Scored 239 articles from 96 feeds; 15 included in digest.
Run ID: run-1789154341524
Generated: September 11, 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 |
|---|---|---|---|---|---|---|---|---|
| WSJ US Business | news | 4 | 16 | 6% | 0.12 | 1% | 8.8h | Stable |
| WSJ Tech | news | 2 | 10 | 20% | 0.21 | 2% | 7.6h | Stable |
| Bloomberg Markets | news | 1 | 18 | 4% | 0.10 | 1% | 3.6h | Stable |
| NYT front page | news | 1 | 17 | 2% | 0.04 | 0% | 5.5h | Stable |
| MyFT | news | 1 | 16 | 11% | 0.11 | 0% | 3.6h | Stable |
| Reddit AntiAI | news | 1 | 14 | 4% | 0.07 | 1% | 5.7h | Stable |
| Tom’s Hardware | news | 1 | 13 | 11% | 0.15 | 5% | 7.6h | Stable |
| Medium Artificial Intelligence (keyword) | commentary | 1 | 10 | 17% | 0.16 | 0% | 0.5h | Stable |
| Medium AI (keyword) | commentary | 1 | 9 | 20% | 0.17 | 0% | 0.5h | Stable |
| FT Alphaville | news | 1 | 3 | ~4% | ~0.10 | ~0% | 2.4h | Low sample |
| Derek Thompson | commentary | 1 | 1 | Collecting data | Collecting data | Collecting data | 9.6h | Collecting |
| Guardian | news | 0 | 25 | 1% | 0.03 | 0% | 8.5h | Stable |
| Hacker News | commentary | 0 | 25 | 4% | 0.07 | 0% | 10.1h | Stable |
| The Verge | news | 0 | 10 | 4% | 0.08 | 0% | 7.5h | Stable |
| Futurism | news | 0 | 7 | 10% | 0.13 | 2% | 6.5h | Stable |
| Seeking Alpha News | commentary | 0 | 7 | 4% | 0.09 | 1% | 1.2h | Stable |
| Ars Technical All News | news | 0 | 5 | 5% | 0.09 | 0% | 8.5h | Stable |
| TechCrunch | news | 0 | 5 | 11% | 0.15 | 1% | 8.2h | Stable |
| WSJ Social Economy | news | 0 | 4 | 3% | 0.09 | 0% | 5.6h | Stable |
| Wired AI News | news | 0 | 3 | ~27% | ~0.24 | ~5% | 9.3h | Low sample |
| CFTC General | policy_release | 0 | 2 | Collecting data | Collecting data | Collecting data | 11.0h | Collecting |
| Economist: Sci & Tech | news | 0 | 2 | Collecting data | Collecting data | Collecting data | 3.0h | Collecting |
| El Reg Offbeat | news | 0 | 2 | Collecting data | Collecting data | Collecting data | 3.2h | Collecting |
| FDIC | policy_release | 0 | 2 | Collecting data | Collecting data | Collecting data | 11.6h | Collecting |
| NYT Economy | news | 0 | 2 | Collecting data | Collecting data | Collecting data | 2.3h | Collecting |
| AI Daily Brief YT podcast | commentary | 0 | 1 | Collecting data | Collecting data | Collecting data | 7.4h | Collecting |
| Ars Technica All Features | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 8.6h | Collecting |
| BIG by Matt Stoller | commentary | 0 | 1 | Collecting data | Collecting data | Collecting data | 4.7h | Collecting |
| Cassandra Unchained by Michael J Bury | commentary | 0 | 1 | Collecting data | Collecting data | Collecting data | 8.4h | Collecting |
| Daring Fireball | commentary | 0 | 1 | ~5% | ~0.08 | ~0% | 7.6h | Low sample |
| Economist: Finance & Economics | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 3.8h | Collecting |
| Economist: United States | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 10.2h | Collecting |
| FRB Press Releases | policy_release | 0 | 1 | Collecting data | Collecting data | Collecting data | 11.6h | Collecting |
| MIT AI Research | research | 0 | 1 | Collecting data | Collecting data | Collecting data | 11.0h | Collecting |
| Net Interest (Marc Rubinstein) | commentary | 0 | 1 | Collecting data | Collecting data | Collecting data | 3.1h | Collecting |
| a16z | other | 0 | 1 | Collecting data | Collecting data | Collecting data | 5.5h | Collecting |
| Plain Bagel RSS YT feed | commentary | 0 | 0 | Collecting data | Collecting data | Collecting data | No recent data | Collecting |
Source: WSJ US Business
Type: news
Included: 4
Scored: 16
28d Digest Rate: 6%
28d Avg Score: 0.12
28d Hotlist Hit: 1%
7d Article Age: 8.8h
28d Confidence: Stable
Source: WSJ Tech
Type: news
Included: 2
Scored: 10
28d Digest Rate: 20%
28d Avg Score: 0.21
28d Hotlist Hit: 2%
7d Article Age: 7.6h
28d Confidence: Stable
Source: Bloomberg Markets
Type: news
Included: 1
Scored: 18
28d Digest Rate: 4%
28d Avg Score: 0.10
28d Hotlist Hit: 1%
7d Article Age: 3.6h
28d Confidence: Stable
Source: NYT front page
Type: news
Included: 1
Scored: 17
28d Digest Rate: 2%
28d Avg Score: 0.04
28d Hotlist Hit: 0%
7d Article Age: 5.5h
28d Confidence: Stable
Source: MyFT
Type: news
Included: 1
Scored: 16
28d Digest Rate: 11%
28d Avg Score: 0.11
28d Hotlist Hit: 0%
7d Article Age: 3.6h
28d Confidence: Stable
Source: Reddit AntiAI
Type: news
Included: 1
Scored: 14
28d Digest Rate: 4%
28d Avg Score: 0.07
28d Hotlist Hit: 1%
7d Article Age: 5.7h
28d Confidence: Stable
Source: Tom’s Hardware
Type: news
Included: 1
Scored: 13
28d Digest Rate: 11%
28d Avg Score: 0.15
28d Hotlist Hit: 5%
7d Article Age: 7.6h
28d Confidence: Stable
Source: Medium Artificial Intelligence (keyword)
Type: commentary
Included: 1
Scored: 10
28d Digest Rate: 17%
28d Avg Score: 0.16
28d Hotlist Hit: 0%
7d Article Age: 0.5h
28d Confidence: Stable
Source: Medium AI (keyword)
Type: commentary
Included: 1
Scored: 9
28d Digest Rate: 20%
28d Avg Score: 0.17
28d Hotlist Hit: 0%
7d Article Age: 0.5h
28d Confidence: Stable
Source: FT Alphaville
Type: news
Included: 1
Scored: 3
28d Digest Rate: ~4%
28d Avg Score: ~0.10
28d Hotlist Hit: ~0%
7d Article Age: 2.4h
28d Confidence: Low sample
Source: Derek Thompson
Type: commentary
Included: 1
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 9.6h
28d Confidence: Collecting
Source: Guardian
Type: news
Included: 0
Scored: 25
28d Digest Rate: 1%
28d Avg Score: 0.03
28d Hotlist Hit: 0%
7d Article Age: 8.5h
28d Confidence: Stable
Source: Hacker News
Type: commentary
Included: 0
Scored: 25
28d Digest Rate: 4%
28d Avg Score: 0.07
28d Hotlist Hit: 0%
7d Article Age: 10.1h
28d Confidence: Stable
Source: The Verge
Type: news
Included: 0
Scored: 10
28d Digest Rate: 4%
28d Avg Score: 0.08
28d Hotlist Hit: 0%
7d Article Age: 7.5h
28d Confidence: Stable
Source: Futurism
Type: news
Included: 0
Scored: 7
28d Digest Rate: 10%
28d Avg Score: 0.13
28d Hotlist Hit: 2%
7d Article Age: 6.5h
28d Confidence: Stable
Source: Seeking Alpha News
Type: commentary
Included: 0
Scored: 7
28d Digest Rate: 4%
28d Avg Score: 0.09
28d Hotlist Hit: 1%
7d Article Age: 1.2h
28d Confidence: Stable
Source: Ars Technical All News
Type: news
Included: 0
Scored: 5
28d Digest Rate: 5%
28d Avg Score: 0.09
28d Hotlist Hit: 0%
7d Article Age: 8.5h
28d Confidence: Stable
Source: TechCrunch
Type: news
Included: 0
Scored: 5
28d Digest Rate: 11%
28d Avg Score: 0.15
28d Hotlist Hit: 1%
7d Article Age: 8.2h
28d Confidence: Stable
Source: WSJ Social Economy
Type: news
Included: 0
Scored: 4
28d Digest Rate: 3%
28d Avg Score: 0.09
28d Hotlist Hit: 0%
7d Article Age: 5.6h
28d Confidence: Stable
Source: Wired AI News
Type: news
Included: 0
Scored: 3
28d Digest Rate: ~27%
28d Avg Score: ~0.24
28d Hotlist Hit: ~5%
7d Article Age: 9.3h
28d Confidence: Low sample
Source: CFTC General
Type: policy_release
Included: 0
Scored: 2
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 11.0h
28d Confidence: Collecting
Source: Economist: Sci & Tech
Type: news
Included: 0
Scored: 2
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 3.0h
28d Confidence: Collecting
Source: El Reg Offbeat
Type: news
Included: 0
Scored: 2
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 3.2h
28d Confidence: Collecting
Source: FDIC
Type: policy_release
Included: 0
Scored: 2
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 11.6h
28d Confidence: Collecting
Source: NYT Economy
Type: news
Included: 0
Scored: 2
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 2.3h
28d Confidence: Collecting
Source: AI Daily Brief YT podcast
Type: commentary
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 7.4h
28d Confidence: Collecting
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: BIG by Matt Stoller
Type: commentary
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 4.7h
28d Confidence: Collecting
Source: Cassandra Unchained by Michael J Bury
Type: commentary
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 8.4h
28d Confidence: Collecting
Source: Daring Fireball
Type: commentary
Included: 0
Scored: 1
28d Digest Rate: ~5%
28d Avg Score: ~0.08
28d Hotlist Hit: ~0%
7d Article Age: 7.6h
28d Confidence: Low sample
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: 3.8h
28d Confidence: Collecting
Source: Economist: United States
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 10.2h
28d Confidence: Collecting
Source: FRB Press Releases
Type: policy_release
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 11.6h
28d Confidence: Collecting
Source: MIT AI Research
Type: research
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 11.0h
28d Confidence: Collecting
Source: Net Interest (Marc Rubinstein)
Type: commentary
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
Source: Plain Bagel RSS YT feed
Type: commentary
Included: 0
Scored: 0
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: No recent data
28d Confidence: Collecting
Published on Medium's The Agentic Enterprise, this article argues that AI models themselves are not the primary source of competitive advantage for enterprises. As models become more capable and widely accessible, the author contends that enterprise differentiation will increasingly depend on context — something models do not come equipped with. The supplied article text is a brief excerpt, with the full argument available via a continue-reading link.
Keywords: competitive advantage, AI commoditization, contextual data, enterprise AI strategy, moat erosion, organizational knowledge, agentic enterprise
Cantor Fitzgerald Co-CEO Christian Wall appeared on Bloomberg Open Interest from the firm's New York headquarters to discuss the company's record performance in 2025, a more complex outlook for 2026, and his view that AI infrastructure investment will generate trillions of dollars in new debt issuance.
Keywords: AI capital expenditure, debt issuance, infrastructure financing, macro transmission channels, credit markets, investment cycles
This article from FT Alphaville is paywalled and its full text was not available for summarization. The title, "Desperately seeking UK data centre data," suggests the piece addresses challenges in obtaining reliable data about the UK data centre sector, but no further detail can be confirmed from the supplied text.
Keywords: data centre capacity, infrastructure transparency, measurement gaps, UK policy, AI infrastructure, systemic importance
Published by the Financial Times, this article contends that AI risks deserve greater attention, observing that governments are promising a light-touch regulatory agenda despite what the piece characterizes as growing threats associated with artificial intelligence.
Keywords: AI regulation, government policy, risk management, light-touch regulation, systemic threats
A cluster of Polymarket accounts recorded significant winnings on prediction markets tied to companies audited by KPMG, according to findings reported by The Wall Street Journal. The report follows earlier WSJ coverage that a KPMG employee is under investigation for alleged insider trading.
Keywords: Polymarket, KPMG, insider trading, prediction markets, information asymmetry, market manipulation
A Wall Street Journal article reports that companies including Aritzia and McDonald's are emphasizing human customer interaction as a strategy for building consumer loyalty, framing the approach as trading screen-based or automated service for personal engagement. The article text provided is limited to the tagline 'Trading screens for smiles in the battle for consumer loyalty,' so no further detail about specific company initiatives is available from the supplied text.
Keywords: Human service, Consumer loyalty, Business strategy, Automation, Retail, Food service, Digital economy
PayPal's CEO is pursuing a standalone strategy to transform the payments company after a reported $50 billion buyout stalled. According to the article, the CEO stands to earn a $25 million bonus if Wall Street accepts his plans to fix PayPal. The available article text does not detail the specific elements of the turnaround strategy.
Keywords: PayPal, CEO strategy, M&A, executive compensation, payments company restructuring
The Wall Street Journal reports that the absence of clear rules and norms around AI use in the workplace is generating distrust among colleagues. The article addresses how companies can help establish guidelines to mitigate these emerging trust issues.
Keywords: workplace culture, trust, AI adoption, organizational norms, internal operations, employee relations
A Tom's Hardware analysis of IFA 2026 argues that the laptop market has polarized into two distinct segments: low-cost machines under $800 with 8GB of RAM, and high-end AI-focused systems costing several thousand dollars with 128GB to 192GB of memory, leaving little for mid-range buyers in the $800–$1,500 range. On the affordable end, multiple manufacturers introduced colorful, budget-oriented laptops aimed at competing with Apple's MacBook Neo. Lenovo's IdeaPad Vibe line offers seven color options and uses Qualcomm Snapdragon X and AMD Ryzen AI 400 chips. Dell's 14S adds HDMI and a headphone jack absent from the XPS 13, and Acer debuted a 16-inch Swift Air variant. The article notes that Apple's influence has revived color options in the Windows PC market. At the high end, systems built around AMD's Ryzen AI Max+ Pro 495 with up to 192GB of unified memory—from Lenovo, Minisforum, and GMKtec—are being positioned as workstations for running local AI agents, with some listed as high as $7,000. Nvidia's RTX Spark N1X platform, set to launch in October, also had new devices shown from Acer and Lenovo, though pricing has not been announced. All high-end systems were demonstrated running AI agent software. The author concludes that without new mid-range silicon from Intel (Nova Lake) or AMD (Medusa Point), the Windows PC market appears content to remain split between these two extremes.
Keywords: AI devices, market segmentation, high-end vs. budget computing, MacBook Neo, product strategy, IFA 2026
Derek Thompson's commentary site features an interview with David Deming, a Harvard economist who oversees undergraduate education, about his controversial 'AI encouragement' policy and the broader state of higher education. The piece opens by cataloguing pressures facing colleges: declining public trust, rising tuition alongside a stagnant college wage premium, rising unemployment among recent graduates, Trump administration actions against universities, and AI-driven disruption to academic integrity and hiring. Deming frames American higher education as having moved through two historical eras—universities as repositories of scarce knowledge, then as centers of specialized expertise—and argues a third era is emerging, one he likens to the medieval collegium, centered on in-person community, shared habits of mind, and virtues that cannot be replicated online. He contends AI will commodify expertise and erode universities' traditional monopoly on it, but cannot substitute for learning in community with peers. On classroom practice, Deming describes a middle path between prohibition and uncritical adoption: having students produce initial drafts under controlled, device-free conditions to establish original thinking, then using AI in supervised revision stages, followed by oral presentations to verify genuine understanding. He endorses dissertation-style oral defenses as an accountability mechanism but notes their difficulty at scale. He describes his AI encouragement policy as having drawn significant concern from faculty and students.
Keywords: Harvard University, AI adoption, educational policy, student use of AI, institutional adaptation
The article explains what Kalshi and Polymarket are, describing them as prediction markets that have grown significantly in popularity and now attract billions of dollars in trades. The article also notes that some U.S. states have attempted to ban these platforms.
Keywords: prediction markets, Kalshi, Polymarket, trading volume, regulatory bans
This Wall Street Journal article covers Oracle's new data platform, with additional coverage of AI's economic implications and a suggestion that studying philosophy may be a strategy for navigating AI-driven workforce disruption. The full article text is not available beyond the headline and a brief subheading.
Keywords: Oracle data platform, AI economic impact, labor displacement, AI-driven disruption, philosophy education
A Reddit post in the r/antiai community, submitted by user bigblueye, raises the question of how trustworthy AI-generated knowledge is given the possibility of manipulating the online content that AI systems retrieve or are trained on. The post links to an image and invites community discussion, but provides no additional article text beyond the title question.
Keywords: AI knowledge reliability, information manipulation, data integrity, online sources, AI trustworthiness
This Medium commentary argues that AI itself is not the primary threat to workers' jobs; rather, the risk comes from other people who adopt and use AI tools effectively. The piece's subtitle frames this as a call for continuous self-updating and skill development, though the article text provided contains only the title and tagline, leaving the full argument unsummarized.
Keywords: AI adoption, labor market competition, skills gap, worker displacement, competitive advantage
The Wall Street Journal reports on Bending Spoons, a tech company described as a roll-up that grows through acquisitions. According to the article, the company relies on aggressive price increases and bespoke metrics as part of its business model. The article notes that rising interest rates pose a potential risk to the company's continued growth.
Keywords: Bending Spoons, price hikes, financial metrics, interest rates, tech roll-up, growth strategy