Figma Stock: Why AI Usage Has a Cost

Figma reported another quarter of substantial growth last week. Revenue increased 43% to $391.4 million, more customers adopted its products, and usage of its AI capabilities continued to expand. The company also raised its full-year revenue outlook, reflecting stronger demand than management had previously anticipated.

One of the most interesting developments sits underneath those growth figures. Figma's non-GAAP gross margin declined from 86% to 82% year over year, while the company disclosed a meaningful increase in infrastructure and hosting costs associated with AI and greater platform usage.

The combination illustrates an important change in software economics. Traditional subscription software can often serve additional users at relatively modest incremental cost. Generative AI introduces activities that may require meaningful computing resources every time a customer uses them.

That does not make AI adoption unattractive. It means investors need to understand both sides of the transaction: the revenue opportunity created by additional usage and the cost of delivering the functionality customers increasingly expect.

Figma's AI adoption is becoming meaningful

According to Figma's November 4 earnings release, 65% of paid customer accounts generating more than $10,000 in annual recurring revenue were using the Figma agent weekly as of October 31, 2026. More than 80% of those accounts were consuming AI credits.

These figures provide encouraging evidence that AI functionality is moving beyond occasional experimentation. Customers are incorporating the tools into their existing workflows, while the platform continues to expand the range of activities supported by AI.

However, the percentages deserve careful interpretation. They refer specifically to paid customer accounts with more than $10,000 in ARR, not Figma's entire customer base. The figures also measure usage rather than the amount of incremental revenue directly attributable to that usage.

A customer using an AI feature does not necessarily mean the company earns additional revenue every time that feature is used. The economic outcome depends on how the functionality is packaged, how much usage is included in existing subscriptions and whether customers purchase additional capacity.

That distinction matters because product adoption and monetization are related, but they are not interchangeable.

Why AI usage changes the software cost structure

Many software companies benefit from attractive incremental economics. After developing a product and building the necessary infrastructure, serving another customer may require relatively little additional spending compared with the subscription revenue collected.

Generative AI can introduce a different cost pattern. Producing an AI-generated design, analyzing a document or generating code may require computing resources each time the request is processed. The cost depends on factors such as model complexity, the amount of computing required, infrastructure efficiency and the type of task being performed.

Figma's latest Form 10-Q provides a useful example. The company reported that technical infrastructure and hosting costs included in cost of revenue increased by $29.5 million compared with the prior-year quarter. Management attributed that increase to AI-related activities and greater usage of the platform by paying customers.

The filing does not separately identify how much of the $29.5 million came from AI inference versus other growth in platform usage. It would therefore be misleading to attribute the entire increase to generative AI alone.

Nevertheless, the disclosure makes the broader economic mechanism visible. Additional customer activity can increase the resources required to deliver the service, even when the underlying software product already exists.

That is a meaningful distinction for long-term investors because revenue growth becomes more valuable when the company can deliver it without a proportionate increase in costs.

The gross margin reveals an important trade-off

Figma reported a GAAP gross margin of 80% during the third quarter, compared with 69% a year earlier. At first glance, that suggests a considerable improvement in the direct profitability of its services.

However, the non-GAAP gross margin moved in the opposite direction, declining from 86% to 82%.

The difference is largely explained by the treatment of certain accounting expenses, particularly stock-based compensation. Figma recognized substantial compensation-related expenses following its July 2025 initial public offering, which affected the prior-year GAAP comparison.

Those expenses were considerably lower in the current quarter. As a result, the reported GAAP gross margin improved even while infrastructure costs increased.

The adjusted measure removes certain expenses and can help investors examine operating trends without the unusually large prior-year IPO-related charges dominating the comparison. However, adjusted gross margin is not automatically a better representation of the company's complete economics. The excluded expenses still have economic consequences for shareholders.

Both measures are therefore useful. GAAP gross margin reflects the accounting result, while the adjusted comparison helps isolate certain changes in the cost structure.

The important observation is that Figma's adjusted gross margin declined by four percentage points despite substantial revenue growth. Higher AI and platform infrastructure costs are part of the explanation, although the available disclosures do not establish that they account for the entire change.

AI credits create a way to connect usage with revenue

Figma has been adapting its pricing model to reflect the economics of AI functionality. In 2025, the company introduced AI credits across its subscription offerings. In March 2026, it began enforcing credit limits and introduced options for customers to purchase additional usage.

These options include AI credit add-ons and pay-as-you-go arrangements. The structure allows Figma to charge for incremental usage beyond what is included in a customer's existing subscription.

From an economic perspective, this is important because AI usage can vary considerably between customers. One organization might experiment with a limited number of AI-generated designs each month, while another might incorporate AI agents throughout its development process.

Providing both organizations with unlimited AI functionality at a fixed price could create substantially different cost structures even if the subscription revenue were identical.

Usage-based pricing provides a potential mechanism for connecting the revenue received with the resources consumed. However, whether it produces attractive economics depends on the price customers pay, the underlying cost of computation and how efficiently Figma manages that usage.

Investors should therefore avoid assuming that higher AI credit consumption necessarily translates into proportionately higher profit.

Three things determine whether AI usage creates economic value

The first consideration is monetization. Figma needs to generate enough revenue from subscriptions, AI credits and related products to justify the additional resources required to provide them. Strong adoption is useful evidence of customer interest, but the financial contribution becomes clearer when usage produces measurable revenue.

The second consideration is computing efficiency. The cost of delivering an AI task can change as models improve, infrastructure becomes more efficient and companies optimize how requests are processed. If Figma can provide the same functionality at a lower cost, its unit economics may improve even without increasing customer prices.

The third consideration is customer value. A feature that meaningfully improves productivity may support higher prices, stronger retention or additional product adoption. A feature that customers use frequently but do not value enough to pay for creates a more difficult economic proposition.

These factors interact. Better models can reduce costs, but they can also encourage customers to use the functionality more frequently. Lower costs per request do not automatically produce lower total spending if the number or complexity of requests grows substantially.

The strongest outcome would be a business in which customer value and monetization increase faster than the cost of delivering the additional usage. That relationship matters more than the adoption percentage alone.

Figma is also investing beyond the direct cost of AI usage

Infrastructure spending does not appear only in cost of revenue. Figma's quarterly filing also identified a $14.5 million increase in research-and-development infrastructure and hosting costs, primarily associated with AI-related activities.

Sales and marketing expenses included an additional $8.8 million of technical infrastructure and hosting costs associated with the growing free-user base and AI functionality being introduced to those users.

These figures reveal different parts of the investment cycle. Developing and improving AI capabilities requires resources. Delivering those capabilities to paying customers creates operating costs. Offering them to free users can create additional expenses before those users generate meaningful revenue.

That last category is particularly interesting for a software platform with a substantial free-user offering. Providing free functionality can help customers discover the product and eventually become paying users. But the economic attractiveness of that strategy depends on how frequently free users convert into paying customers and how much those relationships generate over time.

AI can change the cost of that customer-acquisition process because free users may consume computing resources even before they begin paying.

This does not mean Figma should reduce its free offering. It means the cost of attracting, activating and converting customers deserves attention alongside reported user growth.

What the financial statements say about profitability

Figma's latest quarter illustrates why revenue growth, adjusted profitability and cash generation should be examined separately.

Revenue increased 43% to $391.4 million. Non-GAAP operating income reached $47.0 million, representing a 12% adjusted operating margin. The company also generated $48.4 million in operating cash flow and $41.5 million in free cash flow, equivalent to an 11% free-cash-flow margin.

Those are meaningful indicators of the company's ability to generate cash while continuing to grow rapidly.

At the same time, Figma reported a GAAP operating loss of $119.2 million and a GAAP net loss of $81.3 million. The substantial difference between GAAP and adjusted profitability reflects expenses excluded from the adjusted calculation, including stock-based compensation.

The distinction is particularly relevant when evaluating a relatively young public company. Investors should not treat adjusted operating profit as if it were identical to cash available to shareholders, nor should they assume that a GAAP loss means the business is incapable of generating operating cash.

Instead, the useful question is whether revenue growth increasingly translates into sustainable operating profitability and cash generation after considering the full economic cost of running the business.

AI adoption is one part of that equation. The broader business still has to support research, product development, sales, infrastructure and the other expenses required to maintain its competitive position.

Does collaborative software create a durable advantage?

Figma operates in a market where collaboration is central to the customer experience. Designers, developers and other product teams can work on shared projects, making the platform part of how organizations develop and maintain digital products.

These workflows can create potential switching costs and collaborative network advantages. Moving to another platform may involve transferring projects, adjusting integrations, retraining employees and changing established processes.

However, strong product adoption and integrated workflows do not automatically establish a durable economic moat. Competing products may improve, AI tools may change how design work is performed, and customers may become more willing to move between platforms if compatibility improves.

The current MOATEY evidence is cautious. Figma receives 1 of 20 available Moat points, with Earnings Power Value below Net Reproduction Value and a deteriorating trend in that particular assessment.

Those observations do not prove that Figma lacks useful competitive strengths. They indicate that the framework currently finds limited quantitative support for a clear, lasting competitive advantage.

This distinction becomes more important as AI changes the software industry. A company may have a highly popular platform today while still facing uncertainty about whether its economics will remain attractive as technology and competition evolve.

What MOATEY sees beyond the quarterly growth rate

Following its latest update, MOATEY currently gives Figma a Poor overall rating and an assessment of 20.6 out of 95 points. Its Growth assessment is relatively strong at 12.1 of 15 available points, while Financial Health receives 0 of 15 and Moat receives 1 of 20.

The Growth assessment increased from 11.0 to 12.1 points following the November 5 update, reflecting stronger evidence in that part of the framework.

Importantly, Figma's current Valuation assessment also receives 0 of 30 points, but MOATEY's detailed explanation identifies limitations in the reliability of the valuation estimate. It would therefore be incorrect to interpret the zero as definitive evidence that Figma's shares are expensive.

These are MOATEY framework observations rather than forecasts. The underlying calculations may include proprietary adjustments and are intended to examine multiple dimensions of business quality and valuation. Readers who want to inspect the methodology and supporting historical evidence can review the MOATEY factsheet and documentation.

The broader lesson is that substantial revenue growth does not remove the need to evaluate financial health, competitive durability and the reliability of valuation estimates.

Figma's latest results provide encouraging evidence of product demand and growing AI adoption. Whether that adoption ultimately translates into durable economic value remains a separate analytical question.

What investors should monitor as AI usage expands

The next stage of Figma's development will reveal more about the relationship between customer adoption and the economics of delivering AI functionality.

One useful indicator will be whether AI credit consumption increasingly contributes to paid revenue. The company has introduced mechanisms for charging customers based on additional usage, but investors still need evidence that those arrangements generate attractive economics after the associated computing costs.

Gross margin is another important indicator. If Figma can improve the efficiency of its AI infrastructure while maintaining pricing and customer value, adjusted gross margins may stabilize or improve. If usage costs continue rising faster than associated revenue, margin pressure could persist.

The distinction between paid and free users also deserves attention. Offering advanced AI functionality to free users may support future customer acquisition, but the company must ultimately convert enough of that activity into economically valuable customer relationships.

Finally, investors should follow the relationship between GAAP earnings, adjusted operating profit and free cash flow. Figma's current growth is substantial, but long-term shareholder value depends on more than customer adoption or revenue expansion alone.

The lesson extends beyond Figma

Generative AI is changing how software companies build products, serve customers and charge for functionality. It creates opportunities to provide greater value, attract new users and expand existing relationships. It can also introduce additional operating costs that behave differently from traditional software development expenses.

Figma's latest quarter illustrates both sides. Revenue grew 43%, AI adoption expanded and the company raised its annual outlook. At the same time, adjusted gross margin declined and infrastructure costs associated with AI and greater platform usage increased.

Neither observation invalidates the other. Strong customer adoption can coexist with pressure on unit economics, particularly while a company is still refining its pricing and infrastructure.

For long-term investors, the useful question is not simply whether customers are using AI. It is whether the company can turn that usage into revenue and cash flow at an attractive incremental cost.

Adoption demonstrates demand. Sustainable economics determine how much value that demand can create.