, attributes common among billionaire founders.

Notable Quotes & Memorable Moments

• Chaz:

  "These systems are improving so quickly and the competitive landscape is changing so quickly that really our moat as a business is the speed at which we can learn and therefore ship products." [20:09]

• David:

  “None of them are building linear businesses...it must be compounding.” [32:23]

• Chaz:

  “If you believe in something, I think the key is to just persevere.” [31:44]

• Chaz (on AI advisory for firms):

  “Start, not overthink that initial process and start.” [07:33]

Important Segment Timestamps

• [01:01] – Real-world reporting pain points for investors, “low-hanging fruit” for automation.
• [02:23] – Why workflow automation matters more than chatbots in complex work.
• [04:47] – Private equity client: 80%+ IC memo automation.
• [06:27] – Why capturing more data today is essential for AI-enabled insight tomorrow.
• [10:30] – Prediction: 50-55% productivity gains identified, mostly for junior analysts.
• [13:16] – Lessons from legal tech: “Last mile delivery” and customizing AI for finance.
• [19:38] – Forward deployed engineers and rapid, real-time product feedback loops.
• [22:54] – GP/LP early adopters and organizational adaptation stories.
• [25:44] – Role-play: Where AI can automate media & asset management processes.
• [31:34] – Timeless founder advice: perseverance.

Summary Takeaways for Investors

• Start automating the “boring stuff” now; reporting and monitoring are the easiest entry points.
• Focus on capturing rich, diverse data—especially meetings and calls.
• Be ready for cultural as much as technological change; prepare teams to specialize in AI workflows and adjust roles accordingly.
• Adopt workflow-centric, not chat-centric, AI solutions for the most measurable and impactful gains.
• Real-time customer feedback and quick iteration can be the most powerful competitive moat.
• Perseverance and contrarian conviction are timeless for those aiming to lead, not follow, transformative change.