Attention Economics Explained

A teenager scrolls through TikTok for three hours straight, consuming hundreds of videos but remembering none. A professional opens their laptop intending to work, but finds themselves clicking through news sites, social feeds, and recommendation algorithms for 90 minutes instead. An executive pays $50,000 for a 30-second Super Bowl advertisement competing with dozens of others for a fleeting moment of viewer focus.

These scenarios reflect the fundamental reality of the modern digital economy: attention has become the scarcest and most valuable resource. While information explodes exponentially—millions of videos uploaded daily, billions of posts, endless streams of content—human attention remains stubbornly fixed at 24 hours per day per person. This creates a zero-sum competition where platforms, creators, advertisers, and businesses fight for increasingly scarce eyeball-seconds.

Attention economics is the study and practice of treating human attention as a limited commodity that can be captured, measured, traded, and monetized. In attention-based business models, the goal is not to sell products directly but to capture and hold attention, then convert that attention into revenue through advertising, data collection, or eventual purchases. The platform that captures more attention wins. The creator who holds attention longest grows fastest. The advertisement that breaks through the noise converts best.

This economic framework fundamentally shapes how content is created, how platforms are designed, how information spreads, and ultimately how we spend our cognitive resources. Understanding attention economics means examining why you see what you see online, why platforms work the way they do, what incentives drive content creation, and how the competition for attention affects individuals, society, and culture.

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What Attention Economics Is

Definition and Core Concepts

Attention economics: An approach to managing information that treats human attention as a scarce commodity with economic value, where businesses and platforms compete to capture, retain, and monetize user attention.

Key principles:

• Attention as currency: In digital environments, attention functions like money—limited, valuable, tradable
• Scarcity principle: While information is abundant, attention remains fixed, creating fierce competition
• Opportunity cost: Every moment of attention given to one thing means attention denied to everything else
• Monetization model: Attention converts to revenue through advertising, data, or downstream transactions
• Optimization imperative: Systems optimize for maximum attention capture and retention, regardless of other values

The attention economy transaction:

1. Platform provides free content/service
2. User provides attention (time, focus, engagement)
3. Platform sells access to that attention to advertisers
4. Advertisers pay for impression/click/conversion
5. Platform captures revenue, shares some with creators

Historical context: The concept emerged in the 1970s when psychologist Herbert Simon noted that "a wealth of information creates a poverty of attention." As information technology advanced, this dynamic intensified. Economist Michael Goldhaber's 1997 essay "The Attention Economy and the Net" articulated how attention would become the central economic unit in information-rich environments. By the 2010s, with smartphones and social media, attention economics became the dominant business model for the largest tech companies globally.

Why Attention Became Economic

The transformation from information scarcity to attention scarcity represents one of the most profound economic shifts of the digital age. Understanding this reversal explains why platforms behave as they do.

Era	Scarce Resource	Abundant Resource	Economic Model	User Pays With
Pre-Digital (1950-1995)	Information	Attention	Subscription/Purchase	Money (newspapers, cable, books)
Digital (1995-2010)	Transition period	Transition period	Hybrid models	Mix of money and attention
Attention Economy (2010-Present)	Attention	Information	Free + Advertising	Attention and personal data

Pre-digital era: Information was scarce and expensive. You paid for newspapers, magazines, cable TV, books. The limiting factor was access to information. Attention was abundant relative to available content—people finished newspapers, watched scheduled TV shows, read entire books.

Digital transformation reversed this completely:

• Information abundance: Internet enabled near-zero-cost creation and distribution. Anyone could publish anything instantly to global audiences. Marginal cost of producing one more article, video, or post: essentially zero.
• Content explosion: From roughly 2.4 million web pages in 1998 to over 1.9 billion websites by 2023. YouTube alone receives 720,000 hours of video uploaded daily—more content in a single day than you could watch in 82 years of continuous viewing.
• Fixed attention: Despite exponential information expansion, humans still have only 24 hours daily, limited working memory (7±2 items), finite cognitive capacity (approximately 120 bits/second of conscious processing).
• Mathematical inevitability: Information growing exponentially while attention remains constant creates intensifying competition for a truly scarce resource.

Why platforms turned to attention-based models:

• User reluctance to pay: Early internet culture established expectation of free content. Users resisted subscriptions or paywalls.
• Advertising effectiveness: Proven model from TV/radio: provide free content, sell attention to advertisers.
• Data advantage: Digital platforms could measure attention with unprecedented precision—time spent, click patterns, engagement metrics.
• Network effects: Platforms that captured more attention attracted more creators, which attracted more users, creating self-reinforcing growth.
• Scalability: Once built, platforms could serve millions or billions with minimal marginal cost, making attention-monetization highly profitable.

The attention marketplace: Platforms became attention brokers—intermediaries connecting attention suppliers (users), attention seekers (advertisers), and attention generators (creators). The platform that most efficiently captured and monetized attention won the market.

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How Attention Economics Works

The Attention Business Model

Platform revenue structure:

Free-to-user model:

• Users pay nothing in money
• Users pay everything in attention and data
• Platform captures attention, sells it to advertisers
• Revenue scales with user time and engagement

Example: Facebook/Meta:

• Provides social networking, content, messaging for free
• Users spend average 30-40 minutes daily on platform
• Meta sold that attention to advertisers for $116 billion revenue in 2022
• Business model: maximize daily active users × average session time × ad load per session

Attention metrics platforms optimize for:

Metric	What It Measures	Why Platforms Care	Typical Industry Benchmark
Daily Active Users (DAU)	People using platform each day	More users = more attention inventory to sell	Facebook: ~2 billion DAU
Monthly Active Users (MAU)	Unique users over 30 days	Broader reach for advertisers	YouTube: ~2.5 billion MAU
Average Session Time	Minutes per visit	Longer sessions = more ads shown	TikTok: ~52 min/day average
Engagement Rate	Likes, comments, shares per post	Deep engagement signals quality attention	Instagram: 1.22% average
Stickiness (DAU/MAU)	How habitual usage becomes	Higher ratio = stronger habit formation	Top apps: 50-60% stickiness
Time-to-Return	Hours until user comes back	Frequency creates more ad opportunities	Snapchat: Multiple times daily

These metrics directly correlate to revenue: More users × more time × more engagement = more ad inventory to sell at higher prices. A platform that increases average session time by 10% can increase revenue by 10-15% without acquiring a single new user.

Attention Capture Mechanisms

Platforms don't accidentally capture attention—they engineer for it using sophisticated psychological techniques. Here's how the major mechanisms work:

Mechanism	How It Works	Psychological Exploit	Platforms Using It	Impact on Session Time
Infinite Scroll	Content auto-loads as you reach bottom, no natural endpoint	Removes "stopping cues" that trigger exit decisions	Twitter, Instagram, TikTok, Facebook feed	+30-50% average session time
Autoplay	Next video/episode starts automatically after 3-5 seconds	Leverages inertia—easier to keep watching than actively stop	YouTube, Netflix, TikTok	+70% chance of watching next item
Variable Reward Schedule	Unpredictable mix of interesting/boring content	Same mechanism as slot machines—strongest addiction pattern	All feed-based platforms	Creates compulsive checking behavior
Push Notifications	Alerts interrupt whatever you're doing	FOMO, social obligation, curiosity about feedback	All social platforms, news apps	+25% return visit rate
Social Metrics	Prominent like/view/follower counts	Social proof—"others valued this, so should I"	Instagram, YouTube, Twitter	+40% engagement on high-count content
Algorithmic Personalization	ML predicts what keeps you specifically engaged	Tailored to individual psychological vulnerabilities	YouTube, TikTok, Facebook	Can double average watch time

Real-world example: Netflix autoplay engineering

When Netflix tested autoplay for their next episode, they discovered:

• Without autoplay: 15% of users watched next episode
• With 10-second countdown: 45% watched next episode
• With 5-second countdown: 62% watched next episode
• With 3-second countdown + thumbnail preview: 70% watched next episode

This single feature change increased total viewing hours by an estimated 60%, adding billions in subscription retention value. The feature wasn't designed to help users—it was designed to capture more attention before users could decide to stop.

The attention capture hierarchy:

1. Awareness (notifications, recommendations, social sharing)
2. Click/Open (thumbnails, titles, curiosity gaps)
3. Initial engagement (first 3-10 seconds—the "hook")
4. Sustained attention (pacing, novelty, emotional triggers)
5. Extended session (autoplay, infinite scroll, related content)
6. Return visit (notifications, habit formation, unfinished loops)

Each layer uses different psychological mechanisms, and platforms optimize every stage simultaneously.

The Creator Side

How creators compete in attention economy:

Attention as currency for creators:

• Views, subscribers, followers measure attention captured
• Attention converts to revenue through advertising, sponsorships, products, services
• More attention = more revenue, more influence, more opportunity

Creator optimization strategies:

• Thumbnails and titles: Designed for maximum click-through rate using curiosity gaps, emotional triggers, bold visuals
• Hooks: First 3-10 seconds engineered to retain viewer attention before they scroll/skip
• Pacing: Rapid cuts, constant movement, novelty every few seconds to prevent boredom
• Emotional triggers: Content that makes you laugh, angry, surprised, curious, outraged holds attention better than neutral content
• Controversy and provocation: Strong opinions, hot takes, debates generate engagement through disagreement
• Trend chasing: Jumping on viral topics/formats capitalizes on existing attention allocation

The creator's dilemma:

• Platform incentives: Algorithms reward engagement metrics, not quality or value
• Attention competition: Millions of creators compete for same eyeballs
• Optimization pressure: Must constantly adapt to what captures attention or lose visibility
• Value tension: What captures attention ≠ what provides deep value
• Result: Creators face choice between optimizing for attention (algorithm-friendly) or optimizing for value (audience-friendly)

Example: YouTube evolution:

• Early YouTube (2005-2010): Longer-form content, experimentation, niche communities
• Mid-period (2010-2016): Optimization toward 10-minute videos (ad placement), thumbnails, titles
• Current (2016+): Constant A/B testing, clickbait thumbnails, retention analytics, Shorts competing with TikTok
• Pattern: Platform incentives shaped content creation toward maximum attention capture

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Why Attention Is Valuable

Attention Precedes Action

Fundamental principle: All human behavior starts with attention. You cannot buy what you don't notice, learn what you don't attend to, believe what you don't see, act on what you haven't focused on.

The attention → action pipeline:

1. Awareness: Attention creates awareness something exists
2. Interest: Sustained attention generates interest or curiosity
3. Consideration: Focused attention enables evaluation and thinking
4. Decision: Attention plus consideration leads to choice
5. Action: Decision converts to behavior—purchase, subscribe, vote, share

Every business outcome requires attention first: E-commerce requires users to notice products. News organizations need readers to see articles. Political campaigns need voters to attend to messages. SaaS companies need prospects to watch demos. Attention is the gateway drug to all economic activity.

Attention Enables Persuasion

Persuasion requires attention: You cannot persuade someone who isn't listening. Advertising, marketing, propaganda, education all depend on capturing and holding attention long enough to convey messages and shape beliefs.

Psychological influence mechanisms:

• Mere exposure effect: Repeated attention to something increases positive feelings toward it
• Availability heuristic: Things you've paid attention to recently feel more common/important than they are
• Anchoring: First information attended to disproportionately shapes subsequent judgment
• Emotional conditioning: Attention + emotional experience creates associations that drive future behavior

Example: Advertising effectiveness: Research shows ad effectiveness correlates strongly with attention duration and quality. Ads receiving active attention (measured by eye-tracking) generate 2-3× higher recall and purchase intent than ads receiving passive or no attention. Advertisers pay premium prices for high-attention environments (Super Bowl, YouTube pre-roll) versus low-attention environments (banner blindness).

Attention Generates Data

Attention measurement creates valuable data:

• Behavioral data: What you click, watch, read, linger on reveals preferences, interests, susceptibilities
• Temporal patterns: When and how long you pay attention reveals habits, routines, attention patterns
• Engagement patterns: What triggers shares, comments, saves reveals emotional buttons and social dynamics
• Comparative data: How your attention differs from similar users enables micro-targeting

Data monetization:

• Advertising targeting: Data improves ad relevance, increasing conversion rates and advertiser willingness to pay
• Product development: Attention patterns inform what features/content to build
• Algorithmic optimization: Data trains ML models that become better at capturing attention
• Data sales: Some platforms sell anonymized attention/behavior data to third parties

Example: Facebook advertising: Facebook's advertising platform works because it has detailed attention data on billions of users—what they click, what they watch, what they engage with. This enables advertisers to target ads with surgical precision: "women aged 25-34, interested in yoga, living in Austin, who engaged with wellness content in last 30 days." That targeting capability makes attention captured on Facebook more valuable per-second than generic attention elsewhere.

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Consequences of Attention Economics

Impact on Content Quality

Race to the bottom dynamics:

When platforms optimize for engagement metrics rather than quality, a predictable pattern emerges: content that captures attention crowds out content that provides value.

Content Characteristic	Attention Performance	Value to Audience	Winner in Attention Economy
Nuanced, complex analysis	Low engagement (requires effort)	High long-term value	Loses
Hot takes, strong opinions	High engagement (tribal reaction)	Low informational value	Wins
Accurate but unsurprising	Low shares (not emotionally compelling)	High epistemic value	Loses
Sensational but misleading	High shares (triggers emotion)	Negative value (misinforms)	Wins
Investigative journalism	Expensive, slow, limited viral potential	Very high public good value	Loses
Aggregated outrage content	Cheap, fast, highly shareable	Low to negative value	Wins
Educational, challenging	High cognitive load = low completion	High capability-building	Loses
Entertainment, easy consumption	Low cognitive load = high completion	Moderate entertainment value	Wins

What actually wins the attention competition:

• Novelty over depth: "This ONE WEIRD TRICK" beats "comprehensive guide to complex topic"
• Emotion over reason: Anger, fear, outrage, surprise generate engagement; nuanced analysis gets ignored
• Simple over complex: 280-character hot take beats 5,000-word deep dive
• Controversy over consensus: Disagreement generates comments/shares; agreement gets silence
• Sensational over accurate: "SHOCKING REVELATION" outperforms "incremental research finding"

Real example: News industry transformation 1990-2025

Traditional newspaper model (1990):

• Revenue: Subscriptions ($40/month per reader)
• Optimization target: Subscriber retention (quality, depth, trust)
• Investigative journalism: 15-20% of newsroom budget
• Average article length: 800-1,200 words
• Headlines: Informative, accurate summaries

Digital advertising model (2025):

• Revenue: Display ads ($0.001-0.003 per pageview)
• Optimization target: Pageviews, time-on-site, shares
• Investigative journalism: 3-5% of newsroom budget (cut ~75%)
• Average article length: 300-600 words (shorter attention spans)
• Headlines: Curiosity gaps, emotional triggers ("You Won't Believe...")

Measurable outcomes:

• Quality investigative journalism declined 70% from 2003-2023 (Pew Research)
• Average time spent reading articles: 15 seconds (most don't scroll past headline)
• Sensational headlines increased engagement by 300-400% vs. descriptive headlines
• Opinion content increased from 15% to 45% of output (cheap, engaging)

The economic incentives changed, and content evolved to match—not toward what readers needed, but toward what captured attention most efficiently.

Attention Fragmentation

Continuous partial attention: Term coined by tech consultant Linda Stone describing modern attention state—constantly monitoring multiple streams, never fully present to any single thing.

Causes:

• Notification culture: Constant interruptions from devices, apps, platforms
• FOMO: Fear something more interesting/important is happening elsewhere
• Platform design: Systems engineered to pull attention back frequently
• Habit formation: Repeated context switching creates compulsive checking behavior

Consequences:

• Reduced focus capacity: Practice fragmentation, lose ability to sustain deep attention
• Cognitive costs: Task-switching drains mental energy, reduces overall productivity
• Comprehension decline: Shallow attention reduces ability to understand complex ideas
• Memory impairment: Fragmented attention reduces encoding into long-term memory
• Increased stress: Constant vigilance and interruption elevates cortisol

Research evidence: Microsoft study found average human attention span declined from 12 seconds (2000) to 8 seconds (2013). Later research challenged these numbers, but longitudinal studies consistently show increasing difficulty sustaining focused attention on single tasks.

Psychological Impacts

Addiction mechanics:

• Variable reward schedules: Unpredictable reinforcement (sometimes great content, sometimes boring) creates strongest addiction pattern (same mechanism as slot machines)
• Dopamine hits: Novel content, social feedback, notifications trigger dopamine release, creating reward-seeking behavior
• Tolerance: Need increasingly stimulating content to generate same engagement response
• Withdrawal: Anxiety, discomfort when separated from attention-capturing platforms

Mental health correlations:

• Multiple studies link heavy social media use to increased anxiety, depression, loneliness, especially in adolescents
• Mechanisms include social comparison, fear of missing out, displacement of in-person relationships, attention fragmentation, sleep disruption
• Causation remains debated (does social media cause mental health problems, or do people with mental health problems use social media more?), but associations are robust

Attention fatigue: Constant demands on attention deplete cognitive resources, leading to:

• Decision fatigue (impaired judgment after prolonged attention demands)
• Reduced self-control (attention regulation requires willpower, which depletes)
• Emotional volatility (cognitive fatigue impairs emotion regulation)

Societal-Level Effects

Information ecosystem distortion:

Misinformation advantage: False information often captures attention more effectively than truth:

• Novelty bias favors new false claims over familiar truth
• Emotional content (fear, outrage) spreads faster than neutral content
• Complexity handicap: Nuanced truth harder to convey than simple lie
• Result: Attention economics structurally advantages misinformation

Polarization acceleration:

• Algorithms optimize for engagement, which correlates with outrage and tribalism
• Moderate, consensus positions don't generate engagement; extreme positions do
• Users increasingly shown content that provokes reaction, confirming existing views
• Attention captured through identity-based content, political polarization

Public discourse quality decline:

• Nuanced, thoughtful, complex discussion doesn't compete well for attention
• Soundbites, hot takes, dunks, outrage dominate
• Attention economics rewards speaking to base, not persuading opposition
• Result: Public conversation becomes more performative (playing to audience) than substantive

Economic Concentration

Winner-take-most dynamics:

Attention economics favors massive concentration because:

• Network effects: Users go where other users are, creating self-reinforcing dominance
• Data advantages: More attention = more data = better algorithms = capture more attention
• Algorithmic power: Platforms that control attention distribution control which creators succeed
• Switching costs: Attention habits and social graphs lock users into platforms

Result: Small number of platforms capture enormous share of global attention:

• Google/YouTube, Meta (Facebook, Instagram), TikTok, Amazon, Twitter/X control majority of digital attention
• Top 1% of creators capture disproportionate share of views/revenue
• Smaller platforms, independent creators, niche content struggle to compete

Power implications: Platform that controls attention controls:

• Which ideas spread
• Which businesses succeed
• Which information people see
• What behaviors are reinforced

This concentration of attention control represents unprecedented concentration of influence over information, culture, and society.

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Competing Ethically in the Attention Economy

Building Sustainable Attention

Long-term vs. short-term attention strategy:

Short-term attention capture (clickbait model):

• Optimize for immediate click/view through curiosity gaps, sensationalism, misleading framing
• High initial attention capture
• Low retention, trust, and long-term value
• Works initially, degrades over time as audience feels manipulated

Long-term attention building (trust model):

• Optimize for genuine value delivery, consistent quality, audience trust
• Slower initial growth
• High retention, loyalty, word-of-mouth
• Compounds over time as reputation strengthens

Ethical creators choose long game: Building permission-based attention relationships where audience voluntarily returns because they expect value, not algorithmic manipulation.

Attention With Consent and Value

Principles for ethical attention:

1. Transparent value exchange:

• Be honest about what you're providing and why you deserve attention
• Don't use misleading thumbnails, titles, or hooks
• Deliver on promises made to capture attention

2. Respect attention as precious:

• Don't waste audience time with filler, repetition, unnecessary length
• Get to the point efficiently
• Provide density of value per unit of attention

3. Optimize for audience benefit, not metrics:

• Create what serves audience long-term interests
• Resist platform pressure to optimize for engagement over value
• Measure success by impact, not just views

4. Own the relationship:

• Build direct connections (email lists, communities, memberships)
• Don't be entirely dependent on platform algorithms
• Create attention relationships you control

5. Content quality over content quantity:

• Better to create less frequently with higher quality
• Quality compounds; quantity merely accumulates
• Audience retention improves with value density

Alternative Business Models

Moving beyond pure attention monetization:

Subscription/membership models:

• Users pay directly for content/access
• Removes advertising misalignment
• Enables optimization for subscriber value, not engagement
• Examples: Substack, Patreon, membership communities

Example: Substack: Newsletter platform where writers charge subscriptions. Writers optimize for subscriber retention (value delivery) rather than viral reach (attention capture). Many writers report more sustainable income and creative freedom than attention-monetized platforms.

Product/service businesses:

• Use content to demonstrate expertise, build trust
• Monetize through selling products, services, consulting
• Content becomes marketing, not revenue source
• Optimization: Attract right audience, build authority

Sponsorship/patronage models:

• Brands or patrons support content aligned with their values
• Less dependent on scale of attention captured
• Optimization: Quality and audience fit matter more than pure reach

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The Future of Attention Economics

Emerging Trends

Attention management tools:

• Growing awareness of attention as limited resource
• Tools emerging to help protect attention: website blockers, minimalist phones, attention tracking, notification management
• Corporate attention management: Companies implementing "no meeting days," deep work policies, communication boundaries

Regulatory responses:

• Governments considering regulation of addictive design patterns
• Transparency requirements around algorithmic curation
• Children's attention protection (screen time limits, age restrictions)
• Data privacy laws (GDPR, CCPA) indirectly limit attention monetization through data

Platform evolution:

• Some platforms experimenting with less engagement-optimized feeds
• Chronological timelines making comeback
• "Digital wellbeing" features (though often performative)
• Niche platforms optimizing for different values

Potential Scenarios

Scenario 1: Continued intensification:

• Competition for attention becomes more sophisticated
• AI-generated content floods information space
• Platforms become better at capturing and holding attention
• Result: Attention becomes even more valuable and contested

Scenario 2: Attention rebellion:

• Growing awareness of attention manipulation
• Cultural shift toward slower consumption, attention protection
• Success of attention-respectful platforms and creators
• Result: Bifurcation between attention-extractive and attention-respectful ecosystems

Scenario 3: Regulatory intervention:

• Governments regulate attention-capturing design patterns
• Restrictions on data collection limit targeting effectiveness
• Antitrust action breaks up attention monopolies
• Result: More diverse, less extractive information environment

Most likely: Combination—intensified competition in some domains, rebellion and alternatives in others, gradual regulatory guardrails.

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References and Further Reading

Foundational Academic Sources

1. Simon, H. A. (1971). "Designing Organizations for an Information-Rich World." In M. Greenberger (Ed.), Computers, Communications, and the Public Interest (pp. 37-72). Baltimore: Johns Hopkins Press.

   • Original articulation of attention as scarce resource in information-rich environments

2. Goldhaber, M. H. (1997). "The Attention Economy and the Net." First Monday, 2(4).

   • Seminal essay establishing attention economics framework for digital age

3. Davenport, T. H., & Beck, J. C. (2001). The Attention Economy: Understanding the New Currency of Business. Harvard Business Review Press.

   • Business-focused examination of attention as economic unit

4. Wu, T. (2016). The Attention Merchants: The Epic Scramble to Get Inside Our Heads. Knopf.

   • Historical analysis of attention capture from newspapers to digital platforms

Platform Design and Psychology

5. Eyal, N. (2014). Hooked: How to Build Habit-Forming Products. Portfolio.

   • Inside view of intentional attention-capture design

6. Alter, A. (2017). Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked. Penguin.

   • Psychological mechanisms behind attention-capturing design

7. Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.

   • Analysis of data extraction and attention monetization

Social and Cultural Impacts

8. Carr, N. (2010). The Shallows: What the Internet Is Doing to Our Brains. W.W. Norton.

   • Neurological impacts of attention fragmentation

9. Williams, J. (2018). Stand Out of Our Light: Freedom and Resistance in the Attention Economy. Cambridge University Press.

   • Ethical examination of attention capture

10. Haidt, J., & Twenge, J. M. (2021). "Social Media Use and Mental Health: A Review." Current Opinion in Psychology, 45, 101-107.

    • Research on psychological impacts of attention-based platforms

Misinformation and Information Quality

11. Vosoughi, S., Roy, D., & Aral, S. (2018). "The Spread of True and False News Online." Science, 359(6380), 1146-1151.

    • Evidence that false information spreads faster than truth on attention-based platforms

12. Lorenz-Spreen, P., et al. (2019). "Accelerating Dynamics of Collective Attention." Nature Communications, 10(1), 1-9.

    • Documentation of declining attention spans and accelerating information cycles

Economics and Market Dynamics

13. Evans, D. S., & Schmalensee, R. (2016). Matchmakers: The New Economics of Multisided Platforms. Harvard Business Review Press.

    • Economic analysis of attention-based platform business models

14. Farboodi, M., & Veldkamp, L. (2020). "Long-Run Growth of Financial Data Technology." American Economic Review, 110(8), 2485-2523.

    • Analysis of attention as constraint in information processing

Critiques and Alternatives

15. Lanier, J. (2018). Ten Arguments for Deleting Your Social Media Accounts Right Now. Henry Holt and Co.

    • Critical perspective on attention-based social platforms

16. Newport, C. (2016). Deep Work: Rules for Focused Success in a Distracted World. Grand Central Publishing.

    • Framework for protecting and deploying attention intentionally

17. Rushkoff, D. (2016). Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity. Portfolio.

    • Critique of attention-extraction business models