Scored 274 articles from 95 feeds; 15 included in digest.
Run ID: run-1784661387707
Generated: July 21, 2026 at 03:35 PM ET
Summaries: claude-sonnet-4-6; enrichment 15/15 succeeded
| Source | Type | Included | Scored | 28d Digest Rate | 28d Avg Score | 28d Hotlist Hit | 7d Article Age | 28d Confidence |
|---|---|---|---|---|---|---|---|---|
| Tom’s Hardware | news | 5 | 22 | 11% | 0.14 | 3% | 7.5h | Stable |
| Wired AI News | news | 2 | 7 | ~5% | ~0.16 | ~2% | 9.3h | Low sample |
| Hacker News | commentary | 1 | 25 | 4% | 0.07 | 0% | 8.9h | Stable |
| NYT front page | news | 1 | 23 | 2% | 0.03 | 0% | 4.7h | Stable |
| WSJ US Business | news | 1 | 22 | 5% | 0.12 | 1% | 6.4h | Stable |
| MyFT | news | 1 | 17 | 10% | 0.12 | 0% | 3.6h | Stable |
| TechCrunch | news | 1 | 17 | 11% | 0.17 | 0% | 9.4h | Stable |
| Reddit AntiAI | news | 1 | 14 | 5% | 0.08 | 2% | 8.5h | Stable |
| Medium Artificial Intelligence (keyword) | commentary | 1 | 10 | 20% | 0.16 | 0% | 0.6h | Stable |
| Venture Beat | commentary | 1 | 1 | ~75% | ~0.48 | ~0% | 10.3h | Low sample |
| Guardian | news | 0 | 25 | 1% | 0.03 | 0% | 8.6h | Stable |
| Bloomberg Markets | news | 0 | 17 | 4% | 0.09 | 0% | 4.4h | Stable |
| The Verge | news | 0 | 10 | 3% | 0.09 | 0% | 9.7h | Stable |
| ZD Net | news | 0 | 10 | 4% | 0.05 | 0% | 9.6h | Stable |
| Futurism | news | 0 | 8 | 13% | 0.14 | 3% | 7.5h | Stable |
| Medium AI (keyword) | commentary | 0 | 7 | 12% | 0.15 | 0% | 0.6h | Stable |
| Seeking Alpha News | commentary | 0 | 7 | 5% | 0.11 | 1% | 1.2h | Stable |
| WSJ Tech | news | 0 | 6 | 13% | 0.19 | 1% | 7.5h | Stable |
| Ars Technical All News | news | 0 | 5 | 8% | 0.10 | 0% | 8.7h | Stable |
| SEC Speeches Statements | policy_release | 0 | 3 | Collecting data | Collecting data | Collecting data | 6.7h | Collecting |
| Secure List | news | 0 | 2 | Collecting data | Collecting data | Collecting data | 8.3h | Collecting |
| WSJ Social Economy | news | 0 | 2 | 5% | 0.11 | 0% | 6.5h | Stable |
| Ars Technica All Features | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 8.6h | Collecting |
| Debt Serious | commentary | 0 | 1 | Collecting data | Collecting data | Collecting data | 10.1h | Collecting |
| Economist: Business | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 5.0h | Collecting |
| Economist: China | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 3.9h | Collecting |
| Economist: Finance & Economics | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 6.1h | Collecting |
| Economist: Leaders | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 10.7h | Collecting |
| El Reg Offbeat | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 19.8h | Collecting |
| FT Alphaville | news | 0 | 1 | ~3% | ~0.11 | ~0% | 6.0h | Low sample |
| Grumpy Economist (Cochrane) | commentary | 0 | 1 | Collecting data | Collecting data | Collecting data | 4.0h | Collecting |
| Hugging Face | commentary | 0 | 1 | Collecting data | Collecting data | Collecting data | 5.7h | Collecting |
| IEEE AI | research | 0 | 1 | Collecting data | Collecting data | Collecting data | 7.7h | Collecting |
| MIT Research General | research | 0 | 1 | Collecting data | Collecting data | Collecting data | 10.6h | Collecting |
| NYT Economy | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 3.1h | Collecting |
| a16z | other | 0 | 1 | Collecting data | Collecting data | Collecting data | 5.5h | Collecting |
Source: Tom’s Hardware
Type: news
Included: 5
Scored: 22
28d Digest Rate: 11%
28d Avg Score: 0.14
28d Hotlist Hit: 3%
7d Article Age: 7.5h
28d Confidence: Stable
Source: Wired AI News
Type: news
Included: 2
Scored: 7
28d Digest Rate: ~5%
28d Avg Score: ~0.16
28d Hotlist Hit: ~2%
7d Article Age: 9.3h
28d Confidence: Low sample
Source: Hacker News
Type: commentary
Included: 1
Scored: 25
28d Digest Rate: 4%
28d Avg Score: 0.07
28d Hotlist Hit: 0%
7d Article Age: 8.9h
28d Confidence: Stable
Source: NYT front page
Type: news
Included: 1
Scored: 23
28d Digest Rate: 2%
28d Avg Score: 0.03
28d Hotlist Hit: 0%
7d Article Age: 4.7h
28d Confidence: Stable
Source: WSJ US Business
Type: news
Included: 1
Scored: 22
28d Digest Rate: 5%
28d Avg Score: 0.12
28d Hotlist Hit: 1%
7d Article Age: 6.4h
28d Confidence: Stable
Source: MyFT
Type: news
Included: 1
Scored: 17
28d Digest Rate: 10%
28d Avg Score: 0.12
28d Hotlist Hit: 0%
7d Article Age: 3.6h
28d Confidence: Stable
Source: TechCrunch
Type: news
Included: 1
Scored: 17
28d Digest Rate: 11%
28d Avg Score: 0.17
28d Hotlist Hit: 0%
7d Article Age: 9.4h
28d Confidence: Stable
Source: Reddit AntiAI
Type: news
Included: 1
Scored: 14
28d Digest Rate: 5%
28d Avg Score: 0.08
28d Hotlist Hit: 2%
7d Article Age: 8.5h
28d Confidence: Stable
Source: Medium Artificial Intelligence (keyword)
Type: commentary
Included: 1
Scored: 10
28d Digest Rate: 20%
28d Avg Score: 0.16
28d Hotlist Hit: 0%
7d Article Age: 0.6h
28d Confidence: Stable
Source: Venture Beat
Type: commentary
Included: 1
Scored: 1
28d Digest Rate: ~75%
28d Avg Score: ~0.48
28d Hotlist Hit: ~0%
7d Article Age: 10.3h
28d Confidence: Low sample
Source: Guardian
Type: news
Included: 0
Scored: 25
28d Digest Rate: 1%
28d Avg Score: 0.03
28d Hotlist Hit: 0%
7d Article Age: 8.6h
28d Confidence: Stable
Source: Bloomberg Markets
Type: news
Included: 0
Scored: 17
28d Digest Rate: 4%
28d Avg Score: 0.09
28d Hotlist Hit: 0%
7d Article Age: 4.4h
28d Confidence: Stable
Source: The Verge
Type: news
Included: 0
Scored: 10
28d Digest Rate: 3%
28d Avg Score: 0.09
28d Hotlist Hit: 0%
7d Article Age: 9.7h
28d Confidence: Stable
Source: ZD Net
Type: news
Included: 0
Scored: 10
28d Digest Rate: 4%
28d Avg Score: 0.05
28d Hotlist Hit: 0%
7d Article Age: 9.6h
28d Confidence: Stable
Source: Futurism
Type: news
Included: 0
Scored: 8
28d Digest Rate: 13%
28d Avg Score: 0.14
28d Hotlist Hit: 3%
7d Article Age: 7.5h
28d Confidence: Stable
Source: Medium AI (keyword)
Type: commentary
Included: 0
Scored: 7
28d Digest Rate: 12%
28d Avg Score: 0.15
28d Hotlist Hit: 0%
7d Article Age: 0.6h
28d Confidence: Stable
Source: Seeking Alpha News
Type: commentary
Included: 0
Scored: 7
28d Digest Rate: 5%
28d Avg Score: 0.11
28d Hotlist Hit: 1%
7d Article Age: 1.2h
28d Confidence: Stable
Source: WSJ Tech
Type: news
Included: 0
Scored: 6
28d Digest Rate: 13%
28d Avg Score: 0.19
28d Hotlist Hit: 1%
7d Article Age: 7.5h
28d Confidence: Stable
Source: Ars Technical All News
Type: news
Included: 0
Scored: 5
28d Digest Rate: 8%
28d Avg Score: 0.10
28d Hotlist Hit: 0%
7d Article Age: 8.7h
28d Confidence: Stable
Source: SEC Speeches Statements
Type: policy_release
Included: 0
Scored: 3
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 6.7h
28d Confidence: Collecting
Source: Secure List
Type: news
Included: 0
Scored: 2
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 8.3h
28d Confidence: Collecting
Source: WSJ Social Economy
Type: news
Included: 0
Scored: 2
28d Digest Rate: 5%
28d Avg Score: 0.11
28d Hotlist Hit: 0%
7d Article Age: 6.5h
28d Confidence: Stable
Source: Ars Technica All Features
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 8.6h
28d Confidence: Collecting
Source: Debt Serious
Type: commentary
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 10.1h
28d Confidence: Collecting
Source: Economist: Business
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 5.0h
28d Confidence: Collecting
Source: Economist: China
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 3.9h
28d Confidence: Collecting
Source: Economist: Finance & Economics
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 6.1h
28d Confidence: Collecting
Source: Economist: Leaders
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 10.7h
28d Confidence: Collecting
Source: El Reg Offbeat
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 19.8h
28d Confidence: Collecting
Source: FT Alphaville
Type: news
Included: 0
Scored: 1
28d Digest Rate: ~3%
28d Avg Score: ~0.11
28d Hotlist Hit: ~0%
7d Article Age: 6.0h
28d Confidence: Low sample
Source: Grumpy Economist (Cochrane)
Type: commentary
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 4.0h
28d Confidence: Collecting
Source: Hugging Face
Type: commentary
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 5.7h
28d Confidence: Collecting
Source: IEEE AI
Type: research
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 7.7h
28d Confidence: Collecting
Source: MIT Research General
Type: research
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 10.6h
28d Confidence: Collecting
Source: NYT Economy
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 3.1h
28d Confidence: Collecting
Source: a16z
Type: other
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 5.5h
28d Confidence: Collecting
Chinese AI developer Z.ai (formerly Zhipu), the developer behind the GLM language model, has completed construction of a 1-gigawatt AI data center built entirely on domestically produced Chinese chips, with part of the facility now operational, according to a report cited by Tom's Hardware. The company reportedly runs multiple clusters of 10,000 chips each, with no Nvidia hardware involved.
Keywords: AI infrastructure, domestic chips, supply chain diversification, data center capacity, geopolitical competition, Nvidia alternative
Intel is planning layoffs within its Data Center group, the division responsible for server CPUs, AI chips, and data center architecture, according to Tom's Hardware. The number of cuts has not been disclosed. The report notes the planned reductions come months after the company announced record growth, following what was described as a disastrous 2024.
Keywords: Intel, layoffs, Data Center, server CPUs, AI chips, headcount reduction
Iranian state media and the Islamic Revolutionary Guard Corps (IRGC) claim to have struck and destroyed an Amazon Web Services (AWS) data center in Bahrain using cruise missiles, framing the attack as retaliation for alleged U.S. strikes on an under-construction Iranian nuclear plant. According to the article, the Amazon site has sustained multiple hits since the start of the U.S. bombing campaign in Iran. However, the article notes that Amazon had already moved operations off the Bahrain facility beginning in early April, qualifying the practical impact of the IRGC's claim to have destroyed it.
Keywords: Amazon, AWS, data center, Bahrain, Iran, military strike, critical infrastructure, cloud computing
At VB Transform 2026 in Menlo Park, Xavi Amatriain, Expedia Group's chief AI and data officer, argued that evaluation frameworks ('evals') are replacing traditional product requirements documents (PRDs) as the primary way teams specify what AI systems should do. Amatriain said that as AI-generated code becomes the norm, the core intellectual work of product development will shift to designing evals—including red-teaming and security requirements—before coding begins. On governance, Amatriain described Expedia's three-layer approach—principles, processes and tools, and automation—and a system of 'agent release toll gates' that calibrate the depth of review, red-teaming, and security checks to each agent's risk level. He characterized guardrails as a 'necessary evil' that can distort user feedback loops, arguing that sound design principles reduce the need for after-the-fact guardrails. Other speakers at the event disagreed, contending that high-risk actions still require firm guardrails. Amatriain outlined Expedia's preference for composed, specialized agents over monolithic models, with tools building into skills, then sub-agents, then full agentic systems. He said Expedia's travel AI blends retrieval-augmented generation with direct API calls based on latency needs, and that the agent recommends but does not autonomously complete bookings—a design choice he framed as both a user-agency decision and a security measure. On threats, Amatriain warned that AI systems will increasingly face attacks from other AI agents and stressed that rapid detection and remediation cycles, fed by production monitoring looped back into evals, will be critical. The article cites VentureBeat Pulse research showing 66% of 157 enterprises surveyed allow some production AI deployment without human review, yet only 5% fully trust automated evaluations, and more than half of 107 enterprises surveyed separately have experienced an agent security incident or near-miss.
Keywords: evaluation-driven design, product development workflows, AI governance, risk-calibrated tollgates, composable agents, automated evaluation, organizational adaptation, security-by-design, AI-to-AI threats, agentic systems, internal decision-making, business process change
A New York Times report describes how Meta deployed artificial intelligence to ban accounts on Facebook and Instagram, with some users saying the technology mistakenly deleted their accounts. According to the article, users who sought to resolve these errors still had to rely on AI systems to do so, rather than human support.
Keywords: AI moderation, automated account enforcement, false positives, customer service automation, AI governance, Facebook, Instagram, account deletion, AI error resolution
Altana, a trade technology company, has acquired Cervo AI, an artificial intelligence platform designed to handle customs brokerage tasks. Altana says the acquisition will help speed up customs processes as tariff and trade policies continue to shift.
Keywords: AI automation, customs brokerage, trade technology, tariff policy, operational efficiency, business acquisition
Nvidia held a technical briefing for journalists at its Santa Clara headquarters to detail performance claims for its upcoming Vera Rubin chip system, timing the disclosure ahead of AMD's annual product event. Vera Rubin is Nvidia's successor to its Grace Blackwell hybrid superchip and is designed with a one CPU-to-two GPU ratio; a full NVL72 system pairs 36 Vera CPUs with 72 Rubin GPUs. The company says the system will deliver 10 times more tokens per watt than Grace Blackwell, nearly three times the memory bandwidth, and significantly reduced cabling, which Nvidia is marketing as enabling faster rack installation. The Vera CPU uses a monolithic chip design rather than the chiplet architecture common among competitors, which Nvidia argues improves memory bandwidth efficiency. Nvidia is also selling the Vera CPU as a standalone product and has reportedly indicated it could be available to Chinese customers as soon as August. Early customers for the full system include Microsoft, OpenAI, and Oracle, and OpenAI is said to already have one Vera Rubin rack in use. Nvidia CEO Jensen Huang did not attend the briefing; presentations were led by VP Ian Buck and SVP Andrew Bell. The article notes that Nvidia's benchmarks comparing the Vera CPU against AMD and Intel rivals appeared to use slightly older generations of those competitors' chips. The briefing comes as both Nvidia and AMD compete for large multiyear contracts with major AI hyperscalers and labs.
Keywords: Nvidia, Vera Rubin platform, CPU-GPU integration, AI infrastructure, vertical integration, data center chips, hardware consolidation
Etsy sellers are leaving the platform in growing numbers, citing frustration over an influx of AI-generated and drop-shipped products that they say undercuts handmade goods and erodes buyer trust. The article profiles several longtime sellers who report steep sales declines, including a watercolor artist who says her Etsy revenue fell 30 percent in 2022 and 50 percent the following year, and a UK-based illustrator who reports a 98 percent drop over the past year. Sellers say AI-generated listings frequently go unlabeled despite Etsy's 2024 policy requiring disclosure, and that the company is largely unresponsive to complaints. Etsy's vice president of customer operations acknowledged enforcement is 'complex' and said the company is working to detect and remove non-compliant listings. Etsy reported $2.8 billion in revenue last year, a 2.7 percent increase, and has approximately 5.6 million sellers, though it laid off 11 percent of its workforce in late 2023 and saw declining gross merchandise sales for several years before recent quarterly growth. A new marketplace called Fybe, set to open to vendors August 1, explicitly bans AI-generated content and drop-shipping and plans to use volunteer moderators and direct seller communication to enforce those rules. Displaced sellers who have moved to platforms like Shopify report that driving their own traffic and sales is more difficult than operating within an established marketplace community.
Keywords: Etsy, mass-produced goods, AI-generated content, marketplace competition, seller attrition, platform identity drift, e-commerce
Nvidia VP Ian Buck has stated the company has shipped 'hundreds of thousands' of Grace standalone servers, building on an earlier disclosure of over 2.5 million Grace CPUs shipped in total. Tom's Hardware frames this as part of Nvidia's broader push to establish itself as a data center CPU competitor alongside its dominant GPU business. The shift in messaging is tied to evolving AI workloads: agentic AI applications are changing the hardware balance in data centers, moving from configurations with as many as eight GPUs per CPU toward a roughly one-to-one ratio, increasing demand for CPUs. Nvidia's next-generation CPU, Vera, is designed specifically for these workloads. Unlike AMD and Intel, which use chiplet-based designs, Vera is monolithic with 88 cores on a single die and dedicates substantial die area to its Scalable Coherency Fabric, providing 3.4 TB/s of core-to-core bandwidth and up to 1.2 TB/s of aggregate memory bandwidth via LPDDR5X. Buck acknowledged trade-offs in Vera's architecture and noted the company is not targeting legacy workloads as its primary use case. Nvidia characterizes the data center CPU market as a $200 billion total addressable market opportunity, higher than most industry estimates. Vera is currently in production alongside Nvidia's Rubin GPU infrastructure, and the article notes that Tom's Hardware observed a Vera Rubin NVL72 rack running OpenAI workloads at Nvidia HQ.
Keywords: Nvidia Grace CPU, agentic AI, data centers, Vera processor, GPU to CPU pivot, hardware deployment
Google is reportedly developing a server chip internally referred to as "Frozen v2," which would have part of the Gemini AI model's architecture embedded directly into the silicon. According to the article, engineers project the chip could deliver 6 to 10 times more tokens per watt compared to Google's latest TPUs.
Keywords: custom silicon, TPU optimization, Gemini architecture, computational efficiency, tokens per watt, hardware specialization
A post on Meta's AI blog describes the use of two Meta open-source models — Segment Anything Model 3 (SAM 3) and DINOv3 — within SYNAPS-I, a project under the U.S. Department of Energy's Genesis Mission initiative launched in late 2025. SYNAPS-I involves 60 researchers across five national laboratories, including Lawrence Berkeley, Argonne, Brookhaven, and Oak Ridge, and targets the analysis of scientific imaging data from X-ray and neutron source facilities, which now generate tens of petabytes of data annually due to upgraded detectors. The central bottleneck addressed is image segmentation — identifying and labeling structures within scientific images — which previously required weeks of expert effort per dataset. DINOv3, a self-supervised vision model, provides contextual understanding of image structures, while SAM draws precise pixel-level boundaries. The team fine-tuned both models on beamline data and deployed them across 300 A100 GPUs at national supercomputing facilities. The resulting pipeline delivers a fully reconstructed, semantically labeled 3D volume to researchers in approximately 15 minutes. A demonstration application involved analyzing micro-CT scans of grapevine stems to study drought response at the cellular level, reducing per-time-step annotation from roughly one month to 15 minutes. The article notes that Meta's open-source licensing allows national laboratories to deploy the models within secure government computing environments. DOE Under Secretary Dario Gil is quoted describing the effort as compressing 'discovery time from days to moments.'
Keywords: Meta, AI models, Genesis Mission, AI deployment, business adaptation
The article, published on Medium, argues that marketers are not simply selecting individual AI models but are instead choosing broader integrated systems. It cites Forrester research indicating that Google has overtaken OpenAI as the preferred AI partner among agencies. The available article text is limited to a brief excerpt, so the full details of the argument are not visible.
Keywords: Google, OpenAI, AI vendor selection, platform ecosystems, marketing agencies, AI partnerships, model selection
A Reddit post in r/antiai, submitted by user Spiritual_Owl8439, shares two links related to Australian policy on AI. The first links to a report that Australia has mandated AI data centers to be net power generators and has established water efficiency rules. The second links to a Guardian article dated July 15, 2026, about an Australian government Office of AI addressing artificial intelligence and copyright issues. The post's title is 'Australia W,' and no additional commentary is provided beyond the two linked sources.
Keywords: AI data centers, power generation mandates, water efficiency regulation, copyright, AI infrastructure policy, Australia regulatory framework
A BloombergNEF report projects that U.S. data centers will consume one-fifth of the country's electricity by 2035—four times current levels—driven by surging AI compute demand. Total data center capacity is expected to reach nearly 200 gigawatts over the next decade, with close to half dedicated to AI training and inference. The U.S. is forecast to host 64% of AI chips by power demand by 2033. BloombergNEF's latest estimate is 83% higher than its own December forecast, with other organizations including EPRI and S&P also significantly raising their projections. The report warns that most new data centers will connect to already-strained electrical grids. The PJM Interconnection, spanning Virginia to Illinois, is expected to direct 34% of its electricity to data centers; ERCOT in Texas, 22%. PJM has faced severe grid congestion, paused new connection applications for four years, and saw electricity prices rise 76% over the past year. Utility American Electric Power has threatened to withdraw from PJM amid the strain. Globally, aggressive AI adoption could generate 1,935 terawatt-hours of new electricity demand from data centers by 2033—roughly equivalent to India's annual consumption.
Keywords: data center electricity consumption, energy demand, AI infrastructure, supply constraints, capital investment, power grid capacity
National Grid, the UK utility company, is investing $1.75 billion in a developer focused on supplying power to data centres, positioning itself to capitalize on growing electricity demand driven by artificial intelligence in the United States.
Keywords: National Grid, AI infrastructure, data centre power demand, energy investment, electricity supply