He sold his first 2 startups. His 3rd grew to $1M ARR in 3 months. | Chaz Englander, Founder of Model ML
Serial entrepreneur Chaz Englander breaks down his systematic approach to building and exiting startups, including the tactical playbook behind his latest venture Model ML's explosive growth to $1M ARR in 3 months. He shares contrarian insights on using cold LinkedIn outreach for fundraising, the brutal realities of running high-burn subsidized business models, and why speed becomes your most valuable competitive advantage when you achieve true product-market fit.
Key takeaways
- •True product-market fit shows up as consistent revenue milestones that repeat themselves - hitting $100k in 3 months then doing it again the next quarter.
- •Cold outreach to VCs works when you leverage indirect credibility through mutual connections rather than pitching directly.
- •Running a subsidized business model while fundraising creates extreme pressure because you're literally burning cash to prove market demand.
- •Post-exit founders should consider self-directed investing over wealth managers to stay engaged and maintain control over their financial decisions.
The essay
Most founders treat their first exit like winning the lottery. Chaz Englander treated his like business school tuition. After selling two startups, he and his co-founder made a decision that sounds almost reckless: they fired their wealth manager and started investing their exit money themselves. The crash course in finance and accounting they gave themselves would prove essential when they launched their third company, Model ML, and hit $1 million in annual recurring revenue in just three months.
Englander's approach to serial entrepreneurship breaks the conventional wisdom about taking time off between ventures. Instead of stepping back to enjoy their success, he and his partner dove deeper into the mechanics of business itself. "We're like, well, we don't wanna just, like, give our money to, like, a wealth manager and just do nothing with our day," Englander explains. "What are we gonna do with our day? Why don't we why don't we invest our own money?" That hands-on education in investment banking, private equity, and financial modeling gave them an edge that most technical founders lack when they sit across from VCs.
This financial literacy became crucial because Model ML operated in the high-stakes world of subsidized marketplace businesses. Like many two-sided platforms, the company had to burn cash to acquire users on both sides of their market. Englander describes the pressure: "The burn rate of that business was huge just because, you know, you had to really subsidize riders, it was called in that market. You know, you're you're literally running out of money." The difference between Model ML and countless failed marketplaces wasn't just product-market fit. It was Englander's deep understanding of unit economics and capital efficiency from his self-directed investing education.
The speed of Model ML's growth created its own competitive moat. When Englander says they hit "about a 100k in three months" and "then we kinda did that again in, like, the next three months," he's describing something rare in B2B software: velocity as a defensive strategy. Moving fast in a subsidized business model requires perfect execution on multiple fronts. You need to understand exactly which users to subsidize, how much subsidy creates sustainable behavior, and when to pull back the incentives. Most founders learn these lessons too slowly and run out of money before they find the balance.
Englander's fundraising strategy also defied conventional wisdom about warm introductions and relationship building. Instead of working through accelerator networks or existing investor relationships, he relied heavily on cold outreach that created the impression of warm connections. The key was understanding that every meeting compounds: "If you're meeting one person, it's you're not meeting just them. You're meeting everyone else that they know." This network effect approach to fundraising let him move faster than competitors who were still trying to get the perfect intro to the perfect investor.
The lesson here isn't that every founder should fire their wealth manager and start day trading. It's that understanding the financial mechanics of your business at a deeper level than your competitors gives you options they don't have. When Englander hit product-market fit with Model ML, he knew exactly how to scale efficiently because he understood both sides of the investor-founder equation. He could model different growth scenarios, understand dilution implications, and negotiate from a position of knowledge rather than hope.
For founders building their first company, the takeaway is simple: start learning about finance and investing now, not after you exit. Read the same materials that your potential investors read. Understand unit economics, cohort analysis, and capital efficiency at a level that impresses rather than just satisfies your board. The difference between a good founder and a serial entrepreneur who can scale multiple companies to eight figures isn't just product intuition. It's financial sophistication that lets you move fast when speed matters most.
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