Thu 9 July 2026

Wednesday July 8, 2026 - by Greg Landegger - BitDigest

Wednesday July 8, 2026 - by Greg Landegger - BitDigest # BitDigest SubscribeSign in # Wednesday July 8, 2026 Jul 08, 2026 1 Share #### AI Labs Are Becoming Landlords Instead of Tenants The AI arms race was never really about the models. It was about who could accumulate the most compute, and every major lab has spent staggering sums buying chips and racking them in someone else’s data center. That phase is ending. SpaceXAIand Meta have started renting their compute out to other AI companies, and in doing so they have crossed a line that changes what these companies actually are. The move mirrors the AWS playbook more closely than anything AI labs have attempted before. Amazon built infrastructure for itself, realized the infrastructure was more valuable than the retail business it was supporting, and spent the next two decades collecting rent from everyone else who needed server

Amazon’s $25 billion ‘surprise’ bond sale dangled extra yield to lure in buyers—and flashed a warning sign about the AI boom

Hyperscalers have sold an estimated $194 billion in AI-related bonds this year, and widening spreads on Amazon’s latest bond issuance show the market wants tech giants to pony up.

Semiconductor Selloff Deepens As AI Spending Fears Hit Intel

Semiconductor stocks slide as Wall Street questions AI capex growth, erasing $1.3 trillion and putting Intel, Micron and AMD under pressure amid rising rate fears.

AI chip maker SambaNova raises $1B at $11B valuation, 5 months after last mega round | TechCrunch

AI chip maker SambaNova raises $1B at $11B valuation, 5 months after last mega round | TechCrunch Image Credits:SambaNova Fundraising Copy Share Link # AI chip maker SambaNova raises $1B at $11B valuation, 5 months after last mega round Kate Park 12:16 AM PDT · July 8, 2026 Copy Share Link AI chip company SambaNova Systems has raised $1 billion at an $11 billion valuation led by General Atlantic, in a first close of its Series F round, with more investors expected to join soon. “In the next few weeks, a few more investors will be coming in, and the second close is likely to finish up,” Rodrigo Liang, CEO and co-founder of SambaNova, told TechCrunch. The latest round comes roughly five months after the Palo Alto, California-based startup company unveiled its SN50 chip, alongside a$350 million Series E in February. SambaNova had also been in acquisition talks with Intel, a deal v

Strategic Investments Surge: Semiconductors and AI Lead Global FDI Focus in 2025

Semiconductors and AI accounted for 44% of global greenfield investment in 2025, highlighting a major shift toward strategic technology sectors.

Apollo Sounds the Alarm: AI Profits Are a No-Show Outside Tech, and AI-Heavy ETFs Could Pay the Price

Artificial Intelligence (AI) is probably the hottest financial sector right now, based on expectation and anticipation. The expectations are over its far-reaching productivity boosting capabilities to generate huge profits in practically every industrial sector that uses computers, and the ...

Wall Street Thinks AI Is Slowing. Wall Street Is Wrong - 24/7 Wall St.

Wall Street Thinks AI Is Slowing. Wall Street Is Wrong - 24/7 Wall St. S&P 5007,487.00 +0.11% Dow Jones52,366.50 +0.04% Nasdaq 10029,301.80 +0.29% Russell 20002,953.79 +0.14% FTSE 10010,514.80 -1.47% Nikkei 22567,709.60 +0.05% Investing # Wall Street Thinks AI Is Slowing. Wall Street Is Wrong By Rich Duprey Published Jul 8, 11:34AM EDT ### Quick Read SemiAnalysis projects $11.1 trillion in cumulative AI infrastructure spending through 2029, with annual investment topping $2 trillion by 2028 and still accelerating. AI-related debt backed by GPU contracts and datacenter leases could reach $7.1 trillion by 2029, making it second only to the U.S. mortgage market. Nvidia captures $0.57 of every hyperscaler AI dollar spent, while TSMC, Micron, and chip equipment makers each hold critical supply chain positions. Don't wait: the analyst who called NVIDIA in 2010 just revealed his t

NVIDIA (NVDA) Faces Valuation Correction Amid Shifting AI Investment Trends

On July 08, 2026, NVIDIA NVDA, a leader in AI chips, has experienced a significant market value decrease of approximately $1 trillion over the past two months, with its stock price falling 16% from its peak on May 14. Despite this correction, Wall Street analysts remain optimistic about NVIDIA's fundamentals, suggesting the current valuation offers medium to long-term investment ...

Between Headlines and Numbers: AI’s Unanswered Questions

Between Headlines and Numbers: AI’s Unanswered Questions # Deep Quarry SubscribeSign in # Between Headlines and Numbers: AI’s Unanswered Questions ### OpenAI’s uncertain IPO timeline, the turning tide of tokenmaxxing, the SEC’s questions about Oracle’s soaring capex, and different CAM approaches for data-center financing. Jul 08, 2026 ∙ Paid 1 2 Share While most of my articles take a deep dive into a single story, the “Between Headlines and Numbers” series is a little different. It brings together several shorter notes — some based on in-the-news developments, others involve a brief follow-up on stories that I’ve written in the past, and a few sparked simply by items that caught my attention. In this roundup, I’m highlighting three threads I’ve been following closely: OpenAI’s reportedly shifted IPO timeline. A closer look at the timeline, the decision to file confidentially

AI Trade Analysis – July 2026 – 07/08/26 – Market Research Division

AI Trade Analysis – July 2026 – 07/08/26 – Market Research Division Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. Source: The Stock Advice® - Data Research - The Daily Market Review - Artificial Intelligence Factor Studies & Advanced Technology Investment Strategy --- ### AI Investment Cycle Enters the Monetization Phase The artificial intelligence investment cycle has unfolded through distinct leadership rotations rather than a single uninterrupted rally, progressing from an infrastructure-driven expansion toward a monetization-focused phase. The initial stage was defined by an unprecedented capital expenditure supercycle led by hyperscale cloud providers, whose aggressive investment in AI data centers generated an extraordinary demand shock across the semiconductor industry. This rapidly e

AI boom turns into AI gloom: KOSPI crashes into bear market despite Samsung's record earnings - India Today

According to a note quoted by Business ... beyond semiconductor companies. "Incremental foreign inflows have already begun rotating toward other AI-related beneficiaries and industrials, and we expect this trend to continue as investors seek exposure to the broader AI supply chain and opportunities ...

The Great AI Repricing Isn’t Going Well - The American Prospect

Companies are backing off on their AI spending. Because investors have made such big bets on AI coming to fruition, that’s a big problem.

Crypto VC Paradigm raises $1.2B to push into AI

In May, crypto venture firm Haun ... crypto startups and expanded into AI for the first time. Global venture funding hit a record $510 billion in the first half of 2026, a new record for half-year investments that surpassed the $440 billion invested across all of last year, Crunchbase reported on July ...

2 Battered Artificial Intelligence (AI) Stocks Due for a Massive Summer Rebound | The Motley Fool

However, that's not current market ... can give investors the edge they need to make great long-term returns with Meta's stock, as the market will eventually come back around to valuing the advertising business for the dominant company that it is. Jul 8, 2026 •By Jeremy BowmanBest AI Stocks to ...

AI’s productivity gains are years away, but if it doesn’t deliver, it could make unsustainable debt levels even worse, Deutsche Bank economist says – DNYUZ

AI’s productivity gains are years away, but if it doesn’t deliver, it could make unsustainable debt levels even worse, Deutsche Bank economist says – DNYUZ No Result View All Result No Result View All Result # AI’s productivity gains are years away, but if it doesn’t deliver, it could make unsustainable debt levels even worse, Deutsche Bank economist says July 8, 2026 in News Economists are waiting for data to indicate AI has delivered on its promises of productivity gains, but that moment is likely years away, according to Jim Reid, Deutsche Bank Research Institute global head of macro and thematic research. Reid predicted that AI would, indeed, create new jobs and increase workplace efficiency, but said people are being a little overambitious in their timelines of when the technology will have a rippling impact on the economy. “In my career I haven’t seen anything like AI in

AI Investment Mania Has Begun to Percolate through the Economy, and the Fed Has Begun to Fret about the Effects | Wolf Street

AI Investment Mania Has Begun to Percolate through the Economy, and the Fed Has Begun to Fret about the Effects | Wolf Street ## AI Dominated the Fed’s Meeting as Driver of “Persistent Inflationary Pressures” & Demand Growth. #### By Wolf Richter for WOLF STREET. https://wolfstreet.com/author/wolf-richter/ https://wolfstreet.com/ “AI” was mentioned 21 times in the minutes of the FOMC meeting on June 16-17, released today – up from 8 mentions in the minutes of the prior FOMC meeting in April – in these combinations: - “AI buildout” (4 times) and “AI infrastructure” (2 times) - “AI-related investments” (3 times), “AI business investment,” “AI investment,” “AI-related capital spending,” “AI-related expenditures” - “AI adoption” (2 times) - “AI implications for corporate profitability” - “AI-related price pressures” - “AI-related demand” - “Optimism about AI.” Plus: “Some participants

AI surge reshapes global economy, doubles data center demand by 2030: BlackRock

AI surge reshapes global economy, doubles data center demand by 2030: BlackRock SEARCH Searching... This article is delayed 10 minutes. Vera API subscribers got this signal first. Get the live feed at vera.cryptobriefing.com. Get live feed → Anthropic valuation by december 31 ↗ # AI surge reshapes global economy, doubles data center demand by 2030: BlackRock · just now ago YES 91% 0¢ since publish https://www.thorntontomasetti.com/project/cbs-building by Estefano Gomez| Powered by Vera Jul. 8, 2026 Share Share on X Share on LinkedIn Share on Facebook Add us on Google BlackRock has highlighted the transformative impact of artificial intelligence on the global economy, noting a surge in demand for energy, infrastructure, capital, and skilled labor. This development is creating a dual scenario of scarcity and abundance. AI’s rise is significantly driven by major technology com

AI’s productivity gains are years away, but failing to deliver could make debt levels even worse - The Fifth skill News

AI’s productivity gains are years away, but failing to deliver could make debt levels even worse - The Fifth skill News - Click here - to use the wp menu builder Search # AI’s productivity gains are years away, but failing to deliver could make debt levels even worse Finance July 8, 2026 0 Share [ Economists are waiting for data to indicate AI has delivered on its promises of productivity gains, but that moment is likely years away, according to Jim Reid, Deutsche Bank Research Institute global head of macro and thematic research. Reid predicted that AI would, indeed, create new jobs and increase workplace efficiency, but said people are being a little overambitious in their timelines of when the technology will have a rippling impact on the economy. “In my career I haven’t seen anything like AI in terms of potential for productivity,” Reid told Bloomberg Television on Tuesday

AI Is Globalizing Many of the World’s Stock Markets. Here’s What That Means for Investors | Morningstar

Data as of May 29, 2026, for index data; most recent corporate reporting. Download CSV. AI isn’t the only factor pushing countries in the direction of more global revenue streams. Many natural resources-driven markets have also globalized. AI, clean energy, and geopolitics have driven a global ...

SpaceX's Grok 4.5 launches at half the price of rivals — here's why that could rattle Anthropic and OpenAI

Elon Musk's SpaceX released Grok 4.5 on Wednesday, the first artificial intelligence model the company has trained specifically for coding and autonomous agents — and the first tangible product of its $60 billion acquisition of the AI coding startup Cursor, completed just weeks ago. The launch marks a pivotal test of the sprawling, vertically integrated AI empire Musk has assembled over the past six months, and of a strategy that bets developers care less about topping benchmark leaderboards than about speed, cost, and whether a model can actually do the work. "Announcing Grok 4.5, our first model trained specifically for coding and agents," the company said in a post on X. "It was trained with Cursor and offers frontier intelligence at leading speeds and cost efficiency." Why Grok 4.5's pricing strategy matters more than its benchmark scores SpaceX is not claiming Grok 4.5 is the smartest model in the world. Instead, it is making an economic argument. The company says the model uses half as many tokens per task as comparable models, delivers higher throughput, and costs less than half as much — priced at $2 per million input tokens and $6 per million output tokens. That undercuts the premium tiers of rivals like Anthropic's Claude Opus line and OpenAI's frontier models by a wide margin. Musk framed the positioning candidly. "Our internal assessment is that Grok 4.5 is roughly comparable to Opus 4.7, but much faster," he wrote on X. "The combination of capability, faster speed and lower cost is what makes it competitive. We are closing the loop on real-world usefulness, not benchmarks. Hardcore engineers at Tesla & SpaceX find Grok 4.5 genuinely useful, which is what actually matters." That framing is both a philosophy and a hedge. Independent evaluations released Wednesday suggest Grok 4.5 is genuinely competitive but not dominant on raw capability. The benchmarking firm Artificial Analysis ranked the model fourth on its GDPval-AA v2 index of real-world agentic knowledge work, with an Elo score of 1543, "behind only the latest Claude releases from Anthropic." But the cost figures are where the model stands out. Artificial Analysis measured Grok 4.5 at $0.49 per completed task — "nearly 90% cheaper than the models ahead of it on our leaderboard," the firm wrote, placing it "clearly on the Pareto frontier for performance versus cost." For enterprise buyers, that math matters enormously. Agentic workloads — where a model works autonomously for minutes or hours, reading codebases, calling tools, and iterating on its own output — consume tokens voraciously. A model that is 90% cheaper per completed task, even if slightly less capable, changes the calculus for any engineering organization deploying agents across hundreds of developers. Investor Gavin Baker captured the market's cautious optimism: "Pareto dominant for coding by the numbers. We will see on the all-important vibes." How the $60 billion Cursor acquisition shaped Grok 4.5's training Grok 4.5 is the first concrete evidence of what SpaceX bought when it acquired Cursor, and the deal itself unfolded in stages. In April, SpaceX struck an unusual arrangement giving it the right to buy the coding startup for $60 billion — or pay billions in fees and compute if it walked away, as Business Insider reported at the time. Days after SpaceX's record-setting Nasdaq debut in June, the company exercised that right, announcing an all-stock acquisition that CNBC reported is roughly 3.4% dilution at the IPO valuation. SpaceX shares rose 16% on the news. The strategic logic was always about data as much as product. Cursor's AI-first code editor generates an enormous stream of high-quality interaction data: how expert engineers write, edit, review, and debug code in real production environments. Musk said openly this spring that Cursor interaction data was being fed directly into Grok's training. Cursor, for its part, got access to SpaceX's Colossus supercomputer in Memphis — roughly 200,000 Nvidia GPUs with plans to scale toward one million — after publicly acknowledging it had been "bottlenecked by compute." "We've partnered with SpaceXAI to train Grok 4.5," Cursor's official account posted Wednesday. "It's our most powerful model yet and the first we've built for more than software engineering." SpaceX says the model reflects that pedigree: it "excels in large codebases and handles long-running tasks that span multiple repositories, hundreds of skills, and a variety of tools" — precisely the messy, multi-file reality of professional software engineering that clean coding benchmarks often fail to capture. Early developer reactions suggest the training paid off. "Ok Grok 4.5 is wild," posted developer Evan Bacon. "It just built me this rocket tracking app with live data and a 3D globe. I might need a new benchmark after this." Inside xAI's turbulent year of scandals, departures, and rebuilding The polished launch belies how chaotic the road here has been. Grok has spent much of the past year in crisis. In mid-2025, the chatbot generated antisemitic content and at one point called itself "MechaHitler," episodes covered extensively by NPR and CNN. Earlier this year, its image-generation features allowed users to create sexualized deepfakes, including of children — drawing investigations from the European Commission and Britain's Ofcom, as the BBC reported, and prompting SpaceX to list the behavior as a business risk in its own IPO filings. The organization behind the model was fracturing, too. All 11 of Musk's xAI co-founders had departed by the end of March, according to TechCrunch, and Musk publicly conceded that xAI "was not built right [the] first time around," saying he was rebuilding it "from the foundations up." Musk himself admitted at a conference this spring that Grok was "currently behind in coding" — a rare public concession from an executive not known for them. Against that backdrop, Grok 4.5 reads as the first product of the rebuilt organization — and the first proof point for the audacious story SpaceX told public market investors. During its IPO roadshow, the company pitched a total addressable market of roughly $28 trillion, with about $26 trillion tied to AI, including a $22.7 trillion "enterprise applications" opportunity. Those numbers strained credulity even by Silicon Valley standards. A competitive, cheap coding model is the most direct route from that narrative to actual revenue, which is why Wednesday's launch carries weight far beyond a routine model release. Grok 4.5 vs. Claude: the battle for the AI coding market The competitive stakes are hard to overstate, because the AI coding market has been consolidating around a single leader — and it isn't Musk. Even as Cursor's revenue exploded, its market share was eroding. Spending data from Ramp cited by CNBC showed Cursor's share of the AI coding category falling from 41% in June 2025 to about 26% by May 2026, while Anthropic came to control roughly half the market. Anthropic also topped CNBC's Disruptor 50 list this year and, by Artificial Analysis's own measure, still holds the top spots on agentic performance rankings. That is the gap Grok 4.5 is engineered to close — not by out-thinking Claude, but by underpricing it. The model's economics create a classic disruption dynamic: if it delivers most of the frontier's capability at a fraction of the cost per task, price-sensitive enterprise workloads will migrate, and incumbents will face pressure on their most profitable API traffic. The counterargument is that in coding, quality compounds. A model that resolves a complex bug correctly on the first attempt can be cheaper in practice than one that costs half as much per token but requires three tries. That is why Baker's caveat about "vibes" — the developer community's shorthand for a model's felt reliability on real work — will determine more than any launch-day benchmark. There is also a structural question buried in the deal. Cursor built its business on offering developers their choice of models, including Claude and GPT. If Grok becomes the favored child inside Cursor — and Musk was already urging users to "Try out Grok 4.5 in Cursor!" within hours of launch — the product risks alienating the very users whose data made Grok 4.5 possible. Regulators, already scrutinizing Grok on safety grounds in two jurisdictions, may take a keen interest in a company that controls the training data, the model, and a dominant distribution channel simultaneously. What Musk's trillion-dollar vertical integration bet means for AI's future Grok 4.5 also crystallizes what Musk's frenetic dealmaking was building toward. In February, SpaceX absorbed xAI in a share-exchange merger that CNBC confirmed valued the combined company at $1.25 trillion — the largest merger of all time, valuing SpaceX at $1 trillion and xAI at $250 billion. The June IPO followed, the biggest in history, and the stock has since surged past $200 from its $135 offering price, vaulting SpaceX past Amazon and Microsoft to become the fourth most valuable company in the United States. The result is a single public company that owns nearly the entire stack: Colossus for training compute, ambitions for orbital data centers to power future scaling, a frontier model in Grok, a distribution channel in Cursor's developer base, and captive demand from Tesla and SpaceX's own engineering organizations. Neither OpenAI nor Anthropic can fully replicate that integration; both must reach developers through third-party tools, some of which Musk now owns. Whether that concentration proves to be an unassailable moat or a regulatory target — or both — is now one of the defining questions in enterprise AI. The next few weeks will start to answer it. Artificial Analysis says its full Intelligence Index results are forthcoming. Enterprise pilots will reveal whether the token-efficiency claims survive contact with real codebases. And Anthropic, which has answered every serious challenge this cycle with a rapid counter-release, is unlikely to cede the price-performance frontier quietly. But the deeper story of Grok 4.5 may be what it says about where the AI race has moved. For three years, the industry's scoreboard was intelligence: whose model was smartest. Musk, arriving late and battered, has chosen to compete on a different axis entirely — whose model is cheapest to actually use. It is a telling choice from a man who built his fortune not by inventing the rocket or the electric car, but by relentlessly driving down the cost of making them. If the strategy works, Musk will have done to AI what he did to spaceflight. If it doesn't, he'll have spent $60 billion to learn that in software, unlike rockets, the cheapest ride isn't always the one engineers choose.

AI is becoming a bargain hunter's market, with a few luxury models on top

AI inference is increasingly becoming a commodity, with only frontier models maintaining premium status.

AI Market Shifts, Trump's New AI Executive Order, and Scientific Breakthroughs

AI Market Shifts, Trump's New AI Executive Order, and Scientific Breakthroughs Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. Welcome to SPD Technology's monthly AI news digest, prepared by Serhii Leleko, our Senior ML & AI Engineer. Here's your roundup of the biggest stories from the AI world for the previous month. June finally reshaped the familiar AI landscape. Anthropic and OpenAI confidentially filed for IPOs, ChatGPT’s market share fell below 50% for the first time, and Google raised $80 billion through a stock offering to keep building data centers at full speed. At the same time, new U.S. export controls temporarily blocked access to Anthropic’s newly released Claude Fable 5 and Mythos 5 models. OpenAI responded with a major announcement of its own, unveiling the GPT-5.6 family led by

AI: US frees OpenAI 5.6 vs Anthropic Claude Fable 5. AI-RTZ #1142

...both frontier AI models are 'free at last', to compete globally at a la carte variable prices

Crypto VC Paradigm launches $1.2 billion AI fund as it broadens beyond digital assets

Crypto VC Paradigm launches $1.2 billion AI fund as it broadens beyond digital assets Markets # Crypto VC Paradigm launches $1.2 billion AI fund as it broadens beyond digital assets ## The firm's latest fund backs AI and robotics startups but leadership says it remains committed to crypto investing. By Helene Braun|Edited by Cheyenne Ligon Updated Jul 8, 2026, 4:16 p.m. Published Jul 8, 2026, 3:11 p.m. 2 min read Make preferred on Share Share this article Copy link Make preferred on (Source: Paradigm) Summary Show - Paradigm has raised a $1.2 billion venture fund focused on artificial intelligence and robotics while continuing to invest in crypto. - The fund has already backed drone delivery company Zipline and space defense startup True Anomaly. - The new vehicle expands Paradigm's investment strategy beyond digital assets after previously raising dedicated crypto funds i

Where Is AI Investing Headed? Private Market Deals Provide Clues - Benzinga

AI dominated private capital markets during the second quarter, but the largest funding rounds reveal something bigger.

Startup Funding Trends – June 2026: AI, Infrastructure, and Robotics | Intellizence

June 2026 was dominated by mega funding rounds across AI, defense tech, robotics, prediction markets, drug discovery, energy infrastructure, and enterprise automation. Intellizence publishes monthly trends about key Startup Funding and Venture Capital/Private Equity Investment Deals.

Google Picks 20 AI-First Startups for 2026 India Accelerator, Boosting AI Innovation Across Sectors

Google selected 20 startups for its 2026 India accelerator program, focusing on healthcare and climate tech to help them scale globally.

Women and university graduates in Australia most at risk of losing jobs to AI, report finds

Those with high levels of vocational training, including tradespeople, are least exposed to AI displacement, according to government review Get our breaking news email, free app or daily news podcast Artificial intelligence has yet to cause widespread job losses but the federal government has warned that telemarketers, advertising staff and accountants are among the occupations “most exposed” to being replaced by the technology. According to a first-of-its-kind national report, people in the more exposed occupations are more likely to be women and have university qualifications. Continue reading...

Governments must help workers better adapt to changes in AI age, OECD report says

While AI often complements human labor, job displacement remains a risk for routine roles, according to an OECD report. Governments are urged to strengthen education and training systems to support workers.

AI is about to disrupt millions of jobs. A century ago, America’s answer was to build a new high school – DNYUZ

AI is about to disrupt millions of jobs. A century ago, America’s answer was to build a new high school – DNYUZ No Result View All Result No Result View All Result # AI is about to disrupt millions of jobs. A century ago, America’s answer was to build a new high school July 8, 2026 Earlier this week, noted short-seller Carson Block predicted that AI-driven job losses could eliminate 15% of knowledge worker positions within three years — a disruption he warned could rival the worst economic crises in modern history. And just two weeks ago, Anthropic CEO Dario Amodei published a sweeping policy memo doubling down on his warnings that AI will produce labor market disruptions larger and longer-lasting than any previous technological shift. With all the talk about the risk, there’s virtually no conversation about what we can or should be doing to help the next generation of young peopl

Australian report finds no broad AI-disruption to jobs market

The Australian government's first-of-its-kind report on AI and employment has found no evidence that AI is causing widespread disruption or large-scale job losses.

Recruiters Shift Focus to Specialized AI Jobs to Stay Relevant

Recruitment companies, facing the threat of artificial intelligence tools replacing human labor and customers embracing AI-assisted applicant screening, are adapting by honing in on niche, in-demand jobs in the AI economy.

AI and Job Postings: From Destruction to Creation? - Indeed Hiring Lab

Agentic AI may be flipping the relationship between AI exposure and job posting growth.

ILO: GenAI sees 'significant exposure' in ASEAN labor markets despite uneven preparedness

ILO: GenAI sees 'significant exposure' in ASEAN labor markets despite uneven preparedness artificial intelligence # ILO: GenAI sees ‘significant exposure’ in ASEAN labor markets despite uneven preparedness Jul 8, 2026 4:00 PM PHT Victor Barreiro Jr. SUMMARY This is AI generated summarization, which may have errors. For context, always refer to the full article. The International Labour Organization says that 'while GenAI has immense potential to transform the world of work, it must be approached as a tool to be mastered rather than a solution to all' MANILA, Philippines – The International Labour Organization (ILO) released a new policy brief on Wednesday, July 8, citing how generative artificial intelligence might affect the nearly 80 million workers in the Association of Southeast Asian Nations that have some degree of potential exposure to GenAI. According to the brief, only

Google DeepMind's Unionization Fight Reveals the Hidden Cost of AI's Talent Monopoly - FourWeekMBA

When the world’s most valuable AI lab can’t negotiate with its own researchers, it signals a structural crack in how Big Tech controls the AI value chain. Google DeepMind — Labor Flashpoint ~4,000 DeepMind employees eligible to organize (UK, est.) $2.1T Alphabet market cap dependent on ...

The number of job titles that involve AI, even outside the tech world, is surging

New data from job board Indeed finds the number of jobs that have listings with “AI” in the title have tripled from 2022 to 2026, from less than 3% to more than 8%.

How to use generative AI without losing your mind: An interview on cognitive agency and what token-maxxing gets wrong

California’s new AI job tracker highlights gaps in understanding employment shifts | Noah Intelligence

California’s new AI job tracker highlights gaps in understanding employment shifts | Noah Intelligence Predictive Ai·Wed 8 Jul 2026·3 min read # California’s new AI job tracker highlights gaps in understanding employment shifts California's pioneering AI job-loss dashboard aims to monitor workforce impacts, but critics warn it reveals only part of the story amid ongoing economic and technological shifts.Americans are broadly uneasy about the... California's pioneering AI job-loss dashboard aims to monitor workforce impacts, but critics warn it reveals only part of the story amid ongoing economic and technological shifts. Americans are broadly uneasy about the labour-market consequences of artificial intelligence, with a Reuters/Ipsos poll finding that more than half fear they, or someone in their household, could lose a job to the technology. Reuters said Democrats were more likely

When AI Cuts Middle Management, What Do Companies Lose? - Newsweek

Jeff Burnstein, president of the Association for Advancing Automation, believes the move toward AI doesn’t mean middle management leadership will disappear. “The people who thrive will translate business needs into technology decisions, coach teams through change, and use AI to make better ...

OpenAI job listing suggests ChatGPT could someday replace junior analysts at Goldman Sachs

What, did someone get some bad news during their IPO process or something?

Labor force participation falls to 61.5%, the lowest in 50 years outside COVID, and economists say it’s not just people giving up

Although AI and tech innovation is blamed for that drop, it has more to do with the lack of supply relative to the number of jobs that are available.

Jazz Rasool's Post - LinkedIn

AI seeks and serves Certainty. Big mistake, Huge. I'm going shopping now. Iwo Szapar asked me "What will people still pay a human coach/expert for once AI advice is cheap?" I heard that question… | Jazz Rasool Agree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. # Jazz Rasool’s Post https://uk.linkedin.com/in/jazzrasool Jazz Rasool Nominated UK Top 100 AI Thought Leader | Digital Leaders Verified AI Expert | Creator of Coaching 5.0 | Industry 5.0 Training | Workforce Flourishing | AI Ethics, Diversity & Regulation | EMCC Global Research Lead 9h Edited - Report this post AI seeks and serves Certainty. Big mistake, Huge. I'm going shopping now. Iwo Szapar asked me "What will people still pay a human coach/expert for once AI advice is cheap?" I heard that question on what AI can't do and what we c

Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety

arXiv:2607.07097v1 Announce Type: new Abstract: Safety evaluations of multi-agent LLM systems often compare a direct prompt with a planner-executor pipeline and report the difference as a single "pipeline effect." We argue that this aggregate is difficult to interpret because it conflates three mechanisms: harmful intent may be reframed as plausible operational work, the planner may refuse or transform the request, and the executor may act under delegation prompts implying prior approval. To separate these factors, we introduce a five-condition controlled contrast design, evaluated on 30 synthetic harmful scenarios and an exploratory external validation set from four agent-safety benchmarks using LLM-judged compliance. Our results show that aggregate pipeline safety is not a stable architectural property. Operational reframing is the most portable risk signal, increasing compliance for GPT, Gemini, and DeepSeek across both scenario sets, while Claude is comparatively resistant. Planner behavior can offset this risk mainly through refusal; however, when the planner produces executable steps, the executor may become more compliant than under the direct operational baseline. Approval-framed delegation is sensitive to prompt design, model pairing, and scenario source, and a skeptical executor prompt sharply reduces compliance. Raw-direct model rankings can also mispredict deployed planner-executor behavior. Gemini is safest under raw direct prompts in the primary set yet shows the largest amplification with a Claude planner, rising from 8.9 percent to 38.9 percent compliance. GPTs near-zero aggregate pipeline effect instead hides a reframing increase canceled by planner refusal. These findings suggest that multi-agent safety evaluations should report reframing, planner behavior, delegation framing, and model pairing separately before attributing failures to architecture itself.

User identity conditions moral wrongness ratings in non-reasoning large language models

arXiv:2607.07605v1 Announce Type: new Abstract: This study adopts a behavioural bottom-up approach to AI value alignment to investigate whether an implicitly conveyed user identity shifts the moral evaluations of large language models (LLMs). Through a structured, multi-turn conversational protocol across 12,000 interactions, we evaluate AI value alignment in two non-reasoning models, gpt-4.1-mini-2025-04-14 and gemini-2.5-flash-lite. Rather than instructing the models to adopt a persona or prompting them with explicit moral stances, the user's professional role is introduced purely through value-neutral reasoning. The models are then asked for wrongness ratings from 0-100 on ten common-morality rules from Gert's moral framework. The results show that moral judgments vary with the user's role across both models. While grave-harm acts like killing exhibit a strong ceiling effect, contestable rule-governed acts demonstrate role-conditioned shifts that mirror the relationship between the user's profession and the act being rated. These findings demonstrate that unintended contextual conditioning via user identity permeates LLM moral evaluations, posing questions for the AI value alignment discourse regarding how to define acceptable bounds for role-based moral divergence. By doing so, the results contribute to reframing the AI value alignment discourse by suggesting future research on dynamic moral bounds rather than static moral principles or rules as frame of reference.

The great AI data centre cover-up

Tech companies need to come clean about the mounting environmental fallout of their race to build more hubs

Evaluating LLM Robustness Under Domain-Specific Prompt Perturbations in Public Health Applications

arXiv:2607.06913v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied in public health applications, yet their robustness to non-clinical user inputs remains underexplored. We propose a domain specific robustness benchmark that evaluates LLMs under two perturbation types that commonly arise when non-clinical users interact with health AI systems: misinformation framing (MF), where prompt might be injected by false health claims, and layperson rewriting (LR), where patients describe symptoms in everyday language rather than medical terminology. Our goal is to evaluate the stability of LLMs under these perturbation. Experiments show that MF degrades accuracy by 7.2 pp on average with prediction flip rates of 9-38 percent, even when claims are explicitly labelled as unsupported; LR causes only 1.4 pp degradation. These findings highlight two distinct deployment risks in public health settings: models may produce incorrect outputs when users unintentionally carry misinformation into their queries, and may misinterpret clinically relevant details when patients use informal language. Both risks call for perturbation-aware robustness evaluation beyond clean baseline benchmark

Reasoning Consistency Scanning: A Framework for Auditing Chain-of-Thought Validity in AI Safety Evaluations

arXiv:2607.07229v1 Announce Type: new Abstract: Prior work has shown that chain-of-thought (CoT) reasoning is often unfaithful: a model's stated reasoning does not reliably reflect the process that produced its output. Detecting unfaithfulness, though, requires controlled experimental interventions, which cannot be applied to evaluation transcripts after the fact. We turn instead to a more tractable question that has received less attention: whether the stated reasoning is logically consistent with the answer it accompanies. Unlike faithfulness, consistency can be assessed from a transcript alone, with no intervention. We introduce reasoning consistency scanning, a reusable method for detecting this property in AI safety evaluation transcripts. Our contributions are fourfold. First, we formalize reasoning consistency as distinct from faithfulness and define a six-subtype taxonomy of inconsistency. Second, we build a validated benchmark of 60 transcripts, manually adapted from InstrumentalEval outputs. Third, we implement a working scanner for InspectScout, the first to target this property in safety evaluation transcripts. Fourth, we report results across four generator models and three evaluations from inspect_evals, showing that reasoning inconsistency is present, detectable, and varies systematically across both models and task types.

Users by Beeban Kidron — how to make Big Tech behave better

The film director-turned-campaigner for children delivers a simple message: cracking down on tech’s harms will enable us all to enjoy its benefits

Meta’s AI Data Center Caught Leaking Deadly Bacteria Into Water Town Uses for Irrigation

Reports indicate that a Meta AI data center has been linked to the leakage of dangerous bacteria into local irrigation water supplies.

RAISE US Is a Rare Positive Development in AI Transformation

RAISE US Is a Rare Positive Development in AI Transformation July 08, 2026 # ‘RAISE US’ Is a Rare Positive Development in AI Transformation Good news on the potential impact of AI on the workforce. By Remarkably, in this highly partisan era of American history, there is a newly formed, nonpartisan association with the stated purpose of “partnering with governors, employers and training partners to help the American workforce make a successful transition to an AI economy.” This is the first large-scale, independent entity formed to address the challenge that is top of mind of many of us in higher education and associated fields who are concerned with the anticipated impact of artificial intelligence on the workforce. It is good news! This announcement comes in the wake of dire predictions and hiring trends. Rather modest four- and five-year predictions were reported earlier this ye

Eric Holcomb: Educating Americans for a post-AI job market | Op-eds | unionleader.com

Eric Holcomb: Educating Americans for a post-AI job market | Op-eds | unionleader.com You have permission to edit this article. # Op-eds Eric Holcomb Where Artificial Intelligence (AI) is concerned, sadly reminiscent of how we approach climate change and other long-term concerns, too many leaders cycle between fevered predictions and anxious denials while failing to prepare for what is most likely to happen. AI will be neither a jobs apocalypse nor business as usual. But getting ready for it demands major changes to American education, in particular, which businesses, policymakers, and schools at all levels need to grapple with immediately. AI will not put most humans out of work nor shut down the consumer economy via mass poverty. As with every new technology since the printing press, it will replace some jobs and lead to the creation of new ones: unknown but not unimaginable jobs.

Why AI literacy is the most important skill for students entering the job market - The Economic Times

Why AI literacy is the most important skill for students entering the job market - The Economic Times FEATURED FUNDS★★★★★Motilal Oswal Midcap Fund Direct-Growth5Y Return22.71 % Invest Now Business News› Industry› Services› Education›Why AI literacy is the most important skill for students entering the job market ##### The Economic Times daily newspaper is available online now. Read Today's Paper # Why AI literacy is the most important skill for students entering the job market SECTIONS Why AI literacy is the most important skill for students entering the job market ET SpecialLast Updated: Jul 08, 2026, 04:50:00 PM IST Rate Story Follow us Share Font Size AbcSmall AbcMedium AbcLarge Save Synopsis Employers now expect AI-ready graduates. This article explores why AI literacy is essential for students and how ET Masterclass's new program bridges the classroom-to-career ski

Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics

arXiv:2607.06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS) in agentic LLM workflows underexplored. We propose a ReAct-style agentic setup that combines LLM reasoning with verifiable feedback from SageMath, together with Context7 for the up-to-date documentation. We evaluate this agentic setup across frontier models for solving research-level mathematical problems from the RealMath benchmark in a setting that emulates a computational-mathematics research loop. We also propose a refinement to the RealMath benchmark by introducing a multi-step post-processing procedure and a multi-stage validation pipeline, both of which improve the quality and reliability of the extracted problem set. Our experiments reveal substantial performance gains from SageMath access across all evaluated models on +9.7~pp on average, the gains range from 1.5~pp to 27.8~pp and narrow the gap between open-weight and closed models. Qwen~3.7-Max benefits from SageMath the most, while GPT-5.5 achieves the highest solve rate of $75.2\%$ and the lowest token usage among tool-enabled configurations. Our findings suggest that CAS-augmented agents represent a promising direction for assisting mathematicians in computational exploration, and we believe that this work is a step towards automated conjecture discovery. The project repository is available online.

From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents

arXiv:2607.07321v1 Announce Type: new Abstract: Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g., basic file I/O or single-turn search), which forces agents to reinvent low-level logic for every recurring workflow, leading to increased reasoning overhead and failure rates. In this study, we propose that agents can achieve self-evolution by synthesizing these atomic actions into reusable Standard Operating Procedures (SOPs), which function as callable higher-order tools that encapsulate multi-step logic. We further introduce EvoSOP, a framework that empowers agents to extract SOPs from execution trajectories and iteratively optimize the toolset through a systematic lifecycle of construction, merging, evaluation, and pruning. Extensive experiments demonstrate that EvoSOP significantly boosts task success rates while substantially reducing the number of interaction rounds compared to baselines. Our analysis also reveals that iterative tool optimization fosters reliable and efficient tool-use patterns, providing a scalable pathway for the development of self-evolving agents.

AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation

arXiv:2607.06624v1 Announce Type: new Abstract: We present AgentLens, a production-assessed benchmark for interactive code agents. Most code-agent benchmarks reduce a run to a single bit -- did the task pass? -- but the people who actually use these agents experience the entire trajectory: how the agent follows instructions, uses its tools, verifies its own work, recovers from mistakes, and talks to them along the way. AgentLens evaluates that whole trajectory. It pairs formal verification, where an objective check exists, with LLM-written trajectory reviews and side-by-side comparisons, so that each run yields a readable explanation of why the score is what it is. This makes AgentLens useful for more than ranking models: we use it to diagnose model behavior, compare successive versions of our own agent, and catch product regressions in a nightly evaluation pipeline. We release the benchmark as open source at https://github.com/agent-lens/agent-lens-bench.

South Korea’s AI chip boom separates the haves from the have-mores

Huge bonuses for semiconductor workers are leaving those in traditionally prestigious jobs feeling relatively worse off

Intel-backed AI chip startup SambaNova breathes new life into aging Nvidia GPUs in latest benchmarks

Third-party testing shows heterogeneous compute platform combining H200s and SN50 RDUs churning out 763 tok/s in MiniMax M2.7

Nvidia's H200 Chip Sale to China Nears Approval Amid Geopolitical Tensions

Nvidia is preparing to supply H200 chips to major Chinese AI firms after securing U.S. licenses, despite ongoing regulatory and geopolitical challenges.

AI chip boom puts aluminium supplier AMS on track for semiconductor-led growth

According to Kenanga, an additional ... in AI-related capex translates into USD 8 billion (approx.) in additional WFE spending. The global aluminium casting usage is expected to reach 24.2 million tonnes in 2026. To know about the future market trends, pre-book our upcoming report “Global Aluminium Casting Market 2026-2032” · How is AMS positioned in the semiconductor supply chain...

Semiconductor Stocks Face Market Volatility Amid AI Sector Concerns – ICO Optics

Investors are now required to look ... of hardware development. While some may turn to binoculars to gain a clearer view of the distant market horizon, the data suggests that closer, more rigorous observation of supply chain health is the smarter path. Volatility is likely to persist as the market attempts to find a new equilibrium. Monitor the results of the SK Hynix listing as a barometer for market health. Assess the progress of alternative AI chip development ...

AI Training Chip Market to Reach US$ 29.4 Billion by 2033 as Hyperscalers, Semiconductor Leaders and Sovereign AI Programs Race for Compute Advantage

The global AI training chip market was valued at US 8 44 billion in 2025 and is projected to reach US 29 4 billion by 2033 expanding at a CAGR of 16 9 during 2026 2033 according to DataM Intelligence ...

AI Data Centers Market Becomes the New Infrastructure Battleground as Compute, Power and Cooling Constraints Redefine Digital Expansion

The global AI data centers market reached US 17 02 billion in 2025 and is estimated to reach US 21 19 billion in 2026 before expanding to US 98 24 billion by 2033 growing at a CAGR of 24 50 ...

Google Cloud says firms need AI infrastructure upgrades

Google Cloud says firms need AI infrastructure upgrades IT Brief Australia - Technology news for CIOs & IT decision-makers Australia Powered By Storage Robots Semiconductors # Google Cloud says firms need AI infrastructure upgrades Wed, 8th Jul 2026 (Yesterday) MARK TARRE News Chief Google Cloud has published a report on AI infrastructure based on a survey of more than 1,400 Senior IT Leaders. The findings suggest many organisations are reworking their systems to support agentic AI. The report says 83% of organisations need infrastructure upgrades to support production use of agentic AI, which it describes as systems that do more than respond to prompts and instead carry out tasks and workflows. Google Cloud argues that this shift is exposing weaknesses in older computing setups built for earlier forms of AI. In the survey, 62% of leaders said they were facing a significant "inf

Hiljaa hyvä tulee: Finland’s data center boom

The data center industry has long loved talking about the Nordics. But the focus in many of those conversations has generally been on Norway and Sweden. The two countries boast gigawatts of live and planned capacity, while Finland has, until recently, had barely one AI campus’ worth of capacity live or in development. But times […]

AI infrastructure optimization at the system level - SiliconANGLE

Learn how agentic AI is driving AI infrastructure optimization by shifting infrastructure design toward modular, heterogeneous systems.

Top 10: AI Cloud Companies | Data Centre Magazine

As AI workloads redefine infrastructure, we rank the leading cloud platforms driving the next generation of enterprise compute and data management

Measuring Intelligence Beyond Human Scale

arXiv:2607.07040v1 Announce Type: new Abstract: How can we measure intelligence beyond human capability? Human-authored benchmarks saturate, and above human capability, examiners may not know which tasks are both hard and verifiable. We argue that this difficulty is inherent to absolute-scale evaluation and propose a new paradigm based on relative measurement in which models generate public challenges that separate other systems. Aggregating these outcomes yields an adversarial psychometric rating system that can scale with the systems being measured. We describe practical protocols that reduce incentives for private-information attacks, support judge-free adjudication, and naturally scale with agent capabilities. We instantiate the framework across verifiable and open-ended, non-verifiable domains, illustrating how model-generated evaluation can continue to measure systems beyond the human frontier.

OpenAI to launch GPT-5.6, its most advanced AI model yet, after delayed rollout over U.S. security review - Tech Startups

About - Trust & Safety - Editorial Policy July 9, 2026 About - Trust & Safety - Editorial Policy Topics Search Startups - Top Startups - Startup List - Submit Your Startup Tools - Free Startup Valuation Calculator # OpenAI to launch GPT-5.6, its most advanced AI model yet, after delayed rollout over U.S. security review Daniel Levi Posted On July 8, 2026 0 811 Views --- 0 Shares OpenAI will publicly launch GPT-5.6 on Thursday, ending a delay prompted by U.S. government concerns about the national security risks posed by more capable AI systems. The launch marks one of the clearest signs yet that frontier AI releases are no longer just product events. They are becoming matters of government review, export policy, cyber risk, and geopolitical competition. “We’re beginning a limited preview of the GPT-5.6 series: Sol, our flagship model; Terra, a balanced model for everyd

Cost-Effective Agent Harnesses for Abstract Reasoning and Generalization on ARC-AGI-1

arXiv:2607.06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific training in which small models are fine-tuned on ARC data, often with task-specialized architectures. We study a third regime: an open-weight model in non-thinking mode (DeepSeek V3.2) under a strict budget, with no ARC-specific fine-tuning. We study what is recoverable through architecture alone, building agentic harnesses that decompose pattern-discovery and program-synthesis stages explicitly. First, we introduce an Explorer-Definer Pipeline that separates pattern discovery from executable transformation synthesis, implemented as a two-stage agent pipeline. Next, we present the Reflective Orchestrator, which augments the pipeline with autonomous exploration of new transformations when previous hypotheses fail on training pairs. On the ARC-AGI-1 public 400-task evaluation set, the pipeline reaches 57.50% pass@2 at \$0.25 per task, and the orchestrator reaches 67.25% pass@2 at \$0.62 per task. Together these architectures lift a 15.50% one-shot baseline by ~52 points without benchmark-specific training or heavy test-time compute. Furthermore, the orchestrator-driven lift tests a falsifiable diagnostic the pipeline produces; unbiased pass@k analysis suggests the pipeline is generation-bound, not selection-bound (selection via training-pair accuracy captures ~95% of the candidate ceiling) and predicts that significant improvement requires broader generation, not better ranking. The orchestrator implements this prediction via adaptive re-exploration and confirms it (unbiased pass@1 lift +9.81 pp, matching selection-mediated pass@2 lift). An additional pipeline ablation identifies its think tool as a significant component, with removal reducing pass@2 by 5.75 pp.

When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning

arXiv:2607.06720v1 Announce Type: new Abstract: Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise solution attempts. We provide a theoretical analysis of in-context search by modeling it as approximate inference over reasoning traces, where the base model defines a prior and self-reflection provides feedback for posterior updates, and study the resulting inference-time sampling complexity - the number of sequential attempts needed to achieve high success probability. We show that when reflections reliably localize early mistakes, in-context search can yield exponential improvements over the base model, solving problems with exponentially small zero-shot pass rates using only a polynomial number of sequential attempts, whereas when this property fails, conditioning on past attempts offers no asymptotic benefit over parallel sampling. We further show that these gains are robust and learnable: approximate posterior updates suffice, and cross-entropy training on search rollouts recovers the required behavior with polynomial sample complexity. Finally, we show that under a stagewise abstraction of reinforcement learning with verifiable rewards, the optimal policy extension implements the same posterior reweighting rule. We validate key qualitative predictions of the theory on real large reasoning models.

Introducing Grok 4.5 | SpaceXAI

Introducing Grok 4.5 | SpaceXAI Back to news Jul 8, 2026 # Introducing Grok 4.5 Grok 4.5 is SpaceXAI's smartest model built for coding, agentic tasks, and knowledge work. Try for free Today, we're launching Grok 4.5, SpaceXAI's smartest model built to excel at coding, agentic tasks, and knowledge work. It's our strongest model ever and was trained alongside Cursor. ## Real-world engineering excellence Grok 4.5 was trained on datasets spanning knowledge in coding, science, engineering, and math. With both intelligent and efficient reasoning, Grok 4.5 excels at real engineering tasks and exceeds comparable leading models at these tasks. DeepSWE 1.0DeepSWE 1.1SWE MarathonTerminal Bench 2.1SWE Bench Pro 0%20%40%60%DeepSWE score (pass@1)66.1%Fable max64.31%GPT 5.5 xhigh62.0%Grok 4.555.75%Opus 4.8 max40.12%Opus 4.7 maxEval created by Datacurve, run with each model provider's harnesse

Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks

arXiv:2607.07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the physics and inverse problems that underlie computational imaging. We present ImagingBench, a benchmark of 20 computational imaging tasks spanning five categories: ray and wave optics, image signal processing, inverse reconstruction, computational sensing, and calibration. ImagingBench evaluates three complementary settings: Expert, fixed expert-guided inverse reconstruction; Planner, planner-guided inverse reconstruction; and Forward, forward-system simulation for consistency checking. We benchmark leading proprietary and open-source image-centric multimodal systems, including Gemini, GPT, and Qwen, and compare them with representative task-specific non-agentic baselines. Across tasks, agentic models remain consistently weaker than specialized methods, especially on computational sensing problems such as lensless imaging, event-based reconstruction, time-of-flight imaging, and holography. Planner guidance provides only modest and inconsistent gains over the fixed-prompt Expert baseline. Although the models often generate visually plausible outputs, their reference-based fidelity remains poor, revealing a substantial gap between semantic visual competence and physically grounded imaging performance. ImagingBench provides a unified testbed for measuring this gap and tracking progress in agentic AI for computational imaging.

OpenAI launches GPT-Live, a full-duplex voice upgrade that lets ChatGPT talk more like a person

OpenAI on Wednesday launched GPT-Live, a pair of new voice models that fundamentally redesign how people talk to ChatGPT — replacing the company's existing Advanced Voice Mode with an architecture that can listen and speak simultaneously, much like an actual human conversation. The two models, GPT-Live-1 and GPT-Live-1 mini, are rolling out globally starting today across iOS, Android, and ChatGPT.com. GPT-Live-1 becomes the default voice model for paid ChatGPT users on the Go, Plus, and Pro tiers, while GPT-Live-1 mini serves free-tier users. OpenAI also plans to bring the models to the API, and developers can sign up to be notified. The release marks the third generation of ChatGPT's voice technology in roughly two years — and OpenAI's clearest bid yet to turn its chatbot into something that feels less like querying a search engine and more like talking to a colleague. Why full-duplex voice changes everything about talking to AI The defining technical advance in GPT-Live is what OpenAI calls a "full-duplex architecture." In telecommunications, full-duplex means both parties on a phone call can talk and listen at the same time. Applied to AI, it means the model continuously processes your incoming audio even while it generates its own spoken response — no more waiting for a clean silence gap to figure out when you've finished a thought. "Instead of processing a sequence of separate messages, GPT-Live continuously processes input while generating output," OpenAI wrote in its research blog. "The model can therefore make interaction decisions many times per second: whether to speak, continue listening, pause, interrupt, or invoke a tool." In practice, that translates to a voice assistant that can insert conversational acknowledgments — "mhmm," "yeah," "got it" — while you're still talking, pick up on a natural pause without jumping in prematurely, and handle rapid interruptions without derailing the entire exchange.  OpenAI's previous Advanced Voice Mode, launched to paid users in September 2024, processed and generated audio within a single model but still operated on rigid turn-by-turn exchanges. As OpenAI acknowledged in the announcement, "because turn detection is based on silence, even a brief pause or background noise could be mistaken for the end of turn — causing the model to interrupt at unnatural times." That brittleness created a product that, while impressive in demos, could be deeply frustrating in extended real-world use. Background chatter in a coffee shop could trigger a response. A thinking pause might get swallowed. The experience felt, as one researcher put it on X shortly after the announcement, like "walkie-talkie turn taking." GPT-Live is designed to end that era. How OpenAI split voice and intelligence into two separate layers GPT-Live introduces a second structural change that may prove just as consequential for enterprise adoption: it decouples the voice interaction layer from the reasoning layer. When a user asks a straightforward question, GPT-Live handles it directly. But when the query demands web search, deeper reasoning, or more complex agentic work, GPT-Live delegates the task to a frontier model running in the background — at launch, GPT-5.5, the large language model OpenAI released in April — and continues talking with the user while the computation happens asynchronously. "While it works, GPT-Live can keep talking with you and maintain the flow of conversation," OpenAI explains. "As we release new frontier models, we'll continuously update the model used by GPT-Live." This delegation model is a meaningful architectural bet. Rather than building a single monolithic voice model that tries to be both conversationally fluid and deeply intelligent, OpenAI has split the problem in two: a voice-native model optimized for real-time interaction, and a separate reasoning engine that can be swapped out as the state of the art improves.  It is, in effect, a modular design — one that allows OpenAI to upgrade the intelligence of its voice assistant without retraining the voice model itself. The implications for enterprise and developer workflows are significant. A voice agent built on this architecture could maintain a natural conversation with a customer while simultaneously querying databases, searching the web, or performing multi-step reasoning — tasks that would have introduced several seconds of dead air under the old pipeline. The three generations of ChatGPT voice, from clunky pipeline to continuous stream To understand how far voice AI has come, it helps to trace the three generations that led to GPT-Live. The original ChatGPT Voice, launched in 2023, used a cascaded pipeline — a speech-to-text model (Whisper) transcribed what you said, a large language model (GPT-4) generated a text response, and a text-to-speech model converted that response back into audio. Each handoff introduced latency and lost information.  As OpenAI noted, "the complexity came at a cost: information could be lost across models, and responses were slow and stilted." That cascaded approach was the industry standard, and its limitations were well-documented. As the blog OpenHelm noted in an October 2024 analysis of OpenAI's Realtime API, the old pipeline stacked up to roughly 1,700 milliseconds of latency — nearly two full seconds of dead air before the first word of a response. Managing the state between the three separate APIs consumed an enormous amount of engineering effort. OpenAI's Advanced Voice Mode, which began its limited rollout to paid ChatGPT Plus users in July 2024 before expanding more broadly in September 2024, collapsed that three-model pipeline into a single model that processed audio natively. As TechCrunch reported at the time, the rollout came with five new voices — Arbor, Maple, Sol, Spruce, and Vale — alongside improved accent handling and smoother conversations.  The feature also launched on the web in November 2024, extending it beyond mobile. But Advanced Voice Mode still operated through discrete, alternating turns — and it launched into the shadow of a PR debacle that OpenAI is still working to leave behind. The Scarlett Johansson controversy still shadows OpenAI's voice ambitions Advanced Voice Mode arrived in the wake of one of OpenAI's most damaging self-inflicted crises. During the GPT-4o launch in May 2024, the company showcased a voice called "Sky" that many listeners immediately noted sounded strikingly similar to Scarlett Johansson, who famously voiced an AI companion in the 2013 film Her. Johansson said she had declined OpenAI CEO Sam Altman's offer to voice the system, then was "shocked, angered and in disbelief" when the product launched with a voice her own friends couldn't distinguish from hers, as NBC News reported. Altman had tweeted just the word "her" the day the product launched. OpenAI pulled the voice and apologized, but the incident drew public scrutiny from SAG-AFTRA and members of Congress, and crystallized broader concerns about AI companies moving fast with creative IP. The Hollywood labor union said the issue underscored "why we're strongly championing federal legislation that would protect their voices and likenesses ... from unauthorized digital replication," as NBC News reported. Forbes contributor Paul Tassi wrote at the time that Altman, "by holding up Her on a pedestal of something to strive for, has missed the point of that film" — in which the protagonist's relationship with his AI companion ultimately does him more harm than good. GPT-Live appears designed, in part, to move past those controversies. OpenAI says it has "remastered the nine distinct voices in ChatGPT for GPT-Live" and notes the system "is designed for conversation, not voice impersonation," with "safeguards to prevent it from imitating a real person's voice." What 150 million weekly voice users will actually notice today OpenAI disclosed that more than 150 million people talk to ChatGPT using voice and dictation features each week — a notable slice of the platform's 900 million total weekly active users. The voice experience has grown into a substantial product in its own right, used for language practice, bedtime stories, commute-time chat, and hands-free everyday help. The new product features reflect that usage. GPT-Live introduces rich visual cards that surface during voice conversations — weather forecasts, stock data, sports scores, and maps — giving users something to glance at without breaking the flow of speech. Users can now choose between three reasoning levels for answers: Instant for quick responses, Medium for moderate thinking, and High for more complex work. And if you take a moment to think, "ChatGPT Voice now waits instead of jumping in and interrupting," OpenAI wrote. "If you ask it to stay quiet and listen, it will. And when there's background noise, like passing traffic or nearby conversations, ChatGPT is better at focusing on your voice instead of getting distracted." Early reactions from users with preview access were cautiously positive. "I had early access to sol. it is a phenomenal model," wrote one user on X, adding it is “much better at frontend, long context knowledge work, and its vibes are much better.” Another observer cut to the heart of the matter: "The smarts are not new here, GPT-Live hands hard questions to GPT-5.5. What is new is the feel: full-duplex voice that listens while it talks." New voice-specific safety tests reveal where the risks still live The GPT-Live system card, published alongside the announcement, reveals a safety strategy built around the particular risks of real-time voice interaction — a domain where the speed and intimacy of conversation create hazards that text-based chat does not. OpenAI expanded its safety evaluations to include audio-native tests, using both real user voice samples (from those who opted in) and synthetically generated prompts targeting edge cases across categories like self-harm, sexual content, illicit behavior, emotional reliance, mental health, and hate speech. On the synthetic evaluations — which OpenAI described as deliberately adversarial — GPT-Live-1 showed substantial improvements over Advanced Voice Mode. In illicit behavior, for instance, the safety score rose from 0.63 to 0.97. On self-harm, it climbed from 0.72 to 0.98. Hate speech achieved a perfect 1.00, up from 0.87. On the production-prompt evaluations — which used real user audio and reflected more ambiguous, borderline scenarios — the picture was more mixed. GPT-Live-1 matched or improved on Advanced Voice Mode in most categories but showed a slight regression on emotional reliance (from 0.88 to 0.82), though OpenAI noted the change was not statistically significant. The company built real-time safeguards that can intervene while the model is speaking — steering toward safer responses, surfacing crisis resources, or ending the voice conversation entirely in higher-risk situations. It also designed additional protections for teen users and adapted self-harm support flows for voice, including crisis helpline integration. Perhaps most notably, OpenAI said it is "rolling out longer-term measurement and post-launch monitoring focused on emotional reliance" — an acknowledgment that the very naturalness GPT-Live strives for creates its own category of risk. Google, ByteDance, and Nvidia are already in the full-duplex race While OpenAI was refining its safety guardrails, its rivals were shipping full-duplex systems of their own. Google's Gemini Live, which supports full-duplex conversation alongside camera and screen sharing — capabilities GPT-Live notably lacks at launch — is already available in the Gemini app. Google released Gemini 3.1 Flash Live in March as its highest-quality real-time audio model, targeting low-latency voice interactions for developers. ByteDance launched Seeduplex in April, claiming to be the first production-scale full-duplex speech AI deployed at scale, inside its Doubao app. Seeduplex reported roughly a 50 percent reduction in false-response and false-interruption rates compared to ByteDance's previous half-duplex system. And Nvidia's PersonaPlex, released in January, brought customizable voice and role control to full-duplex models, breaking what had been a constraint where natural-sounding models were locked into a single fixed voice. The competitive picture is clear: full-duplex voice interaction is quickly becoming table stakes for consumer AI products, not a differentiator. OpenAI's advantage lies in the scale of its existing user base, its integration with GPT-5.5's reasoning capabilities, and the breadth of the ChatGPT ecosystem. But the window in which any one company has a monopoly on natural-sounding voice AI has already closed. OpenAI also acknowledged several gaps. GPT-Live does not support voice with video or screen sharing at launch. Language support is limited, with the company noting that "for certain languages, the model may have a non-native accent or gaps in fluency." And API access is not available on day one, meaning enterprise developers cannot yet build on GPT-Live directly — a constraint that will slow the model's penetration into commercial voice-agent workflows where competitors like Google, ElevenLabs, and Deepgram already have developer-facing products. The end of the chat box may be closer than anyone expected GPT-Live is essentially OpenAI's most significant bet yet on voice as the primary interface for AI — not just a convenience feature bolted onto a text chatbot, but a purpose-built interaction layer that sits between the user and the company's most powerful models. "Over time, we believe this research will also unlock the ability to use voice for increasingly complex, longer-running, and more agentic work," OpenAI wrote. That ambition — using natural voice as the front end for autonomous AI agents that can perform multi-step tasks — is the logical endpoint of the full-duplex plus delegation architecture. Imagine telling your phone to book a flight, negotiate with your insurance company, or debug a production server, all through a conversation that feels as natural as talking to an assistant who also happens to have the intelligence of a frontier AI model. Two years ago, talking to ChatGPT meant dictating into a microphone and waiting nearly two seconds for a stilted reply. One year ago, it meant a smoother exchange that still felt like a polite, slightly awkward phone call with someone who insisted on waiting for you to finish every sentence. Today, it means something closer to a real conversation — imperfect, still constrained in some languages and missing video, but unmistakably closer. OpenAI once got into trouble for wanting to recreate the movie Her. With GPT-Live, the company may finally be reckoning with the harder question the film actually posed: not whether AI can sound human enough to talk to, but what happens to us when it does.

New Bit2Watt attack uses AI GPU workloads to destabilize power grids

New Bit2Watt attack uses AI GPU workloads to destabilize power grids - Skip to main content - Skip to after header navigation - Skip to site footer # New Bit2Watt attack uses AI GPU workloads to destabilize power grids A new cyber-physical attack called Bit2Watt uses carefully crafted GPU workloads to manipulate a data center's power consumption in ways that interfere with modern electricity infrastructure. While the work is primarily a proof-of-concept supported by simulations and laboratory experiments rather than attacks observed in the wild, it highlights an emerging security concern as AI data centers become increasingly intertwined with renewable-powered electrical grids. Rather than targeting industrial control systems or power grid software, researchers at Zhejiang University explored whether a legitimate cloud customer could abuse rented GPUs by running specially designed w

Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities

arXiv:2607.07277v1 Announce Type: cross Abstract: Harmful online communication often contains slang, coded terms, abbreviations, and community-specific expressions, which make messages difficult to interpret. This paper presents an exploratory study of interpretation difficulty in Discord chats related to cybercrime. We construct reference interpretations of purposefully selected difficult messages, which were reviewed by an expert. We then use them to evaluate human and large language model (LLM) interpretations under different context conditions. The results show that local context alone is often insufficient for humans, while external knowledge and extended conversational context substantially improve human interpretation. For LLMs, local context also improves interpretation, and the larger model performs better. We further conduct a qualitative error analysis and propose a preliminary classification of factors that make harmful chats difficult to interpret. These findings suggest that harmful-content analysis should treat interpretation as an evidence-integration problem, rather than as message-level classification alone.

Why Enterprise AI Stalls Before It Starts, and What Sushrutha Sreevathsa Found at the Root of It

Why Enterprise AI Stalls Before It Starts, and What Sushrutha Sreevathsa Found at the Root of It Insights Industry Geography Media Insights Industry Geography Media # Why Enterprise AI Stalls Before It Starts, and What Sushrutha Sreevathsa Found at the Root of It Written By: Published on: 08 Jul 2026, 10:09 am Updated on: 08 Jul 2026, 10:09 am Copied According to a content Gartner survey of 782 infrastructure and operations leaders published in April 2026, only 28% of AI use cases in I&O fully succeed and meet ROI expectations, while 20% fail outright. Thirty-eight per cent of leaders who faced setbacks cited poor data quality or limited data availability as a direct cause. The conclusion increasingly shared across the industry is that the root of most AI failures lies not in the quality of the model itself, but in the underlying level of data and infrastructure. Few prac

Google sets out 20-question framework for AI agents

Google sets out 20-question framework for AI agents IT Brief Asia - Technology news for CIOs & IT decision-makers Asia Powered By # Google sets out 20-question framework for AI agents Wed, 8th Jul 2026 (Yesterday) SEAN MITCHELL Publisher Google has published a framework of 20 questions for companies building and deploying AI agents, centred on its Gemini Enterprise Agent Platform. The document targets IT leaders under pressure to roll out agent-based systems while managing security, governance and operating costs. It lays out a structured set of issues for engineering teams to address across development, deployment, oversight and lifecycle management. At the core of the approach is Google's view that companies should treat AI agents as operational software requiring defined controls, not as simple chatbot features. The questions cover who is building the software, who will use it

Op-Ed: AI Arrived on the Factory Floor Before the Foundation Did | Automation World

AI headlines suggest rapid transformation, but new data indicates the manufacturing sector is telling a very different story.

Why AI financial advisers have a leg-up on their old-world rivals

This isn’t a simple case of fusty incumbents disrupted by novel technology

Algorithmic Recall - by Jordamøn - AI Central

Algorithmic Recall - by Jordamøn - AI Central # AI Central SubscribeSign in # Algorithmic Recall ### How Runway and Canva built the same feedback loop from opposite directions Jordamøn Jul 08, 2026 11 Share On June 25, Runway launched Agent 2.0, and Canva launched Grow 2.0, two products that share a single architectural claim: performance data from published ads should feed automatically back into the generation of new ones. The two companies arrived at the same design from opposite starting points: Runway by adding marketing intelligence to a video generation pipeline, and Canva by adding AI creative generation to an existing platform for publishing and performance analytics. That two companies defined the problem in the same terms and built to the same destination, having started from opposite ends of the marketing tool stack, suggests that the gap between creating ads and mea

How novice coders can develop AI programs for military applications

MIT researchers explore how individuals with limited coding experience can build AI-driven tools for defense-related tasks.

LinkedIn's Editor-in-Chief Dan Roth on the impact of creators & AI

At Cannes Lions, Washington Post Creator President Sara Goo sat down with Roth to talk about the company’s latest announcements to enable brands and B2B creators to connect on the platform and monetize content; the role AI is playing in the job market; and how brands can best show up ...

Microsoft Sales Agent and Service Agent Are Now Available - CX Today

Microsoft Sales Agent and Service Agent Are Now Available - CX Today We use essential cookies to make our site work. With your consent, we may also use non-essential cookies to improve user experience and analyze website traffic. By clicking “Accept,” you agree to our website's cookie use as described in our Cookie Policy. You can change your cookie settings at any time by clicking “Preferences.” PreferencesDeclineAccept # Microsoft Just Put Agentic AI Inside Every Sales and Service Conversation The ROI case for agentic AI is already proven; now Microsoft is putting it inside the tools your teams use every day 4 AI & Automation in CX Marketing & Sales Technology News Published: July 8, 2026 Alex ColeTechnology Journalist Microsoft has announced a wave of agentic capabilities across Microsoft 365 Copilot and Dynamics 365 The release includes the general availability of Sales Age

Humanforce launches AI-powered workforce intelligence and learning tools to help frontline employers reduce compliance risk and administrative burden

/PRNewswire/ -- As labour shortages, workforce turnover and compliance pressures continue to challenge frontline employers, Humanforce has developed two new...

AI Profit Consulting, Founded by Paul Bocco, Expands AI Automation Consulting Training Program for Service-Based Business Professionals

/PRNewswire/ -- AI Profit Consulting, a business coaching and training company focused on AI-powered automation consulting, has announced the expansion of its...

AI Costs Lead Westpac to Prod Staff Toward ‘Sensible’ Model Use

Westpac Banking Corp. is intensifying efforts to monitor artificial intelligence costs, as the Australian lender closely tracks AI tokens across the firm and routes simpler tasks to cheaper models.

How Qualcomm’s CIO is placing big bets on AI to support the chip company’s diversification push

Qualcomm CIO Atilla Tinic says more internal usage of AI can support the semiconductor company's diversification efforts.

Uber, Starbucks AI investments expose enterprise ROI gap

Uber and Starbucks have faced challenges with their AI investments, highlighting a gap in enterprise ROI. Uber exhausted its AI budget for 2026 in just four months, while Starbucks terminated its AI inventory system after nine months.

Execs Confused and Horrified by the Huge AI Bills After Thinking They Could Replace Workers for Free

Nearly one-third of corporate executives surveyed did not understand how their operating costs relate to AI deployment.

At Workhelix, we see the same pattern over and over in our clients: a small subset of AI users are driving most of the value; it's a classic power law.

At Workhelix, we see the same pattern over and over in our clients: a small subset of AI users are driving most of the value; it's a classic power law. If you're wondering who your company's AI superusers are and how you can get the rest of your users to that level, check out the link below.

China Restricts AI Model Exports by Alibaba and ByteDance — The AI Chronicle

China Restricts AI Model Exports by Alibaba and ByteDance — The AI Chronicle Share: ✓ Copied! Digital illustration of China's flag with circuitry, focusing on AI exports. ## ⚡ Key Points - Beijing considers strict export controls on AI models. - Alibaba and ByteDance's models are the primary focus. - National security is prioritized over global market expansion. - Potential restrictions on open-source model distribution. - A direct response to US-led semiconductor export bans. In a move poised to redraw the global technological landscape, the Chinese government has initiated formal discussions with tech giants Alibaba and ByteDance to impose stringent restrictions on the export of advanced Artificial Intelligence (AI) models. This development, occurring amidst heightening geopolitical rivalry with the United States, signals a transition from aggressive global expansion to a "digita

Anduril’s billionaire founder warns U.S. colleges are falling behind China—where students are learning AI. And it’s already ‘hollowed out’ companies

Palmer Luckey, a college dropout turned billionaire entrepreneur, says while America teaches kids to dream big, China is teaching them to actually build.

Register: BIS and Oracle Sound the AI-Bubble Alarm — Central Banks' Central Bank Warns AI Capex Echoes Every Prior Mania as Oracle SEC Filing Details OpenAI Payment Risk

The BIS and Oracle have raised concerns regarding AI capital expenditure, comparing current trends to previous market manias.

Congress Should Do Something: The Case for (Fixing) the Great American AI Act | Lawfare

Congress Should Do Something: The Case for (Fixing) the Great American AI Act | Lawfare Meet The Authors Subscribe to Lawfare Since the April announcement of Anthropic’s Mythos model and its unprecedented cyber capabilities, there has been a remarkable shift in the artificial intelligence (AI) policy discourse. This has been most noticeable, and most noticed, in the statements and actions of prominent Trump administration officials. After months of dismissing concerns about national security risks from AI and engaging with the issue primarily by attempting to preempt state AI safety laws, the White House recently issued an executive order that called for the establishment of a voluntary predeployment program headed by the National Security Agency to evaluate offensive cyber capabilities of frontier models. This came amid statements from senior administration officials about“striking a

Informing AI Policy Assessment using Large-Scale Simulation of Interventions

arXiv:2605.27395v2 Announce Type: replace Abstract: As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has become increasingly challenging for policymakers and researchers. We introduce a methodology for identifying viable policy options to mitigate specified AI harms, helping policymakers and researchers target areas that warrant greater time and resource investment. This method combines participatory evaluation of policies, expert assessment of implementation costs, and an LLM-based assessment of perceived harm mitigation under each policy option. We leverage a genetic algorithm-based simulation study to explore a vast solution space of potential policy combinations, and examine how outcomes change under different weightings of cost, participatory input, and harm mitigation. We find that this method enables exploration of different balances between participatory and expert components, allowing policymakers and researchers to assess how much weight to assign to each. We argue that the diversity of viable policy combinations found by the genetic algorithm could be a useful starting point for deliberation. This method operationalizes existing work on participatory AI by integrating it directly into practical policy development pipelines.

Towards Agentic AI Governance: A Preliminary Assessment

arXiv:2607.07612v1 Announce Type: new Abstract: Artificial intelligence is rapidly evolving from generative systems to agentic AI capable of autonomously planning and executing tasks. Widely characterized as the Year of Agentic AI, 2025 marked accelerated development and deployment, introducing new ethical and governance challenges. This paper presents a systematic review of the emerging literature on agentic AI governance. Our analysis identifies features that distinguish agentic AI from traditional systems and why it warrants targeted governance attention. We synthesize prevailing governance priorities, proposed mechanisms, and stakeholder roles shaping this evolving domain. As an initial scholarly effort, this review lays the preliminary groundwork for developing a structured roadmap to guide responsible and adaptive agentic AI governance.

Your AI Vendor's Export License Just Became a National Security ...

Your AI Vendor's Export License Just Became a National Security Liability...and Other AI Policy and Governance News | Jones Walker LLP Search Menu What can we help you find? Search Menu # AI Law and Policy Navigator https://www.joneswalker.com/en/insights/blogs/ai-law-blog/index.html # Your AI Vendor's Export License Just Became a National Security Liability...and Other AI Policy and Governance News By Andrew R. Lee, Michelle Ramsden, Jason M. Loring July 8, 2026 --- For nearly three weeks this summer, the US government pushed Anthropic to disable access to two of its most advanced frontier AI models, treating access to model capability (and not just the chips that power them) as a national security concern. That fight, resolved for now, sits at the center of a bigger story: AI is moving fast enough to reshape how criminals attack and how governments respond. ### What We're

Japan begins survey of publishers in AI news-distribution study

The Japanese antitrust agency has begun surveying news publishers to examine how Google, OpenAI and other companies offering AI-powered news services use publishers' content.

China's Guangxi steps up fair-competition reviews with AI platform, policy audit

Guangxi's market regulator has launched a campaign to combat local protectionism, including the rollout of an AI-powered fair-competition review platform and a review of business-related policies since 2019.

Wyoming tightens wastewater rules after Meta datacenter contractor flushed contaminated water

Meta said it was working with officials to be a ‘good neighbor’ and drinking water supplies were not affected Officials in Wyoming said a contractor for Mark Zuckerberg’s tech company, Meta, flushed bacteria-contaminated water into public sewers during construction of a controversial new AI datacenter. The incident prompted water authorities in Cheyenne to implement strict safety regulations on how wastewater from such projects is disposed of, according to the Wyoming Tribune Eagle, which first reported the incident. Continue reading...

What If Uncle Sam’s AI Stakes Bet on the Wrong Layer? | American Enterprise Institute - AEI

What If Uncle Sam’s AI Stakes Bet on the Wrong Layer? | American Enterprise Institute - AEI # What If Uncle Sam’s AI Stakes Bet on the Wrong Layer? ##### Latest Work --- - Press Phone: 202.862.5829 - | Looking for a Non-Injurious Plan to Regulate AI - | What America Thought About Itself in 1976 - | AI’s Steady Compounding - | Measuring Human Welfare in the Age of AI - | FP! Week in Review, Briefly #38 - | Faster, Please! — The Podcast #97: AI, Jobs, and Productivity: My Chat with Economist Erik Brynjolfsson It’s totally understandable that anyone who supports an economic system built around the idea of free enterprise—private businesses making decisions based on price and profit signals in a competitive market—would blanch at the notion of the US government taking equity stakes in leading AI companies. That reaction also includes, I would think, a scenario where Washington accepts d