OUR ATTENTION IS THE PRODUCT.
WE ARE NOT THE BUYER.
Average sustained attention on a single screen task fell from 150 seconds in 2004 to 47 seconds in 2023 (Mark / UC Irvine longitudinal studies). Global adults spend ~6h40m a day on internet-connected screens (DataReportal 2025); US teens 8h47m on personal devices alone (Common Sense 2024). 27% of OECD jobs now show high exposure to generative AI, up from ~6% in 2020 (OECD Employment Outlook 2024). The platforms are doing what their objective functions tell them to.
Six figures behind the composite.
One 0-100 score.
Heuristic seed snapshot. Median daily screen time among adults is roughly seven hours; US teens sit at ~8h47m of personal-screen use; sustained attention on a single task is now measured in tens of seconds rather than minutes. Generative AI has begun the first measurable wave of white-collar labour displacement. Dark patterns ship in roughly two-thirds of top-grossing apps. OECD survey data shows self-reported autonomy declining since 2010. None of this is the unintended consequence of the platforms. It is what they were optimised for.
Over time.
150 seconds in 2004. 47 seconds in 2023.
Gloria Mark's information-worker studies, measuring actual screen-switching behaviour rather than self-report. Most of the collapse pre-dates short-form video; the floor has held at ~47 seconds since 2016.
OECD jobs with high exposure: 6% to 27% in four years.
OECD Employment Outlook 2023 + 2024 task-exposure methodology. The slope from 2022 tracks the public release of consumer-grade LLMs. Exposure isn't automation; the first-order effect is changed task composition.
What the score is measuring.
Several traditions reading the same data.
Are smartphones bad for us, and if so, in what way?
Social-media platforms can profoundly affect the mental health and wellbeing of adolescents, with magnitudes large enough to warrant public-health-level interventions: age limits, default-off recommendations, school phone-free policies. The current product design is not a neutral tool; the data justifies treating it as a regulated environment.
“We are now experiencing one of the most consequential national experiments in real time.”
The 2010-2015 shift to smartphones + algorithmic feeds explains the simultaneous adolescent mental-health collapse across multiple Anglosphere countries. Four reforms: no smartphones before high school, no social media before 16, phone-free schools, far more unsupervised play. The mechanism is sleep, comparison, attention fragmentation, and the displacement of in-person time.
The harms come from the engagement-maximising business model, not the technology itself. Redesign incentives (subscription instead of attention auction), redesign defaults (no infinite scroll, no autoplay), redesign interfaces (calm tech, intentional friction). The industry can fix this without external regulation if it chooses to.
Treating this as 'attention' miscasts it. What is being extracted, predicted, and sold is behavioural surplus: traces of activity used to predict and modify future behaviour. The platforms are not bad for us as a quirk; they are bad for us as a logical consequence of their economic model, which is incompatible with informed democratic participation.
“We are no longer the subject. We are the raw material.”
Attention is the substrate of meaning. A fragmented attention is a fragmented life. The contemplative answer is not anti-technology but pro-discipline: meditation, sangha, monastic-style intervals away from devices. The traditions had centuries to develop tools for this; the current generation is the first that needs them at scale.
The moral-panic framing collapses several distinct issues into one (gaming, social media, smartphones, the internet). Specific products and uses have specific effects; aggregate dose-response curves are weaker than the headlines suggest. The reasonable interventions are targeted (algorithm transparency, addictive-design bans) rather than blanket (smartphone bans).
From classrooms: phones are the single largest behavioural-control issue teachers raise. From parents: device negotiation is the single largest family-conflict pattern in families with adolescents. The instruments may be debated; the lived-experience signal is large and consistent across continents.
What happens to human autonomy and work as generative AI scales?
AI is a tool that lets one human do what previously required ten. The historical pattern is that labour-saving tools eventually create more jobs than they destroy, by lowering the cost of the underlying activity (more accounting, more software, more medicine). Friction is real; net effect is broadly positive.
Twenty-first century 'automation' is unusually exposed to capture: a small number of compute-owning firms get the productivity gains while displaced workers face year-scale retraining gaps with no compensating wage growth. Without policy, this is the wrong kind of technological change.
The economic story is downstream of an existential one: as systems become more capable than humans across measurable domains, the central question is whose values they encode and whether they remain governable. The labour effects will be massive; the governance effects could be terminal.
What gets called 'AI' is a vast extractive infrastructure built on human-generated data without consent and human labour without protection. The labour displaced first is also typically the labour least visible: data labelling, content moderation, frontline service. The harms are present, gendered, and racialised; the benefits are not.
From the classroom: students who use generative AI well learn faster. Students who use it as a substitute for thinking lose the practice of thinking. The pedagogical task is not banning the tool but making the practice of un-aided cognition still happen alongside it. Many institutions are not equipped to do that.
Sources, weights, and code are open.
Where every number comes from
The composite index is computed from the signals listed on this page, each backed by one or more named sources. Where the source publishes a public dataset or feed it is linked below; where a signal involves qualitative judgement, the LLM-assisted pass is explicitly marked on the signal card.
- ·AlgoTransparency audit
- ·Asurion mobile-attachment research
- ·Brookings GenAI labor analyses
- ·CDC BRFSS
- ·Common Sense Media
- ·Common Sense Media Census
- ·DataReportal
- ·DataReportal Digital 2025
- ·FTC dark-patterns report 2022
- ·Mark et al. UCI Information Worker Study
- ·Microsoft Research
- ·Mozilla TikTok Reporter
- ·National Sleep Foundation Sleep in America
- ·Nielsen Total Audience Report
- ·OECD Employment Outlook 2024
- ·OECD PISA Wellbeing
- ·Pew Research American Trends Panel
- ·Princeton dark-patterns research
- ·Sensor Tower
- ·World Values Survey
Everything is versioned
- → Every hourly snapshot is committed to git with a message naming the signals that moved.
- → A daily snapshot is archived to
data/history-current/for the calibration log. - → Raw scraped article lists are written to
data/raw/so a score is reproducible from its input bundle. - → Signal definitions, weights, and seeded scores all live in plain JSON or TypeScript; anyone can open a PR challenging a value and explain why.
How this pillar is scored.
Methodology & limits
Structural signals (screen time, attention span, autonomy) and faster-moving signals (recommendation concentration, AI displacement) get weighted together. Behavioural attention is treated as a hard measure rather than a soft proxy. Once average sustained attention on a single task drops below a minute, the downstream effects across mental health, polarisation, and learning are largely predictable.
AI displacement sits at 6/10 today. Measurable disruption is clear in specific white-collar sectors (legal research, marketing copy, tier-1 customer support, junior engineering tasks) but not yet across the OECD median worker. The trajectory may push this to 8 or 9 by 2030; the snapshot here reflects current readings, not the projection.
This pillar is built to be read alongside mental health. Many of the adolescent mental-health signals there are downstream of the attention and platform signals here.