Figma (NYSE: FIG) Jumps 29.1%: Insider Buying Meets a New AI Credit Monetization Plan
United States / Software / NYSE: FIG
Figma's stock surged after two signals hit at once: notable insider and institutional buying, and a decision to start charging for AI credits in March 2026. The company reported Q4 2025 sales of US$303.78M, full-year 2025 sales of US$1.06B, and guided 2026 revenue up to US$1.37B. Losses widened (Q4 net loss US$226.56M; full-year net loss US$1.25B), and a US$744.63M shelf registration for ESOP-related Class A stock was filed.
What actually changed
Figma is moving from "AI included" to "AI is a metered utility." Roughly three-quarters of customers already use AI credits weekly. Converting that usage into paid credits will reveal two things fast: price sensitivity of enterprise design workflows and how quickly Figma can cover rising AI infrastructure costs.
For product teams, this means AI feature access will shift from a sunk SaaS fee to a variable line item. Expect budget discussions, usage guardrails, and a closer look at which AI features deliver measurable throughput gains.
Why insider buying matters (and what it doesn't change)
Insider and institutional buying is a confidence marker: those closest to the business added exposure after results. It doesn't erase the core trade-off-Figma's AI adoption is strong, but AI and stock-based comp could keep losses high if monetization lags.
The investment narrative in simple terms
A baseline outlook points to US$1.7B revenue and US$214.1M earnings by 2028. Hitting that requires ~21.2% annual revenue growth and about a US$1.14B improvement from roughly -US$926.1M in earnings today. Some more optimistic models assume ~24% growth and ~US$229.1M in future earnings; more cautious views argue fair value could sit meaningfully below the current price.
Translation for product leaders: pricing power will be tested through AI credit adoption. If enterprises keep using credits at scale, ARPU can rise. If usage drops when paywalls appear, margin expansion stalls.
How this impacts product development teams right now
- Map AI usage by workflow: content generation, variant exploration, prototyping, handoff annotations. Identify where credits concentrate and where they're wasted.
- Forecast AI credit spend: model low/medium/high usage scenarios and tie each to cycle-time and quality outcomes (e.g., time-to-prototype, issue rates at handoff).
- Set admin guardrails: feature access by role, credit caps, alerts, and auto-pause rules to prevent budget overruns late in the month.
- Instrument ROI: track "hours saved per credit," reduced rework, and PRD-to-prototype lead time. If a feature doesn't improve a measurable KPI, cut it.
- Negotiate enterprise terms: seek pooled credits, rollover provisions, and volume tiers that fit your release cadence.
Cost, dilution, and what to watch in the numbers
- Gross margin trend: AI inference is expensive. If paid credits scale faster than compute and model costs, margins should lift; if not, expect pressure.
- Net revenue retention (NRR): the cleanest read on whether AI credits expand accounts without spiking churn.
- Seat vs. usage cannibalization: do credits replace higher-tier seats, or expand overall spend? Monitor mix shifts.
- Stock-based comp and dilution: the US$744.63M shelf for ESOP-related Class A stock signals more supply. Understand how it may affect per-share metrics. What a shelf registration means.
- Security and data controls: verify how prompts/outputs are handled and logged, especially for regulated workflows.
Practical rollout checklist (quarter one)
- Pick two high-leverage workflows for AI credits (e.g., design variant exploration and spec annotation). Limit pilots to keep signal clean.
- Create a simple "credit policy": monthly budget, roles eligible, and a two-minute playbook for when to use AI vs. manual.
- Benchmark before/after: time-to-first prototype, acceptance rate from PM/eng, and handoff issue count.
- Review after 30 days: keep features that show clear gains, kill those that don't, renegotiate volume once you have real data.
Bottom line
The story is straightforward: Figma is testing whether heavy AI usage converts into durable, profitable revenue. Insider buying adds confidence, but the scoreboard will be margins, NRR, and how enterprises respond once credits cost money. If your team proves ROI with tight pilots and guardrails, you'll get the upside without the budget surprises.
Resources for your team
About NYSE:FIG - Figma develops and sells a collaborative, browser-based platform for designing, prototyping, building digital experiences, and subscriptions for access to its platform.
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