Sat 11 July 2026
Daily Brief — Curated and contextualised by Best Practice AI
Experts teach AI their trade, enterprises struggle with capacity, and regulators tighten the net
TL;DRNiam Yaraghi warns that AI-driven productivity gains may be temporary as workers lose the ability to develop core expertise. Meanwhile, 86% of enterprises report their GPUs are running at half capacity or less. US Senator Ed Markey has introduced a legislative package targeting data center energy use and automated hiring. Separately, the Federal Reserve has appointed Marc Andreessen to co-lead a task force on AI's economic impact.
The stories that matter most
Selected and contextualised by the Best Practice AI team
Borrowed expertise: Why AI’s productivity boom may not survive the generation that built it | Brookings
Niam Yaraghi warns AI's productivity gains rely on expertise that today's tools may stop future workers from developing.
Enterprise AI is entering an evaluation gap: Agents are gaining autonomy faster than companies can verify them
Enterprise AI teams are giving agents more freedom at the same moment their confidence in automated testing is collapsing. Half of enterprises have deployed an AI agent or LLM feature that passed internal evaluations and yet still caused a customer-facing failure — one in four more than once — according to the June 2026 VB Pulse survey of 157 qualified enterprise respondents at companies with 100 or more employees. The sample is self-selected rather than a probability sample, so the findings should be read as directional, not precise. But enterprises are not responding by slowing automation: 66% of respondents already permit some production deployment without human review or are building systems intended to do so within the next 12 months. Only 5% say they fully trust the automated evaluations that would make those release decisions. That mismatch is the evaluation gap: the autonomy ceiling is rising faster than the assurance beneath it. It also fits a broader thesis that will be explored at VB Transform 2026: enterprises ship agents first, while the control layers around identity, evaluation, cost, context and orchestration are arriving later. The next year will be a retrofit cycle, with buyers shifting budget toward the systems that make agentic deployments governable and dependable. Why a passing evaluation is not a working agent Traditional software testing usually asks whether a defined input produces an expected output. Agent testing is harder because the system may choose its own sequence of steps, call tools, retrieve data, alter state and respond differently from one run to the next. An agent can make several individually plausible decisions and still reach the wrong result. It may retrieve the correct account but update the wrong field. It may draft a valid refund request but send it without approval. It may call five tools successfully before a sixth step leaks sensitive information or leaves a workflow incomplete. The survey shows enterprises already recognize this limitation. The most common reason for distrusting automated evaluation is poor alignment with real-world outcomes, cited by 29% of respondents. Bias or inconsistency follows at 21%, lack of explainability at 18%, and data leakage or privacy concerns at 17%. That hierarchy matters. Enterprises are saying the score often does not predict what happens when a customer, employee or business process encounters the agent in production — not that automated scoring is too slow or expensive. NIST makes a similar point in its Generative AI Profile: measurements gathered in controlled environments may not transfer cleanly to deployment because behavior changes with prompts, users, context and operating conditions. Its guidance calls for field testing, post-deployment monitoring and clear processes for escalating failures. Capability is not consistency A single successful run proves that an agent can complete a task. It does not prove that it will complete the task reliably. Anthropic’s guidance on agent evaluation distinguishes between measuring whether a system succeeds at least once across repeated attempts and whether it succeeds every time. That distinction is essential for customer-facing or operational workflows. A model that occasionally produces an excellent answer may still be unacceptable if the same task fails unpredictably on the next attempt. Enterprise teams should therefore treat repeatability as a first-class metric. That means running the same scenario multiple times, varying phrasing and context, testing tool failures, and measuring whether the final business outcome remains correct even when the route changes. The evaluation set also has to evolve. Every production incident should become a permanent regression test. Customer escalations, failed tool calls, incorrect approvals and data-handling mistakes should feed back into the pre-deployment suite rather than remaining isolated support cases. Autonomy should expand by risk, not by ambition The survey does not imply that every agent action should require a person. Human review cannot scale across millions of low-consequence decisions. But zero-human operation should be earned by demonstrated reliability and bounded by the consequences of failure. Low-risk actions such as drafting internal summaries or categorizing documents can tolerate broader autonomy. Financial transactions, customer communications, code deployment, access-control changes and data deletion need stricter thresholds, repeated consistency tests, policy checks, rollback mechanisms and clear human escalation paths. The risk isn't evenly distributed by company size, either. Larger enterprises — those with 2,500 or more employees — are moving toward zero-human deployment fastest, at 70% versus 64% for smaller companies, and they're also shipping more agents that go on to fail a customer, at 54% versus 48%. That is the warning for enterprise leaders. Removing the human from the loop does not remove uncertainty. Without stronger assurance, it converts uncertainty into an automated production decision. The market will keep pushing toward greater autonomy because the economic incentive is real. The organizations best positioned won't be those that remove people fastest — they'll be the ones that treat repeatability and regression testing as seriously as deployment speed.
Wall Street is debating the AI buildout. Enterprises just answered: 86% say their GPUs run at half capacity or less
Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack. Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six in 10 enterprises plan to switch or add vendors in each of five control layers within the next 12 months, and roughly a third — depending on the layer — plan to move within the quarter, the research finds. There are five main layers where enterprises are building: identity for agents (which agent is allowed to do what, under whose credentials); evaluation of agent output (whether the work is any good); cost telemetry (what each agent costs to run); the context layer (the business data and definitions agents draw on to answer); and the orchestration control plane (the software that coordinates multi-step agent work). Enterprises are already paying the price for deploying agents ahead of adequate control functions. Fifty-four percent of companies had an agent security incident or near-miss caught before harm in the past 12 months. Twenty-seven percent exercise only reactive control of agent spend — they learn what an agent costs when the invoice arrives, with no per-agent budget or ceiling in place. Here are the five findings that anchor the set — one finding per layer of the tech stack — and what the data suggests doing first in each. Expensive hardware is idle: 86% of GPU operators report utilization of 50% or less Eighty-six percent of enterprises that run their own GPUs report utilization of 50% or less. Wall Street has spent the quarter debating whether the AI buildout is overbuilt. This is buy-side measurement, from the enterprises doing the buying, and the research says the most expensive hardware in buildings of these enterprises runs at no more than half its capacity. The measurement gap compounds it: A minority 44% rigorously track what their AI compute actually costs and returns. Everyone else is only estimating. And the enterprise shopping process continues regardless: 45% of these enterprises say the emerging compute option they are most likely to evaluate in the next 12 months is an AI-specialized cloud (CoreWeave, Lambda, Crusoe, Nebius). However, under 2% of these enterprises report using one of these neoclouds today. Moreover, roughly one in three companies appears to be considering a hedge against Nvidia: Asked which emerging compute option they are most likely to evaluate in the next 12 months, 32% of enterprises named non-Nvidia accelerators (AWS Trainium, Google TPUs, AMD), while 28% named next-generation Nvidia GPUs. The data suggests that enterprises should measure the utilization and per-workload cost of the GPUs they already own before committing budget to new compute — whether that's an AI-specialized cloud contract, new accelerators, or more GPUs. Most deployed "agents" do single-prompt work: 71% say a quarter or fewer complete multi-step tasks on their own Seventy-one percent of enterprises say a quarter or fewer of their deployed "agents" can complete multi-step work on their own; the rest are single-prompt chatbots. Only 10% say true agents are the majority of what they run. To be sure, the respondents reported that they are in a position to know these things: 81% said they recommend or decide AI purchases at their companies. That finding — that most agents are actually just chatbots in trenchcoats — lands amid adoption claims across the industry running well ahead of what enterprises are actually running. Gartner predicted 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. It also warned that the most common misconception is referring to these AI assistants as agents, a misunderstanding known as "agentwashing." Meanwhile, Zapier's enterprise survey said 72% reported deploying or testing autonomous agents; and Writer's 2026 survey has 97% of executives saying their company deployed AI agents in the past year. Those surveys asked whether companies have deployed something called an AI agent, and companies said yes. Our survey asked the people running those deployments a harder question: Of the agents you have in production, how many can complete a multi-step task without a person driving each step? The gap matters for two practical reasons. First, the inflated adoption figures are the benchmark boards and vendors use to pressure technical leaders into moving faster — and this data says the real bar is far lower than the headlines suggest. Second, the label determines the bill: A single-prompt chatbot with a human reading every answer needs none of the identity, evaluation, and cost controls this report covers, while a true multi-step agent needs all of them. 66% let agents push to production on automated evals alone — or are engineering toward it. 5% fully trust those evals Two-thirds of enterprises fall into one of two camps: 34% already allow an AI agent to push a code or system change to production based on automated evaluation results alone, with no human reviewing it, and another 33% are actively engineering their pipelines to allow that within the next 12 months. Only five percent fully trust the automated evaluations that would make that decision. The distrust is earned. Half of enterprises shipped an agent that passed internal evaluations and then caused a customer-facing failure in the past year; a quarter watched it happen more than once. Asked to name the biggest weakness in their current evaluations, more enterprises chose “poor alignment with real-world outcomes” than any other answer — 29% of respondents. And most of the checking happens before an agent ships, then stops. Once agents are live with real users, only 23% of enterprises run real-time quality checks on the answers those agents produce. Another 51% monitor system health only — uptime, request traces, and gateway logs — which tells them the agent is running, and nothing about whether its answers are right. The first move: Before removing human review from any workflow, test your evaluations against production outcomes rather than internal benchmarks, and instrument answer quality, not just uptime. This finding is explored in more depth in VentureBeat's related coverage of the evaluation gap, which found that larger enterprises are moving faster toward zero-human deployment while also failing more often — and outlines a regression-testing framework built on production outcomes rather than internal benchmarks. 69% run credential sharing somewhere in the agent fleet — and those companies get hit far more often Sixty-nine percent of companies allow agent credential sharing somewhere in their agent fleet during runtime – meaning multiple agents operating under one API key or service account. Those companies were far more likely to get hit: Organizations with credential sharing anywhere in the fleet experienced a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent has its own scoped identity. The takeaway for enterprises is this: Give every agent its own scoped identity, starting with the agents that touch production systems. 57% traced a confident, wrong agent answer to their own missing or inconsistent business context Fifty-seven percent of enterprises traced at least one confident, wrong agent answer in the past six months to missing or inconsistent business context: wrong metrics, stale definitions, absent documents. Most of them watched it happen more than once. Most enterprise companies are fixing this, even though they’ve moved forward with agent deployment already: 25% already run a governed semantic layer, or one governed definition of the business that every AI reads from, in production. However, 34% are still building one, and 41% haven't started. The takeaway: Govern the definitions your agents answer from, metrics and entities first, before scaling the agents that depend on them. The quarter where agent technology “portability” became a priority One more shift is worth reporting with its limits stated plainly. In our spring orchestration survey wave, the top concern about provider-controlled orchestration was security and permissioning limits (32%). By June, vendor lock-in led at roughly a third, with security limits at 28%. Those are two snapshots one quarter apart, and here’s one possible explanation for why portability became a top issue for enterprises. Our June survey went into market after a June 12 U.S. Commerce Department export order took Anthropic's Claude Fable 5 offline for enterprises for roughly three weeks. Meanwhile, Chinese company Z.ai released GLM-5.2's open weights under an MIT license on June 16 at roughly one-sixth of GPT-5.5's price; and Tencent's Hy3 arrived July 6 under Apache 2.0; and OpenAI previewed GPT-5.6 on June 26 to a small group of government-vetted partners, opening it broadly on July 9 after the government's review cleared. The open-weight releases in particular promise enterprises more control over their agents, and while we haven't established a causal link here, the timing is worth noting. The posture data matches the mood: 51% now expect their primary control plane for enterprise agents to be hybrid — provider-native plus external orchestration — by the end of 2026, up from 34% in the spring survey wave. Enterprises reporting that they rely purely on provider-managed agent services fell from 12% to 7%. Five layers, no incumbents, 12 months The synthesis across all five surveys reveals a huge “buying” window. In each of the five control layers, 57% to 64% of enterprises plan to switch or add vendors within 12 months — 64% in infrastructure and in evaluations, 59% in agent security, 57% in retrieval and context — and 26% to 38%, depending on the layer, plan to move within a quarter. No layer has an established incumbent: The most common evaluation tooling is the model provider's built-in evals, tied with no dedicated tooling at all (17% each); 82% of respondents name provider-native or hyperscaler controls as their primary agent security layer; and provider-native retrieval leads the context technology layer (RAG, etc) as well. Most enterprises are defaulting today to the built-in tools that ship with the big AI platforms they already use: Anthropic, OpenAI, Google, Microsoft, and AWS. That holds true across every one of these agentic technology layers: enterprises are looking to their primary cloud and model providers to supply the guardrails, evaluations, and retrieval solutions already bundled into those providers' offerings. Those defaults are winning on convenience, and they're also what the coming spending decisions will test. The survey didn't ask which direction that money moves — toward the platforms' built-in tools or toward the specialists challenging them — which is exactly why every contract in these five layers is worth watching over the next four quarters. The Q3 survey wave will measure whether the enterprises made good on these budget plans: whether their agents gained scoped identities, whether evaluations got tested against production outcomes, whether GPU utilization rose, and whether the semantic layers under construction shipped. VentureBeat will release the full Q2 reports across all five VB Pulse trackers at VB Transform, July 14–15 at Hotel Nia in Menlo Park, where we convene enterprise technical leaders building autonomous agents in production. Disclosure: VentureBeat produces both this research and VB Transform
Federal Reserve enlists Marc Andreessen to advise on AI under Warsh - The Washington Post
Fed Chair Kevin Warsh named venture capitalist Marc Andreessen to co-lead a task force on AI 's economic impact — a technology his firm has bet billions on.
Investors sell longer-dated AI debt amid Big Tech borrowing spree
Waning demand highlights scepticism over sector’s long-term profitability
OpenAI and Google sell AI models to blacklisted China groups
US groups have been supplying AI services to Singapore-based subsidiaries of Alibaba, Baidu and Tencent
The Work of Helping A.I. Destroy Work
Start-ups are paying white-collar professionals to teach their jobs to artificial intelligence models. It’s a bonanza. It’s bleak. Where will it end?
‘AI accountability agenda’: US senator unveils package of bills to curb tech’s harms
Exclusive: Senator Ed Markey on why he has proposed legislation aimed at curbing datacenters, automated hiring systems and harm to children US senator Ed Markey is worried about the perils of unregulated artificial intelligence. What part? All of it: the costs associated with thirsty, energy-guzzling datacenters, intrusive workplace surveillance, bias in discriminatory algorithms, AI overriding workers’ judgments, and deepening economic inequality – as those who profit most from AI rake in extraordinary windfalls. Continue reading...
Economics & Markets
SK Hynix Debut Is a Bet That AI Breaks Boom-and-Bust Chip Cycle
South Korean memory chipmaker SK Hynix Inc. just pulled off the largest public listing by a foreign company in US market history. Shares soared 13% on their first day of trading.
Investors sell longer-dated AI debt amid Big Tech borrowing spree
Waning demand highlights scepticism over sector’s long-term profitability
SK Hynix Starts Trading on Nasdaq, Opens 14% Above Offer Price | Bloomberg Tech 7/10/2026
Ed Ludlow sits down for an interview with SK Group Chairman Chey Tae-won from the Nasdaq to discuss SK Hynix's US listing, the biggest-ever by a foreign company. Meanwhile, Bloomberg’s David Gura takes a look at SK Hynix shares as they start trading on the Nasdaq, and dives deeper into the listing's impact on the rest of the US chip sector. (Source: Bloomberg)
AI’s $182 Billion Borrowing Spree Could Become Its Biggest Risk Yet
Six tech giants have flooded the corporate bond market with a level of borrowing that dwarfs anything seen in recent memory, and the bet only pays off if AI delivers returns at a scale that has never been proven.
Daily: Micron increases US investments to $250B
Daily: Micron increases US investments to $250B # Chip Briefing SubscribeSign in # Daily: Micron increases US investments to $250B ### 7.5 min read. Joshua Park Jul 10, 2026 Share Highlights Micron increases US investments. Micron will raise its investment commitment from US$200 billion to US$250 billion in response to heightened demand for their memory chips amid the AI boom. The US chipmaker will focus on boosting domestic production, with an express goal of 40% of DRAM products manufacturing in the US by 2035. Commerce Secretary Howard Lutnick, who seems a major advocate for Micron, spoke at a company event for the need for a stronger US-based supply chain. He even called on Korean chipmakers, Samsung and SK Hynix, to use their proposed investments to boost production in the US. Micron’s stock jumped 9% before settling at ~4.5% today. CXMT to IPO next week. China’s champion
MiniMax Launches ~$1.9B Hong Kong Fundraise
Chinese AI lab MiniMax is raising $1.9 billion through a share placement and convertible bonds to support infrastructure and global expansion.
Special delivery: Italy's postman joins the AI infrastructure race
SpaceX's bankers are preparing to meet investors as early as next week to discuss a bond offering of at least $20 billion, two sources familiar with the matter said on Thursday, as Elon Musk's newly public company seeks funding for an ambitious and capital-intensive AI expansion.
Memory chip giant SK Hynix jumps nearly 13% in Wall Street debut as AI frenzy powers biggest initial share sale in the U.S. by a foreign company
SK Hynix priced its American depositary receipts, or ADRs, at $149 each Thursday. They opened Friday at $170 and closed at $168.01.
SK Hynix Closes Up 13% at $168.01 in Nasdaq Debut, Hits $1 Trillion Market Cap
The Korean memory maker's $26.5 billion offering is the largest US market debut ever by a foreign company, driven by massive demand for AI-related memory chips.
Why tech investors are reevaluating AI investments
Investing.com -- Investors are reassessing artificial intelligence investments as surging infrastructure spending could weigh on earnings growth and valuation multiples, even as demand for AI services remains robust, according to ING.
California's Venture Dominance Reflects AI's Concentration Economy | The WealthAdvisor
California's Venture Dominance Reflects AI's Concentration Economy | The WealthAdvisor Skip to main content California attracted more than $335 billion in venture capital this year, according to PitchBook, far outpacing every other U.S. state. The headline, however, is not simply California's resilience despite persistent concerns over taxes, regulation, and business costs. It is the accelerating concentration of venture capital into a narrow set of AI platforms, founders, and ecosystems that have become increasingly difficult to replicate elsewhere. The data underscores a structural shift in venture capital from financing broad innovation to financing strategic infrastructure. Nearly 90% of invested capital flowed into AI companies, with a handful of mega-rounds accounting for a disproportionate share of total funding. In the second quarter alone, three companies—Anthropic, Project Pr
What 16,000 companies reveal about AI agent adoption? | AI’s next $1 trillion market (and most founders are missing it).
Add Project Prometheus ($12B) and ... global venture activity ... For two straight quarters, one or two companies have carried the headline number, which makes total funding a weak proxy for the actual market. The unicorn stat is the one to sit with. OpenAI and Anthropic pulled in nearly $187B combined in the first half of 2026 - capital that, in any ...
China $93 Billion Hong Kong-Listed Artificial Intelligence Startup Zhipu AI (Knowledge Atlas Technology) Raised $4 Billion in Share Sale, Hong Kong Share Price Increased +1,425% (2026 YTD) from IPO Price in 2026 January (8/1/26) to $93 Billion Market Value, Founded in 2019 by Tsinghua University Professors Tang Jie & Li Juanzi, Key Controlling Shareholders are Founder Tang Jie & Chairman Liu Debing | Caproasia
China $93 Billion Hong Kong-Listed Artificial Intelligence Startup Zhipu AI (Knowledge Atlas Technology) Raised $4 Billion in Share Sale, Hong Kong Share Price Increased +1,425% (2026 YTD) from IPO Price in 2026 January (8/1/26) to $93 Billion Market Value, Founded in 2019 by Tsinghua University Professors Tang Jie & Li Juanzi, Key Controlling Shareholders are Founder Tang Jie & Chairman Liu Debing | Caproasia https://www.caproasia.com/cdn-cgi/content?id=qwR6anZslii6gN2.ePTCihcgf6jT2A8_URwRkKeZt2A-1783682531.8212073-1.2.1.1-4j.TOixBcM.Vp3DU95qd2Ol4R26HcoFt8swW.bECUhM - Capital Market / IPO / Private Equity - Investments / Alternatives - Private Banking / Private Wealth - ESG / Sustainable Investments - Financial Industry - People - Family Office - Billionaire - The Rich - Economy Events - View Events - Professional Investors - Family Office Circle - Alternatives & Private Markets - In
Qiming's Zhou Zhifeng Warns on AI, Robot IPO Rush, Value Needed
Qiming's Zhou Zhifeng Warns on AI, Robot IPO Rush, Value Needed # Qiming's Zhou Zhifeng Warns on AI, Robot IPO Rush, Value Needed NewsGlobeNow Editorial Team 2026-07-10 05:32:31 EDT ## Executive Narrative Qiming Venture Partners' Zhou Zhifeng warns of intense speculation in Chinese AI and robot sectors, expecting a market correction focused on monetization. Zhou Zhifeng, managing partner of Qiming Venture Partners, has highlighted an intense "scarcity premium" driving an IPO rush for AI and physical robot companies in China, but warned that an imminent market correction will demand concrete commercialization results. The remarks were shared during a media session where Zhou discussed Qiming's investment methodology and views on the hot sectors of generative AI and Embodied AI, which integrates AI with physical robot bodies.Zhou noted that while Qiming successfully invested early in
AI Training Startup Mercor Eyes $20 Billion Value
AI Training Startup Mercor Eyes $20 Billion Value Saturday, July 11, 2026 Home Mercor, the AI training and talent platform that connects domain experts with frontier AI companies, is reportedly in talks to raise a new funding round at a $20 billion valuation. If completed, the financing would double the company’s valuation from $10 billion in less than a year, underscoring continued investor enthusiasm for startups powering the generative AI ecosystem. The discussions are still in the early stages, and the valuation, funding size, and participating investors could change before a deal is finalized. ## Mercor Eyes $20 Billion Valuation According to reports, Mercor is seeking fresh capital at a valuation of around $20 billion. If the round closes at that level, it would represent: - A doubling of its valuation from $10 billion. - One of the largest valuation jumps among AI startups
AI Europe 100 · 2026: Six Breeds, 100 Companies - Headline
AI Europe 100 · 2026: Six Breeds, 100 Companies | Headline's Map of European AI Jul 10, 2026 # AI Europe 100 · 2026 Six breeds that explain how we invest in the AI era. Jonathan Userovici Astrid Moullé-Berteaux Cyprien Benoist We revealed the 2026 AI Europe 100 on stage at RAISE Summit. The full presentation is here. Every market map classifies AI companies by sector: infrastructure, enterprise, vertical, health, climate. The Headline AI Europe 100 classifies companies according to what they sell and what they replace. This test sorts an AI company in seconds and predicts business models, metrics, and moats, unlike other market maps. The Europe 100’s six breeds are deliberately heterogeneous. Take a voice model, a sovereign LLM developer, and a biology foundation model: a sector map files them under media, enterprise, and health – three columns with seemingly nothing in common.
Meta stock turns positive on the year on data center plans, new AI model pricing
Meta rose more than 5% on Friday after Mark Zuckerberg said the company is looking into renting out data center computing capacity.
Chipmaker SK Hynix raises $26.5bn in US stock market debut
The Financial Times reported that the share offering was seven-times oversubscribed and more than 500 investment firms had sought to buy shares. Read more: Chipmaker SK Hynix raises $26.5bn in US stock market debut
Fundamentum Launches Rs 2,200 Crore Fund III
Fundamentum launches a Rs 2,200 crore tech fund, emphasizing India-first AI and fintech innovation.
2 Magnificent Artificial Intelligence (AI) Stocks Down 16% to 28% to Buy Hand Over Fist | The Motley Fool
Historically, Nvidia's stock has ... is more AI spending going to happen in the next year. As projections roll out regarding next year's capital expenditures, I'd expect Nvidia's stock to soar on the news, leading to major gains for shareholders. As a result, the time is now to buy the stock, as it could skyrocket by the end of 2026. Jul 10, 2026 •By Adam LevyBest Growth Stocks to Buy in 2026 · Jul 10, 2026 •By Scott Levine5 Best Edge Computing Stocks for 2026 and How to Invest...
The AI race is shifting from bigger models to cheaper, smarter systems
Companies are starting to choose AI models by task, cost and control, not just leaderboard rank.
Reuters AI News | Latest Headlines and Developments | Reuters
ANALYSISA new, inexpensive Chinese AI model is catching up with Anthropic, Open AI on their home turf
OpenAI, Meta Take Aim at Anthropic, Igniting AI Model 'Value War' — BigGo Finance
OpenAI and Meta unveiled new AI ... dominance in the enterprise AI market. OpenAI's GPT-5.6, with its top-tier Sol model, surpassed Anthropic's Opus 5 on key coding benchmarks while slashing computing costs by more than half. Meta launched its first paid model, Muse Spark 1.1, wielding an aggressive pricing strategy at roughly one-tenth the cost of competitors...
Apple sues OpenAI, its employees claiming theft of trade secrets
Apple said in a Friday lawsuit that OpenAI’s nascent hardware business is “rotten to its core.”
OpenAI launches new flagship model GPT-5.6 Sol with midrange pricing, top-tier performance
OpenAI launches new flagship model GPT-5.6 Sol with midrange pricing, top-tier performance This release drew attention because it revealed not only a performance race but also the product lineup and pricing strategy. [Photo: OpenAI] [DigitalToday reporter Jinju Hong] OpenAI has made its new flagship artificial intelligence (AI) model, 'GPT-5.6 Sol', available to general users, moving fully into the next-generation AI race. As it revamps its brand from number-based model names to the 'Sol·Terra·Luna' system, it is targeting the market with both performance and price competitiveness. On July 9 (local time), blockchain media outlet Decrypt reported that OpenAI ended the limited preview service for GPT-5.6 Sol that it had run for about 2 weeks and released the full version. It also unveiled the mid-tier model 'Terra' and the entry-level model 'Luna'. The core of the announcement is a rev
OpenAI says GPT 5.6 is the ‘preferred model’ for Microsoft Copilot 365 amid breakup chatter
OpenAI has publicly endorsed GPT 5.6 for Microsoft Copilot, addressing rumors regarding the partnership between the two companies.
Claude vs ChatGPT (2026): Coding, Pricing & Market Share...
As of May 2026, ChatGPT's share ... to dominate web traffic among generative AI chatbots, holding 52.7% globally and 58. For more context, explore Anthropic Launches Claude Sonnet 5: What You Need to Know and Claude Opus 4.7 vs. 4.8: A Comprehensive Comparison on AI Agents Directory. AAD also lists Competitive Analysis by Omnimind as a relevant directory example to compare against the criteria above. ... Claude vs ChatGPT 2026AI coding assistant marketAnthropic Claude ...
Apple Sues OpenAI, Accusing It of Stealing Company Secrets
The two companies struck a deal in 2024 to offer A.I. services on Apple devices, but their partnership has soured.
Apple Sues OpenAI in Northern California Federal Court for Trade Secret Theft
Apple alleges that OpenAI's leadership directed the theft of trade secrets, noting that over 400 former Apple employees now work at the company.
Meta Slashes AI API Price to a Quarter of Rivals
Meta launched Muse Spark 1.1 with API pricing at roughly 25% of OpenAI and Anthropic. Zuckerberg calls the aggressive pricing a market opening.
The AI adoption race is over. The AI cost war has begun | The National
The AI adoption race is over. The AI cost war has begun | The National News Business Opinion Future Climate Health Culture Lifestyle Sport World Cup 2026 Newsletters TN Magazine - My Profile - Saved articles - Newsletters - Sign out Register UserSign in Sign in Register - Sign in Play in English Play in Arabic Listen to article Save article Share article Future Technology A Samsung memory chip. Every time AI is used, the bill climbs. Bloomberg # The AI adoption race is over. The AI cost war has begun Amit Joshi July 10, 2026 --- For the past few years, companies around the world have been wrestling with the challenge of getting their employees to adopt artificial intelligence. While some businesses initially clamped down on AI use over security concerns, many others pushed hard to drive uptake. That race is now over. Today, companies are confronting a fres
China AI unicorn boom accelerates in 2026 | AnewZ
China AI unicorn boom accelerates in 2026 | AnewZ # China is creating a billion-dollar startup almost every three days An Astribot humanoid robot serving tea at the Robot Valley Exhibition Hall in Shenzhen, Guangdong province, China, 16 April, 2026 Reuters By Mahnoor Makhdoom July 11, 2026 05:00 China's technology sector is producing billion-dollar startups at its fastest pace in nearly five years, with artificial intelligence and robotics driving a new wave of investment that is reshaping the country's innovation economy. China's startup ecosystem is booming once again. After several quieter years, the country's technology sector is producing billion-dollar companies at a pace not seen since 2021, with almost all of the new entrants focused on artificial intelligence or robotics. China created 67 new unicorn startups in the first half of 2026, the biggest increase in almost five
Voice AI Startup Gradium Raises $100M for Global Expansion
Voice AI Startup Gradium Raises $100M for Global Expansion # Voice AI Startup Gradium Raises $100M Seed for Global Expansion Key Takeaways Gradium closed a $100 million seed round after securing a new extension from chipmaker Nvidia. Gradium develops ultra-low latency voice AI models for real-time transcription and multilingual speech synthesis. The capital injection will fund a new office in the United States, recruit top talent and scale globally. Paris-based voice AI startup Gradium has announced a major $100 million seed funding round. The company recently added a $30 million extension to its initial $70 million tranche raised last December. Tech giant Nvidia joined the round as the headline investor. Gradium raises $100M to push its voice AI into its next major growth phase. It highlights the growing enterprise demand for real-time audio systems. Global investors are betting
Labor, Society & Culture
The Work of Helping A.I. Destroy Work
Start-ups are paying white-collar professionals to teach their jobs to artificial intelligence models. It’s a bonanza. It’s bleak. Where will it end?
46% Of Managers Are In AI Denial, And It Could Cost Them Their Jobs
Nearly 80% of managers use AI every day. But 46% don't think it will touch their own job. That gap between using AI and understanding it is about to get expensive.
Is AI Truly Transforming Work in the Asia-Pacific? – The Diplomat
While AI use is proliferating, it is unclear whether it is moving into the areas of the economy where most of the region’s 2 billion workers are actually employed.
Botsitting and Effort Recession: What these new workplace trends mean for HR
Employees are spending hours managing AI instead of benefiting from it, while fewer are willing to go beyond their job descriptions. Together, 'botsitting' and 'effort recession' are emerging as two of the biggest workplace challenges HR leaders need to understand.
Berlin Court Rejects Actors Association's Request Against Voice Actors' Group Over AI Dubbing Agreement
Berlin Court Rejects Actors Association's Request Against Voice Actors' Group Over AI Dubbing Agreement Thu 9th Jul, 2026 The Berlin Regional Court has dismissed an injunction sought by the German Actors Association (BFFS) against the German Voice Actors Association (VDS) regarding public statements on the controversial Netflix AI dubbing agreement. The court ruled on July 8 that all contested remarks made by the VDS were permissible, particularly those attributing partial responsibility for the agreement to the BFFS. The decision is not yet legally binding, and the BFFS retains the right to appeal. The dispute centers around three statements in a VDS press release from April 17, 2026, about the so-called Assignment of Rights (AOR) agreement. This agreement governs the transfer of certain rights from German dubbing actors to Netflix and has been a source of industry contention since t
Who's Gonna Buy All the AI Stuff?
As we lose jobs and the middle class contracts, where will consumer demand come from?
Labor seeks public feedback ahead of nationwide AI survey launch | FedScoop
The agency’s Bureau of Labor Statistics opened a two-month comment period ahead of its January 2027 launch of a survey on public adoption of and time spent on AI.
Stop Saying AI Is the Death of Writing | Writers' Blokke | - Medium
MediumStop Saying AI Is the Death of Writing | by Jose Luis Ontanon Nunez | Writers’ Blokke | Jul, 2026 | Medium Sitemap Sign up Sign in Get app Write Search Sign up Sign in ## Writers’ Blokke https://medium.com/writers-blokke?source=---publication_nav-f968b3008d58-66aa612c2db6--------------------------------------- “A word after a word after a word is power.” — Margaret Atwood Member-only story Writing Technology Learning Artificial Intelligence Writers Blokke # Stop Saying AI Is the Death of Writing ## Why we gladly automate physical labor, but panic when tech learns to write Jose Luis Ontanon Nunez 7 min read 5 hours ago https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Fwriters-blokke%2F66aa612c2db6&operation=register&redirect=https%3A%2F%2Fmedium.com%2Fwriters-blokke%2Fstop-saying-ai-is-the-death-of-writing-66aa612c2db6&user=Jose+Luis+
Meta pulls new AI image feature after days of backlash
Meta's release this week of an AI feature that let people alter Instagram content drew swift blowback.
How to stop Meta from using your Instagram photos to create AI images - The Washington Post
This week, Meta announced the launch of a new artificial intelligence image generator that allows anyone to create AI images based on your Instagram photos.
AI companies are throwing museums a lifeline. What do they want in return?
Touted as a way to engage visitors and boost funding, new tools are triggering concerns around trust and ethics
AI Notetakers: Balancing Efficiency with Privacy Concerns
AI notetakers are raising alarms over privacy and data security, prompting experts to recommend clear consent policies in sensitive industries.
OpenAI Safety Head Heidecke to Leave Firm After Reshuffle: Wired
OpenAI head of safety Johannes Heidecke is leaving the artificial intelligence company following a reorganization, according to Wired.
AI #176 Part 2: Plan B - by Zvi Mowshowitz - Substack
AI #176 Part 2: Plan B - by Zvi Mowshowitz # Don't Worry About the Vase SubscribeSign in # AI #176 Part 2: Plan B Jul 10, 2026 12 5 Share This is part 2 of the weekly, broadly covering speculation, rhetoric and policy, along with alignment research. This does not cover the release of GPT-5.6-Sol. As always, I will be taking a few days to digest what the new model has to offer and to allow others to try it and react. I will cover Sol and its capabilities early next week. I covered the GPT-5.6 system card back on June 28. This also does not cover the release of Plan A, the follow-up to AI 2027. This new scenario is a positive vision of what its authors think we should do going forwards. I do not endorse all of the recommendations or predictions of Plan A, but I do endorse reading Plan A and taking it seriously. Scott Alexander, one of those who worked on it, writes an introducti
Seizing AI job opportunities in the United Kingdom | TechRadar
To successfully bridge the AI skills gap, employers should follow best practices, including integrating AI literacy in different functions to prepare employees across the enterprise for a future in AI and partnering with educational institutions to offer learning programs aligned with business ...
You're Going to Need to Get Comfortable With AI Even Outside Tech Jobs - Business Insider
A new analysis of Indeed job postings suggests that familiarity with AI isn't just in demand in tech.
Technology & Infrastructure
OpenAI introduces ChatGPT Work, a cloud-based AI agent that manages tasks across email, Slack and calendars
OpenAI on Thursday launched ChatGPT Work, a new AI agent embedded inside its flagship chatbot that aims to transform ChatGPT from a question-and-answer tool into an autonomous work platform capable of executing complex, multi-step tasks across users' email, calendars, code repositories, and messaging apps. The product is powered by OpenAI's latest flagship model, GPT-5.6, and is designed to go far beyond generating text. ChatGPT Work can gather context from connected apps, files, and workflows to produce finished documents, spreadsheets, presentations, reports, and websites. The agent takes a stated outcome, breaks it into smaller steps, and stays with complex projects for hours, completing them independently. The launch marks OpenAI's clearest attempt yet to reposition ChatGPT as a workplace platform rather than a chatbot — and it arrives at a moment of extraordinary financial significance for the company. Last month, OpenAI confidentially submitted a draft S-1 registration statement to the SEC, initiating what could become one of the largest technology IPOs in history, with reported valuations clustering between $730 billion and $852 billion and annualized revenue that has blown past $25 billion. In a short demonstration and conversation with VentureBeat on Friday, Ty Geri, a product manager at OpenAI who helped build ChatGPT Work, said the product's mission is to democratize the kind of agentic AI capabilities that OpenAI's internal engineering tool, Codex, has already demonstrated. "What's really exciting is we've seen how much Codex has been able to push the frontier of what we can get done with these AI tools, as opposed to just getting information or answers or guidance," Geri said. "Our internal adoption of Codex is literally an exponential curve across every single product function and every single use case." Why OpenAI built a persistent virtual machine that works from the beach The core architectural bet behind ChatGPT Work is a persistent cloud-based virtual machine that runs on OpenAI's servers, always available to the user regardless of which device they happen to be on. That marks a deliberate departure from competitors whose agents require a local machine to remain powered on and connected. "What's really exciting about ChatGPT Work is that it's a virtual machine in the cloud that's always on for you, and this is available across all of our paid tiers," Geri said. "All Plus users are getting this. I think that's a very unique aspect of this." The mobile-first aspect of the launch is something Geri described as "missing from the market." He pointed to the ability to create a website on a phone and share it with collaborators as a particularly novel capability. "Sites are new in general to Codex. They launched in Codex about a week and a half ago, but now we're launching also in web and mobile. You can create a site on your phone at the beach and share it with your friends," he said. ChatGPT Work will roll out beginning with Pro, Enterprise, and Edu users, and will expand to Plus and Business users over the next few days. In the interview, Geri emphasized that the availability of the product to Plus subscribers — not just premium tiers — is central to OpenAI's strategy. "It's accessible to all paid plans, including Plus users, which in my opinion is a really big feat, and really part of that OpenAI mission, which is about bringing all this power to as many people," he said. How MCP plugins connect ChatGPT Work to Slack, Gmail, and GitHub The product relies on MCP-based plugins to connect to external services like Gmail, Google Calendar, Slack, and GitHub. When asked whether the plugin architecture is based on the Model Context Protocol standard, Geri confirmed: "These are all based on MCP." He added that connecting multiple Gmail accounts — a frequent user request — "is definitely on the roadmap." The experience is designed to be action-oriented from the first interaction. ChatGPT Work offers a personalized onboarding flow that surfaces different suggested use cases depending on the user's role. Geri demonstrated how the system, detecting his role as a product manager, immediately suggested tasks like evaluating AI systems, building research artifacts, and managing his calendar. "You can start with a simple task like catch me up on Slack or Teams or read today's calendar," Geri said. He described a scenario where the system reviewed his calendar, identified scheduling conflicts, flagged meetings requiring preparation, and then — on his instruction — declined, accepted, or rescheduled events directly. Users can also customize the agent by teaching it their writing style, organizing outputs into projects, and — in a lighter touch — choosing a virtual pet that accompanies them in the interface. The interface also introduces a hosted website feature that allows users to build and share interactive sites directly through ChatGPT Work, turning what would typically be a static slide deck into a dynamic, collaborative artifact. "Now we suddenly have a collaborative interface that's actually more exciting and more accessible than a slide deck, which has all these formatting restrictions," Geri said. Scheduling 10 bug bashes at once: what agentic productivity looks like in practice Geri's own usage of ChatGPT Work illustrates the breadth of tasks the system can handle. In the run-up to the product's launch, he needed to organize pre-release testing sessions — known internally as "bug bashes" — across dozens of features and team members. "I just come to ChatGPT Work and say, 'Set up a bug bash for all the distinct features in ChatGPT Work. Add all the people that worked on that feature,' and it can check Slack, it can check GitHub, it can check Docs, and find a time that works for the four highest contributors to that feature," Geri said. "It went and scheduled 10 bug bashes, all coordinated across all those different people. That would have taken me 30 minutes at least." But Geri pushed back against the characterization that ChatGPT Work is limited to rote administrative work. He described using it for analytically complex tasks like identifying the biggest causes of user churn for specific product features and generating product solutions — work he said would previously have taken months. "Things that we would have spent three months doing, we can now spend a week doing — and do much more, and make a much better product," Geri said. "Bugs that we would have found three or four weeks from now, we can now find within two days and fix for our users." He also described handing off the tedium of product testing itself. "It used to be that even though like the most interesting part of my job is like what to test, I would actually end up having to spend most of my job doing the testing, which is like me taking a mouse and like clicking on the same thing over and over again, like five times," Geri said. "Instead, now I can define what do we want to test, and ChatGPT Work or Codex can actually go test it for me, deliver me that bug report, and then we can work on fixing that bug." What OpenAI says about data privacy when AI reads your Slack and email When pressed on data privacy concerns — given that ChatGPT Work pulls sensitive information from workplace tools like Slack, Google Drive, and email — Geri said privacy "is incredibly important, and the most important part of this is it's always in the user's control." He pointed to OpenAI's existing enterprise security infrastructure, noting that "enterprise accounts have ZDR, and users can always opt out of letting their conversations help improve future models, which many users do." The comment aligns with assurances OpenAI made when it first launched ChatGPT Enterprise in August 2023, when the company wrote in a blog post that it does "not train on your business data or conversations." The privacy question carries additional weight now because of the sheer volume of sensitive workplace data ChatGPT Work is designed to access. Unlike a chatbot session where a user voluntarily pastes text into a prompt, ChatGPT Work actively reaches into connected systems — reading Slack messages, scanning calendar invitations, pulling GitHub commit histories — to assemble context for its tasks. That represents a fundamentally different data surface area than anything OpenAI has offered before, and one that enterprise security teams will scrutinize carefully before granting access. ChatGPT Work enters a three-way arms race with Anthropic and Microsoft ChatGPT Work lands squarely in the middle of what has become the defining competitive battlefield in enterprise AI: the race to build autonomous workplace agents that can go beyond generating text and actually execute tasks. The product arrives months after Anthropic took Claude Cowork out of preview and into general availability in April, bringing its AI agent to web and mobile platforms aimed at helping enterprise users monitor and manage long-running AI-driven tasks from anywhere. Meanwhile, Microsoft made Copilot Cowork generally available worldwide on June 16, built in partnership with Anthropic to move beyond chat and into execution. The three products — ChatGPT Work, Claude Cowork, and Microsoft Copilot Cowork — now compete directly for the attention of enterprise IT departments and individual knowledge workers alike. The convergence is striking. All three products share a remarkably similar vision: a persistent AI agent running in the cloud that can break complex tasks into steps, connect to workplace tools via plugins, and produce finished outputs rather than just conversational replies. All three work across desktop, web, and mobile. What distinguishes OpenAI's approach is its raw consumer distribution advantage. ChatGPT has reached 900 million weekly active users, and OpenAI now has 50 million paying subscribers. More than 9 million paying business users rely on ChatGPT for work, and 92% of Fortune 500 companies now use ChatGPT. By making ChatGPT Work available to Plus subscribers at $20 a month — not just Enterprise or Pro customers — OpenAI is betting that broad accessibility will drive adoption faster than any competitor can match. OpenAI's product manager says AI is a partner, not a replacement — with a caveat When asked about the potential impact on the labor market, Geri was careful with his framing. He declined to speak broadly about workforce disruption but offered his personal experience as a product manager whose day-to-day work has been substantially reshaped by the tool. "My job is not to schedule bug bashes and find out who contributed to a specific feature. That's a task I do in my job, but that's not my job," Geri said. "My job is to make an amazing product." He described ChatGPT Work as "a partner" and "an extension of me, certainly not a replacement," adding: "Everybody feels far more productive than before, but is also almost working harder than before, because you get to work on all the things you want to work on as opposed to the drudgery around it." But Geri was also careful not to minimize the sophistication of the work the agent can handle. "I also don't want to say that it's only doing mundane tasks because, like something like hill climbing retention curves on a given feature is not mundane. It's actually really hard to do," he said. The distinction matters. If ChatGPT Work were merely automating calendar invitations and expense reports, it would be a convenience tool. The fact that Geri describes it compressing three months of analytical product work into a single week suggests something with far greater implications for how teams are structured and staffed. An IPO-bound company needs ChatGPT Work to prove enterprise AI can generate revenue The timing of ChatGPT Work's launch is impossible to separate from OpenAI's IPO trajectory. The company needs to demonstrate that it can convert its massive consumer user base into durable enterprise revenue — a narrative that becomes significantly more compelling with a product explicitly designed around professional workflows. OpenAI said it is generating $2 billion in revenue per month, growing four times faster than Alphabet and Meta did at comparable stages, with enterprise now making up more than 40% of revenue and on track to reach parity with consumer by the end of 2026. But OpenAI remains heavily loss-making, and the company does not expect to reach profitability until around 2030, with internal projections suggesting losses of $14 billion in 2026 alone. The competitive dynamics are unprecedented. Anthropic filed for its own IPO on June 1 at a $965 billion valuation, setting up simultaneous public listings from the two most prominent AI startups in history. Whether both can sustain their lofty valuations under the scrutiny of public market investors will depend in large part on whether products like ChatGPT Work and Claude Cowork deliver measurable productivity gains to paying enterprise customers. The launch also caps a product trajectory that began with ChatGPT Enterprise in August 2023, accelerated through the release of OpenAI's Operator agent in January 2025, and continued through Operator's deprecation and shutdown on August 31, 2025, when its capabilities were folded into the ChatGPT agent framework. ChatGPT Work is the consolidation of those efforts into a single, unified product — one that pairs GPT-5.6's three model variants (Sol for power, Luna for speed, and Terra for balanced everyday use) with a persistent cloud environment and an expanding library of MCP plugins. The future of work may already be running in the cloud When asked whether ChatGPT Work signals a shift toward a new kind of operating system — one where users interact with their computers primarily through an AI agent rather than through traditional mouse-and-keyboard interfaces — Geri stopped short of making sweeping predictions. But he hinted at the direction OpenAI sees ahead. "Anybody who has worked with Codex or now ChatGPT Work will realize how exciting it is to interact with your environment and your computer via the agent," he said. "Especially in the desktop app, where the model has access to your entire machine and can interact with websites on your behalf — it's really able to be an extension of you and a real partner, and that certainly feels like the future." At the end of the interview, Geri circled back to something personal. "I've never enjoyed work as much as I have in the last month using ChatGPT Work and Codex," he said — a striking admission from a product manager who, until recently, spent a meaningful share of his days clicking through the same interface five times in a row just to see if it would break. OpenAI is now asking 900 million users to believe that feeling scales. For a company weeks away from one of the largest public offerings in history, the answer to that question is worth roughly $850 billion.
Autonomous AI Marketing Team: LSE's Into-it Pilot
See how LSE's Into-it pilot runs a fully autonomous AI marketing team, reshaping headcount and oversight. Read the case study to rethink your org chart.
OpenAI's Atlas browser doesn't make it to its first birthday
Standalone experiment killed after less than 12 months as model maker redirects agentic ambitions towards workplace productivity
OpenAI launches ChatGPT Work as it broadens GPT-5.6 rollout – Computerworld
OpenAI launches ChatGPT Work as it broadens GPT-5.6 rollout – Computerworld # OpenAI launches ChatGPT Work as it broadens GPT-5.6 rollout news Jul 10, 20265 mins ## The enterprise AI agent combines ChatGPT, Codex, and GPT-5.6 to automate workplace tasks as OpenAI broadens rollout of its latest frontier models. Credit: Juicy FOTO / Shutterstock OpenAI is sharpening its enterprise AI strategy with the launch of ChatGPT Work, a new agentic platform designed to automate workplace tasks, alongside the broader rollout of its GPT-5.6 models, which the company says deliver stronger performance at lower operating costs. According to the company, ChatGPT Work can operate across applications and files, execute long-running tasks, coordinate multiple tools, and produce business documents, presentations, spreadsheets, and websites, allowing employees to delegate more complex workflows rather t
Stop waiting for AI you can trust. Borrow the 500-year-old trick that made untrustworthy agents useful anyway. (Yes, there's a no-code guide!)
AI agents still hallucinate. A live 34-task, $8 multi-agent run shows why executable checks, not better models, make untrustworthy agents safe to delegate to.
Researchers develop 'hierarchical AI agent' that tackles complex errands with ease
Researchers develop 'hierarchical AI agent' that tackles complex errands with ease share this! Tweet Share --- July 10, 2026 # Researchers develop 'hierarchical AI agent' that tackles complex errands with ease by National Research Council of Science and Technology edited by Sadie Harley, reviewed by Andrew Zinin Add as preferred source --- Hierarchical AI Agent. Credit: Electronics and Telecommunications Research Institute (ETRI) Korean researchers have developed a hierarchical AI technology that autonomously plans even complex, long-horizon tasks. The development of this hierarchical task-planning AI technology, which reduces hallucinations and doubles the success rate, is expected to help robots and agents carry out long-term missions. The Electronics and Telecommunications Research Institute (ETRI) developed the hierarchical task-planning artificial intelligence (AI) tech
You all needed to read Output to Outcome eight months ago… | by adrian cockcroft | Jul, 2026 | Medium
You all needed to read Output to Outcome eight months ago… | by adrian cockcroft | Jul, 2026 | Medium Sitemap Sign up Sign in Get app Write Search Sign up Sign in AI Output To Outcome Business Strategy Agentic Ai Cynefin Framework # You all needed to read Output to Outcome eight months ago… 3 min read 23 hours ago https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Fp%2F9b86c8cf6e36&operation=register&redirect=https%3A%2F%2Fadrianco.medium.com%2Fyou-all-needed-to-read-output-to-outcome-eight-months-ago-9b86c8cf6e36&user=adrian+cockcroft&userId=eed29d74b3fb -- 1 https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Frepost%2Fp%2F9b86c8cf6e36&operation=register&redirect=https%3A%2F%2Fadrianco.medium.com%2Fyou-all-needed-to-read-output-to-outcome-eight-months-ago-9b86c8cf6e36&user=adrian+cockcroft&userId=eed29d74b3fb https://medium.c
What is Hill Climbing?. From Pre-Recorded Software to Real-Time… | by Cobus Greyling | Jul, 2026 | Medium
From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
Scaling AI Agents: Building a Prompt Transpiler for Production Agents | by Simerus Mahesh | Google Cloud - Community | Jul, 2026 | Medium
Scaling AI Agents: Building a Prompt Transpiler for Production Agents | by Simerus Mahesh | Google Cloud - Community | Jul, 2026 | Medium Sitemap Sign up Sign in Get app Write Search Sign up Sign in ## Google Cloud - Community https://medium.com/google-cloud A collection of technical articles and blogs published or curated by Google Cloud Developer Advocates. The views expressed are those of the authors and don't necessarily reflect those of Google. # Scaling AI Agents: Building a Prompt Transpiler for Production Agents Simerus Mahesh 10 min read 23 hours ago https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Fgoogle-cloud%2F22b842d08725&operation=register&redirect=https%3A%2F%2Fmedium.com%2Fgoogle-cloud%2Fscaling-ai-agents-building-a-prompt-transpiler-for-production-agents-22b842d08725&user=Simerus+Mahesh&userId=020863e4e171 -- https://medium.co
Why the chips are down despite the AI boom
Some believe the boom-bust cycle in memory chips has ended — the market thinks otherwise
OpenAI unveils long-awaited "super app" as rivalry with Anthropic intensifies
Microsoft said on Monday it is cutting about 2.1% of its workforce, or roughly 4,800 jobs, as the Windows maker restructures parts of its commercial and Xbox businesses, joining other tech giants in a wave of layoffs as companies shift investment toward AI infrastructure.
The Coming Power War That Will Define the AI Era | OilPrice.com
Electricity is emerging as the critical strategic resource of the AI era, creating a global competition among governments, hyperscalers, and infrastructure owners for scarce AI-grade power capacity.
Datacentre and AI infrastructure spending surge accelerates component demand - Astute Group
The global server market expanded by 30.4% year-on-year during the first quarter of 2026 as investment in AI infrastructure continued to outpace component supply, according to IDC figures published by Electronics Weekly. Strong demand for GPU-accelerated systems and sovereign AI programmes ...
AI set to consume more power than conventional data centers by 2027 | TechRadar
Gartner recommends infrastructure providers focus on efficiency upgrades to grids and cooling systems.
Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy
A look at distributed training, from DDP and FSDP to ZeRO stages, and why the wiring between your GPUs matters as much as the strategy you choose.
This time OpenAI announced a novel math proof for a 50 year old problem using a public model (most of the other big math breakthroughs have been with experimental LLMs).
This time OpenAI announced a novel math proof for a 50 year old problem using a public model (most of the other big math breakthroughs have been with experimental LLMs). GPT-5.6 Sol Ultra, using 64 subagents in just under one hour.
Google's TabFM skips per-dataset training and still predicts on tables it's never seen
The vast majority of business data is tabular — living in data warehouses, CRMs, and financial ledgers — yet building a reliable model from it still means training a new one from scratch for every dataset, then maintaining hyperparameter tuning loops, feature engineering, and retraining pipelines to fight data drift. Google Research is proposing a way around that: a new foundation model called TabFM that treats tabular prediction as an in-context learning problem instead. It can generate predictions for a new, unseen table in a single forward pass. For enterprise developers and AI engineers, this reduces the time-to-production from weeks of pipeline engineering to a single API call. The challenge with traditional ML To extract reliable predictions from a gradient-boosted tree, data scientists must build and maintain complex data pipelines. They have to clean messy inputs, impute missing values, encode categorical variables into numerical formats, and engineer custom feature crosses. Once the data is ready, they must run repetitive hyperparameter optimization loops, searching across learning rates, tree depths, subsampling ratios, and regularization grids to find the best configuration. Once deployed, these traditional models "incur ongoing operational debt through data drift monitoring and retraining pipelines to stay accurate," Weihao Kong, Research Scientist at Google Research, told VentureBeat. Meanwhile, the rest of the AI industry has moved on. Generative AI models for text and computer vision have seamlessly shifted to zero-shot inference, where a model can perform a completely new task simply by being prompted with context. Large language models (LLMs) already excel at in-context learning, so why can't we just feed tables into an off-the-shelf LLM? Because LLMs are trained on natural language rather than structured data, they struggle to process tables directly. First, their context limits are exhausted quickly by medium-sized tables containing just a few thousand rows and hundreds of columns. Second, LLMs suffer from tokenization inefficiency, awkwardly splitting numerical values and destroying mathematical precision. Finally, they suffer from structural blindness. When a 2D table is serialized as a 1D text string, LLMs lose track of which value belongs to which row and column as the table grows. "That's why, today, it is far more effective to use an LLM to write the code that handles feature engineering and calls XGBoost than to ask the LLM to read the table itself," Kong said. What is TabFM? To run inference with TabFM, you do not update any model weights. Instead, you take your historical examples (the training rows with their known labels) and your target rows (the new data you want to predict) and pass them to the model as a single, unified prompt. The model learns to interpret the relationships between columns and rows directly from this context at runtime. For example, consider an enterprise analyst trying to predict customer churn. Instead of building a bespoke data pipeline and training an XGBoost model, they can simply pass a sample of historical user session data alongside a new, active session into TabFM. In one forward pass, the model returns an instant churn probability. TabFM overcomes the limitations of LLMs by treating the data as a grid, preserving its structural integrity without forcing it into a single-dimensional text string. To effectively process diverse tabular structures while enabling scalable zero-shot prediction, TabFM synthesizes the strengths of earlier experimental architectures, TabPFN and TabICL. TabPFN, developed by Prior Labs, first proved that a transformer architecture could perform zero-shot classification on small tables, though it struggled to scale computationally to larger datasets. Later, TabICL, developed by France's National Research Institute for Digital Science and Technology, addressed this bottleneck by introducing row compression, allowing in-context learning to efficiently process much larger tables. TabFM combines TabPFN's deep feature contextualization with TabICL's efficient compression into a novel hybrid design built on three key mechanisms: 1. Alternating row and column attention: The raw table is first processed through a multilayer attention module that alternates across both columns (features) and rows (examples). By continuously attending across these two dimensions, the model natively captures complex feature interactions. This deep contextualization does the heavy lifting that would usually require tedious manual feature crafting by data scientists. 2. Row compression: Following this contextualization, the cross-attended information for each row is compressed into a single, dense vector representation. TabICL pioneered this by using CLS tokens to compress a row's rich information into one vector, "in contrast to TabPFN v2, v2.5, and v2.6, which attend over the full cell grid throughout the network," Kong explained. This drastically shrinks the computational footprint. 3. In-context learning (ICL): A causal Transformer then operates on this sequence of compressed embeddings. This Transformer model uses the attention mechanism of TabICL to attend over these dense row vectors, drastically reducing the computation cost and allowing the model to process large datasets efficiently. A major selling point of TabFM is its pretraining recipe. The model was trained entirely on hundreds of millions of synthetic datasets. These datasets were dynamically generated using structural causal models (SCMs) that incorporate a wide variety of random functions. By training exclusively on synthetic SCMs, TabFM learned the fundamental mathematical priors of how tabular features interact without ingesting real-world, confidential CSV files. TabFM in action To test the model's capabilities, Google researchers benchmarked TabFM on TabArena, a comprehensive evaluation suite spanning 51 diverse tabular datasets across 38 classification and 13 regression tasks. On these public benchmarks, TabFM's zero-shot predictions already match or beat heavily tuned supervised baselines. However, Google is careful to note that this does not automatically mean TabFM will universally dethrone bespoke, hyper-optimized production models on every enterprise workload. "Instead of replacing hyper-optimized production models, the true practical business value it unlocks for lean engineering teams is velocity," Kong said. "It allows data analysts and backend engineers to instantly spin up high-quality baseline models without a dedicated data science team managing a complex lifecycle." For advanced practitioners looking to squeeze out maximum accuracy, the research team also introduced a "TabFM-Ensemble" configuration. By running the model through 32 distinct variations and blending the results, TabFM pushes the performance even further. Getting started, trade-offs, and the cloud future The shift to in-context learning for tables introduces a new economic trade-off that engineering teams must consider. With traditional algorithms, training is slow and expensive, but inference is lightning-fast and cheap. TabFM flips this dynamic. While training time drops to zero, inference becomes significantly heavier. Because the model must process the entire historical dataset as context during every single prediction, it requires more compute and memory at runtime. In this new paradigm, "traditional machine learning training becomes the 'prefill' phase (KV caching) in the context window," Kong said. While this prefill cost is steep, it is paid only once per table, and the cache is reused across subsequent queries. "The catch is prediction latency, which no amount of caching removes," Kong added. Every new prediction requires a pass through a large transformer. "Any production API requiring single-digit-millisecond response times cannot tolerate TabFM's forward-pass overhead." For developers looking to evaluate the model today, the barrier to entry is low. Google designed TabFM as a drop-in replacement for traditional ML workflows, offering a scikit-learn compatible API (TabFMClassifier and TabFMRegressor). It natively handles mixed numerical and categorical columns, works directly with pandas DataFrames, and requires no manual ordinal encoders or numerical scalers. The library supports both JAX and PyTorch backends. However, enterprise teams need to be aware of current limitations and licensing restrictions. The model architecture has a hard limit of 10 output classes for classification tasks, and it is optimized for tables with up to 500 features. More importantly, while Google released the underlying codebase under the permissive Apache 2.0 license, the pre-trained model weights are published on Hugging Face under a strict tabfm-non-commercial-v1.0 license. Developers can evaluate the model internally, but it cannot be deployed in commercial products yet. Looking ahead, Google is addressing the commercial deployment friction through its cloud ecosystem. TabFM is being integrated directly into Google BigQuery, allowing analysts to run zero-shot predictions natively via an “AI.PREDICT” command. By putting foundation model inference right next to the data warehouse, TabFM could soon make complex tabular machine learning as accessible as a basic database query. In practice, TabFM shines in rapid prototyping, high data drift environments, and small to medium-sized datasets under 100,000 rows. Conversely, teams should stick to traditional models for strict, ultra-low latency APIs, or massive tables exceeding one million rows, which currently require aggressive row sampling that degrades the foundation model's competitive advantage.
OpenAI's GPT 5.6 class, ChatGPT Work arrive
OpenAI has introduced new developments in its GPT model series and launched ChatGPT Work.
Les Human Quality Raters : données d'entraînement direct, et pas ce que Google a toujours dit | Rankit.fr
Les Human Quality Raters : données d'entraînement direct, et pas ce que Google a toujours dit | Rankit.fr Série complète · 12 articles Cet article fait partie de la série Strategic SEO 2025 : Le ranking de Google enfin documenté, inspirée du travail de Shaun Anderson sur Hobo Strategic SEO 2025. Source : Hobo Strategic SEO 2025 : Shaun Anderson · Chapitre "How Human Quality Raters Are Used" + DOJ v. Google Remedial Opinion Analyse basée sur les témoignages de Pandu Nayak (VP Search Google) au procès DOJ, la Remedial Opinion non censurée, et le livre de Shaun Anderson (@Hobo_Web). En résumé - La Remedial Opinion DOJ révèle que RankEmbed et RankEmbedBERT sont entraînés sur deux sources primaires : logs de recherche + scores des human quality raters - Ce n'est pas du benchmarking périphérique, c'est une donnée d'entraînement fondatrice qui définit comment les modèles ML apprennent à r
One Week, Four Giants: Anthropic, Grok, Meta, and OpenAI All Made Their Move | by Rohit Kumar Thakur | Jul, 2026 | Medium
One Week, Four Giants: Anthropic, Grok, Meta, and Open AI All Made Their Move Fable 5 extensions, a Cursor-powered Grok, Meta’s $4.25 comeback, and GPT-5.6 training itself. If you find this story …
The Download: Claude’s inner workings and OpenAI’s “super app”
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. Anthropic found a hidden space where Claude puzzles over concepts The AI firm Anthropic has got the clearest glimpse yet at what’s really going on inside large language models as they…
AI News Recap: July 10, 2026 - NeuralBuddies
OpenAI launches GPT-5.6 as Sol, Terra, and Luna, Anthropic maps a global workspace inside Claude, and the JadePuffer agent runs a ransomware attack alone.
OpenAI’s latest AI model likely has similar cyber vulnerabilities to one that led to U.S. export controls on Anthropic’s Fable, British agency says
U.K. AI agency found 'universal jailbreaks' that unlocked dangerous cyber capabilities in OpenAI's GPT-5.6
China cybersecurity unit warns against Anthropic's Claude Code
A cybersecurity alert center affiliated with China's industry ministry has raised alarms about Anthropic's AI coding tool over a potential security backdoor.
Adoption, Deployment & Impact
Enterprise AI is entering an evaluation gap: Agents are gaining autonomy faster than companies can verify them
Enterprise AI teams are giving agents more freedom at the same moment their confidence in automated testing is collapsing. Half of enterprises have deployed an AI agent or LLM feature that passed internal evaluations and yet still caused a customer-facing failure — one in four more than once — according to the June 2026 VB Pulse survey of 157 qualified enterprise respondents at companies with 100 or more employees. The sample is self-selected rather than a probability sample, so the findings should be read as directional, not precise. But enterprises are not responding by slowing automation: 66% of respondents already permit some production deployment without human review or are building systems intended to do so within the next 12 months. Only 5% say they fully trust the automated evaluations that would make those release decisions. That mismatch is the evaluation gap: the autonomy ceiling is rising faster than the assurance beneath it. It also fits a broader thesis that will be explored at VB Transform 2026: enterprises ship agents first, while the control layers around identity, evaluation, cost, context and orchestration are arriving later. The next year will be a retrofit cycle, with buyers shifting budget toward the systems that make agentic deployments governable and dependable. Why a passing evaluation is not a working agent Traditional software testing usually asks whether a defined input produces an expected output. Agent testing is harder because the system may choose its own sequence of steps, call tools, retrieve data, alter state and respond differently from one run to the next. An agent can make several individually plausible decisions and still reach the wrong result. It may retrieve the correct account but update the wrong field. It may draft a valid refund request but send it without approval. It may call five tools successfully before a sixth step leaks sensitive information or leaves a workflow incomplete. The survey shows enterprises already recognize this limitation. The most common reason for distrusting automated evaluation is poor alignment with real-world outcomes, cited by 29% of respondents. Bias or inconsistency follows at 21%, lack of explainability at 18%, and data leakage or privacy concerns at 17%. That hierarchy matters. Enterprises are saying the score often does not predict what happens when a customer, employee or business process encounters the agent in production — not that automated scoring is too slow or expensive. NIST makes a similar point in its Generative AI Profile: measurements gathered in controlled environments may not transfer cleanly to deployment because behavior changes with prompts, users, context and operating conditions. Its guidance calls for field testing, post-deployment monitoring and clear processes for escalating failures. Capability is not consistency A single successful run proves that an agent can complete a task. It does not prove that it will complete the task reliably. Anthropic’s guidance on agent evaluation distinguishes between measuring whether a system succeeds at least once across repeated attempts and whether it succeeds every time. That distinction is essential for customer-facing or operational workflows. A model that occasionally produces an excellent answer may still be unacceptable if the same task fails unpredictably on the next attempt. Enterprise teams should therefore treat repeatability as a first-class metric. That means running the same scenario multiple times, varying phrasing and context, testing tool failures, and measuring whether the final business outcome remains correct even when the route changes. The evaluation set also has to evolve. Every production incident should become a permanent regression test. Customer escalations, failed tool calls, incorrect approvals and data-handling mistakes should feed back into the pre-deployment suite rather than remaining isolated support cases. Autonomy should expand by risk, not by ambition The survey does not imply that every agent action should require a person. Human review cannot scale across millions of low-consequence decisions. But zero-human operation should be earned by demonstrated reliability and bounded by the consequences of failure. Low-risk actions such as drafting internal summaries or categorizing documents can tolerate broader autonomy. Financial transactions, customer communications, code deployment, access-control changes and data deletion need stricter thresholds, repeated consistency tests, policy checks, rollback mechanisms and clear human escalation paths. The risk isn't evenly distributed by company size, either. Larger enterprises — those with 2,500 or more employees — are moving toward zero-human deployment fastest, at 70% versus 64% for smaller companies, and they're also shipping more agents that go on to fail a customer, at 54% versus 48%. That is the warning for enterprise leaders. Removing the human from the loop does not remove uncertainty. Without stronger assurance, it converts uncertainty into an automated production decision. The market will keep pushing toward greater autonomy because the economic incentive is real. The organizations best positioned won't be those that remove people fastest — they'll be the ones that treat repeatability and regression testing as seriously as deployment speed.
Employees adopting AI faster than organisations, says McKinsey survey - The Economic Times
Employees adopting AI faster than organisations, says McKinsey survey - The Economic Times Business News Tech AI Employees adopting AI faster than organisations, says McKinsey survey # Employees adopting AI faster than organisations, says McKinsey survey ANILast Updated: Jul 10, 2026, 02:13:36 PM IST Follow us Share Font Size AbcSmall AbcMedium AbcLarge Save ### Synopsis While artificial intelligence (AI) has emerged as the top technology spending priority for many organisations, most are still at an early stage of AI deployment, with employees adapting to the technology faster than the organisations they work for, according to a McKinsey survey. The survey said leaders are introducing AI tools for employees and automating manual processes while encouraging workers to develop new technical skills. However, the outcomes have so far fallen short of expectations. Listen to this
Microsoft 365 Copilot Is Still Below 4.5% Adoption
Despite significant marketing efforts, adoption rates for Microsoft 365 Copilot remain under 4.5% according to recent reports.
Platform Engineering Maturity Is the New Dividing Line in AI Adoption, Perforce Report Finds | The AI Journal
Platform Engineering Maturity Is the New Dividing Line in AI Adoption, Perforce Report Finds | The AI Journal Enthusiasm for artificial intelligence in enterprise technology is not the problem. Execution is. That is the blunt takeaway from Perforce Software’s State of DevOps Report: Platform Engineering 2026, published today, which surveyed 820 technology professionals worldwide and finds that platform engineering maturity – not AI ambition – is now the defining factor in whether organisations achieve lasting value from AI deployment. The report arrives at a critical inflexion point. As AI transitions from experimental tool to production infrastructure, the gap between early adopters and laggards is hardening into something more structural. Organisations that invested in platform engineering foundations are scaling AI with confidence. Those that didn’t are finding that ambition alone d
AI adoption hits 90% as CX deployment paths diverge
AI adoption hits 90% as CX deployment paths diverge # AI adoption hits 90% as CX deployment paths diverge ## AI adoption is nearly universal in CX, but organizations are divided on architecture, governance, and building customer trust while proving ROI. By Constantine von Hoffman, Senior Editor, MarTech Published on July 10, 2026 • Last updated on July 10, 2026 • 4 minutes read ### Share this article ### Table of Contents ### Table of Contents ### Spy on Any Website Get traffic data and keyword intel on competitors instantly. Ninety percent of CX organizations are now piloting or deploying AI, according to Five9’s “2026 Business Leaders CX Report.” Overall, the report found that organizations are largely aligned on adopting AI, but sharply divided on governance, deployment strategies, and how to use it in customer service without creating new complexity or eroding customer tr
7 CFO risks from the high-stakes adoption of AI | CFO Dive
“There’s a risk of losing people who don’t want to innovate or who are scared of how this plays out,” Nintex CFO Burt Chao said.
Council Post: Knowledge Infrastructure: The Strategic Infrastructure For AI Adoption And Scaling
Building strong knowledge infrastructure is essential for successful AI adoption, organizational intelligence and long-term competitive advantage.
AI pressure grows: 86% of Singapore CEOs worry failed strategies could cost them their jobs - Singapore News
AI pressure grows: 86% of Singapore CEOs worry failed strategies could cost them their jobs - Singapore News // Adds dimensions UUID, Author and Topic into GA4 Friday, July 10, 2026 32.2 C Singapore type here... Search Facebook Instagram Twitter Youtube (Photo: Flickr/ILO) Singapore News 2 min.Read # AI pressure grows: 86% of Singapore CEOs worry failed strategies could cost them their jobs By Jewel Stolarchuk July 10, 2026 SINGAPORE: More than eight in 10 chief executive officers in Singapore believe their jobs could be at risk if their companies fail to deliver results from artificial intelligence (AI), reflecting the growing pressure on business leaders to prove that investments in the technology translate into tangible business outcomes. The findings come from Dataiku’s Global AI Confessions Report: CEO Edition 2026, which surveyed 900 CEOs worldwide through researc
The AI power law: Why 2% of AI agents create most enterprise value | ITWeb
The AI power law: Why 2% of AI agents create most enterprise value | ITWeb Sectors Companies About NEWS INSIGHTS EVENTS VIDEOS Search - Home - / - Enterprise Architecture - / - The AI power law: Why 2% of AI agents create most enterprise value # The AI power law: Why 2% of AI agents create most enterprise value ##### The organisations that will lead in AI will be those that identify their power law use cases fastest − and scale them before their competitors do. --- By Bramley Maetsa, IT digital and innovation enablement lead, Sasol.Johannesburg, 10 Jul 2026 --- Bramley Maetsa, IT digital and innovation enablement lead at Sasol. --- Something interesting is happening in enterprise AI right now. Organisations are celebrating how many AI agents they have built. How many pilots are running. How many employees have access to AI tools. How many use cases are in production. Th
Billionaires warned New York would scare off business. Anthropic and Airbnb just made their biggest bets on the city yet
Not only are they expanding their footprint, they’re also doubling headcount. Anthropic said it plans to have more than 1,000 employees by end of year.
Over the last year, we've interviewed thousands of people about AI ...
# Post by Daniela Amodei · LinkedIn · 2026-07-10 **Daniela Amodei**: President and co-founder at Anthropic with 15 years 9 months of experience. Previous roles include VP of Safety and Policy at OpenAI, Engineering Manager VP of People at OpenAI, and Risk Manager Core Operations, User Policy, and Underwriting at Stripe. Based in San Francisco, California, United States [US]. --- Over the last year, we’ve interviewed thousands of people about AI, including a study of 81,000 Claude users on what they want from the technology (which was one of my favorite projects we’ve done at Anthropic). Today, we’re building on that by sharing some of their voices — and the hardest questions they asked us. We won’t get the benefits of AI without addressing the hard questions. You can share your own here: https://lnkd.in/eGxCU_7P ## Comments (9 shown of 50 reported) **[Avishag Bohbot](https://linked
JPMorgan builds AI agents that outperform traditional portfolios in two decades of backtesting
JPMorgan builds AI agents that outperform traditional portfolios in two decades of backtesting SEARCH Searching... # JPMorgan builds AI agents that outperform traditional portfolios in two decades of backtesting The bank's AI-driven strategy beat the classic 60/40 portfolio by 0.7 percentage points annually while delivering lower volatility Share Share on X Share on LinkedIn Share on Facebook Add us on Google by Editorial Team Jul. 9, 2026 JPMorgan Chase has developed AI-powered agents that dynamically shift allocations between stocks and bonds based on market conditions, and the results from backtesting are turning heads. Over two decades of historical simulations, the best-performing AI agent outpaced the traditional 60/40 portfolio by 0.7 percentage points per year. In English: if a standard balanced portfolio returned 8% annually, JPMorgan’s AI would have delivered 8.7%.
AI Formulary Access Strategy: How AI Is Shaping Payer Decisions - Pharma Marketing Network
Discover how an AI formulary access strategy is transforming payer evidence, HEOR, and value communication as AI-assisted reimbursement becomes a reality.
Introducing GPT 5.6 and ChatGPT Work
OpenAI launched GPT 5.6 and ChatGPT Work, an integrated AI workspace that combines conversational AI, coding, and productivity workflows for enterprise users.
How Yorkshire businesses can get the most out of AI: Andrew Brindley
How Yorkshire businesses can get the most out of AI: Andrew Brindley All Sections News you can trust since 1754 Sign In ## Sign up to our Business newsletter Sign up ### Thank you for signing up! Did you know with a Digital subscription to Yorkshire Post, you can get access to all of our premium content, as well as benefiting from fewer ads, loyalty rewards and much more. Sorry, there seem to be some issues. Please try again later. Submitting... This site is protected by reCAPTCHA and the Google Privacy Notice and Terms of Service apply. It’s the quality of their leadership and an understanding that AI adoption is not primarily a technology task, but a question of change management. When businesses introduce new technology, the biggest challenge is rarely the software itself. It’s helping people adapt, building confidence and creating new ways of working. AI is no different, e
r/buildinpublic on Reddit: Would you pay for AI-generated product videos?
Accuracy is the make-or-break part for us too. That's why the AI doesn't invent the UI, it works from your actual screen recording. Before generating the final video, you can review and edit the script, prompts, and other details, plus tweak things like pacing and preview the output.
The New News in AI: 7/10/26 Edition - by Mark McNeilly
The New News in AI: 7/10/26 Edition - by Mark McNeilly SubscribeSign in # The New News in AI: 7/10/26 Edition ### A curated source for the latest AI happenings in the news Mark McNeilly Mark McNeilly Jul 10, 2026 1 Share Should AI have free speech?, How AI lowers your cost of exploring ideas, ChatGPT’s big launch, How to keep AI from ruining how you think, Anthropic tells you how you’re using AI, OpenAI talks with U.S. about a government stake, UN is worried about killer robots, Professors can’t give take home tests anymore, Chinese AI copycats are catching up…and they’re cheaper, the US and China look at limiting AI access to other countries, Can AI models consent to their own constitution?, and more, so… ## AI Quote of the Week --- Greg Lukianoff@glukianoffAI is free speech’s next frontier. It will shape what people read, write, ask, and know. That means the worst response i
Google Photos Debuts AI-Powered Video Remix Feature
Google debuts Video Remix, a generative AI feature in Google Photos, enabling users to transform videos into stylized clips with Gemini Omni.
Wall Street is debating the AI buildout. Enterprises just answered: 86% say their GPUs run at half capacity or less
Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack. Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six in 10 enterprises plan to switch or add vendors in each of five control layers within the next 12 months, and roughly a third — depending on the layer — plan to move within the quarter, the research finds. There are five main layers where enterprises are building: identity for agents (which agent is allowed to do what, under whose credentials); evaluation of agent output (whether the work is any good); cost telemetry (what each agent costs to run); the context layer (the business data and definitions agents draw on to answer); and the orchestration control plane (the software that coordinates multi-step agent work). Enterprises are already paying the price for deploying agents ahead of adequate control functions. Fifty-four percent of companies had an agent security incident or near-miss caught before harm in the past 12 months. Twenty-seven percent exercise only reactive control of agent spend — they learn what an agent costs when the invoice arrives, with no per-agent budget or ceiling in place. Here are the five findings that anchor the set — one finding per layer of the tech stack — and what the data suggests doing first in each. Expensive hardware is idle: 86% of GPU operators report utilization of 50% or less Eighty-six percent of enterprises that run their own GPUs report utilization of 50% or less. Wall Street has spent the quarter debating whether the AI buildout is overbuilt. This is buy-side measurement, from the enterprises doing the buying, and the research says the most expensive hardware in buildings of these enterprises runs at no more than half its capacity. The measurement gap compounds it: A minority 44% rigorously track what their AI compute actually costs and returns. Everyone else is only estimating. And the enterprise shopping process continues regardless: 45% of these enterprises say the emerging compute option they are most likely to evaluate in the next 12 months is an AI-specialized cloud (CoreWeave, Lambda, Crusoe, Nebius). However, under 2% of these enterprises report using one of these neoclouds today. Moreover, roughly one in three companies appears to be considering a hedge against Nvidia: Asked which emerging compute option they are most likely to evaluate in the next 12 months, 32% of enterprises named non-Nvidia accelerators (AWS Trainium, Google TPUs, AMD), while 28% named next-generation Nvidia GPUs. The data suggests that enterprises should measure the utilization and per-workload cost of the GPUs they already own before committing budget to new compute — whether that's an AI-specialized cloud contract, new accelerators, or more GPUs. Most deployed "agents" do single-prompt work: 71% say a quarter or fewer complete multi-step tasks on their own Seventy-one percent of enterprises say a quarter or fewer of their deployed "agents" can complete multi-step work on their own; the rest are single-prompt chatbots. Only 10% say true agents are the majority of what they run. To be sure, the respondents reported that they are in a position to know these things: 81% said they recommend or decide AI purchases at their companies. That finding — that most agents are actually just chatbots in trenchcoats — lands amid adoption claims across the industry running well ahead of what enterprises are actually running. Gartner predicted 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. It also warned that the most common misconception is referring to these AI assistants as agents, a misunderstanding known as "agentwashing." Meanwhile, Zapier's enterprise survey said 72% reported deploying or testing autonomous agents; and Writer's 2026 survey has 97% of executives saying their company deployed AI agents in the past year. Those surveys asked whether companies have deployed something called an AI agent, and companies said yes. Our survey asked the people running those deployments a harder question: Of the agents you have in production, how many can complete a multi-step task without a person driving each step? The gap matters for two practical reasons. First, the inflated adoption figures are the benchmark boards and vendors use to pressure technical leaders into moving faster — and this data says the real bar is far lower than the headlines suggest. Second, the label determines the bill: A single-prompt chatbot with a human reading every answer needs none of the identity, evaluation, and cost controls this report covers, while a true multi-step agent needs all of them. 66% let agents push to production on automated evals alone — or are engineering toward it. 5% fully trust those evals Two-thirds of enterprises fall into one of two camps: 34% already allow an AI agent to push a code or system change to production based on automated evaluation results alone, with no human reviewing it, and another 33% are actively engineering their pipelines to allow that within the next 12 months. Only five percent fully trust the automated evaluations that would make that decision. The distrust is earned. Half of enterprises shipped an agent that passed internal evaluations and then caused a customer-facing failure in the past year; a quarter watched it happen more than once. Asked to name the biggest weakness in their current evaluations, more enterprises chose “poor alignment with real-world outcomes” than any other answer — 29% of respondents. And most of the checking happens before an agent ships, then stops. Once agents are live with real users, only 23% of enterprises run real-time quality checks on the answers those agents produce. Another 51% monitor system health only — uptime, request traces, and gateway logs — which tells them the agent is running, and nothing about whether its answers are right. The first move: Before removing human review from any workflow, test your evaluations against production outcomes rather than internal benchmarks, and instrument answer quality, not just uptime. This finding is explored in more depth in VentureBeat's related coverage of the evaluation gap, which found that larger enterprises are moving faster toward zero-human deployment while also failing more often — and outlines a regression-testing framework built on production outcomes rather than internal benchmarks. 69% run credential sharing somewhere in the agent fleet — and those companies get hit far more often Sixty-nine percent of companies allow agent credential sharing somewhere in their agent fleet during runtime – meaning multiple agents operating under one API key or service account. Those companies were far more likely to get hit: Organizations with credential sharing anywhere in the fleet experienced a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent has its own scoped identity. The takeaway for enterprises is this: Give every agent its own scoped identity, starting with the agents that touch production systems. 57% traced a confident, wrong agent answer to their own missing or inconsistent business context Fifty-seven percent of enterprises traced at least one confident, wrong agent answer in the past six months to missing or inconsistent business context: wrong metrics, stale definitions, absent documents. Most of them watched it happen more than once. Most enterprise companies are fixing this, even though they’ve moved forward with agent deployment already: 25% already run a governed semantic layer, or one governed definition of the business that every AI reads from, in production. However, 34% are still building one, and 41% haven't started. The takeaway: Govern the definitions your agents answer from, metrics and entities first, before scaling the agents that depend on them. The quarter where agent technology “portability” became a priority One more shift is worth reporting with its limits stated plainly. In our spring orchestration survey wave, the top concern about provider-controlled orchestration was security and permissioning limits (32%). By June, vendor lock-in led at roughly a third, with security limits at 28%. Those are two snapshots one quarter apart, and here’s one possible explanation for why portability became a top issue for enterprises. Our June survey went into market after a June 12 U.S. Commerce Department export order took Anthropic's Claude Fable 5 offline for enterprises for roughly three weeks. Meanwhile, Chinese company Z.ai released GLM-5.2's open weights under an MIT license on June 16 at roughly one-sixth of GPT-5.5's price; and Tencent's Hy3 arrived July 6 under Apache 2.0; and OpenAI previewed GPT-5.6 on June 26 to a small group of government-vetted partners, opening it broadly on July 9 after the government's review cleared. The open-weight releases in particular promise enterprises more control over their agents, and while we haven't established a causal link here, the timing is worth noting. The posture data matches the mood: 51% now expect their primary control plane for enterprise agents to be hybrid — provider-native plus external orchestration — by the end of 2026, up from 34% in the spring survey wave. Enterprises reporting that they rely purely on provider-managed agent services fell from 12% to 7%. Five layers, no incumbents, 12 months The synthesis across all five surveys reveals a huge “buying” window. In each of the five control layers, 57% to 64% of enterprises plan to switch or add vendors within 12 months — 64% in infrastructure and in evaluations, 59% in agent security, 57% in retrieval and context — and 26% to 38%, depending on the layer, plan to move within a quarter. No layer has an established incumbent: The most common evaluation tooling is the model provider's built-in evals, tied with no dedicated tooling at all (17% each); 82% of respondents name provider-native or hyperscaler controls as their primary agent security layer; and provider-native retrieval leads the context technology layer (RAG, etc) as well. Most enterprises are defaulting today to the built-in tools that ship with the big AI platforms they already use: Anthropic, OpenAI, Google, Microsoft, and AWS. That holds true across every one of these agentic technology layers: enterprises are looking to their primary cloud and model providers to supply the guardrails, evaluations, and retrieval solutions already bundled into those providers' offerings. Those defaults are winning on convenience, and they're also what the coming spending decisions will test. The survey didn't ask which direction that money moves — toward the platforms' built-in tools or toward the specialists challenging them — which is exactly why every contract in these five layers is worth watching over the next four quarters. The Q3 survey wave will measure whether the enterprises made good on these budget plans: whether their agents gained scoped identities, whether evaluations got tested against production outcomes, whether GPU utilization rose, and whether the semantic layers under construction shipped. VentureBeat will release the full Q2 reports across all five VB Pulse trackers at VB Transform, July 14–15 at Hotel Nia in Menlo Park, where we convene enterprise technical leaders building autonomous agents in production. Disclosure: VentureBeat produces both this research and VB Transform
In-house legal teams struggle to demonstrate ROI on their AI investments - report - The Global Legal Post
In-house legal teams struggle to demonstrate ROI on their AI investments - report - The Global Legal Post Search Register Sign In Search - Deals - Diversity and Inclusion - ESG - In-house Legal Appointments - Law Firm Finances and Profitability - Law Firm Lateral Hires - Law Firm Leadership and Strategy - View all topics Shutterstock By The Global Legal Post ### Sign up for our free daily newsletter You are already a subscriber. More than eight in 10 in-house legal departments globally are unable to measure the return on their AI investments even as all legal teams say they plan to increase spending on AI tools, according to a survey from alternative legal services provider Axiom. The 2026 Axiom In-House Legal AI Report found that 83% of corporate legal departments can’t measure whether AI spending is working, despite 100% of respondents saying they intend to increase their AI
KPMG sees AI surge as firms struggle to prove value
KPMG sees AI surge as firms struggle to prove value IT Brief UK - Technology news for CIOs & IT decision-makers United Kingdom Powered By # KPMG sees AI surge as firms struggle to prove value Fri, 10th Jul 2026 (Today) SEAN MITCHELL Publisher KPMG has reported a sharp rise in organisations embedding artificial intelligence into everyday work, while industry figures warn that adoption without measurement leaves serious gaps in value, cost control and security. The consultancy's latest Global AI Pulse Q2 2026 report found that 22% of organisations are now at the "driving adoption" stage, embedding AI into daily workflows. That is up nine percentage points from the previous quarter. The report also found many projects are under cost pressure, with almost half of businesses scaling back or pausing AI initiatives after concluding the cost outweighed the value. Organisations with full v
Council Post: Why AI ROI Starts Depends On Governance And Training
This is a critical juncture in the growth of AI because there is a disconnect between investment and outcome.
Council Post: AI Agents: Secure Like Software, Manage Like Employees And Budget Like Human CapEx
Here's how AI agents can be secured like software, managed like employees and budgeted like human CapEx.
Geopolitics, Policy & Governance
OpenAI, Google AI sales to blacklisted Chinese firms expose loopholes in US tech curbs – Firstpost
OpenAI and Google’s AI services reached Singapore-based subsidiaries of Chinese companies linked to the Pentagon blacklist, exposing regulatory blind spots and intensifying calls for stricter controls on the global export of advanced AI models.
FT: OpenAI and Google Are Supplying Advanced AI Models to Singapore Subsidiaries of Alibaba, Baidu and Tencent
OpenAI and Google are providing advanced models to Singapore-based subsidiaries of Chinese firms, bypassing some US export controls that target specific entities.
OpenAI and Google sell AI models to blacklisted China groups
US groups have been supplying AI services to Singapore-based subsidiaries of Alibaba, Baidu and Tencent
‘AI accountability agenda’: US senator unveils package of bills to curb tech’s harms
Exclusive: Senator Ed Markey on why he has proposed legislation aimed at curbing datacenters, automated hiring systems and harm to children US senator Ed Markey is worried about the perils of unregulated artificial intelligence. What part? All of it: the costs associated with thirsty, energy-guzzling datacenters, intrusive workplace surveillance, bias in discriminatory algorithms, AI overriding workers’ judgments, and deepening economic inequality – as those who profit most from AI rake in extraordinary windfalls. Continue reading...
On 2 August 2026, Europe's grace period on its landmark AI law ends and the Commission gains the power to fine the makers of the most powerful models — for how they trained them and for what those systems do in the world
On August 2nd 2026, the European Union's rulebook for artificial intelligence stops being a set of deadlines on paper and starts having teeth. From that
Compliance And Enforcement In Global AI Regulation: EU AI Act Risks And International Regulatory Challenges - Technology - Worldwide
The EU AI Act establishes the world's first comprehensive AI regulation, classifying systems by risk and imposing strict compliance obligations on manufacturers developing or deploying AI in European markets. With penalties reaching up to 7% of global revenue and fragmented regulations across ...
Senator Markey Unveils Comprehensive New AI Accountability Legislative Agenda – ICO Optics
A primary pillar of Senator Markey’s ... massive energy consumption driven by data centers. These facilities are essential for modern computing, yet they often impose significant burdens on local power grids and the environment. The proposed legislation requires the Federal Communications Commission to rigorously evaluate new data center projects before construction is permitted. This certification process aims to ensure that these massive infrastructure projects align ...
Antitrust Enforcement on AI Would Be Premature and Slow Progress
Some leading academics and practitioners argue that antitrust is the law of everything, with answers to the toughest questions society faces. But the suggestion that AI development needs significant antitrust intervention is premature, writes White & Case’s Jack Pace.
Bank of England handed powers to regulate key tech firms including Amazon and Google
Direct oversight of ‘critical third parties’ such as Oracle and Microsoft given to ensure resilient cyber-defences and help safeguard UK economy The Bank of England has been handed powers to regulate important tech firms including Amazon and Google from next week, amid fears that system failures could threaten financial stability and harm consumers. From Monday, the Bank and fellow City regulator the Financial Conduct Authority (FCA) will be in charge of ensuring that four large-scale providers of cloud and tech services to banks are resilient and actively reducing the risk of cyber-attacks and major outages that could disrupt services for millions of people and businesses across the UK. Continue reading...
Apple sues OpenAI, alleging artificial intelligence company stole trade secrets
Suit claims OpenAI poached Apple workers, coaxing them to share confidential material in bid to create hardware Apple filed a lawsuit against OpenAI on Friday alleging the artificial intelligence firm stole company trade secrets in a move to create its own hardware device. The suit claims OpenAI poached Apple employees, coaxing them to hand over confidential material, product designs and other tightly held information. Continue reading...
OpenAI Faces Allegations of Evidence Concealment in Copyright Battle
The New York Times and The Daily News claim OpenAI hid evidence in a high-profile copyright lawsuit over AI training on news content.
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