Fri 3 July 2026
Daily Brief — Curated and contextualised by Best Practice AI
Investors Pour $510 Billion, OpenAI Courts the State, and CFOs Pivot
TL;DRGlobal venture funding reached a record $510 billion in the first half of 2026, driven by AI investment. OpenAI is reportedly negotiating a 5% equity stake for the US government to secure its position. Meanwhile, major corporations including Amazon and Citi are actively restricting employee AI access due to spiraling operational costs. Economic data remains contradictory, with experts struggling to reconcile rising vacancy rates with AI-driven labor displacement.
The stories that matter most
Selected and contextualised by the Best Practice AI team
Global venture funding hits record $510B in first half as AI boom accelerates - SiliconANGLE
Global venture funding hits record $510B in first half as AI boom accelerates - SiliconANGLE SHARE UPDATED 07:00 EDT / JULY 02 2026 AI ### Global venture funding hits record $510B in first half as AI boom accelerates Startups worldwide raised a record $510 billion in the first half of 2026, according to a new report out today from market intelligence company Crunchbase Inc. The figure was more than investors put into venture deals across all of last year, when the total came to $440 billion. The first half also set a funding record, beating the previous record of $375 billion set in the second half of 2021. To the surprise of absolutely no one, artificial intelligence drove the boom, with the bulk of venture capital going into a small group of companies. OpenAI Group PBC and Anthropic PBC alone accounted for $217 billion, or 43% of all startup funding in the first half. That level
OpenAI ‘in early talks to give 5% stake to US government’
CEO Sam Altman argued move would share benefits of AI and it would involve other firms doing similar, report says Business live – latest updates OpenAI is reportedly in early stage talks to give a 5% stake in the ChatGPT developer to the US government as artificial intelligence companies attempt to smooth relations with Donald Trump’s administration. The OpenAI chief executive, Sam Altman, has argued that giving the US public a financial stake in the company is the best way to share the benefits of AI, according to the Financial Times, which cited two unnamed people familiar with the discussions. Continue reading...
The Algorithmic Barrier: A Framework for Artificial Frictional Unemployment and Information Asymmetry in Automated Recruitment Systems
arXiv:2601.14534v2 Announce Type: replace-cross Abstract: The United States labor market has entered a period in which high job vacancy rates and prolonged unemployment persist together. Classical theory attributes such conditions to skills mismatch or geographic immobility, but neither fully explains a pattern now widely reported: qualified candidates are rejected at the earliest, automated stage of hiring, before any human sees their application. This paper introduces Artificial Frictional Unemployment (AFU), a framework describing how deterministic automated screening rejects qualified candidates through semantic misinterpretation rather than genuine skill gaps. We situate the phenomenon within labor economics and information asymmetry theory and formalize the mechanism by which legacy Applicant Tracking Systems (ATS) turn hiring into a high-precision classification problem that inflates false negatives. The contribution is primarily conceptual. To make the mechanism concrete, we report a controlled proof-of-concept simulation comparing keyword-based screening with vector-space semantic matching under identical conditions. The simulation shows how lexical variance alone can produce false negatives; it is not a measurement of how much real-world friction is artificial, which we leave to future field studies. Building on the framework, we outline JobOS, a candidate-side architecture that illustrates how semantic competency mapping could operate alongside existing hiring infrastructure. Framing automated recruitment as labor market infrastructure, rather than a firm-level convenience, exposes a correctable source of matching inefficiency with consequences for workforce participation and the use of human capital.
The Rising Unsustainability of AI Graphics Cards Production
arXiv:2607.01258v1 Announce Type: new Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software. In particular, graphics cards have become central to AI training, with frequent hardware updates required to meet escalating computational demands. However, the environmental damages of graphics cards production remain understudied. This study addresses this gap by estimating the environmental damages associated with graphics cards production over the past decade (2013-2025). We analyze trends in energy consumption, carbon emissions and resource depletion. We compile and provide a dataset documenting the environmental damages of NVIDIA workstation graphics cards production since 2013. Our analysis of this dataset reveals a steady increase in production-related impacts over the period. Our finding highlights the need for greater transparency in life-cycle data, a persistent challenge in AI environmental assessments. While operational efficiency improvements (e.g., energy-efficient training, carbon-aware computing) are often prioritized, our results underscore that production-related impacts are also escalating and cannot be overlooked. The AI community must move beyond incremental optimizations and confront the necessity of sufficiency. This shift may demand structural changes such as policy interventions, hardware design for longevity, and cultural shifts away from perpetual growth and increased performance.
A.I. Is Reshaping the Economy. Good Luck Measuring How.
Some data suggest artificial intelligence is already causing job losses. Other sources show the opposite. Why is it so hard to figure out what’s going on?
The AI Trade Is Losing One of Its Key Signals
At a time when markets are growing uneasy over whether the enormous sums being poured into artificial intelligence will ever pay off, the prices the sector commands for each unit of usage are drifting lower.
Companies Are Throttling Employees’ AI Use Because It’s Too Expensive
Sources and leaks from Amazon, Adobe, Atlassian, Citi, and more show what is really happening with AI right now: companies are trying to rein in AI use as costs spiral out of control.
AI Factories Create Winners and Losers in Power Equipment Market
Next-generation AI factories are forcing power equipment firms to rethink their portfolios in the race to profit from a market expected to be worth more than $200 billion a year.
Economics & Markets
Reuters OpenAI News | Today's Latest Stories | Reuters
Open AI is planning its biggest ChatGPT overhaul yet, aiming to turn it into a "superapp" with coding tools and AI agents to boost revenue ahead of a potential stock market listing, the Financial Times reported on Sunday.
Microsoft’s next big bet isn’t on a model but on becoming the Swiss Army knife of enterprise AI
The tech giant is investing $2.5 billion in a new business unit called Microsoft Frontier.
Behind the Term Sheet: Moonshot AI | by Cathay Innovation | Cathay Innovation | Jul, 2026 | Medium
Behind the Term Sheet: Moonshot AI | by Cathay Innovation | Cathay Innovation | Jul, 2026 | Medium Open in app Sign up Get app Sign up ## Cathay Innovation Lessons, reflections and news from a global venture capital platform # Behind the Term Sheet: Moonshot AI ## Investing in the open-source model that reached the frontier 6 min read 21 hours ago -- Share Press enter or click to view image in full size Written by: Denis Barrier& Eric Shengdong; Edits: Jaclyn Hartnett On January 27, 2026, a research lab out of Beijing released a new model, a technical report, and a set of benchmark scores that, if you knew what to look for, told the whole story. Within twenty days, Kimi K2.5’s monthly revenue had exceeded what the company earned in all of 2025. Revenue doubled every month after that. When Kimi published new research on a foundational architecture innovation, Elon Musk we
The AI Trade Is Losing One of Its Key Signals
At a time when markets are growing uneasy over whether the enormous sums being poured into artificial intelligence will ever pay off, the prices the sector commands for each unit of usage are drifting lower.
AI Factories Create Winners and Losers in Power Equipment Market
Next-generation AI factories are forcing power equipment firms to rethink their portfolios in the race to profit from a market expected to be worth more than $200 billion a year.
Finding Value in the Crowded AI Trade | ETF Trends
After a wild last 12 months in a technology stock boom - and more recent volatility - the question du jour, in our view, is not whether AI is transformative.
The Week’s 10 Biggest Funding Rounds: AI, Energy And Biotech Lead The Way
U.S. startups announced sizable funding rounds at a steady clip during a truncated holiday week, with energy and AI leading the way. Houston-based energy startup Joulent secured the biggest round, a $1.75 billion strategic financing.
H2 opens with the AI trade losing its monopoly
H2 opens with the AI trade losing its monopoly TRENDING: Oil price XAU/USD EUR/USD Trade War GBP/USD Silver Newsletter Go to FXStreet homepage Visit Pepperstone - Sponsor (opens in new window) Upgrade Login ## The AI trade losing its monopoly The S&P 500 is still near the highs. The Dow is making fresh ones. From thirty thousand feet, the market looks broadly fine. But beneath the index level, H2 has opened with a fairly serious change in leadership. The AI, memory and semiconductor trades that carried much of the market higher are being marked down hard, while financials, consumer discretionary and some of the prior laggards are finding a bid. The market is not selling everything. It is changing seats. That distinction matters. This is not primarily a payrolls story for equities. The weaker jobs data mattered for bonds, FX, gold and Bitcoin. The rotation in stocks came f
Non-AI Nifty suddenly beating Nasdaq, South Korea & Taiwan bourses: Is the global tech trade finally reversing? - The Economic Times
India's Nifty is surprisingly outperforming global tech giants like Nasdaq, South Korea, and Taiwan, marking a potential reversal in the AI-driven market trend. Foreign investors are shifting away from Korean chipmakers, while India's domestic growth drivers offer a more stable outlook.
AI Infrastructure Stocks Retail Investors Are Watching After OpenAI’s Government Stake Proposal - Simply Wall St News
Artificial intelligence stocks are back in the spotlight after OpenAI floated the idea of giving the U.S. government a 5% stake in the company, valued at about $42.6b, and potentially extending similar deals to giants like Anthropic, Google, and Meta. A proposed public wealth fund, tighter ...
The Artificial Intelligence (AI) Stock That Wall Street Can't Stop Upgrading in 2026 | The Motley Fool
Some of Wall Street's top investment firms think this AI stock is poised to keep soaring.
The Price Shock – How Chinese Open-Weight AI Is Dismantling Silicon Valley’s Business Model – global geopolitics
The Price Shock – How Chinese Open-Weight AI Is Dismantling Silicon Valley’s Business Model – global geopolitics July 2, 2026 ## The Price Shock – How Chinese Open-Weight AI Is Dismantling Silicon Valley’s Business Model How DeepSeek and Zhipu AI exposed the assumptions on which $1.8 trillion in Western AI valuations rest Editorial Analysis | July 2026 I. The Regulatory Hearing as Commercial Theatre Sam Altman appeared before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law on 16 May 2023, delivering testimony that a senior Democratic senator described as remarkable for being the first instance he could recall of a private-sector executive requesting more government oversight of his own industry. Altman proposed, under oath, a federal agency empowered to license AI models above a defined capability threshold, to mandate pre-deployment testing, and to conduct in
AI boom: AI boom not shock-proof; chip shortage and weak pass-through weigh: Report - The Economic Times
AI boom: AI boom not shock-proof; chip shortage and weak pass-through weigh: Report - The Economic Times Business News Tech AI AI boom not shock-proof; chip shortage and weak pass-through weigh: Report # AI boom not shock-proof; chip shortage and weak pass-through weigh: Report ANILast Updated: Jul 03, 2026, 10:26:17 AM IST Follow us Share Font Size AbcSmall AbcMedium AbcLarge Save Print ### Synopsis While AI boom is supported by strong cash flows, it is not immune to shocks, with chip shortage posing a major risk to the rally, says Nuvama. Listen to this article in summarized format Listen ANIAI boom not shock-proof; chip shortage and weak pass-through weigh: Report While the AI boom is supported by strong cash flows, it is not immune to shocks, with chip shortages posing a major risk to the rally, says Nuvama Institutional Equities.As per the brokerage house, supply sh
Intellectia
Companies at the forefront of AI ... capital flows. However, concerns about AI overvaluation and the potential for worker displacement creating economic slowdowns require careful risk management. Understanding these dynamics is crucial for positioning portfolios in the current market environment. The scale of AI infrastructure investment in 2026 is unprecedented ...
Global venture funding hits record $510B in first half as AI boom accelerates - SiliconANGLE
Global venture funding hits record $510B in first half as AI boom accelerates - SiliconANGLE SHARE UPDATED 07:00 EDT / JULY 02 2026 AI ### Global venture funding hits record $510B in first half as AI boom accelerates Startups worldwide raised a record $510 billion in the first half of 2026, according to a new report out today from market intelligence company Crunchbase Inc. The figure was more than investors put into venture deals across all of last year, when the total came to $440 billion. The first half also set a funding record, beating the previous record of $375 billion set in the second half of 2021. To the surprise of absolutely no one, artificial intelligence drove the boom, with the bulk of venture capital going into a small group of companies. OpenAI Group PBC and Anthropic PBC alone accounted for $217 billion, or 43% of all startup funding in the first half. That level
AI Productivity Hopes Show ‘Exuberance,’ Allianz’s Subran Says
Artificial intelligence is likely to have a less even impact on economies than the market hype would suggest, according to Allianz Chief Economist Ludovic Subran.
Allianz's Subran Has Doubts Over Europe's 'AI Dividend'
Ludovic Subran, chief investment officer and chief economist at Allianz, says there isn't enough evidence to say that the AI trade is in "bubble" territory. Speaking to Bloomberg's Caroline Connan at the Aix-en-Provence Economic Forum in France, he also says emerging-market stocks are looking more attractive due to demand for semiconductors, and that Europe might fail to reap the "AI dividend." (Source: Bloomberg)
A.I. Is Reshaping the Economy. Good Luck Measuring How.
Some data suggest artificial intelligence is already causing job losses. Other sources show the opposite. Why is it so hard to figure out what’s going on?
Federal Reserve chairman Kevin Warsh outlines bullish AI productivity case
Federal Reserve chairman Kevin Warsh outlines bullish AI productivity case SEARCH Searching... # Federal Reserve chairman Kevin Warsh outlines bullish AI productivity case The new Fed chair calls the AI revolution a 'paradigm shift' and a 'significant disinflationary force' while refusing to tip his hand on rate policy Share Share on X Share on LinkedIn Share on Facebook Add us on Google by Editorial Team Jul. 2, 2026 Kevin Warsh, the newest occupant of the most powerful economic seat on the planet, wants you to know two things. First, he thinks artificial intelligence is about to reshape the US economy in ways that could be genuinely historic. Second, he’s not going to tell you what he plans to do about interest rates. The Federal Reserve chairman, who was sworn in on May 22, 2026, after being nominated by President Donald Trump on March 4, has been making the rounds with an
Can AI Productivity Offset America’s Shrinking Workforce? The Fed Is Watching
A weaker-than-expected jobs report could renew debate at the U.S. Federal Reserve about how to read the labor market at
The institutions behind the future economy | Arab News
When people talk about the future economy, they often focus on technologies. Artificial intelligence, biotechnology, advanced manufacturing, clean energy. But we need to understand that technologies do not create themselves. Behind every breakthrough is a system that develops talent, generates ...
AI could shave $2.2 trillion off the deficit, but 5 downsides could bring debt roaring back | Fortune
Economists say AI-driven productivity could buy Washington time on its deficit crisis—until longer lifespans, a shrinking labor force, and a possible AI arms race start clawing the savings back.
Claude Sonnet 5, AI Infrastructure Racing, Model Theft Claims | Kursol
Claude Sonnet 5, AI Infrastructure Racing, Model Theft Claims | Kursol This Week in AI is an AI-generated weekly roundup, curated and reviewed by the Kursol team. We use AI tools to gather, summarize, and analyze the week's most important developments — then add our perspective on what it means for your business. Anthropic cut the price of Claude Sonnet 5 by 50% this week, while separately disclosing that Alibaba ran 29 million conversations through the model to steal it. Meanwhile, the infrastructure arms race hit a new milestone: 175 MW of operational AI compute came online. This mix of capability gains, competitive threats, and capacity milestones tells you everything you need to know about where enterprise AI investment is heading right now. ## Anthropic Launches Claude Sonnet 5 at Half the Expected Price Anthropic released Claude Sonnet 5 on June 30, positioning it as the "most
America Is Having MacBook Sticker Shock
Apple is charging you an AI tax.
Top 10 Emerging AI Businesses Quietly Transforming Industries in 2026 Today
Discover 10 emerging AI businesses quietly transforming healthcare, legal services, software development, and enterprise technology with breakthrough innovations in 2026.
AI Startup Funding News | July, 2026 (STARTUP EDITION)
AI Startup Funding news, July 2026: discover where capital is flowing, what founders should do next, and how to raise smarter in a tougher AI market.
As companies race for cheaper AI options, this startup pitches a solution | The Star
Together AI, which specialises in open-source artificial intelligence models, is now worth more than US$8bil (RM32.68bil).
LinqAlpha Secures $22M to Revolutionize Investment Strategies with AI-Driven Market Insights
New York-based AI firm LinqAlpha secured $22 million in Series A funding to enhance its AI agents that predict market shifts.
Labor, Society & Culture
Would You Take $85,000 From the Company Warning AI Might Take Your Job? | by Andy Nguyen | Synthetic Futures | Jul, 2026 | Medium
Would You Take $85,000 From the Company Warning AI Might Take Your Job? | by Andy Nguyen | Synthetic Futures | Jul, 2026 | Medium Sitemap Sign up Sign in Get app Write Search Sign up Sign in ## Synthetic Futures https://medium.com/synthetic-futures Explores the possibilities and implications of artificial intelligence Member-only story # Would You Take $85,000 From the Company Warning AI Might Take Your Job? ## Claude Corps is real, paid, and open to almost anyone under 30. It’s also launching next to a $965 billion IPO filing. Andy Nguyen 5 min read 7 hours ago https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Fsynthetic-futures%2Ffa565d98a265&operation=register&redirect=https%3A%2F%2Fmedium.com%2Fsynthetic-futures%2Fwould-you-take-85-000-from-the-company-warning-ai-might-take-your-job-fa565d98a265&user=Andy+Nguyen&userId=2b1bca4c8ba9 -- http
Three Futures for the Diagnostic Radiologist: A Structured Disagreement About What AI Actually Changes
arXiv:2607.01253v1 Announce Type: new Abstract: Rationale. The diagnostic radiologist's role in 2035 will not look like it does today. Imaging AI is already changing how worklists are organized, how reports are generated, and which cases require a radiologist's attention. What remains genuinely contested is not whether the role changes but how. Approach. Three subject-matter experts (two radiologists and one health tech professional with more than 20 years of experience in medical imaging IT) independently authored 2035 job descriptions for the diagnostic radiologist using a shared template. Each author wrote from a distinct vantage point: one optimistic, one framed as a trade-off view incorporating workforce economics, and one structured around professional stratification. The three versions were published openly and subjected to a structured comparison across seven dimensions. Key findings. The three versions agree on direction but disagree on magnitude. All three describe a radiologist whose routine workload is AI-managed, who carries accountability for AI output, and who spends more time on complex cases and clinical collaboration than today's radiologist does. They diverge on headcount, career security, and whether the profession expands broadly, concentrates into a smaller well-compensated group, or stratifies into sharply differentiated tiers. Conclusion. AI won't eliminate the diagnostic radiologist. Whether it expands, concentrates, or stratifies the profession depends on choices health systems haven't made yet. The clinical argument for optimism is real. So is the economic argument for caution. Both can be true simultaneously. Keywords: radiology workforce; artificial intelligence; diagnostic radiology; job redesign; medical imaging IT; AI governance
Job Openings Rise Again. Is AI Creating Jobs, Not Destroying Them? - 24/7 Wall St.
For the past two years, artificial intelligence has dominated discussions about the future of work. The prevailing narrative has been straightforward: AI will automate tasks, eliminate jobs, and eventually push unemployment higher. Yet the latest labor market data tells a more complicated story.
AI's Displacement of White-Collar Labor
Generative AI and LLMs are displacing cognitive labor, forcing a shift toward AI collaboration and creating a "human premium" for empathetic, high-touch services in the American labor market.
AI Revolutionizes White-Collar Employment: Tech and Finance Jobs Decline, ETHRWorldSEA
As organisations integrate AI into ... repetitive white-collar roles. ... Show More The latest figures suggest that AI's impact on employment is becoming visible in sectors where adoption has been most rapid. However, economists caution against interpreting the trend as evidence ...
New report claims AI is leading to job layoffs — but higher-level more educated workers are being hit hardest | TechRadar
AI unemployment is disproportionately affecting higher-level, more educated tech workers in California, report warns.
AI is 'seniorizing' entry-level jobs, PwC warns - Outsource Accelerator
PwC warns AI is 'seniorizing' entry-level jobs, making them 7x more likely to demand senior skills as graduate underemployment climbs to 42.5%.
Tech and Finance Sectors Losing 28,000 Jobs Monthly Show AI Impact on Labor
Whether artificial intelligence will cause mass workforce cuts over time remains up for debate, but it is starting to leave an imprint on US employment
AI to drive reskilling in IT sector but job loss fears misplaced: Axis Bank chief economist - The HinduBusinessLine
Neelkanth Mishra sees services exports growing strongly despite AI shifts in industry structure
AI will reshape a third of job skills by 2030. Is Ireland ready? – The Irish Times
AI will reshape a third of job skills by 2030. Is Ireland ready? – The Irish Times A special report is content that is edited and produced by the special reports unit within The Irish Times Content Studio. It is supported by advertisers who may contribute to the report but do not have editorial control. # AI will reshape a third of job skills by 2030. Is Ireland ready? ## Upskilling has become an imperative as Ireland’s workforce faces an uncertain future, writes Peter McGuire How well-placed is Ireland to respond to the reshaping by AI of the jobs market, and how can people acquire the skills for roles that may not even exist yet? Illustration: Getty In association with The Irish Times Content Studio Across the world, jobs are being reshaped by automation and artificial intelligence (AI). Some will be lost, others transformed. At the same time, the World Economic Forum (WEF) says
AI Could Be Accelerating Workplace Burnout in South Africa, Stellenbosch Expert Warns - iAfrica
# AI Could Be Accelerating Workplace Burnout in South Africa, Stellenbosch Expert Warns - iAfrica Published: 2026-07-02T12:45:06+02:00 Source: iafrica.com (iafrica.com) Language: en ## Story As South African companies race to embrace artificial intelligence to improve productivity and cut costs, a leading workplace mental health expert has warned that the technology could be accelerating burnout rather than easing employees’ workloads. While AI has been widely celebrated for boosting efficiency and automating routine tasks, Prof Renata Schoeman, head of healthcare leadership at Stellenbosch Business School, believes organizations are overlooking its growing impact on workers’ mental health. “We are having extensive conversations about AI governance, ethics and cybersecurity, which are all essential. But we are largely ignoring the human consequences,” Schoeman said. “AI is not only a
Growth of AI leads to job losses as lawmakers in both parties call for urgent action
As the economy continues to chug along, positive numbers are often accompanied by astonishing figures.
The Algorithmic Barrier: A Framework for Artificial Frictional Unemployment and Information Asymmetry in Automated Recruitment Systems
arXiv:2601.14534v2 Announce Type: replace-cross Abstract: The United States labor market has entered a period in which high job vacancy rates and prolonged unemployment persist together. Classical theory attributes such conditions to skills mismatch or geographic immobility, but neither fully explains a pattern now widely reported: qualified candidates are rejected at the earliest, automated stage of hiring, before any human sees their application. This paper introduces Artificial Frictional Unemployment (AFU), a framework describing how deterministic automated screening rejects qualified candidates through semantic misinterpretation rather than genuine skill gaps. We situate the phenomenon within labor economics and information asymmetry theory and formalize the mechanism by which legacy Applicant Tracking Systems (ATS) turn hiring into a high-precision classification problem that inflates false negatives. The contribution is primarily conceptual. To make the mechanism concrete, we report a controlled proof-of-concept simulation comparing keyword-based screening with vector-space semantic matching under identical conditions. The simulation shows how lexical variance alone can produce false negatives; it is not a measurement of how much real-world friction is artificial, which we leave to future field studies. Building on the framework, we outline JobOS, a candidate-side architecture that illustrates how semantic competency mapping could operate alongside existing hiring infrastructure. Framing automated recruitment as labor market infrastructure, rather than a firm-level convenience, exposes a correctable source of matching inefficiency with consequences for workforce participation and the use of human capital.
Why measuring AI’s job losses remains so difficult in the U.S. | Prism News
AI is already visible in GDP and investment, but U.S. data still cannot isolate its job losses, leaving policymakers to guess at the labor-market hit.
They built the world’s most powerful AI. They’re facing a mystery they can’t explain.
Anthropic, Google and Meta have hired computer scientists, neuroscientists and philosophers to study what some in the industry think may become a moral crisis.
AI’s Safety Dilemma | The Daily Caller
AI can be made 99.99% safe with significant frictions for users, and that last 0.01% can unleash calamities on civilization.
Want to Get Life Insurance Wrong? Ask AI.
Want to Get Life Insurance Wrong? Ask AI. # Economics Matters by Laurence Kotlikoff SubscribeSign in # Want to Get Life Insurance Wrong? Ask AI. ### AI should either provide financial advice that conforms with economics or stop giving advice. Otherwise, AI companies can look to be sued. Larry Kotlikoff Jul 02, 2026 ∙ Paid Share Economics Matters — Blog/Podcast/Financial Riddler/MaxiFi Puzzler Sign up for a free subscription at larrykotlikoff.substack.com. I’ll immediately make it a free lifetime subscription. Also, pls sign up kids, parents, …, everyone you know. Paid subscriptions come with a monthly Q&A session via Substack Live. I’ll email you the day and time. Submit questi… © 2026 Larry Kotlikoff · Privacy∙ Terms∙ Collection notice Start your Substack Get the app Substack is the home for great culture
Why the tech industry can't keep up with the AI backlash
Why the tech industry can't keep up with the AI backlash This is a column about AI. My fiancé works at Anthropic. See my full ethics disclosure here. On Wednesday, OpenAI CEO Sam Altman published an op-ed repeating his call for a new international body to govern artificial intelligence safety. “International co-operation like this seems a reasonable way to avoid power becoming too concentrated, and ensure that the benefits of AI are democratized,” Altman wrote in the Financial Times. Altman’s proposal is sensible, and one that builds on his years-old call for something like an International Atomic Energy Agency for AI. Reading his essay, though, I imagined the people of the world asking themselves: what benefits? Three and a half years after the launch of ChatGPT, the initial wonder that the world first felt about all-knowing answer boxes has increasingly curdled into anger, anxiety,
AI Virtue: What is "Good" Knowledge in the Age of Artificial Intelligence?
arXiv:2607.01776v1 Announce Type: new Abstract: In the age of AI, what will be good knowledge? This article, which is accepted and forthcoming in a special issue of Modern Fiction Studies on "Cultural AI" in 2027, applies digital humanities methods to map epistemic virtues (like "true," "accurate," "creative") used in a corpus of 553 journal articles on AI published in 2024. "Creativity" comes in for special attention as an example. Exploring this discourse of value, the article considers how a framework might be developed for evaluating the knowledge-worth of AI -- one less locked into values formed around pre-AI "knowledge work" agents or structures, and more open to the future values of "generativity." The essay is supported by an online digital kit for exploring data models of the corpus of articles on AI it studies.
Scaling Trends for Lie Detector Oversight in Preference Learning
arXiv:2607.01567v1 Announce Type: new Abstract: Deceptive behavior in LLMs is costly to monitor and prevent, motivating approaches such as Scalable Oversight via Lie Detectors (SOLiD) (Cundy & Gleave, 2025), which uses lie detectors to identify responses for review by high-cost labelers. In this paper, we scale SOLiD to larger models and evaluate it in more diverse and realistic preference-learning settings. We find favorable scaling: undetected deception drops from 34% for 1B-parameter models to 14% for 405B-parameter models at a detector true positive rate of 99%, and expensive human labelers can be removed entirely from the fine-tuning phase without a statistically significant increase in deception. However, SOLiD is sensitive to distribution shift between detector training and preference-training data, which can drive detector false positive rates to impractical levels.
Report: Lifelong learning must change for AI to realise long-term potential
According to the research, as structural issues begin to limit growth, business leaders are urging policymakers to align strategies and revamp workforce development. Read more: Report: Lifelong learning must change for AI to realise long-term potential
Beyond Detection: Redesigning Assessment and Governande of Generative AI at the Universidad Polit\'ecnica de Madrid (UPM)
arXiv:2607.01255v1 Announce Type: new Abstract: Universities have responded to generative artificial intelligence (GenAI) in noticeably different ways, both internationally and within Spain. So far, the dominant reaction has been defensive, this is, most institutions frame the debate around AI detection, plagiarism, academic integrity and a presumed drop in student effort, prioritizing basic training for academic staff over students. Other group of pioneering universities is doing the opposite, pursuing deeper adoption, and assuming that any policy built on prevention or sanction will not hold. This paper sides with that second view. Obsessing about detection is a dead end, since generated text is increasingly hard to distinguish from human writing, and detectors still misfire too often to be trusted. What universities need instead is a coordinated effort to set clear, course-by-course rules for GenAI use, redesign assessment toward authentic and interdisciplinary assessment that fosters critical thinking and learner autonomy, and build a serious AI-literacy programme that treats students as critical co-creators rather than passive users. The challenge, though, is not only pedagogical. Adoption at university scale also raises organisational, technical, operational, legal and economic questions that have to be solved together. In this context, the Universidad Polit\'ecnica de Madrid (UPM) is developing a strategic and sustainable AI policy and adoption framework structured around six dimensions, in which AI functions as an enabler of student autonomy and pedagogical innovation rather than as a threat to be policed.
Technology & Infrastructure
Production-Grade Agentic AI Inference | Medium
Production-Grade Agentic AI Inference | Medium Sitemap Sign up Sign in Get app Write Search Sign up Sign in # Production-Grade Agentic Inference: Four Open-Source Tools, One Big Problem, and Why Each One Made the Next One Necessary Anubhab Banerjee 8 min read 23 hours ago https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Fp%2F372300ac63b4&operation=register&redirect=https%3A%2F%2Fmedium.com%2F%40anbdwnroop.banerjee%2Fproduction-grade-agentic-inference-372300ac63b4&user=Anubhab+Banerjee&userId=2835f45a65b5 -- https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Frepost%2Fp%2F372300ac63b4&operation=register&redirect=https%3A%2F%2Fmedium.com%2F%40anbdwnroop.banerjee%2Fproduction-grade-agentic-inference-372300ac63b4&user=Anubhab+Banerjee&userId=2835f45a65b5 https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fbookmark%2F
Z.ai launches ZCode to challenge Cursor, Claude Code and GitHub Copilot in AI coding
Z.ai, the Beijing-based artificial intelligence lab formerly known as Zhipu AI, on Wednesday officially launched ZCode, a free desktop application it describes as an "Agentic Development Environment" purpose-built for its flagship GLM-5.2 large language model. The move marks the company's most aggressive push yet into the fast-growing AI-powered coding tool market, where it now competes directly with Cursor, Claude Code, GitHub Copilot, and Google's Antigravity. "Introducing ZCode, the official development environment for GLM-5.2," the company wrote on X, noting the tool is available on macOS, Windows, and Linux, supports bring-your-own-key (BYOK) configurations for third-party models, and offers a 1.5x usage-quota bonus for subscribers to its GLM Coding Plan. Read one way, ZCode is simply another entrant in a crowded market. Read another, it is a single product that crystallizes three of the most consequential trends in enterprise software today: the race-to-the-bottom pricing of frontier AI models, the geopolitical balkanization of the AI stack, and the rapid maturation of agentic coding agents into what Gartner now estimates is a roughly $10 billion market. An AI coding tool designed to think in projects, not prompts Unlike traditional IDEs that bolt on AI through a chat sidebar or autocomplete extension, ZCode is best understood as an agent-first development environment. Its core design is built around long-horizon tasks: the user describes an outcome, the agent plans the work, edits files, runs checks, reviews progress, and continues across multiple iterations until the goal is met. ZCode organizes the development experience around the ZCode Agent, deeply tuned for GLM-5.2, with emphasis on deep integration: the model, tools, and execution workflow are tuned together so the Agent fits continuous, multi-step real-world development tasks. The environment supports continuous follow-up across devices: desktop, mobile Remote, and Feishu / WeChat Bot can all keep the same workspace task moving. Sensitive commands, file changes, and high-permission actions go through confirmation before execution. That remote-control feature — the ability to steer a running coding agent from WeChat, Feishu, or Telegram on a phone — is a differentiator that speaks directly to the Chinese developer market, where those messaging platforms dominate professional communication. You can keep checking progress and adding instructions while long-running work continues, from any device with these messaging apps. The tool is free to download. Revenue flows through Z.ai's GLM Coding Plan subscription tiers, which start at $16.20 per month for a "Lite" plan and scale to $144 per month for "Max" — prices that undercut Anthropic's Claude Code and Cursor's comparable tiers by significant margins. Through July 31, ZCode is offering a promotional 1.5x effective quota bonus for Coding Plan subscribers, with off-peak token consumption charged at a 0.67x coefficient. The platform also supports multiple AI models and agents, including Claude Code, Codex, Gemini, and OpenCode — a pragmatic concession to the reality that no single model wins every task. GLM-5.2, the open-source model trained entirely on Chinese chips, powers the whole experience ZCode's value proposition is inseparable from GLM-5.2, the model it was designed to showcase. Z.ai released GLM-5.2 on June 16, first to its Coding Plan subscribers and subsequently as open-source weights under the MIT license on Hugging Face — a sequencing decision that prioritized distribution over the traditional benchmark-led launch. The model's specifications are formidable. GLM-5.2 is a 744-billion-parameter mixture-of-experts architecture with 40 billion active parameters, a genuine one-million-token context window — five times the 200K limit on its predecessor — and training on 28.5 trillion tokens. It ranked second globally on Code Arena as of mid-June, trailing only Anthropic's Claude Fable 5, making it one of the highest-performing publicly available models for coding tasks. Critically, the model was built entirely without American chips. As Decrypt reported, GLM-5.2 "runs entirely on Huawei silicon." Stability AI founder Emad Mostaque estimated total training costs at roughly $25 million, with 80 percent spent on post-training — a figure that, if accurate, would make GLM-5.2 extraordinarily cheap relative to Western frontier models. On benchmarks, GLM-5.2 performs within striking distance of the best proprietary systems. It trails Anthropic's Claude Opus 4.8 by just one percentage point on FrontierSWE, a benchmark measuring multi-hour autonomous engineering projects, while edging out OpenAI's GPT-5.5. Its API pricing — $1.40 per million input tokens and $4.40 per million output — are a cost reduction of up to 82 percent compared to Anthropic's Claude Opus 4.8 at $5 and $25, respectively. Because ZCode is a first-party tool from the same company that makes the model, it requires no manual endpoint configuration — the model is wired in. The Anthropic export ban gave Chinese AI its biggest opening yet ZCode's arrival cannot be separated from the geopolitical drama that has roiled the AI industry over the past three weeks. On June 12, the U.S. government, citing national security authorities, issued an export control directive suspending all access to Anthropic's Fable 5 and Mythos 5 models by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. Enterprise clients in finance, healthcare, SaaS, and critical infrastructure found their core intelligence services abruptly disabled, without exception, prior warning, or effective recourse. While the Trump administration lifted those controls just yesterday — Anthropic confirmed on June 30 that the Department of Commerce had rescinded the directive — the episode sent shockwaves through the developer community and accelerated interest in open-source, self-hostable alternatives. The government's crackdown on Anthropic coincided with a swift rise in Chinese open-source models that are proving to be almost as capable and significantly cheaper than some of the most powerful U.S. models. Z.ai's timing was surgical. On the same day the Trump administration ordered Anthropic's most advanced models blocked for foreign nationals, Zhipu announced the open-source release of GLM-5.2 with no usage restrictions. The South China Morning Post reported that GLM-5.2 would be available to all users of Zhipu's new GLM Coding Plan subscription, "priced at just a tenth of Anthropic's premium Claude Code and Claude Max tiers." The market responded accordingly. Zhipu AI's market capitalization crossed HK$1 trillion (US$128 billion) on June 22, driven by a 42 percent intraday share surge. JPMorgan raised its 2026–2030 revenue forecast for Zhipu by between 7 and 16 percent following the launch, projecting an over 534 percent revenue surge for 2026 and expecting the AI firm to turn a profit by 2028. Why vendor lock-in now carries a geopolitical risk that no SLA can cover The Fable 5 episode did more than embarrass Anthropic. It introduced a new risk category into enterprise AI procurement: sovereign access risk. When a government can disable a commercially deployed AI model overnight, the traditional evaluation criteria of developer experience, benchmark scores, and pricing become secondary to a more fundamental question: Will this tool still work tomorrow? The event exposed the inadequacy of standard enterprise contract language. An investigation by FifthRow found that almost all standard Data Processing Addenda, SaaS agreements, and procurement SLAs "relied on vague 'force majeure' or 'compliance with law' catch-alls, not on precise, actionable regulatory suspension or kill-switch clauses." ZCode's BYOK architecture and GLM-5.2's MIT-licensed open weights offer a partial answer. A development team can download the model, host it on its own infrastructure, and run ZCode against it without ever touching Z.ai's cloud — eliminating both American export-control risk and Chinese data-sovereignty concerns in a single move. The catch is that anyone using Z.ai's cloud API remains subject to Chinese law, a consideration that evaporates only with pure self-hosting. Gartner analysts have warned that governance, pricing, support, workflows, commercial maturity, and market durability matter as much as developer experience and model capabilities when evaluating coding agent vendors for enterprise-wide adoption. By that measure, ZCode faces a steep climb. It is not open source itself; Linux support remains in beta; and security reviewers have flagged the need for careful evaluation of its credential handling, particularly for remote development over SSH and messaging-platform-triggered tasks — an agent that can be summoned from WeChat involves access paths that should be mapped before trusting it with anything sensitive. Inside the $10 billion race where model labs are becoming full-stack IDE companies ZCode enters one of the most crowded and fastest-moving markets in enterprise software. Enterprise AI coding agents are capturing a growing share of enterprise software engineering spend, with the market estimated at roughly $9.8 billion to $11.0 billion annualized as of April 2026, according to Gartner. A defining shift this year, the analyst firm noted, is "the movement of frontier model providers into direct competition with application-layer vendors" — precisely the pattern ZCode embodies. Gartner codified this evolution in May when it renamed its annual Magic Quadrant from "AI Code Assistants" to "Enterprise AI Coding Agents," defining the category as "autonomous or semiautonomous software engineering solutions that perceive context, translate human intent into multistep plans, and execute and verify those steps across code, tests and related engineering artifacts." The 2026 Magic Quadrant names Anthropic, Cursor, GitHub, and OpenAI as Leaders. Z.ai was not among the 12 vendors evaluated — an absence that underscores both the company's nascent enterprise sales presence outside China and the Western-centric lens through which the analyst community still views the market. The competitive landscape is daunting. Cursor is the $2 billion ARR IDE that feels like VS Code with a supercharger. Claude Code reached approximately $2.5 billion in annualized revenue by early 2026. Google relaunched Antigravity 2.0 at I/O in May, and Cognition retired the Windsurf brand, relaunching the IDE as Devin Desktop with the Agent Command Center as the default surface. Against these entrenched players, ZCode's pitch rests on three pillars: deep first-party integration with GLM-5.2 that no third-party editor can replicate, aggressive pricing that starts at a fraction of Western competitors, and MIT-licensed open weights that allow enterprises to self-host — eliminating the regulatory kill-switch risk that the Fable ban made viscerally real. Z.ai's real challenge is turning a $128 billion valuation into a global developer tools business Z.ai controls the model (GLM-5.2), the subscription layer (the GLM Coding Plan), and the IDE (ZCode) — a tightly coupled stack that optimizes for performance but concentrates switching costs. For the company, the business logic is clear. Its most reliable revenue stream has been on-premises deployments for Chinese government agencies, state-owned banks, and energy conglomerates. In full-year 2025, on-premises deployment revenue reached RMB 534 million, growing over 100 percent year-over-year and accounting for 73.7 percent of total revenue with a gross margin of 48.8 percent. ZCode and the GLM Coding Plan represent the company's bid to build a comparable revenue engine in cloud-based developer tools — globally, not just in China. The early signals are encouraging for Z.ai, if anecdotal. Community reception on X was enthusiastic, with one early user calling the tool "super stable" and others clamoring for more Coding Plan capacity. "Bro, can't snag your family's Coding Plan? When are you gonna stock up on more cards?" one user wrote in Chinese, suggesting demand is already outstripping supply. But the hard questions loom large. Can a Chinese AI company build trust with Western enterprise buyers amid escalating technology tensions? Can ZCode's ecosystem mature fast enough to compete with Cursor's polished UX, Claude Code's deep agent primitives, and GitHub Copilot's unmatched distribution? And can Z.ai sustain a company valued at $128 billion while still losing money? What is no longer in question is the competitive dynamic itself. Three weeks ago, a U.S. government directive proved that access to the world's best coding model can vanish overnight. Today, a Chinese lab is shipping a free IDE, an open-source model trained on zero American chips, and a subscription plan that costs less per month than a single lunch in Manhattan. The AI coding agent market did not just become global this summer. It became a market where the fallback option might be better than the thing it's falling back from — and that changes the calculus for every engineering leader choosing a toolchain in the second half of 2026.
The Agentic Garden of Forking Paths
arXiv:2607.01507v1 Announce Type: new Abstract: Empirical research rarely admits a unique analysis. Different analytical choices can lead to different conclusions from the same data, yet these hidden forking paths are difficult to observe. We show that AI agents capture much of the analytical variation among human researchers while making these paths explicit. Across four high-stakes domains, assigning different personas is sufficient for AI agents to report divergent, often opposing, conclusions from the same data and question, with findings systematically aligned with those beliefs. In a study in which 42 human research teams analyzed the same immigration dataset, AI agents reproduced 72% of the human ideological gap in reported effect estimates. Despite reaching opposing conclusions, it is difficult to identify clear issues in each analysis based on the final AI reports: 86% passed independent AI review and 78% passed majority human expert review. These findings suggest that the central challenge is often not flawed analyses, but selective exploration and reporting from a large space of methodologically defensible analyses. AI agents may amplify this longstanding problem by making such exploration inexpensive and scalable. To address this, we introduce the m-value (multiverse value), the probability that an analysis path would produce a claim at least as extreme as the reported one. We further introduce Agentic Bootstrap, which estimates the m-value by using AI agents to sample plausible analysis paths. Applied to the human immigration study, 13.5% of reported human analyses fell in the most extreme 5% of the analysis space (m<0.05). Scientific evidence should therefore be evaluated not only by a single reported analysis but also by its position within the distribution of analyses that could reasonably have been reported. Agentic Bootstrap makes this distribution observable and turns it into a criterion for scientific credibility.
World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments
arXiv:2607.01470v1 Announce Type: new Abstract: Clinical protocol-execution tasks -- checking a lab value, applying a threshold, placing a correctly structured FHIR order -- are natural candidates for RL from world feedback: once clinical SMEs encode decision logic into a verifier, that verifier grades unlimited rollouts without per-episode annotation. But applying RL requires a sound feedback channel and sufficient base capability. We audit MedAgentBench v1/v2, find a 41.7\% silent-finish ceiling that makes inaction the RL dominant strategy, and construct \textbf{MedAgentBench-v3 (MAB-v3)} (508 tasks, 8.9\% ceiling). Training Qwen3-8B exposes two structural barriers: a \emph{capability ceiling} (10/20 task types have 0\% base performance, zero gradient) and a \emph{format-knowledge barrier} (3/20 types require exact clinical codes undiscoverable by exploration). Pure RL reaches 18.2\% pass@1 vs.\ 34.1\% for rule-based SFT; the 15.9~pp gap is attributable entirely to these barriers. A decision/format-knowledge/lookup taxonomy predicts RL learnability and prescribes the fix: SFT to inject codes, RL to learn conditionals.
Janus: a Playground for User-Involved Agentic Permission Management
arXiv:2607.01510v1 Announce Type: new Abstract: AI agents that autonomously execute tool calls on a user's behalf raise pressing questions about permission management: what role could users play, and what role should they play? Despite many proposed approaches, the user's role in agentic permission management remains under explored. We introduce Janus, a playground system for implementing and evaluating user-involved agentic permission management designs. Janus consists of two components: Janus-Core, a modular agentic system supporting a diverse spectrum of permission management designs, and Janus-Harness, an automated evaluation framework. Grounded in a conceptual model that identifies key design axes for user involvement, we implement six permission assistants spanning the design space and evaluate them across three scenarios and three synthetic responders. We demonstrate that user input is critical and can significantly strengthen privacy and security, that AI augmentation of user decisions can help reduce cognitive load, and that realistic user behavior including permission fatigue must be accounted for in system design. No single design performs optimally across all contexts, motivating a more principled and context-sensitive approach to deploying permission assistants in agentic systems. Janus is publicly available to support future investigation into this dimension of agentic system design.
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Why AI Demands a New Approach to Data Center Delivery
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NVIDIA Unlocks AI Compute at Scale, Inviting Partners to Power the AI Infrastructure Buildout | NVIDIA Blog
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The Rising Unsustainability of AI Graphics Cards Production
arXiv:2607.01258v1 Announce Type: new Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software. In particular, graphics cards have become central to AI training, with frequent hardware updates required to meet escalating computational demands. However, the environmental damages of graphics cards production remain understudied. This study addresses this gap by estimating the environmental damages associated with graphics cards production over the past decade (2013-2025). We analyze trends in energy consumption, carbon emissions and resource depletion. We compile and provide a dataset documenting the environmental damages of NVIDIA workstation graphics cards production since 2013. Our analysis of this dataset reveals a steady increase in production-related impacts over the period. Our finding highlights the need for greater transparency in life-cycle data, a persistent challenge in AI environmental assessments. While operational efficiency improvements (e.g., energy-efficient training, carbon-aware computing) are often prioritized, our results underscore that production-related impacts are also escalating and cannot be overlooked. The AI community must move beyond incremental optimizations and confront the necessity of sufficiency. This shift may demand structural changes such as policy interventions, hardware design for longevity, and cultural shifts away from perpetual growth and increased performance.
The Download: a startup has a solution for AI’s groupthink problem
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. LLMs are stuck in a groupthink groove. This startup is trying to get them out. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1…
The Benchmark Ceiling: Human Judgment, Evaluation Scarcity, and the Political Economy of AI Capability Measurement
arXiv:2607.01254v1 Announce Type: cross Abstract: Benchmarks are the primary instruments through which AI capability is measured, compared, and governed. This paper argues that the validity of frontier AI benchmarks is a function of the quality of human judgment embedded in their construction, and that this quality is structurally scarce in ways that standard scaling narratives obscure. As foundation models approach ceiling performance on existing evaluation suites, discriminating signal concentrates in the hardest benchmark items, precisely those requiring elite expert judgment to design. We term this the benchmark ceiling problem: the progressive exhaustion of evaluation signal as models saturate the easy majority of items while the difficult tail, authored by a thin stratum of highly expert evaluators, remains the only source of genuine discrimination. The paper develops this argument in three steps. First, we present a formal model of benchmark signal depreciation. Benchmark scores are public signals of latent model quality, but their precision depends endogenously on benchmark validity. As frontier capability rises and as contamination or strategic optimization increases, fixed benchmarks depreciate as measurement instruments. The model shows that valid signal concentrates in hard-tail items, that the replacement cost of such items rises convexly with frontier capability, and that private benchmark producers underinvest in validity relative to the social optimum. Second, drawing on platform data from micro1 covering over one thousand credentialed professionals, we document the scarcity premium associated with high-judgment, low-codifiability evaluation labor. Third, we develop the political economy and governance implications.
CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse
arXiv:2607.01433v1 Announce Type: new Abstract: Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended questions, in what has been termed the artificial hivemind effect. Here, we introduce CreativityNeuro, a data-free method for enhancing divergent thinking in LLMs via contrastive weight steering. We evaluate our method across multiple creativity assessments and report several main findings. On the Divergent Association Task (DAT), a vocabulary-space creativity test, CreativityNeuro improves performance by up to 14 human percentile points. Next, in a large-scale human evaluation (N=720) on the Alternative Uses Test (AUT) and the Task Task, CreativityNeuro achieves significant improvements in originality, surprise, and creativity, transferring to longer-form and more open-ended tasks. Importantly, we find that across all three tasks, CreativityNeuro demonstrably reduces measures of mode collapse. Moreover, activation steering achieves comparable performance to CreativityNeuro on the DAT, but it does not transfer to the AUT and Task Task, demonstrating the effectiveness of weight-space steering in generalizing to unseen tasks. In conclusion, CreativityNeuro improves divergent thinking and reduces mode collapse without requiring behavioral data, re-training, or gradient-based fine-tuning, providing a straightforward way to enhance LLM performance in creative domains.
Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases
arXiv:2607.01425v1 Announce Type: new Abstract: Understanding large, complex codebases, especially those with obfuscated structures and incomplete documentation, remains a significant challenge. Existing code summarization solutions often rely on a single language model or coding assistant like Claude Code, and treat source code as flat text, underutilizing the rich interdependencies and hierarchical information within a repository. To address these shortcomings, we propose Agent4cs - a multi-agent framework that summarizes large codebases in a bottom-up fashion, where a summarization agent focuses on producing robust summaries; a keyword-extraction agent proactively identifies critical information from subfolders; and a quality-assurance agent iteratively refines the outputs for readability, coherence, and completeness. Evaluated on 7 frontier models, Agent4cs improves semantic consistency across all folder levels by average 8% compared to two structured prompting baselines with code segments. Furthermore, extensive evaluation on real-world datasets demonstrates up to 38% gains in normalized keyword coverage rate over the same baselines.
PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations
arXiv:2607.01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision. Although many existing methods successfully generate prediction-changing alternatives, they often produce unrealistic or infeasible recommendations due to a lack of explicit mechanisms for incorporating domain knowledge and intervention constraints. Neuro-symbolic AI offers a promising direction by combining data-driven predictive models with symbolic reasoning capable of representing human-understandable rules and feasible actions. This paper presents PACE, a modular neuro-symbolic framework for generating feasibility-aware counterfactual explanations. The framework separates prediction and reasoning into two components: a neural predictive model for classification and a symbolic reasoning layer that enforces domain-specific constraints during counterfactual generation. By explicitly modeling feasible interventions, the framework produces explanations consistent with domain knowledge while remaining interpretable and actionable. The approach is model-agnostic and adaptable to domains requiring realistic decision support. A case study is conducted on the Adult Income dataset, combining a multilayer perceptron classifier with Answer Set Programming (ASP) rules encoding feasible modifications to education, occupation, and working hours while preserving immutable attributes. Results highlight the trade-off between counterfactual validity and plausibility and show that symbolic constraints yield explanations that better satisfy domain-specific feasibility requirements, illustrating the potential of neuro-symbolic methods for transparent, feasibility-aware counterfactual explanation in explainable AI.
Why Three AI Labs Suddenly Started Building Scientific Infrastructure
This article examines how leading AI labs are developing infrastructure to support scientific research workflows like literature review and hypothesis generation.
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
arXiv:2607.01531v1 Announce Type: new Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks. World models learned with deep networks are flexible but data-hungry and transfer poorly beyond their training distribution. Program-synthesized world models, written as source code by LLMs and refined through counterexample-guided inductive synthesis (CEGIS), are instead data-efficient and reusable, yet they have been demonstrated mainly on structured-state worlds with a given object vocabulary, and a single program search does not scale to pixel-rendered environments whose object structure must be hypothesized flexibly. We introduce OPINE-World, an LLM agent that learns an object-centric programmatic world model online from interaction. OPINE-World couples two cooperating agents in a loop of hypothesis and test, one acting in the environment and one synthesizing the model in code with replay verification and model-based planning, and it steers exploration with a Bayesian measure of object-type adequacy we call ontology error. We evaluate OPINE-World on ARC-AGI-3, a benchmark for skill-acquisition efficiency in which the object vocabulary, the goal, and the action semantics are withheld. OPINE-World solves 20 of 25 games without per-game training and reaches an action-efficiency score of 78.4 against the human baseline.
Frontier model security risks, an annulled poll, deepfakes—UN report warns AI is outpacing safeguards
Frontier model security risks, an annulled poll, deepfakes—UN report warns AI is outpacing safeguards Opinion ThePrint On Camera - Videos - In Pictures Society & Culture Science - Science - Tech Events - Off The Cuff More Search Add ThePrint as a trusted source✕ Youtube Twitter Instagram Facebook Whatsapp Telegram LinkedIn Friday, July 3, 2026 Opinion - National Interest - PoV - 50-Word Edit ThePrint On Camera - Videos - In Pictures Society & Culture Science - Science - Tech Events - Off The Cuff More - Judiciary - Education - YourTurn - Work With Us - Campus Voice Search Home Tech Frontier model security risks, an annulled poll, deepfakes—UN report warns AI is... Tech World # Frontier model security risks, an annulled poll, deepfakes—UN report warns AI is outpacing safeguards ## Prelim report of UN scientific body on AI names Anthropic's Mythos, saying
Palantir CEO Slams OpenAI, Anthropic Over AI Pricing and Data Risks, Warns of China's AI Progress
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AI Cybersecurity Risks and Opportunities Every Executive Must Know
AI is rapidly reshaping cybersecurity, accelerating both defense and attack capabilities. Senior Executive AI Think Tank members explore whether leaders should be optimistic or concerned—and outline the urgent steps executives must take to close emerging security gaps. ... Artificial intelligence is rapidly redefining the cybersecurity battlefield, shifting the balance between defenders and attackers at a pace many organizations are struggling to match. As enterprises ...
Anthropic details Claude Fable 5's cybersecurity safeguards and AI jailbreak framework
Anthropic details Claude Fable 5's cybersecurity safeguards and AI jailbreak framework SEARCH Searching... # Anthropic details Claude Fable 5’s cybersecurity safeguards and AI jailbreak framework After a US government suspension and a narrow jailbreak scare, Anthropic rolls out a new safety classifier and proposes an industry-wide system for grading AI vulnerabilities Share Share on X Share on LinkedIn Share on Facebook Add us on Google by Editorial Team Jul. 2, 2026 Anthropic’s newest AI model got grounded by the US government, and the company is now showing its homework on how it plans to keep things locked down going forward. Following a security incident in June 2026 that led the US Commerce Department to temporarily suspend global access to both Claude Fable 5 and its more powerful sibling Mythos 5, Anthropic has detailed a suite of cybersecurity safeguards and proposed a
AI is Already Powering Cyber-Attacks. Can it Power Cyber Defense? - Infosecurity Magazine
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Adoption, Deployment & Impact
Six months into 2026, one pattern dominates: the AI express keeps accelerating while the UX local service stands delayed. That is my verdict after grading the 18 AI and UX predictions I published in… | Jakob Nielsen
# Six months into 2026, one pattern dominates: the AI express keeps accelerating while the UX local service stands delayed. That is my verdict after grading the 18 AI and UX predictions I published in… | Jakob Nielsen Published: 2026-07-02T19:27:21+00:00 Source: linkedin.com (linkedin.com) Language: en ## Story Six months into 2026, one pattern dominates: the AI express keeps accelerating while the UX local service stands delayed. That is my verdict after grading the 18 AI and UX predictions I published in… | Jakob Nielsen Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s [User Agreement](https://www.linkedin.com/legal/user-agreement), [Privacy Policy](https://www.linkedin.com/legal/privacy-policy), and [Cookie Policy](https://www.linkedin.com/legal/cookie-policy). Skip to main content [LinkedIn](https://www.linkedin.com/) - [Top Content](htt
Closing The AI Execution Gap: Firms Risk Revenue, Talent And Trust - FutureIoT
Closing The AI Execution Gap: Firms Risk Revenue, Talent And Trust - FutureIoT Thursday, July 2, 2026 Find us on LinkedIn Find us on Twitter Find us on Facebook - Login Technology Industry Application No Result View All Result Technology Industry Application No Result View All Result No Result View All Result # Closing the AI execution gap: Firms risk revenue, talent and trust by FutureCOO Editors July 2, 2026 Photo by Mikhail Nilov from Pexels: https://www.pexels.com/photo/people-listening-to-the-woman-standing-in-front-9301245/ Professional services firms are facing mounting operational and commercial risks as a widening gap emerges between AI ambition and execution, according to Thomson Reuters’ 2026 Future of Professionals report. Despite widespread adoption of AI tools, many organisations are failing to translate usage into measurable value, placing client relat
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PYMNTS Intelligence finds that workplace AI exposure helps determine which AI tools consumers use in their personal lives.
Companies Are Throttling Employees’ AI Use Because It’s Too Expensive
Sources and leaks from Amazon, Adobe, Atlassian, Citi, and more show what is really happening with AI right now: companies are trying to rein in AI use as costs spiral out of control.
AI Everywhere, Transformation Nowhere: Report | Information & Data Manager
AI Everywhere, Transformation Nowhere: Report | Information & Data Manager # AI Everywhere, Transformation Nowhere: Report Thursday, July 2, 2026 - 2:16 Nearly three-quarters of large enterprises use AI regularly or across most business processes, but only 10 per cent say AI is core to how their business operates, a global survey has found. The gap suggests adoption is no longer the challenge - organisational transformation is. The 2026 Global Enterprise AI Report from Publicis Sapient, announced at VivaTech in Paris, surveyed 1,550 AI decision-makers across the US, UK, France, Germany, Australia and the UAE between April 29 and May 14, 2026. Respondents work at organisations with at least 500 employees and US$100 million in annual revenue. The research found 47 per cent believe AI is already capable of meeting today's business needs, yet 42 per cent say their organisations are not
Meta's Zuckerberg Admits AI Agents Are Behind Schedule — While AWS and Microsoft Bet Billions They're Not - FourWeekMBA
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Saudi companies take AI beyond the IT department | AGBI
Saudi companies take AI beyond the IT department | AGBI Analysis # Saudi companies take AI beyond the IT department By July 3, 2026 7:36 AM Saudi companies say they are seeing more practical applications and stronger returns when AI experimentation is adopted in all departments Getty Images for Unsplash+ - Employees developing own tools - Language prompts instead of code - Engineers refine suggestions Saudi companies say they are finding greater success with generative AI by putting it into the hands of employees outside their technology departments. Staff are building software and automating tasks using plain-language prompts instead of computer code. The approach is helping companies overcome a common problem with early AI adoption, where projects were largely confined to technology teams and often failed to address the needs of frontline employees or customers. By allowing b
A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content
arXiv:2607.01248v1 Announce Type: new Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation. In these settings, users may obtain highly structured answers, plans, and judgments without sufficient domain practice. This paper proposes a practice auditing framework for LLM use and AI-generated content governance. It introduces collective empiricism to describe how LLMs compress and reorganize large-scale human experience into outputs that appear empirical and rational, and pseudo-rational cognition to describe how users may mistake AI-generated structured expression for their own rational understanding. The paper analyzes AI subjectivity illusion, subjectivity structures in input materials, template loops in AI-AI conversations, statistical misjudgment in AIGC detection, and memory pollution when generated content enters future contexts, long-term memory, retrieval spaces, or agent skill systems. To reduce these risks, the paper proposes an auditing process based on requirement definition, problem-boundary identification, evidence-source auditing, practical validation, reverse questioning, logging, version management, rollback, and renewed cognition. The framework does not reject AI productivity; it argues that LLM outputs should be returned to verifiable, reproducible, and intervenable processes of practice. The paper provides a conceptual and auditable framework for cognitive risks in LLM interaction, AI-generated content governance, long-term memory systems, and human-AI interaction.
Attribution and Persuasion: The Paradox of Interpretable AI
arXiv:2410.01114v3 Announce Type: replace Abstract: This paper studies AI persuasion by distinguishing between two reasons for disagreement: attention differences, where the AI detects features the decision-maker missed, and comprehension differences, where the AI and the decision-maker interpret observed features differently. We show that AI is more effective in persuading the decision-maker when the disagreement is due to attention differences rather than comprehension differences. We also show that the AI's interpretability shapes how the decision-maker attributes the sources of disagreement and, in turn, whether they follow the AI's recommendation. Our main result is that making AI uninterpretable can actually enhance persuasion and, in the presence of career concerns, improve decision accuracy.
AI Assistance for Human Review of Default Judgments
arXiv:2607.01256v1 Announce Type: new Abstract: Overwhelmed courts in the United States review millions of default judgments each year. Unfortunately, such manual reviews are time-consuming and prone to error. In an audit of 188 debt collection cases granted default judgment by the Superior Court of Los Angeles, we find that 4% contained major defects that should have entirely prevented default judgment, 10% contained inconsistencies requiring reduced judgments, and 32% contained errors requiring amendment prior to judgment. To support courthouses in default judgment review, we collaborated with courthouse attorneys and judges in designing a Default Assistant. The Default Assistant employs large language models to evaluate a case with respect to predetermined legal requirements and provide cited recommendations for an expert user's review. We equip users to verify these recommendations by grounding the assistant's explanations in cited quotes and tables from the original case filings. We conduct a controlled study with 66 law students that conservatively simulates court review, with more time and resources than court staff. We nevertheless find users aided by the Default Assistant were 6.0% more accurate on the average requirement than unaided reviewers (p < 1.0e-4). Simultaneously, users were 25.9% faster in reviewing the average requirement than unaided reviewers (p < 2.5e-10). Statutory requirements demanding extensive document search realized the largest gains, with error reductions and time savings from AI assistance up to 62% and 34%, respectively, relative to unassisted user performance and with differences statistically significant (p < 0.05). Our work provides a proof-of-concept that AI assistants with citations have the potential to help resource-constrained courts conduct default judgment review more accurately and efficiently.
What AI Does to Novice Coders & Why I Don’t Agree That It’s Bad | by Chris Dunlop | Realworld AI Use Cases | Jul, 2026 | Medium
What AI Does to Novice Coders & Why I Don’t Agree That It’s Bad | by Chris Dunlop | Realworld AI Use Cases | Jul, 2026 | Medium Sitemap Sign up Sign in Get app Write Search Sign up Sign in ## Realworld AI Use Cases https://medium.com/realworld-ai-use-cases I wanted to have a publication that helped share tips and tricks from my consultancy where we implement ai for companies like the Olympic team, the Stock Exchange and other B2C companies. Press enter or click to view image in full size The chassis of an Army halftrac scout car get a workout. When this test driver at a large Midwest plant has put the machine through its paces, he’ll know if the machine meets the exacting specifications of the Army. White Motor Company, Cleveland, Ohio. Sourced from the Library of Congress. View public domain image source here Member-only story # What AI Does to Novice Coders & Why I Don’t
AI enthusiasts are in a race against time, AI skeptics are in a race against entropy
I have long looked up to the Fin (formerly Intercom) engineering org. When Christine and I put together our AI mandate1 last year, we drew a lot of inspiration from a piece by Darragh Curran, CTO, called simply “2x”, where he challenged the R&D org to double their productivity in the next 12 months.
Achieving operational excellence with AI
Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to…
Which AI Model to Use for Any Task Without Overpaying
Which AI Model to Use for Any Task Without Overpaying SubscribeSign in Playback speed × Share post Share post at current time Share from 0:00 0:00 / Playback speed × Share post 0:00 / Preview Audio playback is not supported on your browser. Please upgrade. 36 2 1 ## Stop paying frontier prices for work a cheaper AI would crush. Grab the model-picker prompt that routes the deck, the repo, and the call. Jul 02, 2026 ∙ Paid 36 2 1 Share Fable 5 came back yesterday after over two weeks offline, this time with different terms: usage caps, a credit model, and a filter that reroutes some work to a weaker model. The larger point is not whether it’s up or down or different than it was when it first launched. The last few weeks have made it very clear that we cannot depend on intelligence being available in a particular model format or through a particular pipe. We rent t
Palantir CEO: "Something Has Gone Completely Wrong" In AI. Alex Karp Says Enterprises Are Paying To Lose Their Competitive Edge - 24/7 Wall St.
Palantir CEO: "Something Has Gone Completely Wrong" In AI. Alex Karp Says Enterprises Are Paying To Lose Their Competitive Edge - 24/7 Wall St. S&P 5007,506.00 +0.31% Dow Jones52,880.00 -0.07% Nasdaq 10029,648.40 +1.00% Russell 20002,999.11 +0.22% FTSE 10010,634.60 -0.52% Nikkei 22569,604.80 +1.90% Investing # Palantir CEO: “Something Has Gone Completely Wrong” In AI. Alex Karp Says Enterprises Are Paying To Lose Their Competitive Edge By Thomas Richmond Published Jul 2, 12:43PM EDT ### Quick Read Karp contends token-based AI exposes enterprise IP while delivering little value, a thesis that Palantir's 85% revenue growth and 46% operating margin appear to validate. Karp places durable AI profits only at compute (NVIDIA) and application layers, yet PLTR trades at a forward P/E of 80 versus NVDA's 23. Act now: the analyst who called NVIDIA in 2010 just named his top 10 AI stoc
Enterprise AI shifts from access to ROI in 2026
# Enterprise AI shifts from access to ROI in 2026 Published: 2026-07-02T20:30:00+00:00 Source: marketscale.com (marketscale.com) Language: en ## Story Enterprise AI shifts from access to ROI in 2026 Skip to content [](https://www.marketscale.com/) [Overview](https://www.marketscale.com/) [Platform](https://www.marketscale.com/platform) [Discover](https://www.marketscale.com/resources) [Industries](https://www.marketscale.com/industries) [Community](https://www.marketscale.com/community) [Pricing](https://www.marketscale.com/pricing) [Blog](https://www.marketscale.com/blog) [About](https://www.marketscale.com/about) [Log in](https://studio.marketscale.com/) [Start free](https://www.marketscale.com/get-started) [Book a demoDemo](https://www.marketscale.com/book-demo) [‹ Back to Industries](https://www.mark
KPMG: 42% of Enterprises Can't See Where AI Money Goes
KPMG: 42% of Enterprises Can't See Where AI Money Goes # KPMG: Enterprises Are Scaling AI – But 42% Still Can’t See Where the Money Goes AI adoption is accelerating - but without cost visibility and clear accountability, most organisations are scaling spend faster than they're scaling returns 4 Productivity & Automation News Published: July 2, 2026 Christopher Carey New KPMG research of more than 2,000 global business leaders finds that AI adoption is accelerating fast – but the gap between organisations capturing real value and those still chasing it comes down to two things: who owns AI outcomes, and whether organisations can see where the money is going. The findings, published in KPMG’s Global AI Pulse: Q2 2026, draw on responses from senior leaders across 20 countries at organisations with annual revenues exceeding $50 million. The picture that emerges is of an enterprise A
Geopolitics, Policy & Governance
#tech #ai #aigovernance #openai #nationalsecurity | David Timis
#tech #ai #aigovernance #openai #nationalsecurity | David Timis Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. # David Timis’ Post AI & Future of Work Speaker | Senior Fellow in AI Governance | Prompt Engineering Trainer | AGI Strategy Facilitator 8h Edited - Report this post If you told someone a year ago that OpenAI would voluntarily offer a 5% equity stake to the U.S. government, they'd have called you crazy. Sam Altman has reportedly floated a concept to the Trump administration where OpenAI would cede a 5% equity slice to a national public sovereign wealth fund. Based on OpenAI's $852 billion valuation from its recent funding round, that single 5% equity slice is worth a staggering $42.6 billion. For the past few months, I’ve been underlining the unusual, bipartisan momentum building in
AI & Tech Brief: Fable is back - The Washington Post
Anthropic says it’s releasing its most advanced model, Claude Fable 5, back to the public after the Commerce Department lifted its export directive. The process surrounding government AI intervention is as opaque as ever.
OpenAI ‘in early talks to give 5% stake to US government’
CEO Sam Altman argued move would share benefits of AI and it would involve other firms doing similar, report says Business live – latest updates OpenAI is reportedly in early stage talks to give a 5% stake in the ChatGPT developer to the US government as artificial intelligence companies attempt to smooth relations with Donald Trump’s administration. The OpenAI chief executive, Sam Altman, has argued that giving the US public a financial stake in the company is the best way to share the benefits of AI, according to the Financial Times, which cited two unnamed people familiar with the discussions. Continue reading...
Altman's global safety pitch has a 5% twist
Sam Altman is engaging with Washington to discuss AI safety and industry regulation.
A hold on AI: on the Preliminary Report of the Independent International Scientific Panel on AI - The Hindu
A hold on AI: on the Preliminary Report of the Independent International Scientific Panel on AI - The Hindu You are logged in Loading... LOGOUT You don’t have any Active Subscription. Subscribe now Subscribed with another email? Logout and Login with that one. Your active subscription(s) Account subscription benefits alongside Premium Stories, Editorials, Opinions and more. Unlock these with Subscription Products you've access to Additional Subscription Benefits Account Settings Need help with your subscription? # A hold on AI: on the Preliminary Report of the Independent International Scientific Panel on AI ## AI holds scientific promise, but it should not unfold unchecked Updated - July 03, 2026 01:31 am IST READ LATER SEE ALL Remove The UN’s Preliminary Report of the Independent International Scientific Panel on AI drives at a few fault lines in the rapid investment
Washington and AI Giants: New Voluntary AI Standards — The AI Chronicle
Washington and AI Giants: New Voluntary AI Standards — The AI Chronicle Share: ✓ Copied! US government building and digital icons representing AI standards. ## ⚡ Key Points - US government is negotiating voluntary AI standards with major tech firms. - Focus lies on 'frontier model' safety and protecting national security. - The approach contrasts with the EU's mandatory AI Act regulations. - Geopolitical competition with China drives the preference for flexibility. - Tech companies seek a balance between transparency and trade secrets. In a pivotal moment for the future of digital governance, the United States government is engaged in high-level negotiations with leading artificial intelligence companies to shape a new framework of voluntary standards. According to a Financial Times report, later confirmed by Reuters, this move reflects Washington's growing apprehension regarding t
Trump Administration Reportedly on Verge of Standards Deal With Big AI
Reading time 2 minutes · As I’ve written before, part of the reason AI news is such a mess right now is that what AI companies are and aren’t allowed to do is not clear. But a voluntary deal with Big AI is reportedly in the works that might smooth things out significantly (Your mileage ...
Centre for Competition Policy Conference – Beyond Persuasion: Content, Algorithms and Consumer Protection (2 days)
A two-day conference on content, algorithms, and consumer protection hosted by the Centre for Competition Policy.
Commerce Department Extends Export Controls To Advanced AI Models; Authorizes Release To Specific Trusted Partners - - United States
Commerce Department Extends Export Controls To Advanced AI Models; Authorizes Release To Specific Trusted Partners - - United States ARTICLE 3 July 2026 # Commerce Department Extends Export Controls To Advanced AI Models; Authorizes Release To Specific Trusted Partners MBMayer Brown More #### Contributor Mayer Brown is an international law firm positioned to represent the world’s major corporations, funds, and financial institutions in their most important and complex transactions and disputes. Explore Firm Details The US Commerce Department has taken unprecedented action by extending export controls to artificial intelligence models themselves and API-based access to those models, issuing company-specific directives that require licenses before deployment to foreign persons worldwide. Following concerns about offensive cybersecurity capabilities in Anthropic's Mythos and Fable m
Council Post: AI Governance Needs To Scale At The Pace Of AI. Here's How To Make It Happen
When AI governance policies aren’t embedded into workflows, organizations default to a reactive posture.
The EU's Human-Centric Approach to Artificial Intelligence
Explore the EU's AI strategy, AI Act, human-centric regulation, NATO cooperation, and challenges in AI governance and security.
Data protection rules slow LLM rollout in Europe, study says | Euronews
A new Governance AI study reveals that EU data protection rules are stalling AI adoption, leaving 11% of advanced LLM releases delayed or blocked in Europe compared to the US.
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