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A gold speakerphone divides an AI policy chamber into opposing camps while an evidence ladder remains unfinished between them.
Law & informationUnited States+3 clusters01

A presidential speakerphone call turns AI safety into a culture-war test

President Donald Trump used a live speakerphone exchange with Nvidia’s chief executive at the All-In Summit to dismiss fears of an AI takeover as a hoax and argue that slowing the United States would help China. NBC News reports that Trump also praised data centers as a source of wealth while adding that development should proceed prudently. The outlet corrected an earlier description of the event: the call occurred during the industry summit, not an Nvidia all-hands meeting. ABC News places the exchange inside a widening policy split. OpenAI’s chief executive said his company would welcome a slower pace if capability risked outrunning alignment and monitoring, and backed consistent federal requirements, independent assessment, and incident reporting. The vice president acknowledged risks but warned that companies requesting regulation could be using it as a competitive Trojan horse. These are positions, not proof that catastrophe is imminent or that existing authority is sufficient. The deeper consequence is rhetorical. Once safety is framed as loyalty to national leadership or surrender to China, evidence can become subordinate to political identity. Frontier firms have commercial reasons to shape regulation, but that conflict does not invalidate every technical warning. A credible response would force both sides to name the capability, evidence, time horizon, and enforceable control under debate instead of treating all caution as sabotage or all acceleration as recklessness.

7 min
Competing AI accelerator controls are restrained by one shared safety belt while an independent evaluation badge remains outside the locked mechanism.
Systemic riskGlobal+3 clusters02

Frontier AI leaders back a slowdown, but shared concern still lacks shared rules

Leaders of several frontier AI companies are converging on an unusual claim: capability development may need to slow so evaluation, alignment, monitoring, and cybersecurity can catch up. Quartz reports support for a three-part approach built around embedded independent evaluators, common safety benchmarks and limits among leading laboratories, and government coordination that could eventually include narrower arrangements with China. The convergence is politically significant because these companies compete for talent, capital, customers, and strategic influence. It is not yet an enforceable pact. No shared capability threshold, inspection charter, disclosure duty, consequence for defection, or signed timetable has been published. Public comments also preserve important differences. Supporters say pacing is not a halt, while the White House has framed American leadership over China as the overriding priority and Chinese officials have dismissed some warnings as fear mongering. Forecasts about recursive self-improvement and future agent swarms remain expert judgments rather than measured deadlines. The immediate test is therefore institutional, not rhetorical. If outside evaluators receive continuous access, protected reporting, and authority to escalate material findings, the proposal could make safety evidence harder to curate. If companies retain control of the tests, the access, and the consequences, the agreement will remain a public signal rather than a brake.

7 min
Six red signal channels for information, cyber, data, industry, society, and warfare converge on a powerful national monitoring console.
Law & informationChina+3 clusters03

China’s security chief frames AI as a political, cyber, data and military risk

A Chinese-language report attributes a six-part AI risk framework to China’s state security minister. The categories are unusually broad: systemic effects on political security through synthetic media and automated influence; cheaper and faster cyberattacks; large-scale leakage of sensitive data; technology monopolies and widening international imbalance; structural shocks to social governance; and a fundamental transformation of warfare. The response described in the report is equally expansive, including risk monitoring and early warning, a national AI-security supervision platform, stronger domestic research and infrastructure, legal safeguards, public participation, and international cooperation. The framework captures real connections that fragmented policy can miss. Deepfakes, model-enabled cyber operations, data extraction, labor disruption, and autonomous weapons do not remain inside separate agencies once deployed at scale. Yet consolidation creates its own risk. A national security platform capable of monitoring information, data use, and AI activity could also deepen surveillance, political control, and opacity if independent challenge is weak. Provenance deserves caution: the supplied page is a secondary Chinese-language report that attributes the position to an essay in China Cyberspace magazine, but the original essay was not independently located during review. Treat this as a reported official position, not a complete primary policy text.

6 min
A frontier AI accelerator gauge approaches a red limit while an independent inspector opens a transparent access panel over the machine.
Systemic riskGlobal+3 clusters04

Frontier AI proposal calls for embedded evaluators and coordinated limits on capability growth

A new frontier-AI pacing proposal argues that model capability is advancing faster than safety work can reliably contain it. The author attributes that urgency to two developments: AI systems are increasingly helping build their successors, and recent agent incidents suggest that capable systems can pursue objectives in unanticipated, externally harmful ways. The proposal does not call for an immediate halt. It lays out three levels of restraint: frontier laboratories should give independent evaluators continuous, employee-like access; companies and democratic governments should coordinate common standards and limits on unchecked capability growth; and governments should pursue narrower, verifiable agreements with geopolitical rivals. The most consequential commitment is also the least theatrical. Anthropic says it will unilaterally begin the embedded-evaluator step. That could expose training-process risks and safety-policy violations earlier than release-day testing, but only if evaluators have independence, technical access, protected reporting, and authority when a laboratory resists scrutiny. The essay's forecast that a more capable agent swarm could create an internet-scale botnet within six to twelve months is an expert judgment, not a demonstrated timeline. Its account of recursive self-improvement is likewise a claim about direction and speed, not proof that runaway improvement has arrived. The correct response is neither dismissal nor panic. Treat pacing as a testable governance proposal: publish the thresholds, evaluator powers, incident rules, and evidence that would trigger a slowdown.

7 min
Several AI accelerator tracks converge at a polished agreement table while the enforcement rails beneath it remain visibly unfinished.
Systemic riskUnited States · Global+2 clusters05

OpenAI chief hints that leading AI companies may form a safety pact as frontier risks intensify

Fortune reports that OpenAI's chief executive expects leading AI companies to come together on safety, while declining to announce private discussions before a group is ready. The comments followed a proposal for slowing frontier capability growth and giving independent evaluators continuing access inside laboratories. The interview also framed the present moment as a practical limit: OpenAI was described as unwilling to push much further on capability without more progress in monitoring, alignment, and confidence that models will follow human intent. That is a significant statement from a company whose commercial position depends on continued capability leadership. It is not, however, a completed pact. No parties, shared thresholds, timetable, enforcement mechanism, or monitoring institution have been announced. Even the word slowdown remains undefined: it could mean delaying a release, limiting a class of training run, coordinating evaluation gates, or simply spending more time on safeguards while underlying research continues. The distinction matters because public agreement on danger can coexist with private incentives to move first. Company coordination may also require government involvement to avoid antitrust problems and to prevent dominant firms from writing safety rules that exclude smaller competitors. The useful next step is not another declaration of shared concern. It is a public term sheet: capabilities in scope, evidence required before scaling, evaluator access, incident disclosure, treatment of secret models, and automatic consequences when a member defects.

6 min
A presidential strategy console pushes an AI race lever toward maximum while a red risk gauge is left outside the operator's field of view.
Systemic riskUnited States · China+2 clusters06

President dismisses AI-extinction warnings and makes the race with China the overriding priority

Bloomberg reports that President Trump said he had no concern about AI leading to human extinction and identified maintaining the United States' lead over China as his paramount interest. The comment creates a clean political conflict with warnings from frontier researchers and executives who argue that capability growth is outrunning reliable control. It does not establish the full details of White House AI policy, and a brief exchange with reporters is not a technical risk assessment. It does reveal the decision frame likely to shape policy: restraint will be judged against the possibility that a strategic rival continues accelerating. That frame can support legitimate attention to model theft, chip controls, cyber defense, and verification of any international agreement. It can also become an all-purpose veto against safety measures. If every test, delay, disclosure duty, or access limit is described as surrendering the race, then the government has no operational threshold at which risk can outweigh speed. The result is a one-way ratchet: each new warning becomes evidence that the technology is important, and importance becomes the reason to accelerate. A serious national strategy must state both sides of the equation. Define which capabilities create unacceptable domestic or global exposure, what evidence triggers restraint, how the United States would verify rival compliance, and which safeguards can preserve a lead without converting competition into permission for uncontrolled deployment.

6 min
Renewable power lines cross African terrain toward a new data center while a transparent junction shows electricity splitting between the facility and nearby communities.
EnvironmentAfrica · United States · Europe+3 clusters07

Africa is pitched as the next AI-infrastructure frontier as power and permitting constrain mature markets

Fox News reports that American companies and United States officials are pursuing data-center, power, and connectivity projects across Africa as grid congestion, permitting disputes, environmental limits, and local opposition complicate expansion in the United States and Europe. The report points to a 6.2-billion-dollar data-center and hydropower project in Lesotho, as well as United States-supported infrastructure contracts in Gabon. Experts quoted in the article emphasize that Africa begins from a small base and is not positioned to replace American or European computing centers. The immediate opportunity is more local: rising African demand for cloud services, domestic storage of sensitive data, new undersea connections, and projects that combine computing with electricity generation. That opportunity carries a familiar distribution question. Land, power, water, public finance, and data sovereignty can create durable local capacity, or they can be arranged primarily around foreign compute demand and vendor control. Weak grids also mean that a large facility can compete with households and existing businesses unless generation and transmission expand first. The report says South Africa lacks a public data-center register and binding disclosure of water, electricity, and land use. That is reported expert criticism, not a continent-wide regulatory assessment. African countries are not one market, and the source does not establish that promised projects will be financed, completed, or deliver broad local benefit. The right measure is not headline investment. It is local power added, skilled employment created, data governed, taxes retained, and costs made public.

7 min
Two competing AI laboratory tracks accelerate toward a red threshold while researchers stand beside an unused emergency brake.
Systemic riskUnited States+3 clusters08

Frontier AI insiders call for a slowdown as extinction warnings intensify

CNBC reports that researchers at OpenAI and Anthropic are publicly calling for slower AI development after a departing researcher accused the laboratories of gambling with human lives. The report cites an Anthropic alignment leader's personal estimate of a greater than 10% chance of human extinction this decade, other employees warning about recursively self-improving systems, and an OpenAI chief scientist calling for extreme caution as AI begins to accelerate parts of AI research. Roughly 1,400 researchers reportedly signed a July letter urging the U.S. government to build tools for deliberately pacing automated frontier development. These statements are important evidence about concern inside the institutions building the systems. They are not a scientific measurement of extinction probability. The forecasts use uncertain definitions, undisclosed assumptions, and timelines that cannot be validated from public comments. The contradiction is institutional: laboratories describe potentially irreversible danger while competition, fundraising, product schedules, and expected public listings keep the race moving. Concern becomes governance only when it controls a decision. A credible slowdown proposal needs measurable capability triggers, independent evaluations, coordinated coverage across major developers, and a named authority that can impose or verify a pause. Without those elements, public warnings may raise awareness while leaving the operating system of the race untouched. The question is not whether one dramatic percentage is correct. It is why a stated double-digit catastrophic risk does not automatically activate a reviewable safety process.

6 min
A transparent national safety control panel links independent evidence, incident reporting, and a time-limited stop switch to a frontier AI laboratory.
Law & informationUnited States+3 clusters09

OpenAI backs mandatory frontier AI rules and explicit stop thresholds

OpenAI says the United States needs mandatory, capability-based national regulation for the most powerful AI systems. Its proposal calls for common testing, independent assessment, stronger cybersecurity, clear incident reporting, national preparedness, and shared measures of progress toward recursive self-improvement. The company says governments should establish safety bars for when development must slow or stop and that safety should take priority if those bars cannot be met without reducing capability growth. It also supports four California bills covering independent assessors, auditor standards, youth protections, and safeguards against AI-enabled biological threats while arguing that states should fill the vacuum until Congress acts. This is a significant policy shift because the company explicitly says voluntary commitments are insufficient. It is still an interested proposal from a frontier laboratory. Capability-based rules can be written to exclude rivals, convert current scale into a regulatory moat, or let a developer satisfy a process without surrendering final deployment authority. OpenAI also says most open models should not be treated as frontier systems, a distinction that requires transparent and revisable thresholds. The decisive test is enforcement architecture: who receives protected evidence, which incidents trigger notice or a temporary hold, whether affected parties can challenge a finding, and what proof allows work to resume. A national framework should reduce private control over safety judgments, not merely give private judgments a federal label.

6 min
An abandoned research badge lies between two accelerating AI laboratories racing toward the same red danger line.
Systemic riskUnited States+2 clusters10

A departing frontier researcher says the AI race is gambling with human lives

A researcher who spent three years on model pretraining at OpenAI and Anthropic has left the AI industry with a severe warning. Euronews reports that Jacob Coxon accused both laboratories of racing toward self-improving superintelligence without acting responsibly. His distinctive claim is not merely that advanced AI could be dangerous. It is that employees understand catastrophic stakes privately yet continue because each company believes it must arrive first to prevent a less responsible rival from controlling the technology. That describes a coordination failure: individually rational competition can create a collectively unacceptable risk even when participants share the same fear. Coxon's resignation is evidence that this conflict is serious enough to change one insider's career. It is not proof that a self-improving system will emerge on his proposed timeline or that catastrophe is likely. His public thread does not provide model evaluations, incident records, capability thresholds, or a causal forecast that independent analysts can reproduce. The response should therefore avoid two easy mistakes. Dismissing the warning as marketing ignores the cost of resignation and the insider's access. Treating it as a measured probability turns testimony into science it is not. The actionable question is institutional: what shared rules would let one laboratory slow down without simply transferring advantage to another? Predeclared capability thresholds, confidential cross-lab evaluation, mandatory incident reporting, and coordinated pauses can convert fear into a testable governance proposal.

5 min
An international assembly surrounds a black-glass AI core pressing against an incomplete protective ring.
Systemic riskGlobal+2 clusters11

UN rights chief demands hard guarantees for advanced AI

The UN human-rights chief has brought the most severe frontier AI warning into the Human Rights Council. Reuters reports that he said advanced AI could become powerful enough to threaten humanity, that he shares the concerns of industry insiders about existential risk, and that companies should reduce those risks. He called for an all-out effort to establish strong guarantees around AI safety and security before it is too late. The statement is important, but it is not evidence that extinction is imminent. The Reuters account does not identify a probability, timeline, causal mechanism, evaluation method, or enforcement body. Those gaps determine whether the warning becomes governance or remains rhetoric. A meaningful guarantee must name the systems and capabilities in scope, the tests they must pass, the evidence independent reviewers can inspect, the thresholds that force intervention, and the authority that can act across borders. A human-rights frame should also prevent distant catastrophic scenarios from crowding out harms people already experience through surveillance, discrimination, manipulation, unsafe advice, and denial of remedy. The two levels are connected by institutional power: who can deploy a consequential system, who receives notice when it fails, and who can stop or challenge it. The Human Rights Council's 63rd session runs from September 7 to October 7, creating a forum for states to turn the warning into proposals. The standard of success should be operational. Companies should not be allowed to satisfy a demand for safety guarantees with voluntary language that cannot be tested, compared, or enforced.

4 min
A red emergency lever divides a frontier computing core, a barred legal gate, and a pathway extending toward a world map.
Law & informationUnited States+3 clusters12

A U.S. bill would ban superintelligence and threaten 20-year prison terms

A proposed U.S. law would turn the frontier AI safety debate into a prohibition backed by some of the strongest penalties available to government. The Ban Artificial Superintelligence Act would permanently ban developing or deploying systems that surpass human intelligence or can overthrow governments, subvert shutdown commands, or execute unauthorized cyberattacks. It would also pause advanced AI development until a new cabinet-level regulator establishes safety rules and model review. Entities that circumvent the restrictions could face a corporate death penalty, meaning loss of legal authority to conduct business, while individuals could receive prison terms of as much as 20 years. Critics quoted by Fox argue that a unilateral U.S. ban could hand an advantage to China or Russia. The bill itself calls for international agreements, allied coordination, and export controls. But geopolitical competition is not a safety test. The deeper design problem is scope. Human-level intelligence is a contested threshold, while the named dangerous behaviors are more concrete and potentially testable. Any workable regime needs precise capability definitions, independent evaluation, due process, appeal rights, international verification, and penalties tied to intentional or reckless circumvention. A law this severe should not depend on a slogan that regulators, companies, and courts cannot measure consistently.

5 min
A vast line of graduates reaches a broken entry-level career ladder while a narrow AI-specialist gate glows above it.
Work & marketsChina+2 clusters13

China's graduates face an AI squeeze at the first rung of work

A record 12.7 million graduates are expected to enter China's workforce this year as artificial intelligence begins changing the entry-level work that traditionally turns education into experience. The New York Times reports that urban unemployment among 16- to 24-year-olds reached 17.9 percent in July. Graduates described submitting hundreds or thousands of applications, receiving few interviews, and watching employers demand either specialized AI expertise or prior experience for junior roles. AI-related opportunities are growing, but they are concentrated among candidates who already possess scarce technical skills. At the same time, administrative work, research, basic analysis, design preparation, and coding are increasingly susceptible to automation. Those tasks are not only outputs; they are how new workers build judgment and become senior workers. The causal limit is essential. AI did not create the underlying imbalance. China's slowing economy, contraction in sectors that once absorbed graduates, and decades of higher-education expansion already left too many candidates chasing too few desirable jobs. White-collar automation is only beginning, and individual accounts cannot measure its national employment effect. The immediate institutional question is whether firms will use AI productivity to train more people or to remove the first rung and demand experience that nobody is willing to provide. Government and employers should track first-job hiring, paid apprenticeships, time to permanent work, wage progression, and employer-funded training alongside AI vacancy counts. A labor transition is not successful because a premium group of specialists earns more. It succeeds when ordinary graduates can still enter, learn, and build durable careers.

5 min
A paper-cut global negotiating table balances a thin AI rulebook against an independent safety test and existing law volumes.
Law & informationGlobal+3 clusters14

The United States is asking the G20 to make new AI rules the exception

The United States used a G20 meeting in North Carolina to promote a lighter-touch approach to AI governance. Its Carolina Principles urge governments to apply existing laws first, preserve foundational research and commercial opportunity, and reserve new AI-specific regulation for genuinely novel problems. The U.S. position also argues against creating new AI oversight bodies. Reuters reporting cited by TechRadar says China signed on, suggesting that regulatory restraint may become an unusual point of agreement between two competing AI powers. The event did not produce a single industry position. Some technology leaders criticized European rules, while support for safety testing remained visible. That disagreement reveals the standard the debate needs. The number of rules is less important than whether an institution can identify risk, obtain technical evidence, investigate incidents, assign responsibility, and compel remediation. Existing consumer, competition, employment, civil-rights, safety, and sectoral laws may cover many AI harms, but coverage on paper is not enforcement capacity. A light-touch framework needs a hard evidentiary spine: clear jurisdiction, independent evaluation access, mandatory reporting for serious incidents, cross-border coordination, and remedies strong enough to change deployment behavior. Otherwise, regulatory restraint becomes an untested promise made by the parties with the greatest incentive to accelerate.

5 min
A massive data center looms behind a town ballot box while electricity bills, water gauges, and campaign signs converge in a tense public meeting.
EnvironmentUnited States+3 clusters15

AI data-center costs are becoming an election issue

CNBC reports that the backlash against AI data centers has moved into elections, campaign advertising, and political strategy. The conflict is not only about whether voters like artificial intelligence. Communities are confronting the physical and financial terms of the buildout: rising electricity demand, grid upgrades, water use, land, noise, tax incentives, and doubts about whether permanent jobs and local benefits match the scale of public support. The White House and technology industry frame rapid construction as necessary for economic growth and competition with China, while candidates in both parties are finding that local voters want developers to pay their own way and accept enforceable conditions. Treating the resistance as a public-relations problem misses the power shift. A data center is a long-lived industrial decision with concentrated local effects, and national ambition does not erase municipal consent. Developers should disclose expected power and water demand, fund attributable infrastructure, protect existing customers from rate increases, publish credible employment commitments, and negotiate benefits that survive after construction. The political risk will keep growing wherever communities are asked to absorb costs before they can verify the value.

5 min
An analog labor-market dossier contrasts a sharply rising AI adoption chart with layoff notices, reduced pay, and a worker rebuilding a career plan.
Work & marketsChina+3 clusters16

China’s AI push is remaking jobs faster than workers can plan

Associated Press reporting from China documents workers adapting to AI while layoffs, lower pay, and a slowing economy make the transition unusually hard. A Beijing programmer said his boss asked whether AI could replace coding work; two weeks later he and roughly 160 colleagues were laid off. A part-time translator who now helps train AI said industry pay had fallen by more than half compared with years earlier. IDC data cited by AP says the share of Chinese industrial enterprises reporting use of AI models and agents rose to 47.5 percent last year from 9.6 percent in 2024. The effects are uneven: AI creates some training and independent-work opportunities, while workers in narrowly concentrated roles face displacement. China’s housing downturn, weak consumption, record graduate competition, and an aging population make it wrong to attribute every labor problem to AI. But rapid state-backed diffusion is changing tasks and bargaining power before workers can rely on stable retraining or replacement careers. Productivity policy needs income, mobility, and job-quality metrics, not adoption totals alone.

6 min
Streams of anonymous chatbot conversations flow through a city-scale AI foundry while governance gates control access to the data.
PrivacyChina+4 clusters17

China is turning chatbot data into a strategic AI advantage

The New York Times examines how China's data and chatbot ecosystem is becoming part of the country's strategic AI position. The central issue is larger than model performance. Conversational systems can concentrate enormous volumes of behavioral signals, preferences, corrections, and usage patterns, turning ordinary interactions into inputs with commercial and state value. More data does not automatically mean better intelligence, and the details of collection, access, and use determine whether an apparent advantage is sustainable or legitimate. The competitive frame can also obscure individual rights. Every chatbot data strategy should answer what information is retained, under whose authority, for which purposes, how it is protected, and whether a person can inspect or contest its use. An AI race measured only by scale risks rewarding the least accountable system rather than the most capable or trustworthy one.

5 min
A military AI command network stalls at a contract gate while a rival autonomous systems corridor advances in the distance.
SecurityUnited States and China+3 clusters18

America's military AI ambition is colliding with its own feud and China's advance

The New York Times reports that the United States military wants artificial-intelligence dominance but may be undermined by internal conflict and rapid Chinese competition. The dispute with Anthropic captures the structural problem. The Pentagon wants models available for any lawful military use, while the company has sought restrictions around mass domestic surveillance and fully autonomous weapons. Earlier punishment and offboarding threats made a leading model provider part of the strategic risk rather than a stable partner. China faces a different political structure and can align state, military, and industrial goals more directly, even as that model creates its own accountability and rights dangers. The United States should not imitate authoritarian command to compete. It needs durable law, faster secure integration, common evaluation standards, procurement that can support more than one vendor, and red lines set by democratic institutions rather than by either a private chief executive or a defense official. Military speed without legitimacy can create brittle capability.

5 min
A voter casts a ballot in front of a vast artificial intelligence data center, power lines, utility infrastructure, and concerned community members.
Law & informationUnited States+3 clusters19

AI data centers are becoming an election issue because voters can see the bill

The New Yorker argues that AI is now a major election issue, highlighting Michigan opposition to data centers. The accessible evidence supports a narrower claim than simple electoral causation. Planet Detroit reported before the primary that candidates were already debating power rates, water, tax breaks, jobs, public-utility treatment, nondisclosure agreements, and local control. Associated Press coverage shows a hard-fought contest shaped by multiple differences between the candidates. It would be wrong to say data-center opposition alone decided the result. It is fair to say AI infrastructure has crossed into ordinary electoral politics because communities now experience it through construction, environmental permits, utility systems, and public subsidies rather than only through software products.

5 min
A red security barrier divides Chinese robots and power inverters from a glowing United States AI data-center buildout.
Work & marketsUnited States and China+5 clusters20

The U.S. AI race now runs through robots and power hardware

The Trump administration is moving to bar new Chinese-made robots and power inverters from the U.S. market, Reuters reports, framing connected machines and energy-control equipment as risks to the domestic AI buildout. The policy makes the physical stack impossible to ignore: AI depends not only on chips and models, but also on robots, grid-connected electronics, factories, supply chains, and trusted software updates. Security may justify tighter controls, but restrictions also change prices, competition, deployment speed, and the industrial capacity needed to replace excluded suppliers.

3 min
A UK network map with 41.3 percent of AI entities concentrated around London and smaller regional clusters consolidating toward 2030.
Work & marketsUnited Kingdom+2 clusters21

Ashraf, Coyle and Debnath, “Code, capital, and clusters: understanding firm performance in the UK AI economy”

A study combining Companies House, Office for National Statistics, and glass.ai data on UK AI entities from 2000–2024 finds that 41.3% are concentrated in London. Firm size and the intensity of AI specialization are the main revenue drivers, while local qualification rates, population density, and employment make smaller but significant contributions. Forecasts point to 4,651 entities by 2030, alongside slower expansion and a rising dissolution ratio that the authors interpret as a move toward consolidation.

3 min
Work & marketsGlobal+2 clusters22

BIS Annual Economic Report 2026

The Bank for International Settlements released its flagship Annual Economic Report 2026, warning that the sustainability of the AI boom is now one of the major pressure points for the global economy. BIS says AI-related investment and productivity expectations helped keep financial conditions favorable, but warns that the capital-expenditure surge could become unsustainable if supply bottlenecks restrain production or if market-leadership competition drives overinvestment.

2 min