Chaz Englander - 3x founder looking to build $1T company, FT Partners leading the $75m round, learnings and mistakes that others should learn from

Model ML bets AI can replace the junior banker

Model ML is building an AI platform that does the analyst and associate work in financial services. It takes a deal from signal to finished deck, and middle market private equity already puts its pages into investment committee memos. Chaz argues that in AI the main risk is speed, so the company is racing to become the default before peers make not using it a liability.

Takeaways

• Model ML automates the junior banker's workflow. The first use case was a one-page summary of an emailed opportunity, including LinkedIn connections in common. It now runs signals, an LBO check and a take-private deck into an MD's inbox at 9AM, across asset management, consulting and banking.
• Middle market private equity already uses AI-written memos. Many middle market PE clients have entirely AI generated pages in their investment committee memos. One page flagged a weak management team because one person had held four jobs in eight years. Chaz predicts about eighteen months until a fund decides purely with AI.
• Traction is broad and contracts are large. Model ML is live with eight of the top 10 European banks, three of the big four consultancies and tier one asset managers. Revenue splits fairly evenly across its three segments. Contracts are eight figures, and FT Partners led the $75m round.
• Speed is the main risk, so escape velocity matters. Chaz defines escape velocity as the point where not using Model ML becomes a risk for a company's peers. The platform works with Anthropic and OpenAI and swaps models automatically when evaluations show a better one.
• Pricing is heading from seats to outcomes. Pricing is per seat now, with a few large customers on usage. Chaz expects a shift to usage and eventually outcome-based pricing. He declines to share ARR, calls it a vanity metric and points to retention and user activity instead.
• The founders are aiming for a trillion dollars. Chaz says the target is a trillion dollar outcome, not 100bn. Calin cites an Altimeter view that software can outgrow the software market by replacing analysts and associates. Companies in the space raise at 40 to 70 plus times ARR.
• Hiring favors genuine interest and perseverance. Chaz asks candidates what they watch on Instagram or YouTube to gauge real interest in the mission. He and Arne have backed 50 to 70 companies, and he finds Nordic and European founders easier to read than American ones.

How the Englander brothers reached Model ML

1. Fat Lama was a peer-to-peer rental marketplace for high value items such as cameras and drones, with insurance. Product market fit took two to two and a half years. They bought items themselves and routed inquiries to other lenders. Trust was the hardest problem, and the first drone rental ended in theft.