Chegg lost roughly 99% of its value after free AI arrived to do what students had been paying it for. Quentic sold compliance software — the kind of business a glance dismisses as commodity SaaS — and the same wave barely touched it.
The difference isn't luck. It's structure: four layers, each one a reason a customer can't take the work back in-house. Here are both companies read against all four layers — where Chegg had nothing, and where Quentic held.
Chegg: General answers, free elsewhere
This layer is worth something only when the knowledge is proprietary and maintained against a moving target. Chegg's answers were general and static: exactly what a language model now generates for free.
Quentic: Regulation encoded, and kept current
To use an AI's answer safely you have to be able to tell whether it's both correct and complete. In a specialist domain the customer usually can't. And if they could, they wouldn't have needed to ask. Picture an industrial site near Paris storing a given quantity of hazardous chemicals: an AI produces a confident recommendation in seconds, but the operator has to be certain it's compliant with both the EU Seveso III Directive and the relevant French rules, and typically has no way to verify that. Quentic's durability was never "knowing EHS" — it was the regulatory logic encoded into workflows and kept current across jurisdictions. Whether that ran on Java or PHP was irrelevant.
Chegg: Nothing to stand behind
No liability anyone needed transferred, nothing to stand behind.
Quentic: The name on the audit
In EHS, being wrong is expensive: fines, shutdowns, real safety exposure — so customers had little appetite to insource that risk. Being the system of record meant a customer could walk into an audit and pass it easily, the documentation and trail already in defensible form. An LLM won't stand in front of the regulator on their behalf.
Chegg: A library, not an asset
The answer library looked like accumulated data, but it wasn't proprietary or load-bearing. An LLM produces equivalent answers without it.
Quentic: Real friction, but exportable
Years of incident records, audit trails and compliance history raised the cost of leaving — but customers had the right to export all of their own data, which deliberately capped how much lock-in it created. Friction, not a wall.
Chegg: A channel that closed
The funnel came from students typing questions into Google and clicking through. When Google began answering those questions itself, the channel closed at the source.
Quentic: Strong at home, ceded elsewhere
Quentic held this layer, but only within its borders. In Central Europe the footprint was strong enough to be self-reinforcing. Elsewhere, there were whole industries we never entered, because an established competitor already owned the attention there — the experts, the relationships, the standing. In those verticals, deals closed before we even knew they existed. Not a technology gap we could engineer past; a distribution-and-authority gap, and close to unassailable.
Each of those four layers is worth a different amount depending on who your customer is. Encoded domain knowledge is indispensable to an operator working across twelve jurisdictions, and over-engineered for a customer who needs a tenth of it. The layer didn't change. The customer did.
So picture any software market as a pyramid. The most demanding customers sit at the narrow top; the least demanding at the wide base. Now draw a waterline across it. Below the line, AI can build the thing outright — or collapse the need for it entirely. Above it, the complexity still justifies buying.
The line is not fixed. It rises. And when it rises, your layers don't weaken — the set of customers they protect shrinks from the bottom.
“The stack gives you altitude. The waterline gives you rate. You need both to answer the only question an owner actually has: how many years are left, and which layer goes first.”
One exception worth naming: distribution doesn't follow this axis. It varies by market rather than by customer altitude, which is part of why it sits apart from the other three.
Chegg is the clean case. Its layers were thin — but the sharper point is where its customers sat. Students with routine homework questions were the widest, least demanding layer in that market, and Chegg served almost nothing else. The waterline didn't cut through Chegg's category. It rose past the whole thing in one step.
Quentic's layers were pointed at customers with Seveso-grade exposure. Same instrument, opposite answer.
That's the point of the instrument. Chegg looked defensible and was hollow. Quentic looked exposed and held: strong on the first two layers, secondary on the third, bounded on the fourth. A glance gets both backwards; the instrument gets both right. It isn't a checklist you want four out of four on — it's a way to locate where durability sits, and how long it holds.
What the instrument doesn't tell you. It is a durability read, not a growth read. It says nothing about whether this team can execute, whether the pricing is right, or whether the market is expanding. A company can hold all four layers, sit well above the waterline, and still be a poor investment. The instrument answers one question — what survives, and for how long — and it's worth being explicit that this is the only question it answers.
You've seen it run twice. The question travels:
“Which of your customers could do without you in eighteen months — and what share of revenue sits with them?”
Then ask it again about the customers you're still trying to win. That answer is the one that moves the multiple.
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