How ChargeLab Reached $20M ARR by Optimizing for Learning Speed, Not Revenue Growth

How ChargeLab Reached $20M ARR by Optimizing for Learning Speed, Not Revenue Growth

Every founder faces the same impossible tradeoff. Push for faster growth and risk scaling the wrong things. Move slowly to learn and watch competitors capture market share. The conventional wisdom says growth wins—that speed to market matters more than getting everything right.

In a recent episode of Category Visionaries, Zak Lefevre, CEO of ChargeLab, explained why he rejected this tradeoff entirely. By deliberately optimizing for learning speed over revenue growth in the early years, he avoided millions in expensive mistakes and ultimately reached $20 million ARR faster than conventional scaling would have allowed.

The False Choice Between Speed and Learning

Most founders treat learning and growth as competing objectives. You can learn by moving slowly and staying close to customers, or you can grow by scaling quickly and accepting that you’ll learn less per customer. The assumption is that you can’t do both simultaneously.

This framing creates pressure to choose growth. Investors want to see revenue traction. Competitors are raising money and hiring aggressively. The market opportunity feels time-sensitive. So founders hire salespeople, scale marketing, and start optimizing for efficiency metrics before they fully understand what actually drives results.

Zak saw this pattern and recognized the hidden cost. “If we bring on salespeople, they’re just going to be another layer between us and the customer, and we’re going to learn slower,” he explains. “So let’s just keep doing it ourselves until we really feel like we have it nailed.”

The insight was that learning speed and growth speed aren’t actually in conflict—but only if you sequence them correctly. Optimize for learning first, and you can grow much faster later because you’re scaling proven motions rather than experimenting at scale.

What Learning Speed Actually Means

Learning speed isn’t about how many experiments you run or how much data you collect. It’s about how quickly you can identify what’s working, understand why it’s working, and apply those insights to make better decisions.

For ChargeLab, optimizing for learning speed meant Zak personally handling every sales conversation for years. “We didn’t have any sales team for a while. It was really just me,” he says. “I was doing all the sales and closing deals myself, and we had some customer success people that were helping out.”

This approach seems inefficient on the surface. A founder’s time is valuable—surely it’s better spent on strategy and fundraising rather than individual sales calls. But this logic misses what’s actually happening on those calls.

Every conversation revealed patterns. Which messaging made prospects lean in versus tune out. Which objections were fundamental versus superficial. Which features prospects actually cared about versus which features ChargeLab thought they should care about. Which buyer personas could actually close deals versus which just wanted to explore.

These insights don’t come from dashboards or filtered reports from sales teams. They come from direct, repeated exposure to customer conversations. The more conversations Zak took himself, the faster these patterns became clear, and the more confident he became about what actually worked.

The Compounding Nature of Early Learning

The real power of optimizing for learning speed shows up in how insights compound over time. When you learn something fundamental early, it shapes every decision that follows. When you learn that same thing later, you’ve already made hundreds of decisions based on incorrect assumptions.

Consider the alternative path ChargeLab could have taken. Hire salespeople at $500K ARR, give them a rough playbook, let them figure out what works through trial and error. By $2M ARR, you’d have some data on what converts. By $5M ARR, you’d have a more refined playbook. By $10M ARR, you’d finally have it figured out.

But in this scenario, you’ve spent years and millions of dollars learning lessons you could have learned much faster with a different approach. Worse, you’ve built systems and processes around incomplete understanding. Your sales team has internalized messaging that doesn’t quite work. Your product roadmap reflects features that prospects asked for but don’t actually need. Your pricing is based on what you could get away with rather than what creates optimal customer value.

Unwinding these mistakes is enormously expensive. You can’t just update the pitch deck—you need to retrain the entire team. You can’t just adjust the roadmap—you need to deprecate features customers are already using. You can’t just change pricing—you need to handle existing customer contracts and expectations.

By optimizing for learning first, ChargeLab skipped this entire painful unwinding phase. When they finally did scale, every system and process was built on correct assumptions from day one.

The Hidden Costs of Scaling Too Early

The conventional approach to SaaS scaling creates costs that most founders don’t anticipate. These costs don’t show up in the P&L as line items, but they’re real and they compound.

First, there’s the cost of filtered information. When salespeople become the primary interface with customers, founders hear about customer conversations secondhand. The sales team reports what they think is important, filtered through their own interpretation and biases. Critical signals get lost in translation.

Second, there’s the cost of misallocated resources. Without deep customer understanding, product teams build the wrong features. Marketing teams target the wrong channels. Sales teams pursue the wrong deals. Each misallocation costs money directly, but it also costs time and opportunity.

Third, there’s the cost of organizational momentum. Once you’ve hired a team and built processes around a specific approach, changing direction becomes exponentially harder. The team has internalized certain ways of doing things. Customers have expectations based on how you’ve positioned yourself. Investors are tracking metrics that assume continuity of strategy.

Zak avoided all these costs by staying small and learning-focused until the fundamental questions were answered. By the time ChargeLab started scaling, they weren’t guessing—they knew what worked.

When to Shift from Learning to Scaling

The critical question is: when do you make the shift from optimizing for learning to optimizing for growth? Too early, and you scale the wrong things. Too late, and competitors capture market share.

Zak’s answer was elegantly simple: “We really feel like we have it nailed.” Not when they hit a specific ARR milestone. Not when they’d run a certain number of experiments. When they had complete confidence that they understood what drove results and could articulate it clearly enough to teach others.

This confidence came from repetition and pattern recognition. After hundreds of customer conversations, certain truths became undeniable. The messaging that worked was obvious because it worked consistently across different prospects and contexts. The objections that mattered were clear because they appeared in every deal. The buyer personas who could actually close were evident because the pattern repeated itself.

Once you have this level of clarity, adding people becomes a multiplication exercise rather than an experimentation exercise. New hires can execute against a proven playbook immediately instead of spending months figuring things out. Marketing spend goes toward channels and messages that already work at small scale. Product development focuses on features customers have consistently requested.

The Paradox of Moving Slow to Move Fast

The counterintuitive result of ChargeLab’s approach is that moving slowly early allowed them to move much faster later. By spending extra time learning before scaling, they compressed the timeline to $20M ARR because they avoided all the delays that come from scaling too early.

They never had to pause growth to fix fundamental issues with the sales motion. They never had to reorganize the team because the structure couldn’t support the strategy. They never had to rebrand because the early positioning didn’t resonate with the target market. All of these delays plague companies that scale before they’re ready.

The path to $20M ARR wasn’t linear, but it was efficient. Every dollar spent went toward proven tactics. Every hire made the company stronger rather than just bigger. Every product feature addressed real customer needs rather than assumed ones.

The Framework for Your Company

The lesson here isn’t that every company should delay scaling. It’s that the sequence matters enormously. If you scale before you’ve learned what works, you’ll spend years and millions of dollars unwinding mistakes. If you optimize for learning first, scaling becomes straightforward because you’re multiplying what already works.

Ask yourself honestly: do you really understand what drives results in your business? Not at a high level—at a detailed, tactical level. Can you articulate exactly what messaging converts prospects? Exactly which buyer personas close? Exactly what product capabilities matter versus what’s nice to have?

If the answers are fuzzy, you’re not ready to scale. Keep learning. Stay close to customers. Do things that don’t scale. Accumulate the insights that will make scaling inevitable rather than effortful.

If the answers are crystal clear, and you can teach someone else to replicate your results, you’ve learned enough. Now scaling isn’t risky—it’s just execution. The knowledge you’ve accumulated becomes the foundation for efficient, predictable growth that compounds over time.

Zak’s path to $20M ARR proves that the fastest way to grow isn’t always the most direct path. Sometimes it’s the path that takes time to learn deeply before scaling aggressively. The companies that win aren’t necessarily the ones that grow fastest—they’re the ones that learn fastest and then scale what works.