Pay Tokens for Attention
Human taste decides the direction. AI supplies the surface area. Engineering makes it compound.
- #ai
- #growth
- #distribution
- #causally
Growth is not the marketing department’s side task. For an AI-native product studio, growth is part of product design, engineering design, content design, and organizational design. If you build products in an AI-native way but grow them in a pre-AI way, you leave most of your advantage on the table.
What follows isn’t a rigid playbook. It’s a mindset and an operating framework. The core idea:
Pay tokens for attention — spend compute to turn data and context into useful, personalized, distributed artifacts that earn discovery, clicks, shares, replies, and conversion.
But before the AI-native part, we have to respect the fundamentals.
I’ll use one running example throughout: a menu-translation app for travelers — photograph a foreign-language menu, get back translated, visualized dishes. It’s concrete enough to make the abstractions land.
The fundamentals don’t change
A product doesn’t grow because you “do marketing.” It grows because you create a loop:
Product → user value → user action → distribution → more users → feedback → better product.
Every launch and growth effort should be read through a practical founder/operator map:
| Layer | Core question | What good looks like |
|---|---|---|
| Positioning | Who is this for, and why now? | A specific user in a specific high-pain moment |
| Activation | How fast does the user feel the “aha”? | The value is experienced within seconds or minutes |
| Distribution | Where do users come from? | Clear channels, not vague “awareness” |
| Retention | Why would users come back? | The product solves a recurring need, not one-time curiosity |
| Referral / loop | Why would one user bring another? | Sharing is part of the user’s natural workflow |
| Monetization | Can growth be funded sustainably? | CAC, conversion, retention, and margin can work together |
For the menu app, the basic growth logic shouldn’t start from “we need marketing.” It should start from:
| Question | Menu-app answer |
|---|---|
| Who is this for? | Travelers, international students, food explorers — anyone facing a text-only or foreign-language menu |
| What is the painful moment? | Sitting in a restaurant, seeing unfamiliar dish names, not wanting to order blind |
| What is the aha? | Photograph a menu → see translated, visualized dishes |
| What creates sharing? | A visualized menu is naturally useful to your dining companions |
| What creates retention? | Saved menus, food memory, trip use, repeated restaurant discovery |
| What creates monetization? | Subscription, credits, travel pack, premium visualizations, B2B restaurant/menu partnerships |
Traditionally, growth channels are grouped into organic and paid:
| Growth type | What you pay with | Examples | Best use |
|---|---|---|---|
| Organic | Human time, creativity, product work, community trust | SEO, content, referrals, PR, Product Hunt, Reddit, social, product-led loops | Discover durable growth loops and earn trust |
| Paid | Money | Search ads, paid social, influencer sponsorships, affiliate, retargeting | Buy speed, test messages, scale proven loops |
But the more useful distinction is often demand capture vs. demand creation:
| Mode | User state | Example channel | Menu-app example |
|---|---|---|---|
| Demand capture | Already knows the problem and is searching | Google, App Store, SEO, search ads | ”menu translator app,” “Japanese menu translation,” “how to read a French menu” |
| Demand creation | Feels the pain but hasn’t named the category | TikTok, Reels, YouTube, PR, creator content | ”This menu has zero pictures. Let’s use AI to see what I almost ordered.” |
These fundamentals are still true in the AI-native era. AI doesn’t remove the need for positioning, activation, retention, referral, or unit economics. AI changes the production function behind them.
The mindset shift: pay tokens for attention
Traditionally, organic growth is “free” only in the sense that you don’t directly pay the platform. In reality, you pay with human labor.
AI changes this equation:
| Growth production model | Input | Output |
|---|---|---|
| Traditional organic | Human labor + channel knowledge + creative work | Attention |
| Paid | Cash + targeting + creative | Attention |
| AI-native | Data + context + models + workflow automation + human judgment | Attention |
This is what I mean by pay tokens for attention. Tokens don’t buy attention directly the way ads do. Tokens buy the ability to create, adapt, personalize, monitor, respond, and test at a scale human labor could never support.
| Old constraint | AI-native replacement |
|---|---|
| Human writer time | Token-generated pages, answers, captions, scripts, guides |
| Human designer time | AI-generated visuals, layouts, thumbnails, cards |
| Human researcher time | Agents monitoring keywords, communities, trends, competitors |
| Human social manager time | AI-assisted contextual replies, drafts, routing, follow-ups |
| Human SEO labor | Programmatic page generation + AEO/GEO optimization |
| Human ad creative team | Creative variant factory |
| Manual analytics | Automated experiment review and next-test suggestions |
This is not merely “AI helps us write copy faster.” That’s the shallow version. The deeper version is:
Compute becomes a new marginal cost of growth.
You can spend tokens to turn raw data and context into useful artifacts that travel through the internet.
From programmatic SEO to AI-native distribution
Programmatic SEO was an early version of this mindset.
| Era | What scaled | Example |
|---|---|---|
| Manual content | Human-written pages | 100 blog posts |
| Programmatic SEO | Template + structured database | Yelp/Zillow/TripAdvisor-style long-tail pages |
| AI-native distribution | Data + reasoning + multimodal generation + personalization + feedback | Visual menu pages, dish explainers, restaurant guides, contextual answers, short videos |
AI expands “template + database” into something much richer:
| Traditional programmatic page | AI-native artifact |
|---|---|
| Static text page | Multimodal page with images, explanations, translation, FAQs, schema |
| Same template for every entity | Context-aware output by restaurant, dish, cuisine, city, user intent |
| SEO-only | Search + social + community + image discovery + AI-agent readability |
| One-way publishing | Feedback loop from clicks, uploads, shares, saves, payments |
For the menu app, this could look like:
| Raw input | AI transformation | Distributed artifact | Growth value |
|---|---|---|---|
| Restaurant/menu data | OCR, dish understanding, translation, visualization | Visual menu page | SEO, sharing, conversion |
| Dish names | Ingredient/cuisine explanation + generated image | Dish explainer card | Social, image search, education |
| City/area restaurant list | Cluster by cuisine, traveler intent, dietary needs | City food guide | Long-tail search and travel discovery |
| Reddit food/travel question | Contextual answer + optional visualized example | Helpful reply draft | Community discovery |
| Uploaded user menu | Clean share page | A “see what we’re ordering” link | Referral loop |
| Food trend | Hook / script / storyboard generation | TikTok/Reels/Shorts | Demand creation |
The loop becomes:
Menu/restaurant data → AI-visualized artifact → search/social/community distribution → user upload/share/save → feedback data → better artifacts and targeting.
This is the core of AI-native distribution.
The token-to-attention stack
Think about AI-native growth as a stack.
| Layer | Question | Example |
|---|---|---|
| 1. Data substrate | What raw material do we have or can legally access? | Restaurant names, menus, dish names, user uploads, public food questions, city/cuisine data |
| 2. Artifact factory | What useful outputs can AI produce? | Visual menus, dish cards, ordering guides, short video scripts, community answers |
| 3. Distribution agents | Where should each artifact go? | SEO, TikTok, Reddit, Product Hunt, travel blogs, AI-answer-readable docs |
| 4. Feedback engine | Which artifacts actually work? | Impressions, clicks, uploads, shares, saves, signups, payments, repeat use |
| 5. Guardrails | What should we not automate or publish? | Spam, fake experience, copyright issues, hallucinated claims, platform TOS violations |
The practical flow pairs a system action with a human responsibility at every step:
| Step | System action | Human responsibility |
|---|---|---|
| 1 | Monitor opportunities | Define what counts as a real opportunity |
| 2 | Generate artifact candidates | Define creative direction and quality bar |
| 3 | Score quality and risk | Set thresholds and review edge cases |
| 4 | Distribute or queue for review | Protect platform trust and brand tone |
| 5 | Measure downstream behavior | Interpret what the numbers actually mean |
| 6 | Generate next experiments | Decide what deserves more scale |
The goal is not “infinite content.” The goal is infinite specific usefulness under quality control.
Concrete AI-native growth patterns
Here are the patterns worth using as inspiration.
| Pattern | What it means | Example | Main risk |
|---|---|---|---|
| Programmatic utility pages | Generate useful long-tail pages from structured entities | One visual menu page per restaurant / menu / dish / city | Thin SEO spam if pages lack real utility |
| Contextual community participation | Find real questions and answer with useful context | Reply to “what should I order?” posts with visual guidance | Spam, undisclosed promotion, platform bans |
| AI creative factory | Generate many video/post variants and test hooks | 100 hooks around ordering blind, travel menus, dish surprises | Low-quality AI slop, weak brand taste |
| Personalized landing pages | Adapt pages by user intent / audience | Vegan traveler, Japan trip, date-night ordering, student abroad | Over-personalization or hallucination |
| AEO/GEO content | Make content readable by AI answer engines and agents | Structured markdown, FAQs, schema, clear claims, source context | Generic content that agents ignore |
| Shareable artifact loop | Product output itself spreads | A visualized menu link shared with friends before ordering | Output not beautiful / useful enough to share |
| Autonomous experiment generation | Agents create and test many small experiments | Hook × audience × format × CTA variants | Optimizing vanity metrics instead of retained users |
A good AI-native growth system should always ask:
- Is this artifact genuinely useful? Utility protects you from spam dynamics.
- Is it specific to a real context? Specificity beats generic AI content.
- Is it connected to product activation? Attention without activation is noise.
- Can users share or reuse it? Sharing creates compounding loops.
- Can we measure downstream value? You need learning, not just output volume.
- Can this be safely automated? Trust is worth more than volume.
AI-native growth is not humanless growth
This thesis is not “AI replaces the growth team.” It’s the opposite. AI-native growth turns humans from task executors into opportunity spotters, taste setters, workflow architects, and judgment owners.
The new division of labor:
Human instinct identifies raw opportunity → human taste defines creative direction → AI generates high-volume artifacts → human/AI quality systems filter outputs → AI-native engineering automates the workflow → agents distribute and test → humans interpret feedback and update strategy.
Or shorter:
Instinct → Taste → Process → Automation → Distribution → Feedback → Judgment.
The human role moves upstream and becomes higher-leverage.
| Human role | Core job | Why it matters more in AI-native growth |
|---|---|---|
| Opportunity spotter | Notice underserved, high-pain moments before they’re obvious | AI can scan, but humans recognize meaning, urgency, and market asymmetry |
| Creative framer | Turn pain into a compelling hook, story, or meme | AI can produce options; humans sense what feels fresh, native, emotionally true |
| Taste setter | Decide what looks good, trustworthy, premium, or cringe | Low-cost generation makes taste the bottleneck |
| AIGC workflow designer | Build the right model / tool / prompt / control pipeline | One-off AI output is easy; repeatable quality is hard |
| AI-native engineer | Automate the workflow into a reliable system | Growth experiments only compound when they become infrastructure |
| Channel-native operator | Understand platform culture and trust thresholds | Reddit, TikTok, SEO, and Product Hunt each have different norms |
| Judgment owner | Decide what not to publish, fake, claim, scrape, or automate | Low marginal cost creates high temptation and high risk |
| Feedback interpreter | Read weak signals and update strategy | Metrics show what happened; humans infer why |
The key sentence:
AI scales execution; humans decide what is worth scaling.
What it means for each function
This isn’t only for “growth people.” Whatever your function, the question changes.
| Function | Traditional question | AI-native growth question |
|---|---|---|
| Founder / product owner | Does this solve a problem? | Does this product also create a compute-native growth surface? |
| Engineer | How do we build the product? | What repeatable growth workflow can this product power? |
| Designer | Is the product / interface beautiful? | What artifact from this product is worth sharing, indexing, remixing, or posting? |
| Growth / marketing | Which channel should we use? | What data / context can we transform into useful distribution assets? |
| Content / brand | What should we say? | What creative grammar can AI scale without becoming generic or cringe? |
| Operations | How do we keep work organized? | How do we create review queues, QA rules, publishing controls, and feedback loops? |
The highest-leverage person on a team is not necessarily the one who manually does the most tasks. It’s the one who can turn an insight into a repeatable, AI-powered loop.
Product selection should include growth surface
For every new product, evaluate not only feasibility and user pain, but also AI-native growth potential.
| Evaluation question | Why it matters |
|---|---|
| Does usage create shareable artifacts? | Shareable outputs create referral and social loops |
| Does the domain have long-tail entities? | Long-tail entities create SEO/AEO surfaces |
| Are there public demand moments? | Public questions create contextual distribution opportunities |
| Can AI generate useful outputs at scale? | Compute can replace large amounts of manual growth labor |
| Can outputs be personalized by context? | Personalization increases relevance and conversion |
| Can we measure artifact-level performance? | Measurement enables reinforcement learning |
| Does the product improve with more usage? | Feedback loops create compounding advantage |
| Can we avoid spam / legal / trust blowback? | A distribution advantage must not destroy brand trust |
A product with a strong AI-native growth surface is not only easier to build. It’s easier to distribute.
Operating doctrine
Be aggressive in using AI for growth, but disciplined in how you do it.
| Principle | Meaning |
|---|---|
| Utility first | Every generated artifact should help the user, not just promote you |
| Specificity beats volume | A specific answer / page / video for a real context beats generic content at scale |
| Human taste sets the bar | AI can generate, but humans define what is good enough |
| Automate after signal | Don’t prematurely automate workflows before you know they work |
| Measure downstream value | Optimize for activation, retention, referral, and revenue — not vanity impressions |
| Respect platform culture | What’s welcome on one platform may be spam on another |
| Protect trust | No deceptive fake experiences, undisclosed promotion, or unsupported claims |
| Turn winners into infrastructure | A successful experiment should become a repeatable system |
The wrong interpretation: “AI lets us publish infinitely.”
The right interpretation:
AI lets us test and serve infinitely many specific contexts — but only publish where we create net utility.
Or even simpler: we don’t use AI to shout louder. We use AI to appear in more relevant moments with more useful artifacts than a human team could produce.
The final thesis
AI-native growth is not about replacing humans with bots. It’s about changing the growth production function.
| Input | What it contributes |
|---|---|
| Human instinct | Finds raw opportunities before they’re obvious |
| Human taste | Defines what feels valuable, native, beautiful, and trustworthy |
| Human judgment | Decides what to scale and what to stop |
| Data / context | Provides raw material for personalized artifacts |
| Models / tokens | Generate and adapt content, visuals, answers, and experiments |
| Engineering | Turns one-off experiments into repeatable systems |
| Feedback loops | Teach us what actually earns attention and conversion |
The winning team won’t be the one that generates the most content. It will be the one that best combines human insight with machine-scale execution.
This should become part of a studio’s product DNA: every product built with a compute-native growth surface, every growth experiment designed as a feedback loop, every person treating AI not as a shortcut for tasks but as leverage for opportunity, taste, workflow, and scale.
In one sentence:
Human taste decides the direction. AI supplies the surface area. Engineering makes it compound.