---
title: "AI Cost Concentration Turns 1% of Users Into 75% of Expenses, Says SaaS Pricing Veteran"
slug: "ai-cost-concentration-turns-1-of-users-into-75-of-expenses-says-saas-p"
date: "2026-10-26"
category: "MarTech"
author: "Chloe Duval"
correspondent: "Chloe Duval"
correspondentRole: "GTM & Outbound Investigative Reporter"
readingTimeMinutes: 4
tags: ["MarTech"]
originalSourceUrl: "https://www.saastr.com/the-double-hockey-stick-why-1-of-your-users-now-drive-75-of-your-ai-costs-and-what-figma-clay-and-higgsfield-are-doing-about-it/"
canonicalUrl: "https://aurav.ae/ai-cost-concentration-turns-1-of-users-into-75-of-expenses-says-saas-p"
publisher: "Aura-V Intelligence (aurav.ae)"
---

# AI Cost Concentration Turns 1% of Users Into 75% of Expenses, Says SaaS Pricing Veteran

> Ulrik Lehrskov-Schmidt’s “double hockey stick” model shows a tiny slice of customers now drive most AI spend, prompting new pricing guards.

## Key Takeaways (TL;DR)

- A single percent of users can generate up to three-quarters of a SaaS product’s AI bill.
- Flat pricing masks extreme cost-value mismatches, forcing companies to add throttles or tiered credits.
- Figma, Clay and Higgsfield illustrate three of the four cost-value patterns and how they responded.

Ulrik Lehrskov-Schmidt, founder of Willingness to Pay, coined the “double hockey stick” after observing a stark shift in AI-driven SaaS economics. In a recent SaaStr interview he walked through the pattern with Amelia, citing Figma, Clay and Higgsfield as live examples of firms grappling with the new cost curve [The Double Hockey Stick – SaaStr](https://www.saastr.com/the-double-hockey-stick-why-1-of-your-users-now-drive-75-of-your-ai-costs-and-what-figma-clay-and-higgsfield-are-doing-about-it/).

### One Percent, Up to 75% of AI Spend
Lehrskov-Schmidt’s data, drawn from “a couple dozen high-growth AI pricing projects,” shows a power-law distribution of AI consumption. Roughly half of all users consume little or nothing, while the top 1% of users account for 50%-75% of total AI costs; in extreme cases the top 0.1% generate more than three-quarters of the inference bill. In a product with one million users, that extreme case translates to 1,000 heavy users shouldering the bulk of the expense. One client discovered a “least profitable customer” that spent 380 times more on token usage than it contributed in revenue, a loss hidden by flat pricing.

### Flat Pricing Hides a Dual Curve Problem
Traditional B2B SaaS could ignore such variance because serving each customer cost roughly the same. AI introduces a second, independent curve: cost now scales with usage while value may remain flat. The mismatch means that flat fees—whether $5 K, $50 K or $500 K—miss high-willingness-to-pay customers and expose firms to unprofitable heavy users. Lehrskov-Schmidt notes that a French client is negotiating price hikes of up to 30,000 % (about 300 ×) for its top-value accounts, illustrating the scale of the untapped revenue curve.

### Four Cost-Value Configurations, Three Real-World Cases
Lehrskov-Schmidt maps the interaction of cost (flat vs. spiky) and value (flat vs. spiky) into four quadrants. The SaaS landscape currently exhibits three of them:

* **Spiky value, flat cost – Clay**: The data-enrichment platform sees uneven value across accounts but relatively stable AI costs. It introduced “action credits” on top of existing data credits, turning its heaviest users into higher-paying customers without altering the underlying cost structure.

* **Spiky value, spiky cost – Figma**. Gross margins fell from 92 % to 82 % as AI spend rose, but revenue growth outpaced cost, pushing the company past $1 B ARR.

* **Flat value, spiky cost – Higgsfield**: Its tiered plans ($19, $49, $99) include “unlimited AI,” creating a ceiling on revenue per user while cost per user can climb indefinitely. Lehrskov-Schmidt suggests implementing fair-usage limits to prevent 380× customers from eroding margins.

The fourth quadrant—flat cost, flat value—is exemplified by Netflix, which currently enjoys scalable costs aligned with a uniform subscription price.

### Throttles, Kill Points and Gradual Brakes
Lehrskov-Schmidt outlines a pragmatic mitigation framework. First, segment users (new, fraudulent, and the bulk). Next, define a “kill point”—the usage threshold where the service shuts off. Then add “brakes” that reduce utility incrementally (e.g., halving speed after a certain edit rate). Communication channels (email, in-product notice, MCP) are chosen, and brakes are introduced slowly, akin to “boiling the frog.” In practice, affecting roughly 4 % of users can address 60-80 % of the cost problem, allowing firms to recover up to 22 gross-margin points within weeks, according to Lehrskov-Schmidt’s case work.

### Outcomes from the Three Companies
* **Figma**: After layering usage caps and credit systems, the company’s gross margin dipped but revenue grew faster, leading to accelerated growth beyond $1 B ARR. The shift also concentrated revenue among top-value customers, aligning cost and value curves.

* **Clay**: By adding action credits, the platform captured more of the existing value curve. Heavy users now pay several times their prior fees, narrowing the gap between AI cost and revenue without a wholesale pricing overhaul.

* **Higgsfield**: The firm’s unlimited-AI tiers risk a 380× cost-to-revenue ratio. Lehrskov-Schmidt recommends fair-usage limits to protect margins, though he does not confirm whether such limits are already in place.

Our read: The “double hockey stick” frames AI adoption as a structural pricing challenge rather than a marginal cost issue. The evidence—power-law usage, 380× loss cases, and margin swings of up to 22 points—shows that flat pricing models are ill-suited for AI-intensive products. The suggested throttling approach offers a low-friction path to re-balance cost and value, but it relies on careful segmentation and incremental enforcement, which may be operationally complex for fast-growing SaaS firms.

## Aura-V Editorial Verdict

The piece spotlights a structural pricing blind spot that AI has amplified across SaaS. While the suggested throttling tactics are practical, the analysis stops short of quantifying long-term margin impact, leaving readers to infer scalability.

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*Original Think-Tank Source: [The Double Hockey Stick: Why 1% of Your Users Now Drive 75% of Your AI Costs, and What Figma, Clay, and Higgsfield Are Doing About It](https://www.saastr.com/the-double-hockey-stick-why-1-of-your-users-now-drive-75-of-your-ai-costs-and-what-figma-clay-and-higgsfield-are-doing-about-it/)*