VOL. XCIV, NO. 247
★ WIDE MOAT STOCKS & COMPETITIVE ADVANTAGES ★
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Thursday, January 8, 2026
Duolingo, Inc.
DUOL · NASDAQ
Weighted average of segment moat scores, combining moat strength, durability, confidence, market structure, pricing power, and market share.
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Overview
Duolingo is a consumer education software company best known for its mobile-first, freemium language learning app, and it also operates the Duolingo English Test (DET), an online high-stakes English proficiency assessment. The Duolingo App moat is primarily demand- and data-driven: strong brand-led organic acquisition, habit-forming gamification, and a large learner dataset that supports rapid A/B testing and AI personalization. DET moat is driven by institutional acceptance (thousands of programs) plus a convenience advantage versus test-center models. Key pressures include AI-powered substitutes, privacy constraints on data usage, and any loss of trust or acceptance in DET.
Primary segment
Duolingo App
Market structure
Competitive
Market share
—
HHI: —
Coverage
2 segments · 8 tags
Updated 2026-01-06
Segments
Duolingo App
Mobile-first, freemium language learning and adjacent subjects (math, music)
Revenue
93.9%
Structure
Competitive
Pricing
moderate
Share
—
Peers
Duolingo English Test
English proficiency assessment for admissions, visas, and employment (online, on-demand)
Revenue
6.1%
Structure
Oligopoly
Pricing
moderate
Share
—
Peers
Moat Claims
Duolingo App
Mobile-first, freemium language learning and adjacent subjects (math, music)
Revenue share derived from FY2024 10-K disaggregation: Subscription $607.531M + Advertising $54.907M + In-App Purchases $38.653M + Other $1.293M = $702.384M of $748.024M total.
Data Network Effects
Network
Data Network Effects
Strength
Durability
Confidence
Evidence
Large-scale learner interaction data enables rapid experimentation and AI-driven personalization, creating a compounding product-improvement flywheel.
Erosion risks
- AI commoditization lowers the advantage of proprietary learner data
- Privacy regulation reduces ability to collect/use behavioral data
- Competing platforms replicate personalization and A/B testing capability
Leading indicators
- DAU/MAU growth and engagement depth (sessions per user)
- Paid subscriber penetration and retention
- Speed/volume of product experiments shipped (A/B testing cadence)
Counterarguments
- Language learners can multi-home across apps, weakening lock-in
- Open-source models and public corpora can narrow AI personalization gaps
Habit Default
Demand
Habit Default
Strength
Durability
Confidence
Evidence
Gamification (streaks, challenges) builds daily routines that support retention and subscription conversion.
Erosion risks
- User fatigue reduces engagement and streak retention
- Competitors copy gamification mechanics
- Platform policy changes limit notifications or engagement nudges
Leading indicators
- Streak distribution (7-day and 365-day streak counts)
- Paid conversion rate from free users
- Churn and cohort retention metrics
Counterarguments
- Gamification patterns are replicable and not exclusive
- Some users treat Duolingo as casual entertainment, limiting willingness to pay
Brand Trust
Demand
Brand Trust
Strength
Durability
Confidence
Evidence
Strong consumer brand and cultural presence drives organic acquisition and supports premium subscription tiers.
Erosion risks
- Brand damage from product quality, safety, or privacy incidents
- Perceived decline in learning efficacy vs competitors
- Platform controversies or backlash to monetization changes
Leading indicators
- Branded search interest and app store rankings
- Net Promoter Score (NPS) / user ratings trend
- Paid marketing as % of revenue (need for paid acquisition)
Counterarguments
- Brand may not defend pricing in a crowded freemium market
- Large platforms can promote competing learning products at scale
Scope Economies
Supply
Scope Economies
Strength
Durability
Confidence
Evidence
Shared infrastructure across multiple products allows faster feature rollout and lowers marginal engineering cost per new course/product.
Erosion risks
- Product expansion increases complexity and slows iteration
- New subjects fail to reach scale, reducing platform leverage
- Competitors build similar shared infrastructure
Leading indicators
- Time-to-launch for new courses/features
- R&D efficiency (new features per engineering headcount)
- User adoption of non-language courses (math/music) over time
Counterarguments
- Large competitors can also build shared infrastructure; scope economies may not be unique
Duolingo English Test
English proficiency assessment for admissions, visas, and employment (online, on-demand)
Revenue share derived from FY2024 10-K disaggregation: Duolingo English Test revenue $45.640M of $748.024M total revenue.
Design In Qualification
Demand
Design In Qualification
Strength
Durability
Confidence
Evidence
Institutional acceptance acts like a qualification barrier: the test is valuable because thousands of programs accept it for admissions.
Erosion risks
- Institutions rescind acceptance due to security/validity concerns
- Reduced reliance on standardized testing in admissions
- Incumbent tests (TOEFL/IELTS/PTE) defend share via partnerships and policy influence
Leading indicators
- Number of accepting institutions and renewal/retention rate
- Share of international admissions at accepting institutions using DET
- Publicized security incidents or changes to proctoring rules
Counterarguments
- Institutions can switch tests if confidence or policy changes
- Incumbents have entrenched relationships with test centers and regulators
Operational Excellence
Supply
Operational Excellence
Strength
Durability
Confidence
Evidence
An online, on-demand, computer-adaptive test with fast turnaround offers a convenience/cost advantage versus physical test-center models.
Erosion risks
- Cheating/fraud incidents reduce trust and acceptance
- Incumbents replicate online/on-demand delivery
- Regulatory changes restrict remote proctoring
Leading indicators
- Average revenue per test and test volume
- Turnaround time for scoring and certification decisions
- Institution acceptance growth in key destination countries
Counterarguments
- Convenience advantage narrows if incumbents move online at scale
- High-stakes exams face ongoing arms races in security and anti-cheating
Brand Trust
Demand
Brand Trust
Strength
Durability
Confidence
Evidence
For a high-stakes credential, trust in validity/security is essential; acceptance by top programs can reinforce perceived credibility.
Erosion risks
- Loss of confidence in validity/security
- Negative media coverage regarding cheating or fairness
- Policy shifts away from English testing requirements
Leading indicators
- Security incident rate and remediation speed
- Institution acceptance churn (rescissions)
- Score correlation studies vs other standardized tests
Counterarguments
- Legacy tests may retain higher perceived prestige in some markets
- Trust can be fragile and sensitive to isolated security failures
Evidence
Our millions of learners complete over a billion exercises every day, creating what we believe to be the world's largest learning dataset.
Scale of usage generates a proprietary dataset that can improve teaching efficacy and engagement.
The greater the scale of our learner base, the more we can use insights from data analytics to improve both engagement and efficacy.
Management explicitly describes a data-driven flywheel linking scale to analytics to better product to more scale.
Large data moat... [W]ith over a billion exercises completed every day... the world's largest collection of language-learning data.
Duolingo explicitly characterizes its dataset as a large data moat.
We build gamification features into our platform... and... run thousands of A/B tests to optimize each feature for maximum engagement.
Explicitly ties gamification and experimentation to engagement, a prerequisite for habit formation.
As of December 31, 2024, there were about 32 million daily active users with a 7-day streak... and about 10 million... with a 365-day streak or longer.
Large base of long streak users indicates durable daily habit behavior.
Showing 5 of 14 sources.
Risks & Indicators
Erosion risks
- AI commoditization lowers the advantage of proprietary learner data
- Privacy regulation reduces ability to collect/use behavioral data
- Competing platforms replicate personalization and A/B testing capability
- User fatigue reduces engagement and streak retention
- Competitors copy gamification mechanics
- Platform policy changes limit notifications or engagement nudges
Leading indicators
- DAU/MAU growth and engagement depth (sessions per user)
- Paid subscriber penetration and retention
- Speed/volume of product experiments shipped (A/B testing cadence)
- Streak distribution (7-day and 365-day streak counts)
- Paid conversion rate from free users
- Churn and cohort retention metrics
Curation & Accuracy
This directory blends AI‑assisted discovery with human curation. Entries are reviewed, edited, and organized with the goal of expanding coverage and sharpening quality over time. Your feedback helps steer improvements (because no single human can capture everything all at once).
Details change. Pricing, features, and availability may be incomplete or out of date. Treat listings as a starting point and verify on the provider’s site before making decisions. If you spot an error or a gap, send a quick note and I’ll adjust.