At a glance

Seven areas · Select a tile to explore its charts.

Favourable trendMixed pressuresHigh pressure

Counts include dedicated and shared charts; a shared chart appears in each relevant area. Editorial assessments · reviewed 17 October 2026. Colours combine direction and consequences; they are not automatic risk scores or labels for how fast a curve grows.

Why these colours? Read the evidence and limits

01 / ACCELERATING WORLD - MONITORING SEVEN AREAS

Every indicator.
Room to see the whole curve.

Sixteen indicator cards with 20 main graph panels, plus recovery details and thematic and scenario explorers. Each panel has its own dates, time window and trend context.

02 / PEOPLE & DISTRIBUTION

Growth for whom?
Concentrated where?

Average progress can hide different experiences. Distribution, ageing and the geography of growth belong beside the headline totals.

DISTRIBUTION

Who holds the wealth?

Compare the top tenth with the bottom half. Start with wealth, or switch to income for a different view of inequality. The world view and the distribution within a country answer different questions.

WID estimates, equal-split adults. Income is pre-tax national income after pensions; wealth is net personal wealth. Current downloaded histories end in 2023. They are not the separate 2025 snapshots in WIR 2026.

World Inequality Database ↗

URBAN CHANGE

More people.
More density?

Not necessarily. A city can grow outward while its population grows. Compare population, people per square kilometre and concentration in a country's cities.

Six illustrative cities, not a representative global sample. UN WUP 2025 uses harmonised DEGURBA settlement extents, not administrative boundaries. Density is not a direct measure of crowding or service quality.

UN World Urbanization Prospects 2025 ↗

THE GEOGRAPHY OF DEMOGRAPHIC CHANGE

Different places, different futures.

UN World Population Prospects 2024, processed by Our World in Data. Estimates end in 2023; 2024 onward, including 2026, is a medium-variant projection. Dashed lines are projections, not observed outcomes. Source ↗

03 / THE COMING OF AI

A new capacity to act.
A new question of control.

AI connects the trends above: computation and data become tools that can help people analyse, create and act. The question is how to turn expanding capability into useful, widely shared progress.

MEASURING CAPABILITY

What has changed?

Selected tests show rapid gains in language, scientific questions, coding and computer use. Several cross their human reference level; others remain below it. These are different tasks, not one measure of intelligence.

Stanford AI Index benchmark trajectories, 2012–2025, relative to each task’s human baseline of 100 percent. Several exceed the reference level, while agent computer use remains below it.
Stanford HAI, AI Index 2026, Figure 2.1.1 · data through 2025. Each line has its own human reference; 100% is not perfect accuracy. Different systems can supply the results across tasks and years. Tap the chart to enlarge.

A higher benchmark score opens possibilities. Dependable performance outside the test requires separate evidence.

Benchmarks can saturate, contain errors or reward familiarity with a test. Read this as evidence of changing task capability, not proof of general judgment or safe autonomy. Source and interpretation ↗

WHY NOW?

Three advances reinforce one another.

Better data

Useful examples, careful selection and checked demonstrations shape what a model can learn. Quality and coverage matter alongside volume.

Better learning methods

Architectures, training objectives and feedback change how a model uses its examples. Pre-training builds broad patterns; post-training shapes behaviour and task performance.

Better computation

Accelerators, memory and parallel processing make larger training runs and more experiments feasible. Efficient software helps turn hardware into useful work.

Better examples improve learning; better methods use resources more effectively; more usable computation expands what can be tried.

Conceptual explanation, not a measured allocation of progress between three causes.

From microprocessors to AI chips

The transistor curve spans more than half a century. Moore’s roughly two-year doubling became an industry reference, not a law guaranteeing performance. Modern GPUs add parallel computation and advanced packaging; some combine multiple compute dies. Transistor totals therefore do not measure speed, efficiency or useful work on their own.

Memory, data movement, numerical precision and software also matter. The same hardware can deliver different results depending on how it is used.

Explore the transistor and computing charts in the Atlas ↑

FROM POSSIBILITY TO DIFFUSION

Capability matters more when people can use it.

Stanford’s 2025 AI Index reports that the price of querying a model at a fixed GPT-3.5-level benchmark threshold fell from $20 to $0.07 per million tokens between November 2022 and October 2024—more than a 280-fold reduction. This compares a particular performance threshold, not every model or the full cost of deploying AI.

Lower prices can widen access. Useful adoption still depends on skills, reliable infrastructure, suitable workflows and who controls the tools.

Stanford AI Index 2025 · inference prices ↗

POSSIBLE DIRECTIONS · NOT A FORECAST

Where could this lead?

From answers to actions

Agents could coordinate longer workflows across software. The test is successful completion with bounded permissions, clear accountability and a way to stop or undo actions.

From analysis to discovery

AI could help propose molecules, materials and experiments. The test is independently validated results, useful treatments and wider access—not simply more generated candidates.

From screens to the physical world

Robotics could extend assistance into factories, care and daily life. The test is reliable operation in varied environments, at an acceptable cost and level of risk.

These are possible paths and proposed tests of progress. Their speed and reach remain uncertain; no date for general intelligence is implied.

How AI could affect all seven areas

Illustrative opportunities and pressures to examine, not measured net effects.

AreaPotential contributionQuestion of control
PopulationCare assistance and planning for changing needsDoes support preserve autonomy and reach underserved communities?
EconomyProductivity and new servicesWho gains, who is displaced, and how concentrated are the returns?
TechnologyFaster design, coding and experimentationCan testing and oversight keep pace with deployment?
DataBetter analysis and cyber defenceHow do we limit surveillance, misuse and automated attacks?
EnvironmentResource optimisation and environmental monitoringDo verified savings exceed added energy and material demands?
HealthResearch and clinical assistanceAre benefits validated, affordable and reliable across populations?
Freedom & societyAccess to knowledge and participationCan people challenge decisions and retain independent judgment?

Faster capability is not the objective by itself.

AI could increase the pace of change and improve our ability to respond. Which effect dominates depends on deployment, institutions and the distribution of power. The aim is a sustainable pace of useful progress, with the capacity to detect harm, correct mistakes and share the gains.

Continue to Acceleration & Absorption ↓

04 / ACCELERATION AND ABSORPTION

Acceleration and absorption.
From growth to the capacity to respond.

The shape of a curve changes the demands a system must absorb. Compounding, connectedness and cycles help explain those demands; our ability to observe, act and correct determines how well we respond.

SCHEMATIC CURVES · NOT FORECASTS

Three curves, three different stories.

A rising level is not enough to establish an increasing percentage growth rate. Read the shape, the time horizon and what is growing.

Linear

Equal absolute additions. The level rises, while percentage growth declines.

Exponential

A constant percentage applied to a growing base. Absolute additions become larger.

Saturation / S-curve

Growth first gathers pace, then slows towards a limit. Stabilisation is a possibility, not a universal outcome.

Illustrative shapes on a shared linear index scale, starting at 1. Time is schematic; these are not fitted histories.

01 / COMPOUNDING

Growth builds on growth.

Each increase expands the base for the next. Several drivers can also reinforce one another. A small sustained rate can create a large change in scale.

02 / CONNECTEDNESS

Consequences cross domains.

A change in one area can alter demands in another: more data can require more energy and stronger security. Interactions may amplify or dampen pressure.

03 / CYCLICITY

Trajectories can turn.

Feedback, constraints and recurring cycles can interrupt expansion or bring reversal. Neither endless growth nor inevitable collapse follows from a rising curve alone.

ARITHMETIC ILLUSTRATION

What a century does to a small percentage.

Every line starts at 1. The annual percentage stays constant; the amount added each year grows.

All four rates

Lower rates, closer view

Linear axes in both panels, with different vertical ranges. Level = (1 + annual rate)years. Constant rates, no shocks or saturation: mathematical scenarios, not forecasts. Multipliers show the level reached, not the sum of annual flows.

What grows matters.

If the rising quantity is an annual physical burden, each year can demand more collection, treatment or infrastructure. Unresolved flows can accumulate into a stock even when their growth slows. Capacity, prevention and ecological limits determine the consequences.

FROM SCALE TO RESPONSE

Can the capacity to respond keep pace?

Absorption means the practical ability to handle change: understand it, act on it and correct its consequences. A one-time improvement can create breathing room, but sustained growth may use it up. The following scenario compares pressure and response on one assumed scale; it does not claim a measured global absorption limit.

ILLUSTRATIVE SCENARIO · CYBERSECURITY

A one-time capacity gain buys time.
It does not stop workload growth.

Common workload index: manual capacity = 100. All future lines are assumptions, not observed global cyber threats or a forecast of attacks.

What comes from evidence, and what is assumed?

This example uses a Microsoft October 2025 randomised study of 167 professional analysts. Its workflow calculations imply roughly 3.1–6.5× correctly identified malicious emails per analyst-minute under particular prevalence, precision and recall assumptions. The lower 3.1× case assumes 20% malicious mail and 80% precision and recall, with automated benign-mail handling.

The 20% annual workload increase is illustrative. Staffing, task mix and effectiveness are held constant; implementation overhead, errors and automation bias are not modelled. A task-specific vendor study does not establish a 3.1× reduction in breaches or a universal productivity gain.

Microsoft research · study and assumptions ↗
01

Observe & interpret

Measure detection delay, error rates and the human ability to check an answer. Faster output alone does not establish better judgment.

02

Decide & respond

Measure the time from a warning to action, trained staff and available infrastructure. Capacity is specific to the task.

03

Correct & recover

Measure appeal times, fallback readiness and successful recovery. A response system needs a practical way to change course.

Control can enable useful speed. Explore how prevention, greater response capacity and shorter delays change the gap in Pace and Control Simulator ↓.

05 / PACE AND CONTROL SIMULATOR

Pace and Control Simulator.
Choose the pace. Build the response.

Explore the conceptual relationship between pressure, adaptive capacity and exposure. These controls express assumptions; they do not measure the world's current state.

Acceleration potential A
Adaptive capacity C
Relative pressure Ω

A = ∛(P × I × N)   C = ∛(G × R × L)   Ω = (A ÷ C) × E

Dimensionless conceptual ratios, not calibrated indicators, probabilities or safety thresholds. The time paths are an illustrative extension: pressure = 100 × A × E × (1 + p)t; capacity = 100 × C × (1 + c)max(0, t − delay). Equal values only mean equality on this assumed common scale. No empirical global “absorption capacity” is claimed.

06 / GOVERNANCE FOR CONTROL

Who decides?
Who can question the decision?

A model can reveal a gap. Institutions need the authority, knowledge and accountability to act on it. These possible structures are proposals for discussion, not a claim that one design fits every society.

Control with checks, participation and correction.

AI can help detect risks, compare options and evaluate results. Decisions affecting rights, public resources and acceptable risks need accountable human authority. No single operator should both make consequential decisions and be the sole judge of their effects.

OPTION A / PUBLIC AUTHORITY

Democratic mandate.
Independent scrutiny.

Elected institutions set objectives and limits. A competent public authority supervises implementation, while independent auditors and courts provide scrutiny and avenues for remedy.

Strength and vulnerability

A clear mandate and enforcement powers can support action. Political capture, slow procedures and limited technical capacity can weaken oversight. Published reasons, conflict-of-interest rules and periodic review are essential safeguards.

OPTION B / SHARED OVERSIGHT

Bring affected people
into the decision.

A public-interest council brings together citizens, domain experts, workers, operators and public authorities. It examines trade-offs and recommends conditions; a named accountable authority retains responsibility for binding decisions.

Strength and vulnerability

Broader participation can reveal overlooked harms and priorities. Unequal resources and unclear responsibilities can distort deliberation. Balanced representation, transparent selection and clear decision rights help address these weaknesses.

OPTION C / COORDINATED LEVELS

Local action.
Shared safeguards.

Local and sector bodies handle decisions close to those affected. National institutions set minimum protections and hear escalations. International cooperation addresses impacts that cross borders.

Strength and vulnerability

Decisions can reflect local conditions while sharing knowledge. Gaps between jurisdictions and fragmented enforcement remain risks. Common reporting, explicit escalation routes and coordination agreements help connect the levels.

A correction process for any structure.

These structures can be combined. Each needs a visible route from evidence to action, and from a contested decision to a remedy.

  1. Observe & disclosePublish indicators, incidents, uncertainties and who is responsible. Protect privacy and legitimate confidential information.
  2. Deliberate & decideHear affected groups, compare alternatives and record reasons, limits and review dates.
  3. Act & evaluateUse bounded implementation, monitor outcomes and check whether benefits and burdens are fairly distributed.
  4. Challenge & appealGive affected people an accessible way to request an explanation, independent review and correction or reversal.
  5. Pause, repair & reviewDefine emergency triggers and who may pause a system. Provide safe fallback, time-limited powers, independent review and conditions for restart.

Appeals need published response deadlines and protection against retaliation. Emergency intervention must be proportionate: stopping a critical service can itself cause harm. Review findings should feed back into the next decision.

Measure whether control works.

Track time from warning to action, access to appeals, correction and recovery times, recurrence of failures, distribution of impacts and concentration of decision power. Assess these together: a fast decision is not necessarily a good one, and a low appeal count may reflect barriers to challenge.

Return to Pace and Control Simulator ↑

SOURCES & METHOD

Sources & method

What the domain colours mean

The seven domain tiles are dated editorial assessments. Green marks a favourable measured outcome, orange marks mixed progress or competing pressures, and red highlights worsening burdens or institutional deterioration. They are not calculated from curve speed, do not imply that all measures in a domain agree, and do not measure global absorption capacity. The evidence and limits for each judgement are available under “Why these colours?” above the chart gallery.

What the curve labels mean

Each indicator chart pairs two horizons: the long view uses all available history, and the recent view uses up to ten years ending at the latest source date. Their dates are printed beside the tags. These tags stay fixed when the chart window changes, so the comparison is always visible. The Explore panel also describes its selected window. Automatic labels are descriptive heuristics, not statistical diagnoses or forecasts; they require at least six observations spanning five years. Sparse records receive a direction-of-change description instead of a fitted growth claim.

The two-year transistor reference is 2,300 × 2(year − 1971) / 2, anchored to the Intel 4004 and held fixed across windows. It is an illustration, not a fitted curve or a forecast. CPU milestones, single-die GPUs and two-die GPU totals are shown separately. On a log scale, exponential growth appears straight.

Ten- and fifty-year buttons count backward from the latest historical estimate in that series, not necessarily from today. Missing historical observations are not filled in. The waste panel separately shows explicitly labelled projected extensions to 2026. Annual changes use adjacent years only. Connecting lines between sparse estimates are visual guides, not annual measurements. Log scales show proportional change and require positive values.

CO₂ and temperature

The environment comparison opens as an overlay with CO₂ in ppm on the left and temperature anomalies in °C on the right. The scales are independent; line crossings and relative slopes are not physical equivalences. Both series use the same observed years in the overlay, 1979–2025. Temperature alone extends to 1880, and its baseline is 1951–1980, not pre-industrial conditions. Temperature trends are least-squares slopes in °C per decade. Percentage growth, exponential labels and logarithmic scales are not used for temperature anomalies. Annual values remain unsmoothed, and incomplete 2026 data is excluded.

Primary energy and its sources

Energy now uses annual physical-energy-content estimates for 1965–2025 throughout, replacing the previous substitution-method snapshot. By source stacks oil, coal, natural gas, nuclear, hydropower, other renewables and biofuels, wind and solar. The top edge is their sum; statistical differences from the published aggregate are below 0.1%. Focus a source to see its own trajectory; focused shares still use all global primary energy as their denominator. These are not electricity shares: thermal fuels include conversion losses, while wind and solar PV count electrical output. Commercial coverage excludes traditional biomass. Stacks use linear levels and zero baselines; the Total view also offers annual changes and log scales.

Biodiversity decline

The combined panel plots five distinct indicators, each normalized to 100 in its own first shown year. Regions, measures and baseline dates differ. Solid lines connect annual estimates. The amphibian circles are approximate readings of the All Species mean occupancy points in Adams et al. (2013), Figure 1D, for 2002–2011. Dashed lines connect two published endpoints only; no annual values between them are asserted. These are not percentages of species remaining. Baselines do not reset when the time window changes. The 10-year view spans 2014–2024 and explicitly identifies studies without points in that window. The study-period summary retains each indicator’s full original period. Uncertainty bands are omitted; overlapping butterfly and broader insect indicators must not be summed.

Urbanisation and megacities

The urbanisation panel uses harmonised UN DEGURBA city-only coverage throughout 1975–2026. It replaces the previous urban percentage based on national definitions. All-city residents are shown in billions on the green left axis; megacity residents are shown in millions on the dashed orange right axis. Both axes start at zero, but their heights, gaps and slopes are not directly comparable. Historical CAGR uses 1975–2025 only and stays fixed across display windows. Dotted extensions, shading and hollow diamonds identify the 2026 UN projection. Megacities have at least 10 million residents in each year; crossing that threshold changes membership and can produce jumps. This measures population concentration, not density, pollution or governance performance. The Kinshasa waste example is a separate, locally scoped World Bank finding.

Global waste and projected extensions

The two-panel comparison separates annual plastic and electronic waste flows from accumulated ocean plastic stock. Plastic and ocean history ends in 2019; the published OECD central baseline (2022 model) extends both to 2026. E-waste history covers 2010–2022. Its 2023–2026 values are our linear extrapolation of the 2017–2022 average annual addition, approximately 2.22 million tonnes. Dotted extensions, shaded regions and hollow endpoint diamonds distinguish projections from historical estimates. Historical CAGR tiles exclude all projected values. The axes are independent, and these overlapping categories must not be added. No prediction interval is claimed. No complete observed 2026 annual totals are represented.

Evidence, projections and assumptions

Source estimates include reconstructed and modelled historical values. UN projections are dashed and explicitly dated. The curve shapes, compounding scenarios, cybersecurity scenario and Pace and Control Simulator section are illustrative. The cyber evidence card uses sourced tracking and explicitly marked reconstructed estimates. Shading shows the arithmetic gap only when both lines share a meaningful unit or an explicitly assumed scale.