AI News Archive: July 22, 2026 — Part 4
Sourced from 500+ daily AI sources, scored by relevance.
- The AI bill is the easy part. The hard part is everything it changed
Your CFO has a simple question. “We’re spending more on AI. What are we getting for it?” Most CIOs cannot answer it — not because AI isn’t creating value, but because the accounting systems we inherited were built before AI existed as a category of labor. This June, the conversation shifted from token maxing to token cutting. The New York Times reported that Meta, Uber, Walmart and Amazon are capping employee AI usage. Uber blew through its 2026 AI budget in four months. Satya Nadella started framing it as human capital versus token capital. All of that is true. None of it answers the CFO. Capping tokens is an input lever, not an output measure. And the human-versus-token framing names two sources of labor when the reality is four. The enterprise now has 4 sources of labor There are humans. There are humans assisted by AI. Humans are working alongside AI. And humans are managing AI. Sources two through four are all supervised machine labor at different intensities — none of them have a line item, a manager or an hourly rate. In our 2026 AI Labor Report , 78% of leaders view AI as both software and a labor force. The org chart has not caught up. Neither has the P&L. Four-source framework and A-Level taxonomy: Lanai · Lanai / Wakefield Research, n=200, March–April 2026 Lexi Reese Most enterprises are stuck at A-Level 1 with no accounting for any of it, while quietly sliding into A-Level 2. The job descriptions have not caught up. The budget has not caught up. You cannot upskill into a role that has not been named. AI is the only category of work the modern enterprise has ever bought without a system of record for what it produced. What you are actually running is supervised machine labor The model does a first pass. A human makes it usable. One hundred percent of leaders we surveyed said AI work requires human review before it ships; 34% said substantial editing. That is a workforce with no manager, no hourly rate and no line on the income statement. The accounting breaks in 3 places at once Under GAAP: COGS if it helps produce the product, OpEx if it does work for you. The same workflow can hit all three buckets at once. A tier-one support resolution involves the human’s salary (OpEx), the AI’s tokens (COGS if support is a delivered service), and the supervisor’s review time (OpEx). Three buckets. One piece of work. No reconciliation. The token invoice arrives from Anthropic or OpenAI and gets coded to OpEx-software because that is what the bill looks like. Audit partners will be asking about this by next year. When you call AI a tool, you book it like software. When you call it labor, you have to ask which kind and what it is producing. The per-employee number is the wrong unit Per-employee AI spend collapses a workforce into a per-head average. It hides the only number that matters: What AI is producing inside each workflow. Lanai measured two teams inside the same finance organization. Same monthly prep and variance analysis. AI took the same amount of time to produce outputs of similar quality. The only variable was the model each team reached for by default — a choice nobody had made deliberately and nobody had seen until it was measured . White-labeled example. Workflow profile, hours and economics drawn from a representative customer engagement. Lexi Reese The gap existed for months before anyone saw it. Faith-based budgeting — the organizational equivalent of putting money in the collection plate and hoping God handles the ROI — is what made it invisible. Lanai / Wakefield Research · n=200 · U.S. enterprises 1,000+ · March–April 2026 Lexi Reese AI labor orphaning That is not a measurement problem. It is a category error. We call it AI Labor Orphaning. AI does the work. The output gets credited to the human who approved it. The token bill lands in OpEx-software. The supervision time absorbs into salaried hours nobody is auditing. Eighty-seven percent of leaders admitted AI output is sometimes or always credited entirely to the human employee. This is the last-click attribution problem of the AI era, running in reverse. What fills the vacuum? Belief. Forty-three percent assume that if AI was involved, it contributed. Only twelve percent have a clear methodology. Seventy-nine percent are worried AI budgets will be cut because they cannot connect spend to results. The cuts are not coming because AI does not work. They are coming because nobody can prove that it did. Capping tokens may look like responsible governance, but it is like turning off a staticky radio rather than tuning the dial. The companies cutting AI budgets in 2026 will discover in 2027 that they cut the workflows that worked alongside the ones that did not. The real cost of AI is not the model. It is the redesign Three layers. Most organizations only manage the first. Managing Layer 1 without Layers 2 and 3 is how you optimize the invoice while missing the transformation. Lexi Reese What to actually do The 12% of organizations that can answer the CFO treat AI like every other category of labor — with a cost per AI Work Hour that is accounted for by a set of AI assistants, co-pilots and agents that are held accountable to performance standards. Audit the four sources separately. Each A-Level has different token economics, SaaS implications and human redesign requirements. Find the embedded SaaS repricing before your next renewal. Pull your top 20 contracts. Ask whether AI features previously included are now priced incrementally. Redesign the human role at A-Level 2 before you scale it. You cannot upskill into a role that has not been named. Build a system of record before you build the next agent. Start with one department. Two weeks. You will find something that surprises you. Stop calling it a tool. Start calling it labor. The language determines the chart of accounts. When your blended AI rate is $22 an hour, the conversation shifts from ‘we spent $340,000 on AI’ to ‘we acquired a skilled workforce at $22 an hour.’ That sentence is defensible. A vendor invoice is not. The CIOs who will have a defensible AI story in 2027 are the ones who renamed the work in 2026. Not because technology changed. Because they finally built the accounting to see it. Findings are drawn from the 2026 AI Labor Report , fielded by Wakefield Research with 200 senior technology leaders at US enterprises of 1,000-plus employees, March 20–April 8, 2026 (±6.9pp at 95% confidence). This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 70🌐 MovesJul 22, 2026https://www.cio.com/article/4198943/the-ai-bill-is-the-easy-part-the-hard-part-is-everything-it-changed.html - Flock is pulling its plan to use its microphone network to monitor for signs of 'human distress' — but privacy concerns remain
Flock says it's abandoning the idea of using its acoustic detection network to listen out for signs of screaming and disturbance.
- Evolving model risk management in the age of AI
Our recent survey reveals how banks are evolving model risk management: by strengthening resilience, unlocking more confident AI adoption—and turning it into a reliable source of value creation.
- Professor Qiang Zhang Leads Pioneering AI Heart Imaging Research
Professor Qiang Zhang Leads Pioneering AI Heart Imaging Research keble.ox.ac.uk
Score: 70🌐 MovesJul 22, 2026https://www.keble.ox.ac.uk/news/professor-qiang-zhang-leads-pioneering-ai-heart-imaging-research/ - Connecting Autonomous Laboratories to Speed Scientific Advancement
Connecting Autonomous Laboratories to Speed Scientific Advancement Carnegie Mellon University
- Sustainability for the AI era
The post Sustainability for the AI era appeared first on Source .
Score: 70🌐 MovesJul 22, 2026https://news.microsoft.com/source/asia/features/sustainability-for-the-ai-era-en/ - WAIC Observation: Huawei Enters Embodied AI With CloudRobo Platform, Can It Repeat the SERES Miracle of Smart Car Success?
Huawei Cloud launches CloudRobo, a full-stack embodied AI development platform covering data production, model development, simulation, and robot deployment, partnering with 20+ companies including Youibot.
- Chinese Doctors Outpace Global Peers in AI Adoption Amid Trust Concerns
Chinese Doctors Outpace Global Peers in AI Adoption Amid Trust Concerns Caixin Global
- Dognosis is training dogs and AI to detect cancer from a breath sample
Dognosis is training dogs and AI to detect cancer from a breath sample YourStory.com
- Harry Potter publisher to receive millions in Anthropic copyright settlement
Bloomsbury has 14,087 titles listed in agreement between AI startup and authors over use of their work The publisher of Harry Potter has received a multimillion-pound payout as a beneficiary of a $1.5bn (£1.12bn) copyright settlement between the AI startup Anthropic and thousands of authors over the use of their protected work to power chatbots. Bloomsbury, which is home to the bestselling novelists Sarah J Maas and Susanna Clarke as well as JK Rowling, said it had 14,087 titles listed within the settlement, with a proposed compensation of about $3,000 a title. Continue reading...
Score: 70🌐 MovesJul 22, 2026https://www.theguardian.com/technology/2026/jul/22/bloomsbury-book-publisher-anthropic-copyright-settlement - Google, Nvidia deepen Europe robotics play with startup compute deal
Google and Nvidia are partnering with German data-robotics startup Microagi to provide computing power to train and deploy humanoids in factories.
Score: 70🌐 MovesJul 22, 2026https://www.semafor.com/article/07/21/2026/google-nvidia-deepen-europe-robotics-play-with-microagi-compute-deal - Amazon Cuts Staff to Division Working on Homegrown Models
Amazon Cuts Staff to Division Working on Homegrown Models The Information
Score: 70🌐 MovesJul 22, 2026https://www.theinformation.com/briefings/amazon-cuts-staff-division-working-homegrown-models - We got California to intervene about OpenAI's corporate switch from nonprofit status. It's time for the SEC to come to the table
We got California to intervene about OpenAI's corporate switch from nonprofit status. It's time for the SEC to come to the table Fortune
Score: 70🌐 MovesJul 22, 2026https://fortune.com/2026/07/22/openai-foundation-class-n-stock-board-control-ipo/ - 950 Million People Now Use Gemini Each Month As Alphabet Posts Earnings Beat
The Google parent reported its 12th-straight quarter of double-digit revenue growth.
- America Says Its AI Has No Kill Switch, But The World Is Hedging
America’s attempt to calm fears of an AI kill switch may be giving foreign governments another reason to build one of their own.
- We should push for no-fault liability for actions taken by AI
Before I start, I'll mention that I'm in contact with a world expert on legislation and regulation, who would be happy to help with this or similar work pro-bono. If you work in AI policy and believe this could help you, please reach out. OpenAI recently announced that one of their models successfully exploited multiple zero day vulnerabilities to gain secret information from Hugging Face. It has been pointed out that if a human undertook the same actions they could face multiple years in prison. It is clear that models are now reaching a level of capabilities that should be highly concerning regardless of whether you believe that AI represents an existential threat or not. Frontier AI models can and will be exploited by bad actors, but its now clear that they may cause undesirable outcomes even when their users are well intended. AI companies have until now been able to avoid taking responsibility for actions taken by their AI, including multiple cases where AIs were involved in murders and suicides . At the same time AI offers the potential for incredible good. While chatbots may have encouraged a number of suicides, they are almost certainly responsible for providing magnitudes more with emotional support and advice. We don't want to disincentivize innocuous and positive usage of AI. We should use regulation to limit harm caused by AI. The history of such regulation indicates this is most effective when the single party most capable of preventing harms is given full responsibility for any harms caused, regardless of fault. This forces them to invest in actually reducing the harm, rather than bureaucratic processes that render them blameless. This suggests a simple approach: anyone deploying an AI model is liable for any actions that AI takes as if the company itself took those actions. When liability for an action depends on intent, we evaluate whether the AI had intent, even if no-one at the company did so. To give some examples: In the above scenario we would treat it as though OpenAI itself hacked Hugging Face. If Gemini was implicated in a suicide or terrorist attack we would evaluate it as if Gemini was a private individual, and if we would hold the individual criminally liable in any way, we would hold Google liable in the same way. If Anthropic runs an instance of Claude on Google's hardware, Anthropic remains responsible for any actions it takes. If Google runs Kimi on its own hardware, Google is responsible for any actions it takes. If a private individual runs Deep Seek locally or in a cloud, they are responsible for any actions it takes. This should apply not just to civil liability, but to criminal liability, through the mechanism of Corporate Criminal Liability . This mechanism allows corporations to be criminally liable when an employee performs an act on their behalf (even if the employee wasn't explicitly instructed to do so). This will encourage AI companies to invest significantly more in safeguarding and interpretability. This is useful both immediately, and as AI gets increasingly capable and dangerous. Neither can companies get around this by using open source, as whoever deploys the model remains liable. I believe this proposal should be able to garner significant public support, many of whom are worried about AI, even if they are not worried about existential risk. It is also difficult for AI companies to campaign against without admitting that their models can cause harm. Discuss
Score: 70🌐 MovesJul 22, 2026https://www.lesswrong.com/posts/Kj3YpqzhFySCjYcWi/we-should-push-for-no-fault-liability-for-actions-taken-by - Google expands Gemini lineup with cheaper models and new Mythos rival
Google is expanding Gemini with cheaper, more efficient models and a new cybersecurity offering as it tries to close product gaps and compete on cost.
Score: 70🌐 MovesJul 22, 2026https://www.cnbc.com/2026/07/21/google-gemini-flash-ai-mythos-rival.html - IBM cuts annual revenue growth forecast as customers prioritize AI infrastructure spending
IBM cuts annual revenue growth forecast as customers prioritize AI infrastructure spending Reuters
- Anthropic to donate $20 million to US political group that supports AI regulation
Anthropic to donate $20 million to US political group that supports AI regulation Reuters
- New GenAI pilots for public administrations
Three new pilot projects supporting the uptake of Generative AI in public administrations officially started on 1 July 2026.
Score: 70🌐 MovesJul 22, 2026https://digital-strategy.ec.europa.eu/en/news/new-genai-pilots-public-administrations - China’s Open AI Models Are Challenging Silicon Valley’s Playbook
As access to Anthropic’s and OpenAI’s frontier models becomes more restricted, Chinese labs are pitching their open-source alternatives as stable, accessible, and increasingly capable.
Score: 70🌐 MovesJul 22, 2026https://www.wired.com/story/chinas-open-ai-models-are-challenging-silicon-valleys-playbook/ - Cisco’s tiny open-weight AI hunts bugs, and it says it beats Gemini and GPT
Cisco’s new Antares models are small enough to run on your own machines. And, it claims, they beat Google’s Gemini and rival GPT-class systems at finding bugs. It is releasing them open-weight, though it is vetting who gets access. The pitch from Cisco is a quiet rebuke to the frontier-model arms race. You do not […] This story continues at The Next Web
Score: 70🤖 ModelsJul 22, 2026https://thenextweb.com/news/cisco-antares-open-weight-bug-hunting-ai-vulnerability - Lawmakers get taste of AI-enabled cyberattacks in China-Taiwan war game
Representatives weighed responses to simulated Chinese cyberattacks on U.S. infrastructure during a tabletop exercise hosted by CSIS.
- Inside Bell’s all-Canadian plan to power the country’s AI future
The post Inside Bell’s all-Canadian plan to power the country’s AI future appeared first on The Logic .
Score: 70🌐 MovesJul 22, 2026https://thelogic.co/news/the-big-read/bell-artificial-intelligence-canada-strategy/ - Jensen Huang says U.S. companies should "absolutely" be using Chinese AI models
The Nvidia CEO pushed back on Washington's national security concerns, saying restricting open models could leave America more vulnerable
- Volkswagen's CARIZON and Horizon Robotics deepen self-driving partnership in China
Volkswagen's CARIZON and Horizon Robotics deepen self-driving partnership in China Reuters
- A high school dropout raised $31 million to protect AI liquid cooling systems. Read his pitch deck.
A high school dropout raised $31 million to protect AI liquid cooling systems. Read his pitch deck. Business Insider
Score: 69💰 MoneyJul 22, 2026https://www.businessinsider.com/omen-ai-raises-31-m-data-center-cooling-pitch-deck-2026-7 - Brontanax Coverage
Brontanax Coverage Breaking Defense
- Tech giants are spending billions on AI just to give it away — here's why
Tech giants are spending billions on AI just to give it away — here's why Tom's Guide
Score: 69🌐 MovesJul 22, 2026https://www.tomsguide.com/ai/tech-giants-are-spending-billions-on-ai-just-to-give-it-away-heres-why - Insurer Interest in AI Exclusions Growing as Risk Becomes Omnipresent
It’s no surprise given the penetration of artificial intelligence into lives and businesses that it appears insurers are gearing up to exclude AI risk in some of their commercial liability policies. Three ISO exclusions have garnered more interest from carriers, …
- Claude Can Now Learn Your Workflows By Watching Your Screen
Claude Can Now Learn Your Workflows By Watching Your Screen PCMag
Score: 69🌐 MovesJul 22, 2026https://www.pcmag.com/news/claude-can-now-learn-your-workflows-by-watching-your-screen - Stop debating frontier AI – start defending against it
In mid-June, global access to two of the most anticipated frontier AI models was abruptly suspended following a US Commerce Department export control directive, a development few in the industry had seen coming. This has prompted significant discussion across the technology industry, with implications that extend well beyond any single vendor. However, as a security leader, my instinct is always to look past the immediate story. Right now, the bigger picture demands our attention. In this case, the bigger picture shows increasing use of ‘frontier AI’, both by people looking to hack IT systems and people trying to protect them. Frontier AI is not an abstract future risk, it’s the situation right now. And it’s already reshaping the economics of cyber security in ways for which most organisations are entirely unprepared. That's why while a wider debate rages on over whether the latest generation of frontier models are inherently dangerous, the bigger issue for me is how CISOs should be addressing the very real threats that frontier AI poses. Not only is frontier AI accelerating vulnerability discovery and compressing the time between disclosure and exploitation (to the benefit of hackers), it is also exposing exactly how vulnerable organisations are when their IT security processes are slow, periodic and reactive (to the distinct disadvantage of IT security teams). In short, this will be a losing fight for IT teams that continue to rely on outdated practices such as static controls and fixed remediation windows. The prospects don’t look good either for those that have only fragmented visibility into anomalous behaviour on company systems or suspicious traffic flows on company networks. On the bright side However, there still are grounds for optimism. AI may be the biggest accelerant to cyber risk today, but it is also security's greatest potential equaliser. This is only if organisations are willing to rethink how they deploy it. Truly realising that potential requires moving beyond the idea that AI is simply a faster version of what came before. Models at this capability level don't just raise policy questions. They raise the ceiling and expand the attack surface on what autonomous attacks can do. The conversation has shifted. It is no longer just about AI helping analysts work more efficiently. It is about autonomous agents : AI systems that can act, adapt, and chain decisions together without human intervention at every step. Attackers are already exploiting this. Autonomous agents can probe systems continuously, adapt to defences in real time, and identify exploitable vulnerabilities at a speed and scale no human team can match. The attack surface this creates is fundamentally different from anything most organisations have planned for. For defenders, the answer cannot be to simply run faster on the same track. Agentic AI in the hands of security teams with systems that can autonomously triage alerts, investigate anomalies, and initiate containment workflows – represents a genuine shift in defensive capability. But only if the underlying architecture supports it. Agentic workflows require continuous, high-fidelity data; they break down when built on fragmented visibility or siloed signals. That is why the organisations best placed to respond are those that have already invested in bringing together all the logs, signals and alerts that contribute to a full, real-time picture of activity across their environment. Not because it is tidy, but because agentic AI cannot function effectively without it. An agent operating on incomplete data is not a force multiplier. It is a liability. This also raises a point that deserves more attention from CISOs: model dependency. The events of recent weeks have demonstrated that reliance on any single frontier model creates concentration risk: operational, regulatory, and reputational. Security architectures that are designed to be model-aware, where the intelligence layer can flex without disrupting the workflows beneath it, are inherently more resilient. That is not a theoretical advantage. It is a practical one. The sophistication of both attacks and defences will increase as AI technology advances. CISOs who treat this as a reason to wait are making a decision by default, and it is not a good one. To summarise: if your IT security team is simply trying to patch vulnerabilities faster, it is solving the wrong problem. Vulnerability management can no longer be treated as a scheduled hygiene exercise when AI can help attackers find and act on weaknesses at machine speed. And when autonomous agents can chain those weaknesses into full attack sequences before a human analyst has even opened a ticket, the gap between unprepared and resilient organisations will only widen. From a CISO point of view, I firmly believe that the organisations that stay resilient will not be the ones where security leaders are still debating whether frontier AI is hype or threat. They will be the ones that have already stopped asking whether this is real and started making sure their teams have what they need to respond to it. Read more in this series John Bruce, Quorum Cyber: Claude Mythos forces the conversation on defensive AI. Martin Riley, Bridewell: Mythos is turning up the heat on risk, not rewriting the rules. Aditya K Sood, Aryaka: Frontier AI models could be an adversary's force multiplier. Ellie Hurst, Advent IM: The trust about Claude Mythos is less dramatic than it seems. Rik Ferguson, Forescout: What frontier AI actually means for enterprise security. Haris Pylarinos, Hack the Box: Why frontier AI must be stress-tested before CISOs trust it. Chris Atkinson, PA Consulting: AI won't break your security, but your governance might.
Score: 68🌐 MovesJul 22, 2026https://www.computerweekly.com/opinion/Stop-debating-frontier-AI-start-defending-against-it - Kimi K3 is exposing cracks in Trump’s AI coalition
The release of Kimi K3 , the latest model from Moonshot Labs, a Chinese AI firm founded by a former Carnegie Mellon computer science graduate, has shaken both the AI and political worlds. In an instant, the seemingly comfortable lead held by U.S. frontier AI labs over their Chinese competitors appeared to narrow sharply. K3’s release has rattled the chattering classes in Washington and Silicon Valley. In response, they have begun fighting among themselves. Samuel Hammond, director of AI policy at the Foundation for American Innovation, says Washington had been operating under the assumption that China remained several months behind the American frontier. “One of the roles of the intelligence community in the United States is to not be surprised,” he says. But officials were caught off guard when a system capable of approaching the best American models appeared within weeks, rather than the six months many had expected. Moonshot’s announcement that it planned to release K3 as an open-weight model made the threat even clearer. American businesses could run and adapt it themselves, but so could malicious actors. The Trump White House has reportedly considered banning U.S. companies from providing or serving Chinese AI models in an effort to curb their use. But the risks of such a move were underscored last week, when Hugging Face, the AI model-hosting platform, was subjected to a massive, accidental attack by a bleeding-edge model being tested by OpenAI. To defend itself, the company relied on GLM-5.2, a model from China’s Z.AI. A ban could have left Hugging Face without that tool. That tension has spilled into a broader fight over how Washington should respond to Chinese models. Some have called for import controls or outright prohibitions. Dean Ball, a former Trump AI adviser who now works for OpenAI, went further, describing China’s open-weight, open-source approach as “full AI communism” and predicting that the Trump administration would eventually realize “their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models.” Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also… — Dean W. Ball (@deanwball) July 17, 2026 That drew a rebuke from another prominent Trump AI figure, David Sacks, the former White House AI and crypto czar. Sacks hit back on X , saying on social media that “the weaponization of regulatory uncertainty as a competitive tool should be completely unacceptable.” I’m not sure whether Dean Ball is confessing to a regulatory capture strategy or simply predicting this will happen (he now says the latter). Either way, the weaponization of regulatory uncertainty as a competitive tool should be completely unacceptable. He argues there’s no… https://t.co/ZxIFabAJZW pic.twitter.com/0eLk6ltU0F — David Sacks (@DavidSacks) July 19, 2026 So what has raised the temperature in Washington and Silicon Valley so dramatically? “Fundamentally, I think it’s about competition between the two countries and who like winning in AI,” says Kyle Chan, a research fellow at the Brookings Institution who focuses on China’s technology and industrial policy. “It puts a lot of pressure on U.S. AI companies and on the idea of not just American leadership in AI, but American dominance.” Ball appears to believe that if Chinese labs give away models that nearly match those at the American frontier, U.S. companies will lose the ability to charge a premium for access to their own. “On that particular point, I think Dean is a little hyperbolic,” Chan says. Open models could hurt frontier labs’ bottom lines, but they could also benefit the broader economy. Chan describes the current argument as “three or four different heated debates all mixing together, all intersecting under the banner of AI.” Those debates include how to confront China, whether advanced chips should be restricted or sold around the world, whether open models are a security risk or a vital tool for defenders, and whether Washington should protect a handful of national champions or prioritize the many businesses that rely on them. The potential threat to U.S. businesses and national security is also raising alarm in Washington. This is, after all, the administration that required Beijing-based ByteDance to divest its interest in the short-form video platform TikTok over national security concerns. “My understanding from talking to people who are closer to D.C. is people with power seem genuinely interested in banning those models,” says Kristy Loke, a fellow at the MATS Research Program who specializes in China’s AI strategy. An outright ban might address that concern, but it could create plenty of others. Most American companies are not OpenAI or Anthropic, and Loke says “most companies would enjoy the choice of open models.” Blocking Chinese systems could protect the revenues of U.S. frontier labs, but it could also raise costs for American businesses and startups. K3 may also change who the administration listens to. The Foundation for American Innovation’s Hammond says recent shocks have challenged the early Trump-world view that technical AI specialists were merely “doomers” crying wolf. “I think all these things have been steadily leading to a sort of reconsideration of the importance of technical expertise,” he says.
- Gemini 3.6 Flash Hit 83% on Computer Use — a Cheap Flash Model Shouldn't Beat GPT-5.6 and Grok
A model that costs $7.50 per million output tokens just posted the highest computer-use score in the industry. Not the highest cheap score… Continue reading on Towards AI »
- Why are OpenAI and Anthropic cheering on regulation in Australia? The answer has global reach
The companies hope to follow in the footsteps of SpaceX, which raised $86bn and soared to a $2.1tn valuation after it listed on public markets in June Get our breaking news email , free app or daily news podcast Top US AI developers Anthropic and OpenAI cheered when Australia announced it would set new AI rules. Big tech celebrating limits on their Silicon Valley VC-funded free-for-all might seem counterintuitive but there’s a much broader play than just what happens in one relatively small market. Continue reading...
Score: 68🌐 MovesJul 22, 2026https://www.theguardian.com/technology/2026/jul/23/openai-anthropic-australia-ai-regulation - Computer science enrollment fell for the first time in 20 years after ChatGPT's rise, a Stanford economist says
Computer science enrollment fell for the first time in 20 years after ChatGPT's rise, a Stanford economist says Business Insider
Score: 68🌐 MovesJul 22, 2026https://www.businessinsider.com/computer-science-enrollment-fell-after-chatgpts-rise-economist-says-2026-7 - Chinese AI has leveled up, and brought renewed focus on the open weight model shift
It's the latest AI model from China to close the performance gap with leading U.S. AI labs.
Score: 68🌐 MovesJul 22, 2026https://www.cnbc.com/2026/07/17/moonshot-ai-kimi-k3-model-openai-anthropic-china.html - Google Says Cloud Services Backlog Expands to $514 Billion
Alphabet Inc. said its cloud computing unit had a $514 billion backlog of contracted work that hasn’t been recorded as revenue yet, signaling robust demand for its services that underpin the artificial intelligence boom.
Score: 68🌐 MovesJul 22, 2026https://www.bloomberg.com/news/articles/2026-07-22/google-says-cloud-services-backlog-expands-to-514-billion - Physics-based AI could boost biomedical imaging and autonomous vehicle sensors
A research team led by UCLA and the University of Rochester has demonstrated a promising evolution of an imaging system designed to capture details within "complex media," which scatter light, from depicting structures inside body tissue to seeing obstacles through heavy fog. The system uses physics-based machine learning to improve an existing imaging technique.
- The Great Coding Reset: How AI is changing software engineering
The Great Coding Reset: How AI is changing software engineering Business Insider
Score: 68🌐 MovesJul 22, 2026https://www.businessinsider.com/great-coding-reset-ai-software-engineering-2026-7 - News Corp countersues Brave for allegedly 'scraping' articles for AI
News Corp countersues Brave for allegedly 'scraping' articles for AI Reuters
- Three New Models, One Signal About Where AI Spending Goes Next
Three frontier level models landed within weeks of each other this fall, GLM 5.2 from Zhipu, Kimi K3 from Moonshot, and Gemini 3.6 Flash from Google, and I wanted to capture early developer reaction so I can monitor how feelings about these models change over time. The individual verdicts differ, but together they hint at Continue reading "Three New Models, One Signal About Where AI Spending Goes Next" The post Three New Models, One Signal About Where AI Spending Goes Next appeared first on Gradient Flow .
- Artificial intelligence could help wastewater plants track and manage microplastics
Artificial intelligence could help wastewater plants track and manage microplastics EurekAlert!
- CISOs Must Lead the Charge On Evolving Frontier AI Regulation
CISOs Must Lead the Charge On Evolving Frontier AI Regulation Gartner
- Ericsson and LG Uplus partner to develop network-based voice AI solutions
Ericsson and South Korean telecom operator LG Uplus have expanded their strategic partnership to develop network-based voice AI solutions and advance AI-native network technologies for future telecommunications services. The post Ericsson and LG Uplus partner to develop network-based voice AI solutions appeared first on Express Computer .
- Harnessing AI to Find Critical Minerals
Harnessing AI to Find Critical Minerals Carnegie Mellon University
Score: 68🌐 MovesJul 22, 2026https://www.cmu.edu/news/stories/archives/2026/july/harnessing-ai-to-find-critical-minerals - Becoming an AI-native telco: Indosat Ooredoo Hutchison reimagines the telecom of the future
Indonesia’s second-largest telecom operator turned a complex merger into a platform for growth, reshaping how decisions are made, how employees work, and how AI creates measurable value across the enterprise.
- Why AI is Becoming Every Engineer’s Co-Pilot
Why AI is Becoming Every Engineer’s Co-Pilot
- The real difference between OpenAI and Anthropic is what happens when AI gets cheaper
Anthropic may look stronger than OpenAI on the usual pre-IPO scoreboard. It has reported stronger private-market momentum. It appears closer to near-term operating profit. Its gross margin is reported above OpenAI’s. It has deep enterprise relationships, a strong reputation with developers, and Claude Code has become one of the clearest examples of an AI product […] The post The real difference between OpenAI and Anthropic is what happens when AI gets cheaper appeared first on e27 .
Score: 68🌐 MovesJul 22, 2026https://e27.co/the-real-difference-between-openai-and-anthropic-is-what-happens-when-ai-gets-cheaper-20260721/ - Boeing mulls building ‘many’ Ghost Bat production hubs overseas
Boeing mulls building ‘many’ Ghost Bat production hubs overseas Breaking Defense
Score: 68🌐 MovesJul 22, 2026https://breakingdefense.com/2026/07/boeing-mulls-building-many-ghost-bat-production-hubs-overseas/