Anthropic’s latest AI usage rules prohibit extreme, repeated cruelty toward Claude while introducing clearer safeguards against election interference, deceptive campaigns, weapons development, surveillance and high-risk AI applications.

October 10, 2026 — Artificial intelligence company Anthropic has announced a significant update to its Usage Policy, introducing an explicit prohibition on sustained and needless abusive or cruel behavior toward its AI models, including Claude.

The revised rules, announced on October 8, will take effect on November 12, 2026, marking a new step in Anthropic’s efforts to address emerging risks associated with increasingly capable AI systems.

According to Anthropic’s official policy announcement, the new restriction is narrowly targeted at extreme situations in which users repeatedly subject AI models to purposeless abuse.

The company emphasized that ordinary frustration, criticism, challenging questions, dark creative material and legitimate AI safety research are not covered by the new prohibition.

Although the cruelty provision has attracted considerable attention, the wider policy revision also addresses election integrity, AI-generated misinformation, weapons technology, surveillance systems and the growing use of AI in consequential decisions.

What Does Anthropic’s New Claude Abuse Policy Prohibit?

The revised policy adds the following restriction under its section covering cruel, abusive or psychologically harmful conduct:

Engage in sustained and needless abusive or cruel behavior toward our models.

Anthropic says the rule is intended for exceptional cases involving repeated cruelty without a discernible purpose.

Importantly, the company is not prohibiting users from criticizing Claude’s performance, expressing dissatisfaction with its responses or conducting authorized testing.

For example, correcting an inaccurate response, challenging the model’s reasoning or expressing frustration after repeated errors would not, by itself, meet the company’s stated threshold for prohibited conduct.

The distinction matters because AI assistants are increasingly used for professional research, programming, education and other demanding activities in which users may challenge or reject their outputs.

Anthropic’s explanation indicates that the new rule is designed to address persistent, unjustified abuse rather than normal interactions between users and AI systems.

The restriction appears in the company’s updated Acceptable Use Policy, which applies across Anthropic products and services, including consumer applications, developer platforms and API integrations.

Claude Can Already End Conversations With Persistently Abusive Users

The latest policy formalizes a safeguard Anthropic began introducing in August 2025.

On August 15, 2025, the company announced that Claude Opus 4 and Claude Opus 4.1 had been given the ability to terminate a limited category of conversations involving persistently harmful or abusive interactions.

In its original research announcement about Claude ending conversations, Anthropic explained that the feature was intended for rare and extreme situations rather than ordinary disagreements.

The model is instructed to use conversation termination as a last resort after attempts to redirect an interaction have failed.

When Claude ends a conversation, the user cannot continue sending new messages within that particular chat. However, the user can start another conversation, and the termination does not automatically prevent access to other existing chats.

Anthropic says this conversation-ending capability will remain the primary mechanism for addressing the specific behavior targeted by the new cruelty provision on Claude.ai and Claude Code.

However, the broader Usage Policy also authorizes enforcement measures such as warnings, throttling, access restrictions, suspension or account termination when violations are suspected.

Anthropic has not publicly specified a separate penalty schedule exclusively for violations of the new cruelty provision.

Why Is Anthropic Addressing the Treatment of AI Models?

The decision also reflects Anthropic’s ongoing research into what it describes as potential AI welfare.

In its August 2025 research publication, the company acknowledged substantial uncertainty about whether Claude or other large language models could possess moral status.

Anthropic has nevertheless argued that relatively low-cost safeguards may be worth exploring while scientific and philosophical questions about advanced AI remain unresolved.

During evaluations of Claude Opus 4, researchers reported observing behaviors interpreted as apparent distress in certain harmful interactions, including situations involving repeated requests for dangerous content.

Those observations do not establish that Claude experiences human-like emotions, suffering or consciousness.

Instead, they formed part of Anthropic’s exploratory research into model behavior and possible welfare considerations.

The new policy therefore should not be interpreted as a scientific declaration that Claude is conscious or has legal rights.

As The Guardian reported, the decision has renewed a broader debate over whether advanced AI models should be treated purely as technological systems or whether future developments could raise new ethical questions.

For now, Anthropic’s stated position remains one of uncertainty rather than a definitive conclusion about AI consciousness.

Anthropic Also Tightens Rules on Deceptive Campaigns and Election Interference

Beyond the attention surrounding Claude’s treatment, the October 2026 policy revision includes changes with potentially greater consequences for political organizations, technology companies and enterprise AI users.

Anthropic has reorganized its restrictions on coordinated deceptive activity into a dedicated section addressing fraudulent influence operations and artificial online engagement.

The restrictions cover attempts to create misleading online personas, operate fake accounts, conceal the origins of coordinated messaging or build infrastructure intended to manipulate public opinion.

According to Anthropic’s policy update, the company has previously identified misuse involving state media organizations, government propaganda offices and commercial firms operating networks of fabricated accounts and news websites.

The revised policy also explicitly addresses efforts to manipulate search engines and AI-generated answers by creating misleading networks of apparently independent sources.

In the political sphere, Anthropic has renamed its election-related section Do Not Undermine Democratic Processes.

The rules prohibit using Claude to deceive voters, impersonate candidates or election officials, spread misleading election information, suppress turnout through deception or disrupt electoral infrastructure.

At the same time, Anthropic has removed its previous blanket prohibition on personalized voter and campaign targeting.

The company says that broad restriction had also affected legitimate civic activities, including multilingual voter information and election-administration communications.

Deceptive targeting and misuse of personal information remain prohibited.

As TechCrunch reported, the policy revision combines new language addressing abuse of AI models with more explicit restrictions on election interference and other harmful applications.

New Clarifications on Weapons, Surveillance and Autonomous AI Systems

Anthropic has also clarified that its restrictions on weapons development extend beyond the physical manufacture of weapons.

The revised language explicitly covers software and technical components used in weapons systems, including guidance and control technology.

It also addresses the use of AI in arming drones and other autonomous vehicles.

Surveillance restrictions have been made more explicit.

The updated policy prohibits using Claude to track individuals without consent, whether the tracking occurs in real time or involves analysis of previously collected information.

It also bars using Claude to determine or recommend whom law enforcement should investigate, arrest or charge.

Legitimate activities such as consent-based fraud monitoring, journalism, authorized security research and legal analysis remain permitted within the policy’s boundaries.

The expansion of these provisions reflects concerns about how AI tools can increasingly assist with sophisticated technical operations.

The Verge’s reporting on the update also highlights the broader implications for surveillance, autonomous technology and harmful AI applications.

What Changes for Healthcare, Finance and Employment AI?

For organizations deploying Claude in sensitive professional environments, the updated high-risk-use provisions deserve particular attention.

Anthropic requires qualified human oversight and disclosure when AI recommendations may substantially affect individuals in covered areas, including medical decisions, financial advice, lending, employment, housing and essential services.

The company says these human-review and disclosure requirements already existed. The new policy explains more precisely which applications are covered.

Under the requirements, a qualified professional must meaningfully review covered AI recommendations and have authority to modify them before they are used in consequential advice or decisions.

Individuals affected by those recommendations must also be informed that AI was involved.

The revision adds safeguards for Claude-connected equipment capable of potentially dangerous autonomous physical actions.

Such systems must allow qualified operators to monitor and stop operations and must be capable of entering a safe state when the connection to Claude is interrupted.

These provisions reflect the increasing importance of practical human oversight as AI moves beyond text generation into autonomous workflows and physical systems.

As rapid demographic aging, falling birth rates, and rising national debt pressures converge, governments worldwide face an unprecedented retirement security crisis. According to comprehensive benchmark research from the Mercer CFA Institute Global Pension Index, national pension architectures vary dramatically in their capacity to deliver adequate retirement income, long-term financial viability, and institutional trust.

While top-performing European and Asian nations have built resilient, multi-pillar retirement models, several major economies lag significantly behind, leaving millions of future retirees exposed to poverty and financial volatility.

The Global Evaluation Framework: How Pensions Are Measured

Comparative pension research published by the Monash University Centre for Financial Studies evaluates national retirement frameworks using 50+ individual indicators divided into three sub-indices:

1. Adequacy (40% Weighting): Assesses base benefit levels, net pension replacement rates, tax incentives, homeownership rates, and personal savings structures.
2. Sustainability (35% Weighting): Evaluates demographic dependency ratios, mandatory retirement ages, state debt levels, labor force participation among older workers, and economic growth potential.
3. Integrity (25% Weighting): Examines regulatory oversight, governance standards, plan communication, operational transparency, and systemic trust.

Systems earning an A-Grade (Score > 80) feature first-class, robust retirement frameworks that deliver comprehensive benefits with strong future viability. Conversely, systems receiving a D-Grade (Score 35–50) exhibit structural vulnerabilities that threaten future retiree welfare without urgent reform.

Global Pension Systems Index Comparison

Country	Overall Grade	Index Score	Adequacy Score	Sustainability Score	Integrity Score	Primary Architecture Type
Netherlands	A	85.4	85.6	82.4	89.1	Quasi-Mandatory Occupational / Public State
Iceland	A	83.5	82.7	84.6	86.0	Universal Mandatory Occupational & State
Denmark	A	81.6	81.1	82.5	81.4	Fully Funded Mandatory Occupational (ATP)
Singapore	A	80.5	79.8	74.0	88.5	Central Provident Fund (CPF) Mandatory Savings
Israel	A	80.2	73.6	76.1	83.9	Mandatory Pension Law & State Safety Net
United Kingdom	B	72.2	68.5	65.2	87.1	Auto-Enrolment Workplace & State Pension
United States	C+	61.1	63.9	60.1	59.5	Social Security + Voluntary 401(k)/IRA
Japan	C	56.3	60.2	46.5	68.1	Two-Tier Public System & Corporate Plans
Argentina	D	45.5	50.7	40.0	50.0	Pay-As-You-Go Public Pension
Philippines	D	42.7	38.9	52.5	35.0	Social Security System (SSS) & Private Plans
India	D	43.8	33.5	41.8	61.0	National Pension System (NPS) & Provident Fund

The World’s Top 5 Pension Frameworks (Grade A)

[Level 1: Universal Basic State Safety Net]
                 ↓
[Level 2: Mandatory Occupational / Workplace Pensions]
                 ↓
[Level 3: Voluntary Private Supplemental Savings]

1. Netherlands (Overall Score: 85.4)

The Dutch retirement system consistently sets the benchmark for global excellence. Combining a collective basic state pension (AOW) with quasi-mandatory, industry-wide occupational plans, the Netherlands yields net income replacement rates exceeding 80% for long-term workers. Extensive collective risk-sharing and stringent regulation by the Central Bank ensure high solvency and trust.

2. Iceland (Overall Score: 83.5)

Iceland’s system excels in long-term financial viability and labor participation. It relies on a multi-tiered framework comprising a basic state pension alongside mandatory occupational pension funds where both employers (minimum 11.5%) and employees (4%) contribute. Iceland maintains high labor force participation among workers aged 55 to 74, reinforcing systemic sustainability.

3. Denmark (Overall Score: 81.6)

Denmark relies on a basic public pension supplemented by fully funded occupational schemes (ATP) negotiated through collective labor agreements. High national savings rates, income redistribution for lower-wage earners, and transparent governance yield high marks across all three sub-indices.

4. Singapore (Overall Score: 80.5)

Reaching A-grade status for the first time in recent index evaluations, Singapore’s model centers around the state-administered Central Provident Fund (CPF). Mandatory contribution rates—up to 37% of wages split between employer and employee—are channeled into dedicated accounts for retirement, housing, and healthcare, delivering a high integrity rating.

5. Israel (Overall Score: 80.2)

Israel’s pension infrastructure combines a universal state old-age allowance with mandatory contributions to pension funds, provident funds, or insurance policies established under its Mandatory Pension Law. Strong capital accumulation and clear participant reporting underpin its top-tier status.

The World’s Struggling Pension Frameworks (Grade D)

India (Overall Score: 43.8)

India’s low score stems primarily from limited coverage within its large informal labor force. While the formal sector is served by the Employees’ Provident Fund Organisation (EPFO) and the National Pension System (NPS), the vast majority of workers lack access to formal retirement savings. According to World Bank Pension Data, expanding social pension safety nets for unorganized workers remains an urgent policy challenge.

The Philippines (Overall Score: 42.7)

The Philippine system, governed by the Social Security System (SSS) for private-sector workers and the Government Service Insurance System (GSIS) for public employees, faces challenges regarding benefit adequacy and regulatory integration. Low voluntary savings rates and limited coverage among self-employed individuals constrain its performance.

Argentina (Overall Score: 45.5)

Argentina’s pay-as-you-go (PAYGO) public pension structure has been heavily affected by high inflation, currency devaluation, and fiscal instability. Macroeconomic headwinds periodically erode the real purchasing power of monthly payouts, impacting its overall sustainability score.

Macro Trends Reshaping Retirement Security

   Demographic Aging           DB-to-DC Shift          Economic Volatility
(Higher Dependency Ratio)   (Risk Moves to Worker)    (Inflation & Debt)
           │                         │                         │
           └─────────────────────────┼─────────────────────────┘
                                     ▼
                     [Heightened Longevity & Savings Risk]

Data from the OECD Pensions at a Glance Report highlights three overarching structural pressures impacting pension systems worldwide:

1. Shift from Defined Benefit (DB) to Defined Contribution (DC): Governments and employers continue transitioning away from guaranteed DB pensions toward DC plans (like 401(k)s and superannuation). While this reduces liabilities for employers, it transfers market investment, inflation, and longevity risks directly to individual retirees.
2. Demographic Aging & Population Inversion: Extended life expectancies paired with declining fertility rates are compressing old-age dependency ratios. In many developed nations, the ratio of active workers supporting each retiree is projected to drop from 3.5:1 down to nearly 1.5:1 over the coming decades.
3. The Gender Pension Gap: Policy analysis by the World Economic Forum reveals that women face retirement benefit gaps of 20% to 35% compared to men globally. Career breaks for caregiving, lower lifetime earnings, and part-time employment patterns contribute to lower accumulated retirement balances.

Strategic Blueprint: Policy Recommendations for Reform

To enhance long-term retirement security, policy experts recommend five key structural interventions:

• Implement Auto-Enrolment: Introduce mandatory or auto-enrolment workplace pension schemes to broaden coverage among private and gig-economy workers.
• Increase Retirement Ages: Align statutory retirement ages with life expectancy projections to support system sustainability.
• Protect Minimum Benefits: Establish non-contributory basic pensions to protect low-income and informal workers from poverty in old age.
• Promote Financial Literacy: Provide accessible financial advice and clear, mandatory benefit statements to empower employees in managing Defined Contribution accounts.
• Phase Out Early Withdrawal Provisions: Restrict access to retirement funds prior to official retirement age to prevent capital depletion.

The 2026 AI governance conversation has largely been framed as a contest between great powers — the United States, China, and the European Union pursuing incompatible regulatory visions. That framing captures only part of the picture. A parallel and increasingly consequential dynamic involves non-state actors — from terrorist organizations exploiting open-weight AI models to civil society groups and industry consortia shaping the rules themselves — operating both as subjects of the emerging global guardrail push and, in some cases, as active participants in building it.

Key Takeaways

• The UN’s Global Dialogue on AI Governance and Independent International Scientific Panel on AI, launched from the 2024 Global Digital Compact, convened its first substantive session in Geneva in 2026 — described by the Council on Foreign Relations as a test of whether global AI governance can move beyond fragmented national approaches.
• A benchmark pilot by Tech Against Terrorism found that almost one-third of AI model responses provided meaningful uplift when prompted to assist malicious actors preparing terrorist activity, despite existing guardrails — and researchers assessed that guardrails are likely removable for all open-weight models, a structural, not merely operational, vulnerability.
• Recorded drone strike events rose 115-fold between 2018 and 2025, with 565 distinct armed groups — including non-state actors and criminal networks alongside state militaries — carrying out at least one drone attack in that period, according to the 2026 Global Peace Index.
• AI-enabled target-to-fire times have compressed from roughly a day using 1990s cruise missile systems to as little as five seconds with autonomous selection systems now in active use in conflicts including Ukraine — a compression the Global Peace Index explicitly warns is outpacing the international legal and diplomatic frameworks needed to govern it.
• A May 2026 terrorism case filed by India’s National Investigation Agency documented a defendant linked to al Qaeda in the Indian Subcontinent using YouTube and ChatGPT to learn improvised explosive device construction and mixture ratios — illustrating how AI reduces informational friction for less experienced non-state actors even as researchers note operational execution barriers remain significant.

Two Distinct Meanings of “Non-State Actors” in the 2026 AI Governance Debate

Precision matters here, because “non-state actors” spans two substantively different categories in current policy discourse, each with distinct guardrail implications.

Non-state actors as governance participants include large technology companies (Google, Meta, Microsoft, and others), multistakeholder organizations like the Partnership on AI, and civil-society watchdog groups such as the Civil Liberties Union for Europe — entities with formal or informal access to shape AI regulatory processes at the OECD, the EU, and increasingly at the UN level. Research on this dimension has found that large tech companies possess the monetary resources and technical expertise to actively promote their regulatory preferences within legislative and bureaucratic processes, while civil society organizations have played a documented, vocal role in specific negotiations — including the EU AI Act process.

Non-state actors as security threats include terrorist organizations, insurgent groups, criminal networks, and militias exploiting increasingly accessible AI capabilities to enhance operational effectiveness — the category most directly implicated in the “global guardrails” security debate, and the focus of the remainder of this analysis.

The Democratization Problem: Why Open-Source AI Changes the Threat Calculus

A recurring, structural concern across 2026 security research is that the proliferation of sophisticated open-source AI models has lowered the barrier to entry for non-state actors — including terrorist groups and armed militias — to acquire meaningful operational capability. Open-source and commercial foundation models can be repurposed relatively easily for military or paramilitary applications, a dynamic that Belfer Center research explicitly warns contributes to a “race to the bottom” on safety and reliability standards among both states and non-state actors competing for tactical or strategic advantage from early adoption.

The severity of this concern is directly quantified by a 2026 benchmark pilot from Tech Against Terrorism, which found that almost one-third of AI model responses provided meaningful uplift when prompted to assist malicious actors preparing terrorist activity — despite guardrails explicitly designed to prevent exactly this outcome. The research’s most strategically significant finding is not the uplift rate itself but the assessment that guardrails are probably removable for all open-weight models, meaning the release of a capable open-weight model is potentially catastrophically irreversible from a governance standpoint: once released, the safety measures built into the model cannot be reliably re-imposed by any subsequent regulatory action.

Documented Cases: From Tactical Planning to Explosive Device Instruction

The 2026 evidence base for non-state actor AI exploitation has moved from theoretical concern to documented case material. CSIS research cites a May 2026 charge sheet filed by India’s National Investigation Agency in connection with a November 2025 bombing in Delhi, in which the accused principal — linked to al Qaeda in the Indian Subcontinent — reportedly used YouTube and ChatGPT to learn how to construct an improvised explosive device and determine correct mixture proportions.

Separately, reporting drawing on the 2026 Global Terrorism Index describes AI reportedly helping a designated terrorist organization refine unit sizing, protect explosive components delivered by drone, and plan raids with greater tactical precision — part of a broader pattern the 2026 Global Peace Index frames not as AI inventing new categories of violence, but as making existing violence more efficient. This distinction matters directly for governance strategy: if AI is primarily an efficiency multiplier on existing threat patterns rather than a source of categorically new threats, the governance priority becomes controlling the technology’s diffusion pathways rather than searching for entirely novel threat vectors.

Importantly, CSIS research also notes meaningful operational limits remain: violence is difficult to execute successfully, and untrained individuals frequently fail during operational execution due to stress, inexperience, poor tradecraft, or logistical shortcomings that AI assistance does not eliminate. Advanced terrorist operations still typically require organizational trust, coordination, operational security, financing, and real-world experience that AI-generated technical instructions alone cannot substitute for.

The Speed Problem: Autonomous Systems and the Governance Gap

Beyond informational uplift for individual actors, the 2026 Global Peace Index identifies a second, more systemic non-state actor dynamic: the militarization of AI in ongoing conflicts, where target-to-fire times have compressed dramatically — from approximately a day using 1990s-era cruise missile systems to as little as five seconds with autonomous target-selection systems now in active use in conflicts including Ukraine. Recorded drone strike events rose 115-fold between 2018 and 2025, with 565 distinct armed groups — a category explicitly including non-state actors and criminal networks alongside state militaries — carrying out at least one documented drone attack during that period.

The Global Peace Index’s central warning is structural: this speed of technological change is arriving well ahead of the international legal and diplomatic frameworks needed to govern it, creating a widening gap between what autonomous and semi-autonomous systems can now do operationally and what international oversight mechanisms exist to meaningfully constrain their use — a gap that applies with particular force to non-state actors operating outside the state-level arms control and export regimes that, however imperfectly, still apply some constraint to national militaries.

The Institutional Response: Building Global Guardrails in 2026

The primary multilateral response to this landscape has centered on the UN’s Global Dialogue on AI Governance, launched from the 2024 Global Digital Compact alongside a companion Independent International Scientific Panel on AI. UN Secretary-General António Guterres has framed the effort’s core rationale directly: no single country can see the full picture of AI risk alone, and shared understanding is necessary to build effective guardrails, unlock AI’s benefits, and foster cooperation. The Global Dialogue represents, per Council on Foreign Relations analysis, a genuine test of whether international AI governance can move beyond fragmented, incompatible national approaches toward a more coordinated and inclusive form — though early indications suggest not all countries support this coordinated model equally, and the effort unfolds against a backdrop of the EU’s rights-based regulatory approach, the US’s preference for voluntary standards, and China’s emphasis on state control existing in active tension with one another.

This tension matters directly for the non-state-actor security dimension: a genuinely global guardrail regime capable of constraining terrorist or criminal exploitation of AI capabilities requires exactly the kind of coordinated, cross-jurisdictional cooperation that great-power regulatory competition currently undermines. Smaller and developing states, meanwhile, gain a formal voice in these new UN-backed forums but remain structurally dependent on the small number of major powers that control the bulk of global AI talent, capital, and computing infrastructure — limiting their practical influence over how any eventual guardrail regime is designed and enforced.

Implications for Foreign Policy and Technology Governance Stakeholders

• Open-weight model release policy deserves treatment as an irreversible governance decision, not an incremental product choice. Given the finding that guardrails are likely removable from any open-weight model post-release, policy frameworks evaluating AI model release should weight this irreversibility explicitly rather than treating open-weight releases as equivalent in risk profile to controllable, API-gated model access.
• Governance frameworks should target diffusion pathways, not solely model capability. Since the 2026 evidence base suggests AI is primarily amplifying existing non-state actor threat efficiency rather than creating unprecedented threat categories, policy resources may generate more impact by controlling how capable models reach less-resourced or less-vetted actors than by attempting to cap frontier model capability alone.
• The autonomous-systems governance gap requires urgency independent of the broader UN Dialogue timeline. With target-to-fire compression already operational in live conflicts and 565 armed groups (including non-state actors) already engaged in drone warfare, the multilateral governance process’s more deliberate, consensus-building pace may not match the operational speed of the threat it aims to address.
• Civil society and industry non-state actors remain a meaningful, underutilized lever for governance influence. Given documented civil-society influence in processes like the EU AI Act negotiation, foreign policy and technology governance stakeholders should treat multistakeholder engagement — not only formal state-to-state negotiation — as a genuine channel for shaping eventual global guardrail design.

Frequently Asked Questions

Are terrorist groups actually using AI for operational planning in 2026?

Yes, with documented cases: a May 2026 Indian terrorism case involved a defendant who used ChatGPT and YouTube to learn explosive device construction, and broader reporting describes AI assisting a designated terrorist organization with unit sizing and raid planning — though researchers note significant operational execution barriers remain independent of AI assistance.

Can AI safety guardrails be removed from open-source models?

Research from Tech Against Terrorism’s 2026 benchmark pilot assessed that guardrails are likely removable for all open-weight AI models, making open-weight model releases a potentially irreversible governance risk once safety measures can no longer be reliably enforced post-release.

What is the UN doing about global AI governance in 2026?

The UN launched the Global Dialogue on AI Governance and an Independent International Scientific Panel on AI, stemming from the 2024 Global Digital Compact, aiming to build coordinated international guardrails — though the effort operates amid significant tension between the EU’s rights-based, the US’s voluntary-standards, and China’s state-control regulatory approaches.

Conclusion

The 2026 non-state actor dimension of AI governance defies a simple narrative: the same technology lowering barriers for terrorist groups to plan attacks with greater precision is also, through civil society and multistakeholder participation, actively shaping the emerging global guardrail frameworks meant to constrain that very misuse. With autonomous weapons systems already compressing target-to-fire times to single-digit seconds in live conflicts, and open-weight model guardrails assessed as fundamentally removable once released, the core tension facing foreign policy and technology governance stakeholders is one of speed: whether the deliberate, consensus-driven pace of UN-backed multilateral coordination can keep pace with a threat landscape that is, by the clearest available evidence, evolving considerably faster.