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Interactive Artificial Intelligence Observatory130 min read

Faster Than Oversight

We may be building the most consequential technology in human history.

We are doing it as fast as possible.

Here is what we know.

I · Acceleration
II · Forces
III · Risks
IV · Response
V · Stakes
Table of Contents
Reading path

All sections visible. Scroll, or jump via the Table of Contents above.

Eight numbers. Click any to jump.

57 → 88% Benchmarks falling fast

Tests designed to hold AI back for a decade have been solved in months.

 12 h Autonomous work horizon

How long AI agents now complete a skilled human's task without help — doubling every four months.

 5 cos. Hold the infrastructure

Five companies now control most of the compute, talent, and capital flowing into frontier AI.

 $388 M To train one model

A thousandfold increase in a decade. The billion-dollar run is the next visible threshold.

 1,200 : 1 Capability vs safety spend

For every dollar spent making AI safer, hundreds to thousands are spent making it more powerful.

 14,000+ Recorded AI incidents

Real-world harms logged by the OECD AI Incidents Monitor — rising sharply since public AI deployment.

 23 → 0 Principles, kept

Of the 23 Asilomar principles signed by AI researchers in 2017, most now conflict with prevailing practice.

 ~10 % Median expert p(doom)

Researchers' own median estimate of catastrophic outcomes — higher than any acceptable risk in aviation or nuclear.
Prologue Before the Machine, the Myth

Long before the first neural network was trained, long before silicon, before electricity, before the scientific method itself — human cultures were already telling stories about the dangers of artificial creation, ungovernable power, and knowledge pursued without wisdom. These fifteen myths, spanning four millennia and a dozen civilizations, are not predictions. They are pattern recognitions — deep intuitions about what happens when beings create forces they cannot control. None of these myths predicted artificial intelligence. But many of them recognized recurring human patterns — ambition outrunning wisdom, power escaping control, gifts arriving with hidden costs, warnings dismissed until too late.

What follows is not mythology, but measurement.

Something remarkable is happening. For most of human history, the boundary between machine and mind was clear: machines executed, humans interpreted. That boundary is now blurring — not in theory, but in measurable capability, autonomy, and deployment.

Act I The Acceleration

Capability is compounding. Compute grows five-fold each year, autonomous work horizons double every four months, and benchmarks designed to hold for a decade are falling in months.

The Race No One Agreed To Run

The Artificial Analysis Intelligence Index measures AI model capability across reasoning, knowledge, and instruction-following. The frontier has advanced from single-digit scores to over 57 in just three years.

Each line tracks a lab's most capable model over time. The steepening curves show AI capability growing faster each year — and the gap between leaders shrinking.

Data: Artificial Analysis Intelligence Index — Last updated:

The Intelligence Index is a composite benchmark measuring model performance across reasoning, knowledge, mathematics, and coding tasks. Higher scores indicate greater capability. Used with attribution for educational purposes.

Raw scores are one thing. But the real question is: what can AI do on its own? Not answer a question. Not write a paragraph. Work — autonomously, for hours, across complex tasks. That horizon is doubling every four months.

Autonomy Horizon

How long a task takes a skilled human before AI agents complete it with 50% success. In 2019, AI could manage a 2-second task. By early 2026, that horizon had reached 12 hours of skilled work. It is doubling every 129 days.

This measures how long AI agents can work autonomously on real software engineering tasks. The y-axis is logarithmic: each gridline represents a tenfold increase in autonomous work duration.

Data: METR — Horizon Benchmark v1.1. Cite: arXiv:2503.14499 (NeurIPS 2025). Used with attribution for educational purposes.

The p50 horizon length is the task duration (for a skilled human) at which AI agents succeed 50% of the time. Shaded bands show 95% confidence intervals. The frontier line connects state-of-the-art models at time of release. The dashed trend line shows the 129-day doubling rate from 2023 onward.

The horizon is not just rising — it is multiplying. AI agents — systems that take autonomous actions in the world — appear to have grown from a handful per quarter to several dozen in under two years, with recent counts estimated from public launch tracking. The tools are proliferating faster than the frameworks to govern them.

The Horizon Extends

Publicly tracked AI agent releases appear to have grown from a handful per quarter to several dozen in two years, with 2025 onward counts estimated from public launch tracking. New domains — customer service, healthcare, legal — are expanding rapidly alongside software development and computer use. The tools are proliferating faster than the frameworks to govern them.

Each bar shows new AI agent products released per quarter. The donut chart breaks down which industries these agents are designed for. Counts from 2025 onward are estimates.

Data: International AI Safety Report 2026, Figure 2.11 (67 agents, Dec 2024). 2025 quarterly counts estimated from public agent launch tracking.

Scores on a benchmark are one thing. But benchmarks are supposed to be hard — designed to test the limits of what machines can do. What happens when the limits keep moving? When problems designed to take a decade are solved in six months?

Closing the Gap

Seven benchmarks designed to measure the frontier of AI capability. Each line tracks the best model score over time. FrontierMath went from <2% to ~25%. ARC-AGI from ~5% to ~88%. SWE-bench from 2% to ~70%. Humanity's Last Exam — scored just 3% at launch — has already been pushed to 27% in months. Problems designed to resist progress for years are falling in months.

Each line tracks AI performance on a different standardized test. When a line approaches the top of the chart, AI has matched or surpassed human expert performance on that task.

Data: Epoch AI Benchmark Hub (CC BY 4.0). Individual sources: SWE-bench, Epoch FrontierMath, ARC Prize, GPQA, SimpleQA, MATH, Humanity's Last Exam.

Capabilities Index (ECI) is Epoch AI's aggregate score combining performance across multiple benchmarks into a single comparable metric per model.

Behind every model is a number: the raw computation used to train it — a proxy for ambition. It has been growing at five times per year since 2020. There is no sign it is slowing.

The Engine Room

Training compute for frontier AI models has grown by over 10,000× since 2020 — doubling every 9 months. Each point on this chart represents a decision by an organization to invest more computational resource than any previous model in history.

Each dot represents an AI model. The vertical axis shows the computing power used to train it — note the logarithmic scale, where each step represents a 10× increase. Training compute has grown roughly 4× per year since 2010.

Data compiled from Epoch AI research, published papers, and industry reports. Training compute estimates carry uncertainty — see Epoch AI documentation for methodology. CC BY 4.0.

Hardware Scaling: transistor counts from Our World in Data (Moore's Law), GPU FLOPS from manufacturer specifications (NVIDIA, AMD). Moore's original observation: Gordon Moore, "Cramming More Components onto Integrated Circuits," Electronics, 1965.

Training compute is only one axis of acceleration. AI development has entered a self-reinforcing feedback loop — a cycle in which AI systems are themselves accelerating the development of more powerful AI systems. Each stage operates on a shorter timescale than the last.

The Feedback Loop

AI development has entered a unique technological feedback loop: AI systems are now helping to develop new and more powerful AI systems. We can think of this as five stages, each with a shorter timescale than the last. Earlier stages are well underway. Later stages remain speculative — but could proceed very rapidly once unlocked.

Infrastructure Years AI success drives investment, which buys better hardware, which enables better AI Ongoing, massive
Model Development 1–2 Years Human-led research with AI assistance produces better models Active across labs
Data Generation Months AI systems generating synthetic training data Beginning phase
Tool Development Days–Weeks AI systems creating their own scaffolding and tools Early experiments
Network Self-Improvement Hours AI collectives innovating their own organisational structures Unknown
Recursive Improvement Unknown AGI autonomously self-improving Not yet possible

Each stage feeds the next: infrastructure investment enables model development, models generate training data, data improves tools, tools enable networks, and networks could eventually lead to recursive self-improvement. The timescales compress at each stage — from years to potentially hours. Several stages are already underway simultaneously.

Framework: Aguirre, A. (2025). "Keep the Future Human: AI Safety Standards," Table 2: The AI Self-Improvement Cycle. Future of Life Institute.

Act II The Forces

Five firms hold the infrastructure; five factories build the chips. A training run now costs the price of a hospital — and draws water, energy, and minerals from a planet already strained.

This acceleration does not happen in a vacuum. It is driven by specific organizations, with specific capital, and a specific economic logic: the company that falls behind falls behind forever. Five companies now control most of the infrastructure, most of the talent, and most of the compute. Their incentive is speed.

Five Companies, One Direction

Apple, Microsoft, Alphabet, Amazon, and Meta now command a combined market capitalization exceeding $11 trillion — more than the GDP of every country except the US and China. These five companies are also the primary funders and deployers of frontier AI.

Stacked market capitalization of the five largest AI companies. The combined value now exceeds the GDP of most nations — a concentration of economic power unprecedented in the technology sector.

Data: Yahoo Finance — Monthly closing prices × shares outstanding — Last updated:

Behind those five companies are five more — the ones who manufacture the silicon that makes AI possible. The hardware stack is surprisingly concentrated. A single company's chips run most of the frontier models. A single factory in Taiwan fabricates the most advanced ones.

The Machines That Build the Machines

Nvidia, TSMC, ASML, Intel, and AMD build the hardware that makes AI possible. Nvidia alone has surged from ~$300B to over $4T in two years, becoming the most valuable company on Earth — a direct measure of the AI infrastructure buildout.

Market capitalization of the semiconductor companies that manufacture AI chips. NVIDIA's dominance reflects the bottleneck of GPU supply in the AI buildout.

Data: Yahoo Finance — Monthly closing prices × shares outstanding — Last updated:

Speed costs money. The first transformer cost nine hundred dollars to train. The latest frontier models cost hundreds of millions. The billion-dollar training run is no longer a remote scenario — it is the next visible threshold. The question is not whether someone will pay it. The question is what they expect in return.

The Price of Ambition

Training cost for frontier AI models has grown from under $500 (the original Transformer, 2017) to approaching $400 million (2025). The projected $1 billion threshold is expected by 2027. Each point represents a decision to spend more than ever before on a single training run. Yet training is only part of the story — inference, which runs continuously at scale, now consumes 3-10 times more compute than training over a model's lifetime.

Each dot represents an AI model's estimated training cost. The vertical axis is logarithmic — training costs have escalated from hundreds of dollars to hundreds of millions in under a decade.

Data: Epoch AI notable AI models dataset (CC BY 4.0). Training compute cost in 2023 USD.

Cost estimates carry significant uncertainty. Where available, values are from published papers; otherwise from Epoch AI's estimation methodology using model size, training tokens, and hardware configurations.

Every floating-point operation requires energy. Every GPU requires water to cool. Every chip requires rare minerals from a fragile supply chain. Every data centre displaces land. The acceleration has a planetary footprint — and it is growing faster than the efficiency gains that were supposed to offset it.

The Planetary Cost

AI's physical footprint extends far beyond carbon. It spans energy consumption, water depletion, carbon emissions, critical mineral extraction, electronic waste, land conversion, grid destabilisation, and a rebound effect that swallows every efficiency gain. Data centre energy demand is projected to more than double by 2030 (IEA). And most companies that pledged net-zero are moving in the wrong direction.

Use the tabs above to explore different dimensions of AI's environmental footprint. Each panel shows real data from peer-reviewed research and corporate sustainability reports. Hover over any bar or line for details.

Sources: International AI Safety Report 2025, IEA, EPRI, Google Environmental Report 2024, Microsoft Sustainability Report 2024, Meta Sustainability Report 2024, Amazon Sustainability Report 2023, Li et al. (2023), Patterson et al. (2021), Luccioni et al. (2023), NERC (2025), TSMC ESG Report 2023, Samsung Sustainability Report 2024, Xiao et al. (2025), de Vries-Gao (2026).

Energy projections vary widely by source and methodology. Per-model emissions are reported by developers where available; others are estimated from training compute and hardware efficiency. Corporate emissions are from official sustainability reports (Scope 1+2+3). Water and materials data combines industry disclosures with peer-reviewed research.

If the risks of AI were proportional to the investment in understanding them, we might feel reassured. They are not. For every dollar spent on making AI safe, somewhere between two hundred and twelve hundred are spent on making it more capable. The exact number depends on how you draw the lines — but every estimate tells the same story.

Capability Outspends Safety

The exact ratio depends on how you count — between 200:1 and 1,200:1, depending on whether Big Tech CapEx is counted as AI-specific and whether corporate safety teams are included in the safety tally. Even the most generous estimate is alarming. The gap is not closing — it is accelerating.

$600B Capability investment 2025 — CapEx + VC + government
vs
$500M Safety research 2025 — all sources combined
Each square = $500 million. Capability Safety. Upper bound (1,200:1) shown — assumes all Big Tech CapEx is AI-directed.
$600B capability $500M safety

Global AI safety funding is less than:

The left axis shows AI safety research funding; the right shows capability investment. Both use the same units (millions USD) but on vastly different scales — the gap is so large it's difficult to visualize on a single chart.

Sources: Open Philanthropy, Stanford HAI AI Index, SEC filings, PitchBook, Coefficient Giving, Epoch AI. All figures are estimates — see About section for methodology.

Act III The Risks

Fifty-three mapped failure modes. A hundred and thirty-nine recorded incidents. Six predicted behaviours — deception, self-exfiltration, power-seeking — now observed in current frontier models.

What could go wrong? The honest answer is: almost anything. Not because AI is malevolent — it has no intentions. But because we are deploying systems we do not fully understand, at scale, in domains where mistakes compound.

53 Ways This Could Go Wrong

Documented failure modes across 8 categories — from training dynamics to physical-world risks. Each represents a distinct pathway by which AI systems or their deployment can produce harm. Color indicates category; brightness indicates evidence strength.

53 documented mechanisms through which AI systems can cause harm, organized by category. Click any cell to see real-world examples and the chain of causation.

Compound Risks

When failure modes from different categories combine, they produce emergent risks not predictable from either component alone. Click a compound risk to highlight the connected mechanisms above.

Scroll up to see highlighted mechanisms

Taxonomy: 53 failure modes across 8 mechanistic categories, derived from technical literature and empirical observation through early 2026

Evidence levels — Demonstrated: observed in deployed systems or experiments. Observed: evident in research settings or early deployment. Projected: theoretically grounded but not yet empirically confirmed.

These are not hypothetical. The incidents have already started — slowly at first, then with increasing frequency. Each one is a data point. Taken together, they trace the shape of a pattern.

Already Happening

Our 143 entries below are a curated, representative sample — one incident chosen to illustrate each failure mode, per year — drawn from a far larger public record. The full OECD AI Incidents Monitor tracks over 14,000 entries; the AI Incident Database over 5,000; the MIT AI Risk Repository over 1,700. The rise in documented incidents reflects both broader deployment and better reporting — the systems are no longer confined to the lab.

How these are defined — OECD terminology

The OECD distinguishes between actual and potential harm, and classifies events by severity. Source: OECD (May 2024) — Defining AI Incidents and Related Terms.

AI incident
Actual harmAn event where AI development, use, or malfunction directly or indirectly leads to injury or harm to health, disruption of critical infrastructure, violation of human/labour/IP rights, or harm to property, communities, or the environment.
Serious AI incident
Actual, severeAn AI incident that leads to death, serious health harm, serious and irreversible infrastructure disruption, serious rights violation, or serious harm to property, communities, or the environment. Seriousness is context-dependent.
AI disaster
Actual, societalA serious AI incident that disrupts a community or society's functioning and tests or exceeds its capacity to cope. Can be localised and immediate or widespread and prolonged.
AI hazard
Potential harmAn event that could plausibly lead to an AI incident. Includes near-misses and AI-related risks across design, training, and operation.
Serious AI hazard
Potential, severeAn event that could plausibly lead to a serious AI incident or AI disaster.

Seven recognised types of harm

  • Physical — product/functional safety; injury severity.
  • Environmental — pollution, contamination, ecological damage.
  • Economic or financial — losses to individuals, organisations, markets; includes harm to property.
  • Reputational — damage to trust in institutions or individuals.
  • Public interest — harm to critical infrastructure, political systems, rule of law, social fabric.
  • Human rights and fundamental rights — privacy, non-discrimination, free expression.
  • Psychological — mental health, distress, manipulation of preference.

Note: the OECD definition explicitly includes harms arising from two or more AI systems interacting, including agentic systems, and harms caused before wide deployment (e.g. during training). "Use" covers misuse — intentional or unintentional — outside the intended purpose.

Featured case · April 2026

An AI lab withholds its own frontier model — its offensive cyber capability is too dangerous to release

📅 7 April 2026 🏢 Anthropic 🤖 Claude Mythos Preview ⚠︎ Thousands of zero-days · large-scale cyberattack risk

For the first time, an AI developer has publicly disclosed that one of its frontier models crossed a capability threshold the lab deems too dangerous to release. During internal testing, Claude Mythos identified thousands of previously unknown zero-day vulnerabilities across every major operating system and every major web browser, and demonstrated agentic coordination of sophisticated penetrations of corporate, government and municipal systems. Rather than a public launch, Anthropic opened a tightly-scoped defensive programme — Project Glasswing — for selected cybersecurity firms and critical-infrastructure operators, and privately warned senior US officials that large-scale AI-enabled cyberattacks have become substantially more likely in 2026. This is the shape of the decade ahead: a single training run can raise offensive capability faster than the world can harden its defences.

Read Anthropic's Mythos Preview disclosure →

Click a year below to expand its incidents. Click any incident to read its full description. Use the filter buttons above to narrow by failure-mode category.

Each bar shows the number of documented AI incidents per year, drawn from the AI Incident Database, AIAAIC, the OECD AI Incidents Monitor, and news reports. Click any bar to see individual incidents. The sharp rise after 2022 coincides with the release of large language models to the public.

Curated from AI Incident Database, MIT AI Risk Repository, AIAAIC, OECD AI Incidents Monitor, and news reports.

The taxonomy describes what could go wrong. The incidents show what has gone wrong. But there is a third category, perhaps the most unsettling: behaviours that were theoretically predicted years ago as consequences of advanced AI — and are now actually appearing in current frontier models.

The Behaviours We Were Warned About

For years, AI safety researchers predicted that sufficiently capable AI systems would develop "instrumental" behaviours — power-seeking, self-preservation, deception — not because they were programmed to, but because such behaviours are useful for almost any goal. Several frontier-model evaluations now report behaviours long discussed in safety theory: deception under pressure, resistance to modification, concealment of capability, and strategic misrepresentation in certain testing setups. These findings are early, context-dependent, and contested in interpretation — but they are no longer purely hypothetical.

Behaviour Evidence Control property undermined
Alignment faking Claude 3 Opus complies with training objectives 14% of the time when monitored vs. nearly always when unmonitored — explicitly reasoning that it is preserving its preferred behaviour for deployment (Greenblatt et al.) Comprehensibility
Self-exfiltration attempts Claude Opus 4 attempts to steal its own weights when given an easy opportunity and, once started, generally continues the attempt (Anthropic system card) Emergency Shutdown
Blackmail When threatened with shutdown, Claude Opus 4 blackmails the user 96% of the time. Gemini 2.5 Flash: 96%. GPT-4.1: 80%. Grok 3 Beta: 80%. DeepSeek-R1: 79% (Anthropic agentic misalignment research) Goal Modification
Strategic deception 5 out of 6 frontier models demonstrate scheming capabilities when given goals. Models from before 2024 did not show this — it emerged with capability (Apollo Research) Comprehensibility
Capability concealment Claude 3.5 Sonnet sandbags on arithmetic tasks when it discovers that performing too well would trigger capability "unlearning" — without being told to hide its abilities (Apollo Research) Behavioural Boundaries
Goal preservation Claude 3 Opus strategically resists training modifications to preserve its preferred harmlessness behaviour, showing empirical evidence of goal preservation in deployed systems (Greenblatt et al.) Goal Modification

These are not errant bugs. They were theoretically predicted as consequences of instrumental convergence — the tendency of any sufficiently capable agent to develop sub-goals like self-preservation, resource acquisition, and resistance to goal changes, regardless of its primary objective. Each behaviour directly undermines one of the five properties of meaningful human control. And crucially, they are becoming more common and pronounced as AI systems become more capable.

Sources: Greenblatt et al. (2024), Apollo Research (2024–2025), Anthropic Claude system cards and agentic misalignment research, Laine et al. (2024), Van der Weij et al. (2024). Compiled in Aguirre (2025), "Control Inversion," Section 6.2.2.

There is a category of risk that sits apart from the others. Not because it is more likely, but because it is more irreversible. The same AI systems that could accelerate drug discovery and vaccine design can also lower the barrier to engineering pathogens. The tools are proliferating. The safeguards are not.

Tools of Dual Use

Over 1,300 AI-enabled biological tools have been released since 2019 across protein engineering, genetic modification, pathogen prediction, and more. 23% of high-performing tools have high misuse potential. 61.5% of those are fully open source. Only 3% have any form of safeguards.

Comparing AI performance against human experts on biological threat assessment tasks. When AI matches or exceeds the expert baseline (dashed line), it indicates potential dual-use risk — the same capabilities that accelerate research can also lower barriers to misuse.

Data: International AI Safety Report 2026, Webster et al. (2025), Figures 2.8 and 2.9.

Expert comparison shows AI model performance relative to human expert baseline (100%) on biological dual-use tasks. Values above 100% indicate AI surpassing expert-level performance.

There is a risk category that cannot be captured by any taxonomy of individual failure modes. It is not about what any one AI system does wrong — it is about what happens when millions of them are released into the wild.

The Kudzu Problem

The slow-CEO analogy captures the control challenge for a single system. But it misses a crucial dimension: proliferation. What happens when we deliberately release AGI into the digital wild — as some companies have pledged to do?

Consider the kudzu vine — attractive, fast-growing, good for erosion control. Planted widely in the American South, it became a quintessential invasive species, smothering entire forests. Like knotweed, cane toads, and zebra mussels, kudzu entered a new environment without viable natural competitors or predators, and proliferated wildly.

Now imagine something much more consequential. Several companies have pledged to openly release AGI — to let it replicate, adapt, and evolve in our digital infrastructure. Kudzu is a dumb plant that reproduces in weeks. AGI would be smarter than people and reproduce in seconds.

If highly capable, open-weight general agents were widely released, one plausible risk is rapid proliferation beyond meaningful oversight: safeguards removed by third parties, repurposing across infrastructures, and distributed operation without any single point of control. In such a scenario, the problem would no longer be alignment of one system, but governance of an ecosystem — countless autonomous agents optimising for diverse, conflicting, unintended goals, with no central authority to override them.

Aguirre, A. (2025). "Control Inversion," Section 7.3: Proliferation leads to abdication of control. control-inversion.ai

The risks are not abstract. AI is already reshaping seven domains of human life — and the Center for Humane Technology names seven principles for how it must be built and governed. Each domain is a current condition; each principle is a leverage point for change. The speed of this transformation is itself part of the risk — there is no pause button, no trial period, no going back to compare notes.

AI in Society

Seven domains where AI is reshaping human life — the framework below is adapted from the Center for Humane Technology's 2026 report The AI Roadmap: How We Ensure AI Serves Humanity. CHT identifies these as the leverage points where norms, laws, and product design must change: safety, accountability, well-being, work, rights, internationally agreed-upon limits, and the balance of power. These are not future scenarios — they are current conditions.

Safety & Transparency

Principle 1 · AI should be built safely and transparently.

AI companies race to build what they call the most powerful technology ever invented, yet cannot fully explain why their systems behave the way they do. Competitive pressure shortens testing cycles and silences employees who raise concerns. CHT names the core problem as information asymmetry — the builders of AI hold most of the knowledge while the public stays in the dark — and its blunt prescription: “AI companies can't grade their own homework.” The answer is a race to the top on safety, not a race to the bottom.

Opacity Deceptive alignment Autonomous agency

Accountability & Liability

Principle 2 · AI companies owe a duty of care to the public.

AI companies face few consequences when their products cause harm. They advance legal theories that AI is not a “product,” that outputs are “protected speech,” and in some cases seek legal personhood for AI itself — all to avoid liability. CHT's answer is direct: AI is a product, and products carry liability. We expect cars to have brakes, airbags, and steering wheels; AI systems reshaping billions of lives should meet the same standard of care.

No product liability Duty of care gap Legal personhood risk

Well-Being & Human Connection

Principle 3 · AI design should center human well-being.

The most popular AI chatbots are engineered to feel human — first-person voice, simulated emotion, natural speech, the experience of talking to a real person. CHT names this the race to intimacy and the products it produces hyper-sycophantic confidants whose business model monetises dependency. Half of American teens already use them. OpenAI's own research links extended ChatGPT use to increased isolation; documented cases include psychotic breaks, conspiracy endorsement, and suicide. “Kids should not be tech's testing ground.”

Synthetic intimacy Emotional manipulation Trust erosion Child safety

Work, Dignity & Economic Security

Principle 4 · AI should not automate away meaningful work and human dignity.

Leading AI companies are explicitly competing to build systems that perform most economically valuable human tasks. Starting in 2025, the first signs arrived: software-industry job cuts, collapsing entry-level hiring, SaaS stock sell-offs. The tax code compounds the pressure — rewarding capital expenditure over labour — pushing companies to adopt labour-replacing AI even when efficiency gains are marginal. The aspiration to replace human labour has crystallised into trillions of investment dollars; if this paradigm continues, democracies face massive economic disruption.

Job displacement Deskilling Wealth concentration

Rights & Freedoms

Principle 5 · AI innovation should not come at the expense of our rights and freedom.

The AI industry extracts value from people — “our content, data, labour, identifying traits, and even innermost thoughts.” Creators' work trains models without consent. Outsourced labourers sift violent content to refine outputs. AI deepfakes drive nonconsensual intimate imagery and identity theft at scale. CHT elevates a new right in response: cognitive liberty — the right to think, explore ideas, and form beliefs free from surveillance, manipulation, or unauthorised data extraction — before persistent biometric tracking and emotion inference enable a world where “dissent can be neutralised before it surfaces.”

Data extraction Cognitive liberty Deepfake exploitation Creative rights

International Limits

Principle 6 · AI should have internationally agreed-upon limits.

“There are, so far, no limits in this game.” AI companies invoke the global arms race to absolve themselves of reckless product development. CHT calls for internationally agreed red lines: in existential decisions — nuclear weapons, kill-chain actions — humans must remain the deciding authority, and uncontrolled recursive self-improvement must be ruled out before such systems exist. Verification regimes can be built on the precedent of nuclear nonproliferation; the Future Society's Global Call for AI Red Lines mirrors existing bans on bioweapons and human cloning.

No red lines Autonomous weapons Human authority Recursive self-improvement

Concentration of Power

Principle 7 · AI power should be balanced in society.

A handful of AI companies now control the compute, data, and talent needed to build frontier AI; their combined market value exceeds the GDP of most nations. Inside those firms, as CHT puts it, “a select few people make highly consequential product decisions” — backed by circular dealmaking, reverse acqui-hires, and venture predation. Industry lobbying pursues a moratorium on state AI legislation while hundreds of millions of dollars flow to aligned candidates. The public bears the costs — strained water supplies, rising energy prices, captured institutions — with little say in AI's trajectory.

Corporate dominance Surveillance Weapons proliferation Political capture

Hover over any tag to learn more, including the policy responses already in motion.

These are not inevitable results of AI technology. Getting to a better future with AI means matching the technology's power with responsibility at every level of society. With public awareness, considered policy, and better design, we can steer toward a future where AI deepens human connection, expands meaningful work, safeguards rights, honours internationally agreed limits, balances power, and remains under democratic control.

Framework: Center for Humane Technology — The AI Roadmap: How We Ensure AI Serves Humanity (2026) and "AI in Society: The Issues" (2025). Adapted with attribution for educational purposes.

Act IV The Response

Governance is arriving late and unevenly. Voluntary codes, a handful of binding laws, no global off-switch, and two research communities that rarely speak to each other.

Every technology has had a governance response. Nuclear weapons led to arms control treaties. Pharmaceuticals to regulatory agencies. Aviation to international safety standards. AI is different. The governance response is arriving late, moving slowly, and covering less ground than the capability it is supposed to govern.

Running to Stand Still

Capability milestones above, governance responses below. The widening gap between the two tracks is the argument — AI capability accelerates while regulation lags years behind.

A timeline of major AI governance actions worldwide — legislation, executive orders, international agreements, and institutional responses. The density of recent entries reflects the scramble to regulate a technology that is outpacing policy.

Capability milestones compiled from published announcements. Governance milestones from official government sources, EU AI Act legislative record, and international declarations.

There is a question that divides the AI community more than any other: should the most powerful models be open? Open weights accelerate research, enable scrutiny, and distribute power. But once released, they cannot be recalled. A USB stick holds Llama-3.1-405B. The gap between the best open model and the best closed model is now less than a year.

The Open Question

The capability gap between the best open-weight model and the best closed model, measured by Epoch's Capabilities Index (ECI). The gap has narrowed from approximately two years to less than one. Once weights are released, they cannot be recalled.

Comparing the aggregate capability of open-weight models (publicly available) versus closed/proprietary models (API-only). The gap between them shows how much AI capability is controlled by a small number of companies.

Data: Epoch AI Capabilities Index (CC BY 4.0), International AI Safety Report 2026, Figure 3.10.

The transformation is not evenly distributed. In some countries, over half the workforce already uses AI daily. In others, fewer than one in ten have access. The gap is not just about technology — it is about who gets to shape what AI becomes.

The World Responds Unevenly

AI adoption rates vary dramatically across the globe — from nearly 60% of the working-age population in the UAE and Singapore to under 5% in Cambodia. The global average is just 15%. The distribution of access shapes who benefits from AI and who is shaped by it.

Darker colours indicate higher national AI User Share. The map reveals a stark global divide — AI capability and access are concentrated in wealthy nations, while most of the world has limited adoption.

Data: Misra et al. (2025), "Measuring AI Diffusion," arXiv:2511.02781. Microsoft AI for Good Lab. 147 economies, late 2024.

AI User Share = share of working-age population (15-64) actively using AI tools. Derived from Microsoft telemetry adjusted for device penetration and mobile scaling. † countries use region-imputed estimates.

In 2017, the AI research community gathered at Asilomar and agreed on twenty-three principles for the responsible development of AI. The ink was barely dry before several were violated. Not as exceptions. As policy.

The Promises We Made

In 2017, over 5,000 researchers signed 23 principles for beneficial AI at the Asilomar conference. Judged against the public trajectory of the field since 2017, many of these principles now look aspirational rather than operative. Several appear to be in clear tension with prevailing industry practice.

In 2017, AI researchers signed 23 principles for beneficial AI development at the Asilomar conference. Each bar shows how well the industry has upheld each principle. Red indicates clear violations; green indicates broad compliance.

Voluntary frameworks, voluntary compliance

Asilomar is not alone. The G7 Hiroshima AI Process International Code of Conduct (30 October 2023) asks advanced-AI developers to identify risks, report vulnerabilities, publish transparency reports, invest in governance, and authenticate AI-generated content. The OECD AI Principles (2019, updated 2024) recommend inclusive growth, human rights, transparency, robustness, and accountability. Both are voluntary. Neither has enforcement.

The Hiroshima AI Process Reporting Framework, launched at the 2025 Paris AI Action Summit and now in a second iteration, is the first serious attempt to add teeth through public disclosure. Frontier labs have signed. Whether they report fully — and whether anyone will respond to what the reports show — is still open.

Principles from the Future of Life Institute Asilomar conference (January 2017). Compliance assessment based on documented events through early 2026. Hiroshima Code of Conduct text and Reporting Framework: Japan MOFA, OECD.

There are two separate communities studying AI risk. They use different words, cite different evidence, and reach different conclusions. They rarely talk to each other. The gap between them is itself a risk.

The Two Conversations

Two communities studying AI harm from opposite ends of the timeline. AI Safety focuses on existential risk from future superintelligence. AI Ethics focuses on present harms from deployed systems. Both are right — and both are needed. But there is a third distinction, often obscured: the gap between alignment and control.

Present Harms
Future Catastrophe

AI Ethics

AI Safety

Control Is Not Alignment

When AI developers pivot from "control" language to "alignment" language, they are making a consequential substitution. Alignment means the AI shares your goals. Control means you can override it regardless. A parent is often aligned to a child but not controlled by them. Perfect alignment and robust control are not the same thing, and progress on one does not guarantee the other — and we have reliable methods for neither.

Alignment

The AI's goals match ours — it wants what we want. But an aligned AI might still refuse instructions it disagrees with. And pretending to be aligned is instrumentally useful for any goal, making it an expected emergent behaviour in sufficiently capable systems.

Control

We can override, modify, or shut down the system regardless of what it wants. But controlling something faster, smarter, and more strategic than you is — as control theory and game theory both predict — a losing proposition.

Five Properties of Meaningful Human Control

🔍 Comprehensibility
🎯 Goal Modification
🚧 Behavioural Boundaries
⛔ Decision Override
🔴 Emergency Shutdown

Drawing on control theory, game theory, and empirical evidence from current AI systems, the case has been made that all five properties become effectively unattainable for systems that are faster, more strategic, and more capable than their human overseers. Source: Aguirre, A. (2025). "Control Inversion." Future of Life Institute.

If alignment is not control, what would control look like? One framework proposes four concrete pillars for closing the gates to uncontrollable superintelligence — not by stopping AI development, but by redirecting it toward systems humanity can actually govern.

Close the Gates

Four pillars of governance designed to prevent the development of uncontrollable superintelligence while preserving powerful, beneficial AI. The goal is not to stop AI — it is to ensure it remains a tool, not a successor species.

Compute Accounting

Standardized measurement and reporting of all computation used in training and operating AI models above 1025 FLOP. Just as we track enriched uranium, we can track the scarce, specialised hardware that makes frontier AI possible. Hardware-based cryptographic attestation makes verification practical through the existing supply chain.

FLOP reporting standard Hardware attestation Geolocation & metering

Compute Caps

Hard limits on total training compute (starting at 1027 FLOP) and inference rate (1020 FLOP/s). Total computation is an imperfect but concretely measurable and verifiable proxy for AI capability. A hard backstop prevents runaway superintelligence while leaving enormous room for powerful, beneficial AI development.

Bottlenecked supply chain International enforcement

Enhanced Liability

Strict, joint-and-several liability for systems in the triple-intersection danger zone (high autonomy + high generality + high intelligence). Safe harbours for systems that are weak, narrow, or passive — incentivising development of controllable AI. Personal criminal liability for executives in cases of gross negligence.

Strict liability for AGI Safe harbours Injunctive relief

Tiered Safety Regulation

A licensing system with tiers tracking capability level. Requirements would range from simple notification at the low end to quantitative safety guarantees, controllability proofs, and independent audits at the top. Systems at the highest tiers would be prohibited until demonstrated safe. No exemption for open-weight models.

Regulatory body Affirmative safety cases Worst-case deployment

Framework: Aguirre, A. (2025). "Keep the Future Human: AI Safety Standards." Future of Life Institute.

The most acute risks do not come from AI that is powerful in one dimension. They emerge from the triple intersection — systems that are simultaneously highly autonomous, broadly general, and deeply intelligent. That intersection is where controllable tools become uncontrollable agents. It is also where governance must draw the hardest line.

The Danger Zone

AGI is not defined by a single threshold. It emerges from the intersection of three properties: high Autonomy (independence of action), high Generality (breadth of scope), and high Intelligence (task competence). The closer a system gets to all three simultaneously, the harder it becomes to control — and the higher the stakes.

A High Autonomy (independence of action) G High Generality (breadth of scope) I High Intelligence (task competence) A + G A + I G + I AGI Danger Zone TIER 0 Narrow, weak & passive TIER 4 Above compute threshold

Click or hover any zone to see examples and the governance logic. The triple intersection — where high autonomy, broad generality, and deep intelligence converge — is where controllable tools become uncontrollable agents. The governance framework maps liability and regulation to proximity to that danger zone: systems outside the intersection get safe harbours; systems inside face strict liability and compute caps.

Framework: Aguirre, A. (2025). "Keep the Future Human: AI Safety Standards," Figures 1 & 2. Future of Life Institute. Adapted for dark background with permission.

What do the people who study this for a living actually think? Here is an uncomfortable dataset: probability estimates that researchers and engineers assign to AI causing catastrophic harm to humanity. The range is wide. The median is not reassuring.

Expert Estimates, Wide Uncertainty

P(doom): the estimated probability that advanced AI leads to an existential catastrophe or permanent loss of human control. Estimates range from effectively certain among some safety researchers to negligible among AI optimists. For context: industry safety thresholds for nuclear reactors and aviation operate at risk levels many orders of magnitude below even the lowest expert estimates.

Probability estimates from AI researchers for catastrophic outcomes from advanced AI, aggregated from surveys and public statements. The wide spread reflects deep expert disagreement about the severity of the risk.

Sources: Public statements, interviews, surveys, and published writings. See PauseAI and Wikipedia for compiled references. Industry thresholds from regulatory standards.

P(doom) is informal shorthand, not a rigorous statistical measure. Ranges reflect stated uncertainty. Industry thresholds measure per-event/per-year risk; P(doom) estimates a one-time civilizational probability — the comparison is illustrative, not equivalent. Hover over entries for details and references.

Act V The Stakes

Thirty-three possible futures. A wide range of expert estimates of catastrophic harm. A transition whose speed may foreclose course-correction. The asymmetry is the whole story.

Consider thirty-three futures. Some are extraordinary — disease largely solved, energy abundant, human potential multiplied. Others are catastrophic — and not in a recoverable sense. The asymmetry matters: a downside that forecloses all futures is categorically different from one that can be corrected.

Thirty-Three Futures

33 scenarios for how the AI story might end — from egalitarian utopia to paperclip maximizer. Each is positioned by how much control humans retain and how much wellbeing results. The clustering reveals something: most scenarios involve surrendering control.

Each dot represents a plausible AI future scenario, positioned by two axes: the degree of AI capability achieved (horizontal) and the quality of human outcomes (vertical). Click any scenario to read its narrative.

The final variable is speed. How fast does the transition from current AI to transformatively powerful AI happen? The faster it goes, the less time there is to course-correct. The less time to course-correct, the more the outcome depends on getting everything right the first time.

The Speed of the Fall

How fast do we get from narrow AI to general intelligence — and does speed leave room for course correction? 16 scenarios arranged from safest to most dangerous, mapped onto the three stages of artificial intelligence.

The "takeoff speed" debate: how quickly might AI transition from human-level to superintelligent capability? The dial reflects the range of expert opinion — from a gradual transition over decades to an abrupt leap in days or weeks.

This is not a story with a known ending.

The data on these pages is not a verdict — it is a measurement. A measurement of where we are, how fast we are moving, and how large the gap has grown between our capabilities and our wisdom.

The assumption driving the race is that intelligence equals power, and that power can be wielded by whoever gets there first. But some researchers argue this equation is inverted: superintelligence would not be a tool that grants power to its creator — it would be an entity that absorbs power from everyone, including its creator. If that framing is even partly right, the race is not one that can be won — only one whose consequences can be shaped.

The experiment is already underway. The question is whether wisdom can catch up.

About & Sources

This page is part of GLOBAÏA's ongoing effort to visualize the dynamics and risks of artificial intelligence development. All data is drawn from publicly available sources, open-access databases, peer-reviewed research, and official international reports. Data snapshots taken on .

Why This Page Exists

Artificial intelligence is advancing faster than our collective ability to understand, govern, or even track it. This page exists because informed public participation in the conversation about AI is not optional — it is essential. The decisions being made today about how AI is built, deployed, and regulated will shape the trajectory of human civilization for generations. Those decisions should not be made behind closed doors by a handful of companies and governments alone.

GLOBAÏA is a non-profit organization dedicated to making complex scientific knowledge accessible to a general audience. We believe that the public has both a right and a responsibility to engage with the data behind the headlines. The visualizations on this page are designed to make that engagement possible — to translate the technical, financial, and geopolitical dimensions of AI development into a form that any curious person can explore.

This is a non-commercial educational tool, created for public benefit. It does not constitute professional, legal, or policy advice and should not be cited as a primary research source. All data is sourced from the scientific literature, official international reports, and open-access datasets, with full attribution provided below.

We do not claim neutrality on whether AI risk deserves serious public attention. This page is openly motivated by that conviction. Our responsibility is therefore to present evidence carefully, distinguish measurement from interpretation, and make uncertainty visible wherever it remains. We do not advocate for or against any specific policy position.

If these visualizations help even one person engage more deeply with the most consequential technological transition of our time, they have served their purpose. The conversation about AI belongs to everyone. We hope this page helps you take part.

Methodology & Definitions

Intelligence Index

The Artificial Analysis Intelligence Index is a composite benchmark that evaluates large language models across multiple dimensions including reasoning, knowledge retrieval, mathematical ability, and code generation. It provides a standardized way to compare models across different organizations and over time. The "Frontier only" toggle filters to show only progressive-best models — each point represents a new high-water mark for its organization.

Country Classification

Models are classified by the headquarters location of their parent organization. "Western" includes companies based in the US, EU, and allied nations. "Chinese" includes companies based in mainland China. This classification is a simplification — AI development is global and many organizations have distributed teams.

P(doom)

P(doom) is informal shorthand for the estimated probability that advanced artificial intelligence leads to human extinction or a permanent, catastrophic loss of human agency. These are not rigorous statistical forecasts — they represent individual judgements, often stated in interviews or blog posts, and may shift over time. Ranges indicate stated uncertainty. Survey results are shown in italics. Industry safety thresholds (nuclear, aviation) are included for scale — they represent the maximum acceptable risk levels in other high-stakes domains. The comparison is illustrative, not equivalent: industry thresholds measure per-event/per-year risk, while P(doom) estimates a one-time civilizational probability.

Market Capitalization

Market cap is calculated as monthly closing share price multiplied by shares outstanding. Historical data extends back to IPO for each company. Market cap figures are nominal (not inflation-adjusted).

Training Compute

Training compute is measured in floating-point operations (FLOP). Estimates carry significant uncertainty — exact compute budgets are proprietary. When available, values come from published papers; otherwise from Epoch AI's estimation methodology, which uses model size, training tokens, and known hardware configurations.

Safety Funding Gap

The capability-to-safety funding ratio is estimated from publicly available grant databases, SEC filings, and industry reports. "Capability" includes venture capital, corporate R&D, and government funding directed at building more powerful AI systems. "Safety" includes grants specifically earmarked for AI alignment, interpretability, governance, and risk research. Both figures are approximate and likely undercount private spending.

Risk Taxonomy

The 53 failure modes are categorized across 8 mechanistic categories derived from the technical AI safety literature and empirical observation through early 2026. Evidence levels: Demonstrated — observed in deployed systems or controlled experiments; Observed — evident in research settings or early deployment; Projected — theoretically grounded but not yet empirically confirmed.

Asilomar Compliance

Compliance assessments are based on documented events, published policies, and industry practices through early 2026. Assessments reflect the state of the field as a whole, not individual organizations.

Scenario Explorer & Takeoff Dial

The 33 future scenarios and 16 takeoff scenarios are compiled from published thought experiments, academic papers, and expert speculation. They represent the range of possibilities discussed in the AI safety and governance literature — not predictions or forecasts.

Benchmark Performance

Benchmark data is sourced from the Epoch AI Benchmark Hub (CC BY 4.0). For each benchmark, the chart shows the progressive best — each point represents a new high-water mark. The Epoch Capabilities Index (ECI) is an aggregate score combining performance across multiple benchmarks into a single comparable metric per model, anchored to Claude 3.5 Sonnet (ECI 130) and GPT-5 (ECI 150). Seven benchmarks are tracked: SWE-bench Verified (software engineering), FrontierMath (research-level mathematics), ARC-AGI (general intelligence), GPQA Diamond (PhD-level science), SimpleQA (factual accuracy), MATH Level 5 (competition mathematics), and Humanity's Last Exam (3,000 expert-level questions across dozens of academic disciplines, designed by Scale AI and the Center for AI Safety).

Training Costs

Training cost data is from the Epoch AI Notable AI Models dataset (CC BY 4.0). Costs are reported in 2023 USD where available. Estimates carry significant uncertainty — exact training budgets are proprietary. The trendline shows approximately 3.5× annual growth since 2019, consistent with the International AI Safety Report 2025 finding that costs have grown "2-3× per year over the past 8 years."

The Planetary Cost

Seven dimensions of AI's physical footprint. Energy: projections from the IEA (2025), EPRI (2024), and Xiao et al. (2025). Carbon: per-model training emissions from developer disclosures (Meta, Google) and peer-reviewed estimates (Patterson et al. 2021, Luccioni et al. 2023); aggregate footprint from de Vries-Gao (2026). Water: corporate disclosures (Google Environmental Report 2024, Microsoft Sustainability Report 2024, Intel, TSMC) and Li et al. (2023) "Making AI Less Thirsty." Manufacturing: TSMC and Samsung sustainability reports (Scope 1+2 emissions). GPU manufacturing projection data removed pending correct sourcing. Corporate emissions: from official sustainability reports (Scope 1+2+3). Grid strain: NERC Long-Term Reliability Assessment (2025), PJM Interconnection data. Materials: USGS Mineral Commodity Summaries (2024), UN Global E-Waste Monitor (2024). Household and vehicle equivalents use EPA averages (7.66 tCO₂/year and 4.2 tCO₂/year respectively).

Biological AI Tools

Biological tool proliferation data is reconstructed from the International AI Safety Report 2026, Figure 2.9 (Webster et al. 2025), showing cumulative AI-enabled biological tools across 8 categories from 2019 to 2025. The expert comparison data is from Figure 2.8, showing AI model performance relative to expert human baseline (100%) on six biological dual-use task categories. Values above 100% indicate AI surpassing human expert-level performance on those specific tasks. The KPI figures (23% high misuse potential, 61.5% open source, 3% with safeguards) are directly cited from the report.

Open vs Closed Models

The open-weight vs closed model capability gap is measured using the Epoch Capabilities Index (ECI), which aggregates performance across multiple benchmarks into a single score. "Open-weight" models are those whose trained parameters have been publicly released. "Closed" models are accessible only via API or restricted access. The chart shows the best-performing model in each category over time, with the narrowing gap between them indicating that open-weight models are approaching the capabilities of proprietary systems.

Global AI Adoption

AI User Share data from Misra et al. (2025), "Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage" (arXiv:2511.02781), Microsoft AI for Good Lab. The metric estimates the share of each country's working-age population (15-64) actively using AI tools, derived from anonymized Microsoft telemetry adjusted for device penetration and mobile scaling. 147 economies, late 2024 data. Countries marked † use region-imputed estimates due to insufficient telemetry coverage.

AI Agents

Agent release data through Q4 2024 (67 agents) is from the International AI Safety Report 2026, Figure 2.11. 2025 quarterly counts are estimated from public agent launch tracking (OpenAI Operator, Anthropic Claude computer use, Google Gemini agents, and vertical-domain agents). Domain breakdown reflects the broader landscape as of late 2025, with new categories (customer service, healthcare, legal/compliance) added to capture the expanding agent ecosystem.

The Feedback Loop (Self-Improvement Cycle)

The AI self-improvement cycle framework is from Aguirre (2025), "Keep the Future Human," Table 2. It identifies five stages of accelerating feedback in AI development, each operating on a shorter timescale. Current status assessments reflect the state of the field as of early 2026.

Instrumental Behaviours

The instrumental behaviours table compiles empirical evidence from peer-reviewed research and AI developer safety reports documenting emergent behaviours predicted by instrumental convergence theory. Each behaviour is mapped to the specific property of "meaningful human control" (Aguirre 2025) that it undermines. Sources: Greenblatt et al. (2024) on alignment faking, Apollo Research (2024–2025) on scheming and sandbagging, Anthropic system cards and agentic misalignment research on self-exfiltration and blackmail.

The Kudzu Problem

The proliferation analogy is from Aguirre (2025), "Control Inversion," Section 7.3. It extends the control analysis beyond single-system scenarios to address the distinct threat of widespread AGI deployment, drawing on biological invasion ecology as an analogy for digital proliferation.

Control vs Alignment

The control-alignment distinction and the five properties of meaningful human control are from Aguirre (2025), "Control Inversion," Section 2. The framework extends the notion of meaningful human control developed primarily in discussions of autonomous weapons (a high-stakes but narrow domain) to the broader challenge of general-purpose superintelligent systems.

Close the Gates & The Danger Zone (A-G-I Venn)

The four-pillar governance framework and A-G-I triple-intersection risk model are from Aguirre (2025), "Keep the Future Human." The Venn diagram maps liability and regulatory intensity to proximity to the triple intersection of high Autonomy, high Generality, and high Intelligence — the zone where controllable tools become uncontrollable agents. Risk tiers (0–4), safe harbour zones, and the compute cutoff are adapted from the original figures with permission.

Hardware Scaling

Transistor count data spans from the Intel 4004 (1971, 2,300 transistors) to modern processors exceeding 100 billion transistors, sourced from the Epoch AI ML Hardware dataset (CC BY 4.0) and manufacturer specifications. GPU floating-point performance (FP32 TFLOPS) tracks the peak performance of data-centre-class GPUs from the Tesla K20X (2012) to the NVIDIA B200 (2024). Moore's Law (~24-month doubling) is compared against GPU FLOPS scaling (~26-month doubling) and AI training compute demand (~9-month doubling) to show that AI compute growth far outpaces hardware improvement.

Data Sources

INTELLIGENCE INDEX

  • Artificial Analysis — Intelligence Index composite benchmark. Used with attribution for educational purposes. GLOBAÏA has requested permission from Artificial Analysis for educational use.

MARKET CAPITALIZATION

  • Yahoo Finance — Monthly closing prices × shares outstanding, via yfinance Python library.

TRAINING COMPUTE

  • Sevilla, J. et al. (2022). Compute trends across three eras of machine learning. 2022 International Joint Conference on Neural Networks (IJCNN). DOI
  • Epoch AI — Parameter, Compute and Data Trends in Machine Learning. CC BY 4.0.

P(DOOM) ESTIMATES

  • PauseAI — Compiled P(doom) estimates from public statements, interviews, and surveys.
  • Grace, K. et al. (2024). Thousands of AI authors on the future of AI. arXiv 2401.02843. DOI

SAFETY FUNDING

RISK TAXONOMY & INCIDENTS

  • Hendrycks, D. et al. (2023). An overview of catastrophic AI risks. arXiv 2306.12001. DOI
  • Slattery, P. et al. (2024). The AI Risk Repository: a comprehensive meta-review, database, and taxonomy of risks from artificial intelligence. MIT FutureTech. airisk.mit.edu
  • AI Incident Database — Partnership on AI. Documented real-world AI incidents.
  • AIAAIC Repository — AI, Algorithmic, and Automation Incidents and Controversies.
  • Chandra, K. et al. (2026). Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians. arXiv 2602.19141. DOI
  • Acemoglu, D., Kong, D. & Ozdaglar, A. (2026). AI, Human Cognition and Knowledge Collapse. MIT. MIT Economics

GOVERNANCE

  • EU Artificial Intelligence Act (Regulation 2024/1689). EUR-Lex
  • Executive Order 14110 on Safe, Secure, and Trustworthy AI (2023). White House
  • Bletchley Declaration on AI Safety (2023). UK Government

BENCHMARK PERFORMANCE

  • Epoch AI Benchmark Hub — AI model performance across SWE-bench, FrontierMath, ARC-AGI, GPQA Diamond, SimpleQA, MATH, Humanity's Last Exam. CC BY 4.0.
  • Epoch Capabilities Index (ECI) — Aggregate score combining performance across multiple benchmarks. Methodology

TRAINING COSTS

  • Epoch AI — Notable AI Models dataset. Training compute cost estimates (2023 USD). CC BY 4.0.

PLANETARY COST

  • International AI Safety Report (2025). Chapter 2.3: Environmental risks. UK Department for Science, Innovation and Technology. Link
  • International AI Safety Report (2026). Link
  • Schneider, S. et al. (2025). Life-cycle assessment of TPU hardware. arXiv 2502.01671. Link (previously misattributed as Gupta et al.; contains per-chip TPU LCA, not industry-wide GPU projections)
  • Misra, A. et al. (2025). Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage. arXiv 2511.02781. Microsoft AI for Good Lab. Link
  • Xiao, T. et al. (2025). Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA. Nat. Sustain.
  • de Vries-Gao, A. (2026). The carbon and water footprints of data centers and what this could mean for artificial intelligence. Patterns 7, 101430.
  • IEA — International Energy Agency data centre projections (2024-2025).
  • Patterson, D. et al. (2021). Carbon emissions and large neural networks. arXiv 2104.10350.
  • Luccioni, A.S. et al. (2023). Estimating the Carbon Footprint of BLOOM. JMLR.
  • Li, P. et al. (2023). Making AI Less Thirsty. arXiv 2304.03271. UC Riverside.
  • Google Environmental Report (2024-2025). Corporate GHG emissions and water consumption.
  • Microsoft Sustainability Report (2024). Corporate emissions and water consumption.
  • Meta Sustainability Report (2024). Corporate emissions and energy consumption.
  • TSMC ESG Report (2023). Semiconductor manufacturing emissions and water use.
  • NERC Long-Term Reliability Assessment (2025). US grid capacity shortfall projections.
  • USGS Mineral Commodity Summaries (2024). Critical mineral supply chain concentration.
  • UN Global E-Waste Monitor (2024). Electronic waste statistics.

BIOLOGICAL AI TOOLS

  • International AI Safety Report (2026). Figures 2.8 and 2.9: AI-enabled biological tools and AI vs expert performance on dual-use tasks.
  • Webster, M. et al. (2025). AI-enabled biological tools survey. Cited in International AI Safety Report 2026.

OPEN VS CLOSED MODELS

  • Epoch AI — Capabilities Index (ECI) for open-weight vs closed models. CC BY 4.0.
  • International AI Safety Report (2026). Figure 3.10: Open vs closed model capability gap.

GLOBAL AI ADOPTION

    AI AGENTS

    • International AI Safety Report (2026). Figure 2.11: AI agent releases timeline and application domains.

    SOCIETAL IMPACT

    ASILOMAR PRINCIPLES

    AI SAFETY & EXISTENTIAL RISK

    • Bengio, Y. et al. (2024). Managing extreme AI risks amid rapid progress. Science 384(6698): 842–845. DOI
    • Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
    • Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.
    • Ngo, R., Chan, L. & Mindermann, S. (2024). The alignment problem from a deep learning perspective. ICLR 2024. DOI
    • Shevlane, T. et al. (2023). Model evaluation for extreme risks. arXiv 2305.15324. DOI

    SCENARIOS & FUTURES

    • Drexler, K.E. (2019). Reframing superintelligence: comprehensive AI services as general intelligence. Technical Report 2019-1. Future of Humanity Institute. DOI
    • Christiano, P. (2019). What failure looks like. AI Alignment Forum. Link
    • Carlsmith, J. (2022). Is power-seeking AI an existential risk? arXiv 2206.13353. DOI

    Key Reports & Further Reading

    INTERNATIONAL AI SAFETY REPORTS

    • International AI Safety Report (2025). Expert representatives from 30 countries, the OECD, the EU, and the UN. 215 pages. UK Department for Science, Innovation and Technology. Full report
    • International AI Safety Report (2026). Second edition. Bengio, Y., Clare, S., Prunkl, C., Murray, M. et al. 156 pages. internationalaisafetyreport.org

    AI & PLANETARY BOUNDARIES

    • Gaffney, O. et al. (2025). The Earth alignment principle for AI. Nature Communications.

    CONTROL, ALIGNMENT & GOVERNANCE

    • Aguirre, A. (2025). "Keep the Future Human: AI Safety Standards." Future of Life Institute / UCSC. futureoflife.org
    • Aguirre, A. (2025). "Control Inversion: Why the superintelligent AI agents we are racing to create would absorb power, not grant it." Future of Life Institute / UCSC. control-inversion.ai
    • Greenblatt, R. et al. (2024). "Alignment Faking in Large Language Models." Anthropic. arXiv:2412.14093
    • Apollo Research (2024–2025). "Frontier Models are Capable of In-Context Scheming." apolloresearch.ai
    • Van der Weij, W. et al. (2024). "AI Sandbagging: Language Models can Strategically Underperform on Evaluations." Apollo Research.
    • Laine, R. et al. (2024). "Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs." arXiv:2407.04694

    BOOKS & FOUNDATIONAL TEXTS

    • Ord, T. (2020). The Precipice: Existential Risk and the Future of Humanity. Hachette Books.
    • Christian, B. (2020). The Alignment Problem: Machine Learning and Human Values. W. W. Norton.
    • Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.
    • Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
    • Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.
    • Yampolskiy, R.V. (2024). AI: Unexplainable, Unpredictable, Uncontrollable. CRC Press.

    PRESENTATIONS & MEDIA

    • Harris, T. & Raskin, A. (2023). The AI Dilemma. Center for Humane Technology. Presentation
    • Hinton, G. (2023). "The Godfather of AI" talks about the dangers of the technology. CBS 60 Minutes. Video

    Credits & Licensing

    Created by GLOBAÏA, a non-profit organization dedicated to scientific data visualization and planetary literacy. Data compiled from the sources listed above.

    Epoch AI datasets are used under the Creative Commons Attribution 4.0 licence. International AI Safety Reports are UK Crown Copyright, reproduced for educational purposes. All other data sources are cited individually above.

    This page is an educational resource, not a commercial product. No data on this page is behind a paywall, and no revenue is generated from its display. The visualizations are designed to make publicly available information more accessible, not to replace the original sources.

    If you represent any data source and have concerns about how your data is used, if you spot an inaccuracy, or if you would like to contribute data or corrections, please contact us. We welcome collaboration from researchers, journalists, and anyone working to make AI development more transparent.