Emerging Behaviours is a market-sensing framework that identifies shifts in how younger generations interact with technology, media, and each other — and treats those shifts as leading indicators of mass-market demand before incumbents recognize them.
Section 1
How It Works
The core insight is deceptively simple: the way 16-year-olds use technology today is the way 36-year-olds will use it in five years. Younger cohorts — unburdened by legacy habits, institutional expectations, or sunk costs in existing platforms — adopt new interaction patterns first. Those patterns don't stay niche. They migrate upward through age demographics with remarkable consistency, and the companies that build for those patterns early capture the market before incumbents even notice the shift.
This works because of a well-documented asymmetry in how behavioral change propagates. Older demographics don't adopt new behaviors because they read about them in trend reports. They adopt them because the infrastructure, social norms, and product ecosystems built by younger users eventually become unavoidable. When teens started communicating through images instead of text in 2011, it wasn't a fad — it was a permanent rewiring of communication preferences that Snapchat and Instagram captured and that eventually reshaped how every age group interacts with their phone. When Gen Z began defaulting to short-form vertical video over horizontal long-form content around 2017, it wasn't a quirk of youth culture — it was a signal that attention economics had fundamentally shifted. TikTok read that signal. YouTube, Netflix, and Instagram spent the next four years scrambling to catch up.
The underlying principle is that technology adoption follows behavior, not the other way around. Most founders build technology and hope behavior changes to match. The Emerging Behaviours framework inverts this: you observe the behavior first, then build the technology that serves it. This dramatically de-risks product development because you're not betting on hypothetical demand — you're betting on demand that already exists in a younger cohort and is migrating toward the mainstream.
The framework also exploits a structural blind spot in incumbent organizations. Large companies optimize for their existing user base, which skews older and more profitable. They systematically underweight signals from younger users who don't yet have purchasing power. This creates a recurring window — typically 3–7 years — where an emerging behavior is clearly visible but commercially underserved.
"I skate to where the puck is going to be, not where it has been."
Section 2
When to Use This Framework
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Best Conditions for the Emerging Behaviours Framework
| Dimension | Ideal conditions |
|---|
| Founder profile | Culturally fluent observers — founders who spend time in online communities, understand platform dynamics, and can distinguish between a fleeting trend and a durable behavioral shift. Age matters less than cultural proximity; a 40-year-old who genuinely understands Discord culture can apply this as well as a 22-year-old. |
| Stage | Ideation through Series A. The framework is most powerful when choosing what to build. It becomes less useful once you have a product and are optimizing unit economics — at that point, you're executing against a behavior you've already identified. |
| Market conditions | Best during platform transitions (mobile → mobile-first video → AI-native), generational inflection points (Gen Z entering workforce, Gen Alpha entering teen years), or when new hardware creates new interaction modalities (AirPods normalizing audio, Vision Pro testing spatial computing). |
| Competitive environment | Ideal when incumbents are large, profitable, and optimizing for existing demographics. The bigger the incumbent's revenue from older users, the slower they'll pivot to serve emerging behaviors — creating a wider window for new entrants. |
| Inputs needed | Ethnographic observation (spending time on TikTok, Discord, BeReal, Roblox), app store analytics (Sensor Tower, data.ai), social listening tools, Pew Research generational studies, and — critically — direct conversations with 15–25 year olds about how they actually spend their time. |
The framework is unusually potent right now for two reasons. First, Gen Alpha (born 2010–2024) is the first generation raised entirely on algorithmic feeds, voice interfaces, and AI-generated content — their baseline behaviors are radically different from even Gen Z's. Second, the gap between youth adoption and mainstream adoption is compressing: TikTok went from teen novelty to the most-used social app in the world in roughly three years, compared to the decade it took Facebook to make the same journey. The observation window is shrinking, which means founders who can read emerging behaviors early have an even larger advantage.
Section 3
When It Misleads
The framework's greatest strength — pattern-matching from youth behavior — is also its most dangerous failure mode:
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Failure Modes & Blind Spots
| Blind spot | What goes wrong |
|---|
| Confusing fads with shifts | Not every youth behavior migrates upward. Clubhouse captured a real behavior (live audio socializing) but it turned out to be pandemic-specific, not generational. The behavior evaporated when the context changed. You need to distinguish between behaviors driven by durable structural forces (shorter attention spans, mobile-first interaction) and those driven by temporary conditions. |
| Monetization lag | Young users adopt fast but pay slowly. Snapchat had 100 million daily active users before it had a credible revenue model. Building for emerging behaviors often means building for a demographic with limited purchasing power, then waiting years for monetization to catch up. If your capital runway doesn't match this timeline, you die before the market matures. |
| Incumbent fast-follow | Large platforms have become dramatically faster at copying emerging behaviors. Instagram cloned Stories from Snapchat in 2016 and Reels from TikTok in 2020. If the behavior you've identified can be replicated as a feature inside an existing platform, your window may be months, not years. |
| Observer bias | Founders tend to see what they want to see. If you're already excited about a product idea, you'll find "emerging behaviors" that confirm it. The framework requires genuine ethnographic discipline — observing without a hypothesis, not cherry-picking signals that support a predetermined conclusion. |
The single most common mistake is building for a behavior that's real but not durable. The test isn't "are young people doing this?" — it's "are young people doing this because of a structural change in how they interact with technology, or because of a temporary social context?" Ephemeral messaging (Snapchat) was structural — it reflected a genuine shift toward privacy-conscious, low-stakes communication. Audio-only social networking (Clubhouse) was contextual — it reflected pandemic isolation, not a permanent preference for voice over video.
Section 4
Step-by-Step Process
Step 1 — ImmerseEmbed yourself in youth-native platforms and communities
Spend a minimum of 5 hours per week on platforms where 15–25 year olds are the primary users — not reading about those platforms, but using them. Create accounts, follow creators, join Discord servers, play Roblox games. You're looking for interaction patterns, not content. How do people communicate? What do they share? What do they refuse to do? Keep a running log of behaviors that surprise you or that have no equivalent in older demographics.
Step 2 — CategorizeDistinguish structural shifts from surface trends
For each behavior you've logged, apply the structural test: Is this behavior enabled by a technology change (e.g., better phone cameras → visual communication)? Is it driven by a generational value shift (e.g., privacy consciousness → ephemeral content)? Or is it driven by a temporary context (e.g., pandemic boredom → Clubhouse)? Only structural and value-driven behaviors are worth building for. Cross-reference with quantitative data — if a behavior is growing 30%+ year-over-year across multiple markets, it's likely structural.
Step 3 — MapIdentify the commercial gap between behavior and product
The behavior exists. Now ask: what product would perfectly serve this behavior, and does it exist yet? If teens are spending 3 hours a day on short-form video but the only monetization tool for creators is brand deals, there's a gap. If young professionals are using AI tools for work but no enterprise product is designed for their workflow, there's a gap. The gap between observed behavior and available product is your opportunity space.
Step 4 — ValidateTest with the cohort that exhibits the behavior
Build a lightweight prototype or landing page and put it in front of 50–100 users from the target demographic. Don't ask "would you use this?" — observe whether they actually engage. Run a 2-week test in a Discord server or a TikTok comment section. The validation signal you're looking for isn't enthusiasm — it's repeated, unprompted usage. If users come back without being asked, the behavior-product fit is real.
Step 5 — TimeEstimate the migration window and build your capital plan accordingly
The hardest question: how long until this behavior goes mainstream? Study analogous adoption curves. Short-form video went from teen niche to mass market in roughly 3 years. Ephemeral messaging took about 4 years. Mobile payments in the U.S. took nearly a decade. Your capital plan must match the migration timeline — if you'll need 5 years of runway before the mainstream arrives, your fundraising strategy needs to reflect that from day one.
Section 5
Questions to Ask Yourself
DiscoveryWhat are 15–22 year olds doing on their phones that 35-year-olds find confusing or pointless?
Which apps are growing fastest among users under 25 that have no equivalent for older demographics?
What communication norms have shifted in the last 2 years (e.g., voice notes replacing texts, memes replacing words)?
What do young users actively refuse to do that older users take for granted (e.g., phone calls, email, long-form reading)?
ValidationIs this behavior growing because of a structural technology shift, a generational value change, or a temporary context?
Can I find quantitative evidence (app downloads, time-spent data, survey data) that this behavior is accelerating, not plateauing?
Have I observed this behavior across at least two distinct cultural or geographic contexts, suggesting it's not locally specific?
Is there a clear product gap — something users are doing despite inadequate tools, not because of great tools?
ExecutionCan an incumbent replicate this as a feature within their existing platform in under 6 months?
Does my product require network effects to work, and if so, can I achieve critical mass in the target demographic before the behavior goes mainstream?
What's my monetization timeline — and do I have enough capital to survive until the behavior migrates to demographics with purchasing power?
Am I building a product that grows with the cohort as they age, or one that only works for a narrow age window?
RiskWhat would cause this behavior to reverse or plateau — and how would I detect that early?
If TikTok, Instagram, or YouTube added this as a feature tomorrow, would my product still have a reason to exist?
Am I projecting my own excitement onto a behavior that might be less durable than I think?
Section 6
Company Examples
Section 7
Adjacent Frameworks
Emerging Behaviours rarely operates alone. It's a sensing framework that feeds into execution frameworks:
Pairs well withSpot the fringes — what are nerds doing on weekends
The natural complement. Emerging Behaviours looks at youth demographics; Spot the Fringes looks at enthusiast communities. Together, they create a two-lens system for detecting demand before it becomes obvious — one generational, one subcultural.
Pairs well withCategory creation
Once you've identified an emerging behavior, Category Creation gives you the playbook for building a new market around it rather than competing in an existing one. TikTok didn't enter "social media" — it created "short-form entertainment."
In tension withFocus on what won't change
Jeff Bezos's framework asks you to build on permanent human needs (low prices, fast delivery, wide selection). Emerging Behaviours asks you to build on changing human behaviors. The tension is productive: the best companies do both — serve a permanent need through a new behavioral channel.
In tension withBuild a Copycat
Copycat says replicate what's proven. Emerging Behaviours says build for what's forming. If you copy a product built for yesterday's behavior, you may arrive just as the behavior shifts. The frameworks pull in opposite directions on the risk spectrum.
Section 8
Analyst's Take
Faster Than Normal — Editorial ViewMy honest read: this is the most powerful and most misused framework in the entire library.
It's powerful because every major consumer technology company of the last 15 years was built on an emerging behavior that incumbents dismissed. Facebook was built on the behavior of college students wanting to see each other's photos and relationship statuses. Instagram was built on the behavior of smartphone users wanting to share visual moments. TikTok was built on the behavior of teens preferring algorithmic entertainment over social feeds. Twitch was built on the behavior of young people watching other people play games. In every case, the behavior was visible years before the winning product was built — and in every case, older observers dismissed it as trivial, juvenile, or temporary.
It's misused because most founders confuse observing a behavior with understanding it. They see teens using a new app and conclude "I should build something like that." But the app is the surface. The behavior underneath is what matters. Vine proved that short-form video was a real behavior. But Vine failed because it didn't understand the behavior deeply enough — it thought the behavior was "watching 6-second loops" when the actual behavior was "discovering entertaining content from strangers through an algorithmic feed." TikTok understood the deeper behavior. That's why TikTok won.
The founders who apply this framework best share a specific trait: they're participant-observers, not analysts. They don't read reports about Gen Z. They're in the Discord servers. They're on the TikTok For You Page at 11pm. They're watching what their younger siblings or employees actually do, not what surveys say they do. The data is in the behavior, not the self-report.
The biggest risk right now is that the migration window is compressing. It used to take 5–7 years for a youth behavior to go mainstream. Now it takes 2–3. TikTok went from "that weird Chinese app teens use" to the most-downloaded app in the world in about 30 months. This means the opportunity window for founders is shorter, the capital requirements are higher (you need to scale faster), and the incumbent fast-follow threat is more severe. Instagram launched Reels within 18 months of TikTok's U.S. breakout. YouTube Shorts followed shortly after. If you're building for an emerging behavior in 2025, you need to assume you have 18–24 months before a platform with a billion users copies your core mechanic.
One more thing worth saying plainly: this framework has a built-in age bias that founders should be honest about. If you're over 35, your ability to naturally observe emerging behaviors degrades. Not because you're less intelligent, but because you're no longer embedded in the communities where behaviors emerge. The fix isn't to pretend you're still 22. The fix is to build a systematic observation practice — hire young researchers, embed in youth-native platforms, and treat your own instincts about "what young people want" with deep skepticism.
Section 9
Opportunity Checklist
Use this scorecard to evaluate whether a specific emerging behavior represents a viable product opportunity. Score each item as yes (1 point) or no (0 points).
Emerging Behaviours Opportunity Scorecard
The behavior is observable across at least two distinct platforms or communities, not confined to a single app.
The behavior has been growing for at least 12 months, suggesting durability rather than a momentary spike.
The behavior is driven by a structural force (technology change, generational value shift) rather than a temporary context (pandemic, viral moment).
No existing product perfectly serves this behavior — users are cobbling together workarounds or tolerating poor experiences.
Incumbents are either ignoring the behavior or serving it as a secondary feature rather than a core product.
The behavior is migrating upward in age demographics — early signs of adoption among 25–35 year olds, not just teens.
I can articulate the behavior in one sentence without referencing a specific app or platform (proving it's a behavior, not a product preference).
Section 10
Top Resources
01BookChen's framework for how network-effect products launch and scale is essential reading for anyone building on emerging behaviors. The early chapters on how products find their first atomic network — often a young, tight-knit community — map directly onto the Emerging Behaviours framework. The Tinder and
Slack case studies are particularly relevant.
02BookOnce you've identified an emerging behavior, you need to build a product that captures it through habit formation. Eyal's
Hook Model (trigger → action → variable reward → investment) explains why some products successfully harness new behaviors while others fail to retain users. The framework is especially useful for understanding why TikTok's infinite scroll works and why Quibi's scheduled content didn't.
03BookChristensen's most underrated book applies disruption theory to prediction — specifically, how to identify which emerging signals will become mainstream disruptions and which will fizzle. The framework for analyzing "nonconsumption" (people who want to do something but can't with existing products) maps perfectly onto emerging behaviors that lack adequate product solutions.
04BookMoore's classic explains the gap between early adopters and the mainstream — the exact transition that the Emerging Behaviours framework depends on. Understanding why some products cross the chasm (Instagram, TikTok) while others stall in the early-adopter phase (Vine, Clubhouse) is critical for timing your build and your fundraising.
05EssayThompson's foundational essay explains how the internet enables companies to aggregate demand by owning the user relationship rather than the supply. This is the structural mechanism that makes emerging behaviors so valuable: the company that captures a new behavior first aggregates the demand, and aggregators tend to win permanently. Essential for understanding why being early to an emerging behavior creates durable competitive advantage.
The behavior has a plausible monetization path within 3 years — either through the users themselves or through adjacent revenue (ads, commerce, subscriptions).
