Mental Models
The human brain loves straight lines. If a project is failing, it feels like it'll fail forever. If our new product saw 20% growth in users this month, our default assumption is that it'll keep growing 20% every month until the end of time, provided we keep doing exactly what we're doing. This linear thinking is comfortable, but it's rarely how the real world operates. This mental model is the antidote to that fallacy.
Relationships, markets, and growth phenomena almost never follow a straight line. They follow an S-curve (or sigmoid curve, if you want to get mathematical about it).
ENTER THE S-CURVE
Picture a line graph shaped like a stretched-out letter "S" leaning forward. It has three distinct phases:
The Slog (bottom tail): You put in a ton of effort, time, or money, and practically nothing happens. Growth is slow, flat, and unrewarding.
The Rocket Ship (steep middle): You hit an inflection point. Things suddenly "click," and growth takes off, fast. A little effort yields massive results.
The Plateau (top tail): The wild growth tapers off. You're hitting a ceiling—market saturation, physical limits, diminishing returns—and no matter how much more effort you pour in, the needle barely moves.
Now, here's a subtle but important point that's easy to get wrong, and that I'll admit I used to get wrong myself. It's tempting to describe that steep middle as "exponential growth." It feels exponential when you're in it. But true exponential growth has no ceiling—it just keeps compounding forever. An S-curve, by definition, does the opposite: it bends back down. So the steep middle was never really unbounded growth. It was constrained growth that simply hadn't met its constraint yet.
This matters because of what it implies about the plateau. The plateau isn't some new villain that shows up in the third act to ruin the party. The ceiling was baked into the system from the very beginning. A finite market has a finite number of customers in it whether you've reached them or not. The diminishing returns weren't introduced; they were always latent, waiting. The S-curve's whole value as a model is that it describes systems that are bounded from the start. Once you see it that way, the plateau stops being a surprise and starts being a forecast.
Let's look at two business examples.
EXAMPLE 1: ADOPTING A NEW WORKFLOW OR TECHNOLOGY
Say your team is rolling out a new CRM or project management system.
When you first roll it out, productivity actually drops. People are confused, they're breaking old habits, and everything takes twice as long. This is the slog, and it's the most dangerous phase, because if you panic here, you abandon the tool prematurely—right before it was about to pay off.
But if you push through to the steep part of the curve, the team masters the tool and efficiency climbs sharply. New habits lock in, the friction disappears, and suddenly the thing that was slowing everyone down is the thing making them fast.
Eventually, though, you squeeze all the inherent value out of that software. And notice why: it's not that the team got lazy or that the tool broke. It's that there was only ever a fixed amount of efficiency to be gained from this particular system, and you've now captured most of it. Sending the team to three more training seminars on the CRM won't yield another massive spike—it'll just be a wasted afternoon, because the ceiling was a property of the tool all along.
EXAMPLE 2: SCALING A NEW PRODUCT OR SERVICE
Say your company launches a new enterprise software tool.
For the first six months, it's a grind. You're spending heavily on marketing, iterating on bugs, and fighting for every single early adopter. Morale might be low because the input-to-output ratio is terrible. (The slog.)
Then you finally hit product-market fit. Word of mouth catches on. Your customer acquisition cost plummets, and your user base doubles every quarter. The team is popping champagne. Leadership, high on the momentum, updates the five-year forecast by drawing a straight diagonal line pointing to the moon. (The rocket ship.)
But a year later, growth suddenly slows. Panic ensues. The executive team demands to know what went wrong. Did marketing get lazy? Is the product broken?
The reality is much less dramatic, and this is exactly where the "it was always bounded" framing earns its keep. Nothing went wrong. You've simply worked your way through the early and mainstream adopters who were always the easiest to win. The customers who remain are late adopters—harder to convince, more expensive to acquire—and there are only so many of them, because the total addressable market was a fixed quantity from day one. The plateau wasn't an event that happened to you. It was the shape of the market revealing itself. The straight-line-to-the-moon forecast was the fantasy; the curve was the truth the whole time.
WHY DO I FIND THIS SO POWERFUL?
The S-curve changes the nature of the conversation in the room, in two crucial ways.
First, it manages emotional expectations and prevents reactionary decisions. When you understand that the initial slog is a feature, not a bug, you don't cancel a promising initiative too early. And when you understand that the plateau is structurally inevitable rather than a failure, you don't punish your team when gravity finally catches up to the growth. It stops leaders from confusing market saturation with team incompetence—two things that look identical on a dashboard but call for completely different responses.
Second, it forces you to time your next move. This is the part that turns the S-curve from a description into a decision.
If you wait until you're flatlining at the top of the curve to start innovating, you're too late. Remember that every new initiative has to pay its own slog tax up front—that initial flat stretch where effort goes in and nothing comes out. So if you only start your next project once your current one is already declining, you're stacking a brand-new slog on top of an existing decline. That's a dangerous dip in overall performance, exactly when you can least afford it.
The move, then, is counterintuitive: you start building your next S-curve while you're still riding the steep, highly profitable middle of your current one. It feels wasteful—why divert resources from the thing that's working?—but the timing is the whole point. By the time your first curve taps out, your second curve is just hitting its inflection point, and the handoff is seamless. You never flatline because you've already got the next rocket lit.1
The S-curve is a simple visualization, but if you keep it handy, it'll save your team from making straight-line predictions in a curved world.