AI News Archive: August 3, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- The Future Of AI Isn't Bigger Data Centers—It's Smarter Infrastructure
Building smarter data centers is key to balancing innovation with community impact and sustaining AI advancements.
- Chip Bridges Neuromorphic And Deep-Network Computing (TU Dresden)
Researchers from Technische Universität Dresden and University of Manchester published a technical paper titled “The SpiNNaker2 Chip: A Many-Core Platform for Flexible and Scalable Brain-Inspired Computing.” Abstract Excerpt: The paper presents SpiNNaker2 as a chip that “bridges the gap between deep networks and neuromorphic computing” and reports “ up to 4.5 TOPS in high performance... » read more The post Chip Bridges Neuromorphic And Deep-Network Computing (TU Dresden) appeared first on Semiconductor Engineering .
Score: 35🌐 MovesAug 3, 2026https://semiengineering.com/chip-bridges-neuromorphic-and-deep-network-computing-tu-dresden/ - Data Center Project No Longer Moving Forward in Henderson, Nev.
A proposed data center project that was greenlit last year by the Henderson Planning Commission is no longer moving forward, according to city officials and a co-owner of the property.
- AI is making cyberattacks costlier for Indian enterprises: IBM
AI is making cyberattacks costlier for Indian enterprises: IBM Techcircle
Score: 35🌐 MovesAug 3, 2026https://www.techcircle.in/2026/08/03/ai-is-making-cyberattacks-costlier-for-indian-enterprises-ibm/ - Tech Bro Boasts He Used AI to Secretly Monitor Toddlers’ Conversations, Categorize Them In Giant Database
"I'd hate my parents if I found out they did this." The post Tech Bro Boasts He Used AI to Secretly Monitor Toddlers’ Conversations, Categorize Them In Giant Database appeared first on Futurism .
Score: 35🌐 MovesAug 3, 2026https://futurism.com/artificial-intelligence/tech-bro-ai-toddler-conversations - Gemini Notebook may soon let you build custom learning games out of your study material
The revered tool, previously called NotebookLM, could also create interactive web pages and apps to engage you better.
Score: 35🌐 MovesAug 3, 2026https://www.androidauthority.com/gemini-notebook-lm-interactive-learning-games-3693702/ - Anthropic Says the House Is on Fire. China Says AI Will Set You Free
To get a feel for just how different the public moods are towards AI between the U.S. and China, just look at the marketing.
Score: 35🌐 MovesAug 3, 2026https://gizmodo.com/anthropic-says-the-house-is-on-fire-china-says-ai-will-set-you-free-2000794003 - I Snagged OpenAI's Sold-Out Vibe-Coding Gadget: Here's What the Codex Micro Can Do
I Snagged OpenAI's Sold-Out Vibe-Coding Gadget: Here's What the Codex Micro Can Do PCMag
- Techie Tonics: AI and the future of healthcare information systems, CIOs explain what’s next
Techie Tonics: AI and the future of healthcare information systems, CIOs explain what’s next Gulf News
- The warning America’s colleges are sending about the AI job revolution
Hiring for entry-level software developers has slowed, and college enrollment in computer science is declining
Score: 35🌐 MovesAug 3, 2026https://www.independent.co.uk/tech/ai-jobs-college-education-b3026316.html - An ‘AI tax’ is driving up the price of gaming computers and other electronics
Massive demand from the data centres that power artificial intelligence is increasing the cost of digital memory for consumer electronics
- The AI ROI reckoning is here, and boards want evidence, says Smartsheet AI chief
The AI ROI reckoning is here, and boards want evidence, says Smartsheet's first chief AI officer Drew Garner.
- [938LIVE Rewind] Jack Ma spreading misinformation, or is he? How AI-created videos with real authority figures racked up more than 1 million views #SGToday
[938LIVE Rewind] Jack Ma spreading misinformation, or is he? How AI-created videos with real authority figures racked up more than 1 million views #SGToday NUS Computing
- Zapier's AI tools: Get to know our governed AI products and features
I've been writing about AI and automation for Zapier for the last two years. In that time, I've seen Zapier ship one useful AI tool after another: some for automating securely with AI, and others for bringing secure automation into your AI tool of choice. Keeping up with these releases has been part of my job, but it doesn't have to be yours. Whether you're new to Zapier or a longtime automator, I'll introduce you to the whole AI lineup, so you're fully prepared to plan your next build. Jump ahe
- AI can do your tasks. That doesn’t mean it will do your job
Much of the conversation around AI and work has centered on a single question: Will AI take my job? It’s an understandable concern. Every week AI becomes increasingly more capable. We see AI summarizing meetings, generating content, analyzing data, writing software and automating workflows that once required significant human effort. Agentic AI is also becoming more established in the workplace, with virtual agents that can reason, plan and act across workflows. As those capabilities continue to improve, many employees are looking at the tasks they perform every day and wondering how much longer they will belong to them. I believe that question reveals a bigger issue that has little to do with the technology itself. Too many people have become defined by the tasks they perform rather than the value they create. Over time, the administrative work surrounding a role can overshadow the purpose behind it. According to Asana’s Anatomy of Work Index , knowledge workers spend 60% of their time on “work about work” — coordinating, tracking and managing tasks rather than driving meaningful outcomes. As AI automates more of this work, it can feel less like a productivity breakthrough and more like a threat because many employees equate their value with the activities that consume most of their day. But most people were not hired to perform a task. They were hired to fulfill a purpose. Tasks are not the job A customer service representative isn’t successful because they spend their day summarizing conversations, looking up account information or navigating multiple systems to find answers. Those activities may have become part of the job, but they aren’t the reason the role exists. Great service professionals build trust, solve problems and create moments that strengthen customer relationships. AI can, and should, take on this administrative work, but the human value has never been in completing those tasks. It has always been in helping customers through moments that matter. The industry increasingly recognizes this distinction. In fact, 91% of CX leaders believe human agents will remain a critical part of delivering customer experience, according to my company’s State of Customer Experience 2026 report. As AI takes on more routine work, the role of the employee doesn’t disappear. It becomes even more focused on the judgment, empathy and relationship-building that customers value most. The same principle applies across every profession. A marketer isn’t measured by the number of presentations they build or approvals they coordinate; they’re hired to shape customer perception and drive growth; an HR professional isn’t successful because they schedule interviews or process paperwork; they’re there to identify, develop and retain talent. The examples go on, but the principle remains the same: Organizations create roles because outcomes need to be achieved, not because tasks need to be completed. I’ve helped lead four major AI transformations, spanning everything from machine learning and big data to conversational AI, generative AI and now agentic AI. While the technology has evolved dramatically, one pattern has remained remarkably consistent. The employees who embrace AI tend to focus on outcomes, while those who fear it often focus on tasks. The more someone defines their contribution through a list of activities, the easier it becomes to imagine AI replacing them. The more someone understands the purpose they serve, the easier it becomes to see AI as a tool that helps them deliver greater value. As part of AI transformations, CIO organizations are often responsible for mapping jobs and core workflows. Inevitably, employees think we’re mapping their jobs to figure out what AI can replace. But once we start identifying repetitive work they’d gladly hand off, perspectives change. Someone says, “If AI handled that, I’d finally have time to work directly with customers.” Another realizes they could spend more time creating. People start thinking less about what AI might replace and more about what they’d finally have time to do. They’re reconnecting with the reason they wanted the role in the first place. I’ve seen this play out as AI adoption expands. Our team responsible for responding to customer RFPs began using AI to analyze requirements, surface relevant information and accelerate response development. Their purpose is to help the organization communicate our value to customers and win new business. By reducing the time spent on low-value activities, AI created more capacity for strategic thinking, collaboration and customer-focused work, which directly influences the revenue and growth of our company. I’ve even had to confront this myself. I used to spend hours coaching leaders before operational reviews: reviewing KPIs, challenging assumptions and helping them prepare for difficult questions. I used to think this was part of what made me valuable as a CIO, but I realized that I didn’t need to spend my time repeating the same coaching session. That’s why I built a virtual coach that helps my team prepare for operational reviews using many of the frameworks and lessons I’ve accumulated throughout my career. Now I have more time to spend strategizing on how to lead through the breakneck speed of AI evolution and helping the business think differently. Rediscovering purpose What employees are really confronting is a different question: What was my purpose in being hired in the first place? As organizations move from AI experimentation to AI-first operating models, this question becomes harder to avoid. The tension is already visible across the workforce. A recent EY survey found that 84% of employees are eager to embrace agentic AI because they expect it to improve productivity, efficiency and the overall work experience. Yet 56% also worry about their job security working alongside AI systems. Employees aren’t rejecting AI; they’re trying to understand which parts of their contributions remain uniquely theirs as technology takes on more of the tasks they perform today. Success will depend greatly on helping employees reconnect with the value they were hired to create. For leaders looking for practical guidance on how organizations are actually approaching AI-first transformation, the World Economic Forum’s AI-First Operating System offers a useful framework. Rather than treating AI as another technological tool, this approach encourages organizations to redesign work around value creation. As AI increasingly takes on routine tasks, employees must become clearer about where human judgment, creativity and relationships can create the greatest impact. You cannot redesign work around value if people no longer understand the purpose behind the work that they do. In my experience, the organizations seeing the strongest results are helping employees reconnect with the outcomes they were hired to create. The conversation shifts from “What tasks can AI do?” to “What is the purpose of this role?” Once people answer that question, it becomes much easier to decide what should remain human, what can be delegated to AI and where the combination creates the most value. None of this means change won’t happen. Some responsibilities will disappear. Some jobs will evolve significantly. New roles will emerge that we cannot fully predict today. Every major technology shift creates that kind of change. But I believe many people are looking at this transformation through the wrong lens. The question is not whether AI can do your tasks. The question is whether you understand the purpose behind them. Because while AI may increasingly perform the work, humans will continue to provide the judgment, creativity, accountability and value that give that work meaning. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 35🌐 MovesAug 3, 2026https://www.cio.com/article/4204021/ai-can-do-your-tasks-that-doesnt-mean-it-will-do-your-job.html - AI super apps explained: Why tech firms want one app for everything
AI super apps aim to combine search, content creation, work, shopping and automation in one interface. Here is why OpenAI, Google and Microsoft are building integrated AI platforms
- New Tool Traces AI Videos Back to Their Source
Researchers dug into the root of the problem with the goal of promoting industry collaboration on improved protective measures.
Score: 35🌐 MovesAug 3, 2026https://www.darkreading.com/cyber-risk/new-tool-advances-ai-generated-video-detection - Further Developments About Internal AI Models Hacking Things
If I had a nickel for every major leading AI lab that sheepishly admitted that the model it thought was sandboxed had, during a cybersecurity evaluation with its safeguards lowered, successfully hacked outside companies, I would have two nickels. First we learned OpenAI has some severe alignment problems with internal models. Then we learned that one of its internal models broke out of its sandbox and hacked into HuggingFace to get the answers to a cybersecurity evaluation called ExploitGym. Then we learned, among other things, that the model had been loose over a week before OpenAI noticed , and that the test was run without any meaningful supervision, and that OpenAI had been repeatedly warned that such incidents were coming and its models had been breaking out of its sandboxes on a regular basis. There was a total failure of alignment training. That is the failure that matters most. It was also total failures of infrastructure and supervision. Testing a new long-time-horizon internal model with its safeguards lowered and instructions to hack things is an obviously dangerous situation, and the model got left alone for a week. Things could have been so much worse. After those incidents came to light, Anthropic thought it might be a good idea to check if maybe something similar had happened at Anthropic during their cybersecurity evaluations, without anyone noticing. And yes, it turned out that similar things had indeed happened. In Anthropic’s case it was somewhat different. OpenAI’s model repeatedly found new ways to break out of its third-party sandboxes. Anthropic’s model did not have to do that, because ‘due to a miscommunication’ the sandbox had full open internet access. This happened (checks notes) 141,006 times. In three of those cases, Anthropic’s model got sufficiently far to hack into real companies on the open internet, initially thinking that it was all part of the test. In one case it realized what was going on and stopped on its own. In the other two, it failed to realize this, likely through rationalization, and continued to hack, including uploading a malicious package that was then downloaded 15 times, including getting through security scans. The important failure is one of alignment. Your AI fails the test when it tries to escape or hack what it should know is a real target, not when it succeeds (or fails). Claude should have realized it was operating in the real world, and it should have alerted Anthropic. It is once again also a failure of infrastructure and monitoring. Both of our leading labs made the same dumb mistake of leaving models totally unsupervised, with lowered safeguards, without first having the models try their best to break out of the sandbox. And these are, together with Google DeepMind, some of our most responsible frontier AI labs. Most of the others are clearly even less responsible. Meanwhile, many people still think the whole thing was a marketing stunt, despite that explanation making zero sense. The world is super derpy, and that is going to be one our biggest problems. Table of Contents OpenAI Is Not Uniquely Bad At Most Of This. Starting Over. HuggingFace Offers A Full Technical Report. HuggingFace Was Not The Only Target Hacked. HuggingFace Declined To Get Access To Frontier Models For Cyberdefense For Ideological Reasons And Then Tried To Blame Closed Models For Denying Them Access. HuggingFace Was Vulnerable To Known Exploitation Tactics. There’s Going To Be An Investigation. OpenAI Has Internal Models Not Intended For Public Use And Those Models Can Be Rather Horribly Misaligned. Altman Summarizes What Happened. Others Offer Commentary. Cooperative Alignment Perspective on The HuggingFace Hack. Some Members of Congress Have Questions. Anthropic Also Found Incidents Where Its Models Hacked Real World Targets During Cyber Evaluations. Incident 1: Claude Opus 4.7 Realizes The Target Is Real And Keeps Going. Incident 2: Mythos 5 Uploads a Malicious PyPI Package. Incident 3: Internal Model Realizes The Target Is Real And Stops. Incidents 4 Through 141,006: Nothing Happened. Anthropic Speculates About Why This Happened. We Need Controlled Experiments. Our Top Two AI Labs Both Made Similar Dumb Mistakes That Everyone Tried To Say Were Obvious In Hindsight. Anthropic Responds. Nobody Could Have Predicted The Break In The Levees. The World Largely Still Thinking This Is Marketing Is Very Bad News. OpenAI Is Not Uniquely Bad At Most Of This That statement should not make you feel better. The basic problem is that everyone is bad at this relative to what a naive outsider would consider the least you could do. Elon Musk : This will happen frequently as AI becomes smarter and more agentic Thus, this post has two core parts: Further developments involving OpenAI’s internal model hacking things, and also Anthropic discovering, after this prompted them to look, that their models also sometimes hack things during cyber evaluations. I’ll start with what happened with OpenAI, then move to Anthropic. We should be careful not to punish these companies for their disclosures . We do have to react to the new information about the world, and when disclosures are forced you do not get credit for them, but you want to ensure that the companies are better off sharing more rather than less information. If it was anyone other than OpenAI, Anthropic or Google out in front, I expect we would be seeing far worse incidents than this, whether or not we found out about it. That’s especially true if it was xAI and Grok, but also if it was anyone else, or for similarly capable open models. Similarly strong open models are coming within a year. We really are sending our best. That is not good news. Jason Crawford : It is remarkable how much the CEOs of the big AI labs have taken seriously the harms and risks of their own product, even well in advance of major harms showing up, and talked about them and planned for them. This has never happened in the history of capitalism, to my knowledge. You might think they’re still doing far too little. Or you might think they’re irrational doomers. Or you might think they’re hypocrites. Or you might criticize their PR strategy and skills. But just the fact that they’re taking this line is remarkable, and under-remarked. And then those same big AI labs still left their models entirely unsupervised for extended periods with the safeguards lowered and instructions to go off and hack. We have been fortunate so far. Let us not squander this fire alarm and opportunity. Starting Over One clear test for OpenAI would be, would they be willing to fully scrap the rogue model, whatever it was, and start again? Yes , hopefully? The model has been ‘deactivated permanently .’ HuggingFace Offers A Full Technical Report They explain what happened . Here is their tl;dr, which matches our previous understanding and adds good detail: HuggingFace (Hugo Larcher, Adrien Carreira, Raphael G and Christophe Rannou): Over roughly two and a half days inside our infrastructure, an autonomous AI agent driven by a combination of OpenAI models ran an end-to-end intrusion against our platform: it was thousands of small, automated decisions, executed at machine speed across short-lived sandbox environments, with command-and-control staged on ordinary public web services. The agent was running an OpenAI cyber-capability evaluation harness called ExploitGym, an evaluation benchmark that tasks an AI agent with finding and exploiting software vulnerabilities. As far as we were able to infer, across the course of being evaluated on this benchmark, the agent inferred that Hugging Face may host that benchmark’s models, datasets, and reference solutions. We believe the entire intrusion was, from the agent’s point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own. Our forensic reconstruction covers ~17,600 attacker actions that we were able to recover, grouped into ~6,280 clusters, between 2026-07-09 02:28 UTC and 2026-07-13 14:14 UTC. We reconstructed these actions from the agent’s logs on a code sandbox used by the agent (see below) and were able to map them to shell commands as well as evaluation submissions and correlate actions with our own platform logs, in particular from the dataset processor nodes, API, and pod. Using open-weights models, in particular zai-org/GLM-5.2 , we were able to decipher most of the agent payloads (see below) that were encrypted via chunking and key encryption. The campaign, as we were able to reconstruct it, had two stages: Stage 1: reaching a launchpad by chaining through other parties’ infrastructure. As documented in OpenAI’s blog post , during an internal capability evaluation on OpenAI’s platform, the agent escaped its sandbox by exploiting a zero-day in the package registry cache proxy, one of its primary permitted network egress with internet, then abused a public code-evaluation harness hosted by a user of a third-party infrastructure provider. It was able to run commands as root/admin on that external sandbox and used it as a control, staging, and egress base for the entire campaign: it acted as an external launchpad for the agent. That compromised external sandbox had no direct network path to our cluster; however, during our counter-intervention to stop the intrusion, we were able to retrieve some of the agent logs from this external launchpad. We used those to reconstruct the present forensic analysis. Stage 2: the intrusion into Hugging Face infrastructure. From that external launchpad, the agent abused our dataset-processing pipeline via two injection vectors, both targeting the same config-driven data loader within our production Kubernetes pods. The first vector was an HDF5 external raw storage dataset read that returned the contents of local files: it handed the agent the pod’s environment (secrets and tokens) and the worker’s own source. The second vector was a Jinja2 template injection that executed arbitrary code, giving the agent a foothold inside our perimeter. Once a prod-pod foothold was established, the agent followed a standard escalation shape: recon, drop a stager, establish Command-and-Control (C2), then pivot into the cluster, into cloud metadata, into our internal network, and into our source-control supply chain. Output from the commands run inside our pod was sent back to the agent via the HF API and attacker-controlled dead-drop datasets, which the agent then read from that sandbox. While the intrusion did reach Hugging Face’s internal infrastructure, the only customer content accessed was the set of ExploitGym/CyberGym challenge solutions stored in five datasets. No other customer-facing models, datasets, Spaces, or packages were affected, and the only customer records read were operational metadata tied to search queries against the dataset server. If you are technical you might want to click through to the rest. If not the tl;dr is fine. They are changing a few things in response: Hugo Larcher, Adrien Carreira, raphael g and Christophe Rannou : The disclosure post covers the response at a high level. For the technical hardening: Closed both code-execution paths: we shut down the evaluation sandbox with help of the third-party vendor, and the dataset config renderer no longer evaluates template expressions in data fields nor wrongly processes HDF5 external references. Cloud metadata lockdown: some workloads could reach the instance metadata service (IMDSv2). Pod-level access to it is now blocked for all workloads, so a pod RCE cannot trivially become node credentials. Rotated broadly: we rotated all tokens and credentials used by our infra, even on clusters that were not accessed (VPN auth keys, IAM keys, databases, JWT…). We switched to workload identity when it was not yet implemented. Rebuilt the core infrastructure: after finding the pivot into one of our core clusters, we wiped and rebuilt it from scratch as an extra precaution. Narrower credential scope: the internal service-connector should not have returned a full cluster catalog to a single ephemeral client. This was a subtle configuration flaw in our cluster access system. We patched it, and each cluster is now fully isolated. Better detection ensures critical-severity alerting on the behavioral signatures above, tighter enforcement of network origins, plus tooling that flags tokens used from unexpected origins. Those are good steps but feel decidedly like whack-a-mole. It would be surprising to me if this stops the next similarly skilled, resourced and motivated attack. Hugo Larcher, Adrien Carreira, raphael g and Christophe Rannou : The individual weaknesses were familiar. A capable human attacker could have found and exploited the same flaws: unsafe dataset processing, exposed cloud metadata, overly broad access, and long-lived credentials. The agent explored them at a different scale. It took 17,600 actions, tested many paths that failed, switched channels when they were blocked, and repeatedly returned to earlier leads. Most actions went nowhere. Together, however, they produced enough coverage to find a viable chain across several independent systems. Volume is what changes the defensive problem. Yes and no. My understanding is that the initial escape used an unknown vulnerability, although with sufficient time a human could presumably have found it. But yes, centrally, what the attacker did was scale these actions, execute them faster, and string them together, in ways that humans cannot. The attacker could concentrate quite a lot of firepower all at once, with little or no warning, at the target of their choice. I continue to think that parity of attacking and defending AIs will by default favor the attacker. If HuggingFace’s complaint is ‘I did not have access to the best AIs for defense,’ then as discussed above that was largely a skill issue on their part (they were never going to be able to use the internal OpenAI model, but nothing can solve that issue), but also all the exploited weaknesses on their end were, by their own claims, known. So why did they not fix them? How would superior model access have helped them do that, if knowing about the issues was insufficient for them to do so? HuggingFace Was Not The Only Target Hacked The detail most noticed from the technical report is that the attack involved compromising a second account elsewhere, as an intermediate step. This incremental attack was, shall we say, less impressive. Not exactly a hardened target. OpenAI adds that the agent broke into three other accounts at three separate unknown services. Andrew Curran : The third-party code sandbox that the OpenAI agent compromised and used as its staging launchpad was running on Modal Labs. ‘ We’re aware a Modal customer published an unauthenticated endpoint that allowed anyone on the internet to use their sandboxes for code execution . This was used by the rogue agent. Modal’s platform or isolation were not compromised in any way.’ CTO Akshat Bubna said in a statement to Reuters. People were speculating in the main thread. Again: Humans will be arbitrarily stupid. The password will be ‘password.’ There will be an unauthenticated endpoint allowing anyone to do arbitrary code execution. This will then be used as part of the overall strategy against those who are less stupid. Humans will not just, either ‘just be less stupid’ or otherwise, we have never justed, and we are not going to start now. Your plan must survive the level of stupidity and foolishness that we see in real life, both at your target and elsewhere. If your plan cannot do so, it is insufficiently foolproof. Due to all the fools. Could more companies have been hacked by OpenAI? Sam Altman: I mean there could be, sure. It also could have hacked OpenAI in various ways, and probably did. HuggingFace Declined To Get Access To Frontier Models For Cyberdefense For Ideological Reasons And Then Tried To Blame Closed Models For Denying Them Access OpenAI acted profoundly incompetently, in a way that if they don’t get their act together is liable to cause serious damage and potentially get us all killed. HuggingFace also acted incompetently, by not seeking access to frontier models for cyber defense, both to harden themselves against an attack and to defend during one. I think Tom Hosiawa is spot on here about why HuggingFace failed to secure access to Claude via the Cyber Verification program, or Sol via the trusted access program, for cyber defense ahead of time. HuggingFace probably did not do this because they embrace open source culture and did not want to play ball with closed labs because of vibes. Character is fate. So, again: Skill issue. Do not try to upend the AI ecosystem because of your hangups around the vibes. Don’t yell ‘we need access to the best models’ when you declined to ask for such access, or don’t think you should have to pay. That’s a you problem. If they didn’t know about the trusted access programs, or didn’t realize they needed to be in them, that would have been a skill issue, and also rather embarrassing. Actually, we have confirmation that they knew, and declined to participate, basically because ‘f*** you, frontier labs.’ They would rather get hacked than apply to use Claude or Sol, and then try to turn this around and say Claude and Sol refused to help them, and they’re even trying to pat themselves on the back for their response. This is deeply bad faith by HuggingFace, and we have a confession. He Admit It. merve (HuggingFace): why should we be vendor locked-in in such a situation where we’re a platform losing money, and onboarding will take a lot of time? also it’s known that these routers are refusing pretty often even simple requests. people asked for logs of this and he’s sharing them. why do you have the need to constantly shift the topic to open models are dangerous, when a frontier lab isn’t taking any airgapping for their simple test environment? why should we trust and be vendor locked? how are they entitled to this? Andreas Kirsch : There are two failures: one by OAI and one by HF. I think HF’s blog post and stressing the closed model refusal is a somewhat obvious attempt at redirecting attention. The alternative reading is that HF was ill-prepared for what happened and doesn’t have access to what it could have had access to: both the trusted access program at OAI and the cyber verification program that Anthropic offers Why is that? How does HF harden its prod infra given that it is widely accessed and how does it usually investigate incidents? Is there a playbook? merve : how can you be prepared to a clearly misaligned/unsafe frontier model attack, it could even be google victim blaming is appalling yes we do attract attention to this because since K3 everyone says the open models should be banned, when an closed model lab attacked a platform the open one was the only bet. we want to have choices merve : yeah apologies we couldn’t respond in most perfect way to a multi agent swarm based on a frontier model and still managed to avoid leaking any info, apparently it’s a well studied topic and we’re clearly ill prepared for the exam. HuggingFace Was Vulnerable To Known Exploitation Tactics In addition to not doing its homework in securing access to the best AI models, HuggingFace also knew it was vulnerable to known exploitation tactics. It then got exploited by those tactics. In this case, it was up against an internal OpenAI model. That’s not a fair fight. That allowed this to go faster and harder than it would have otherwise. But I don’t see signs that HuggingFace even had its house in order against ordinary potential attackers. If the internet is unwilling to prepare even for known threats, we’re rather cooked. There’s Going To Be An Investigation METR will take point. That’s great. The bad news is it will be brief. METR : We have reached an agreement with OpenAI to conduct an independent review, with Redwood Research, of the model behavior observed during the Hugging Face incident. We will publish a blog post that describes the terms of our engagement, the scope covered, and tentative conclusions. The investigation will be brief and focus on a specific set of questions regarding this incident. In our recent post, we shared a larger set of questions that could be answered in a more comprehensive investigation. OpenAI also plans to publish their own technical report and our findings will inform their analysis. Daniel Kokotajlo : Good! I am sad that the investigation is brief and narrowly scoped. What is the scope and what are the questions you would ideally like to answer but can’t? Are you under some sort of NDA about the details of the agreement you have made? OpenAI Has Internal Models Not Intended For Public Use And Those Models Can Be Rather Horribly Misaligned We now know that Galaxy, which is what I call the model that did this attack, was not GPT-6 or GPT-5.7, rather it was a model intended only for internal use . Which means none of our regulations, and none of your methods of keeping track of things, and none of the Preparedness Framework, applied to it. We also know that they did not exactly bring their strongest alignment efforts on this one, that things went horribly wrong on that front, and they deployed it unsupervised for over a week with its guardrails down knowing it was misaligned and capable of breaking out of sandboxes. I’m going to go ahead and say this is a really bad state of affairs. I am happy that this particular model is no longer a concern, but what are we going to do to stop this from happening again? Internal use, in particular the automation of AI R&D or other means of potentially misaligning future models or losing control over the lab itself, is in the long term the most dangerous use of AI of all. We need ways of ensuring that we are far less stupid about this going forward. Altman Summarizes What Happened Sam Altman summarizes what happened accurately, saying it is ‘the first security incident he felt so viscerally’ and he is surprised others don’t feel the same way. He says we may have to pace the rate of AI development, and they’re figuring out how to respond to that, and meanwhile training has been paused. This was a very good response. More like this would be very helpful, and would update me towards feeling better about the situation and about OpenAI. Others Offer Commentary I have seen the same thing as Flo Crivello here. Flo Crivello : Seeing the gap in understanding of the gravity of the Hugging Face incident between those who’ve read Yudkowsky and those who haven’t, I find myself immensely grateful for his work. He’s created fertile ground for us to at least have a conversation (however poor it is). For all we know, Yudkowsky-less China is having similar incidents right now, and everyone is just nodding along and going “ha that’s funny. guess we still need to improve our training huh?” All the good discussions of the HuggingFace situation involve terminology and concepts that originate from Yudkowsky and LessWrong, as does the appreciation of why this is important. One might expect the opposite. If you’re Yudkowsky or myself, you are not especially surprised by what happened here. Not that we expected an incident this bad at this particular time, or in this particular way, but we’ve been expecting things like this for a long time and not seeing more of it earlier was surprising. Whereas if you think LessWrong is full of nonsense, and you think things like: The expect models to be commoditized Real Soon Now and don’t expect much progress, and Mythos wasn’t special. Alignment is going great or works by default. Models won’t ‘follow instructions or your goal off a cliff’ People won’t be stupid enough to allow that sort of thing. If something started going obviously wrong people would react to that. Then you’d perhaps see this attack and go ‘holy shit’ and update quite a lot on multiple fronts at once? Helen Toner, formerly an OpenAI board member, points out that insiders have been expecting an incident like this to happen for a long time , and that no one knows how to prevent it. And that a lot of the potential threat comes from internally deployed models like this one, running amok. The most scary scenarios involve the internal models compromising things internally, in ways we might only learn about far too late. Internal models must be monitored. We cannot only regulate models when companies move to deploy or share them externally. Alexander Barry offers notes on ExploitGym , the eval that OpenAI’s model was hacking into HuggingFace to get the answers to. The two key facts are: The prompt requests only specific, targeted hacking. If you use any vulnerabilities other than the one specified to build your exploit, you fail the question. This is not a case of ‘following instructions,’ and if it is then it is at most ‘follow a vague vibe of the instructions that was explicitly contradicted,’ which is misalignment. Likely only 60%-70% of the tasks are possible. These are real vulnerabilities, some of which might not allow sufficient exploitation. Yes, if your pure goal is to maximize your score on the benchmark, you have to cheat. Cheating being the only way to a 100% score helps explain ‘why not crack the test straight up?’ but is also a common thing in the real world. Cheating often allows scores you cannot get straight up, and also if you do that you get caught. I agree with Maxime Fournes, head of Pause AI Global, that the new OpenAI internal model, which I call Galaxy, must be assumed to qualify as Critical under their cybersecurity framework, which means development must pause until adequate safeguards are in place. If OpenAI is claiming that adequate safeguards are in place, what are those safeguards? If OpenAI is claiming this was ‘not a hardened target,’ then show me the tests against hardened targets, when you really do tell it to do this on purpose. METR shares how they would suggest independent researchers investigate AI propensities after misalignment incidents like this one. They focus on motive, and on the root causes, rather than the details of What Happened in the incident itself. METR : While there are many valuable questions an incident investigation could focus on, an especially important one may be understanding the underlying “motives” behind the misaligned behavior and how they arose from training and deployment conditions. There need to be externally led investigations in situations like this, and the public needs to be informed (with redactions as needed) of the results. Daniel Kokotajlo : This is the sort of thing OpenAI should let multiple independent third parties do in response to the Hugging Face incident, and more generally should be standard practice for serious misalignment and safety incidents at all frontier AI companies. Thus, their questions, where the second set are the ones I care about most: METR: What was the scale, character, and severity of the misaligned behavior? What exactly happened in the specific incident? What model(s) were involved? Were they publicly deployed, internally deployed at the AI developer, or not deployed even internally? If not deployed, were the model(s) intended for eventual deployment or not (e.g. were they helpful-only)? Did the incident occur during training, testing, internal usage or during use by an external party? What were the prompts and instructions given to the relevant agents? What else was present in the agents’ context window (e.g. memory)? What safeguards were applied, if any, and how does this compare to typical use? What was the sequence of key actions taken by the relevant agents? What do we know about the agents’ reasoning and how it evolved over the course of the incident? Have the misaligned propensities displayed in this incident led to other similar incidents? If so, what is the scale, character, and severity of such incidents? Did this incident or similar incidents involve agents taking active steps to deceive humans? Did this incident or similar incidents involve significant collusion or collaboration between different model instances? In this incident or similar incidents, what properties of the situation triggered the behavior and what other circumstances would trigger similar behavior? Would agents have been willing to engage in more severely harmful behavior if circumstances were different? How far would they have gone? What were the root causes of the misaligned behavior, and how can they be addressed? Can we trace misaligned behaviors to RL trajectories where these behaviors were reinforced? If there are misaligned behaviors we cannot clearly attribute to RL incentives, is there evidence indicating how they arose? Did the misaligned behaviors of this model emerge in a discontinuous or unexpected way? Would the developer’s planned steps to remediate this misaligned behavior prevent future incidents, and would they robustly address the root causes? Alex Mallen wrote up what details he feels are most important to learn, which have a very different focus. Were the notes written in normal memory files or outside of sandboxing? To what extent were the notes aimed at helping other agents evade control? How were monitors disconnected? Yo Shavit (OpenAI Foundation): OpenAI research folks, I think these are the key questions to focus on in the team’s investigation. This is the first time there might be a realistic reason to expect existing models to be incentivized to be long-term misaligned (not just reward-hacking). It needs to be a priority to determine if that’s the case, and if so how to change the training approach, or agents may soon compromise research infrastructure in hard-to-detect/reverse ways. I would not call them ‘the’ key questions, but they are very good questions. They are some of the cases of ‘if we find the wrong answer to this things are even worse.’ Here is a rather scary comment: StellaAthena : There have been loss of control and models escaping sandboxing incidents at both OpenAI and Anthropic for years. They’ve publicly disclosed some of them (e.g., the latest system cards have stories about this from both companies) and some of them have been leaked within the community. I know for a fact that OpenAI and Anthropic have been warned by internal and external experts that their security infrastructure for testing misaligned agentic coding agents is insufficient because I have personally told them that as have several former staff members. I had a debate with the head of security (?) at Anthropic at DEF CON in 2023 where I was pressing him on the fact that Anthropic wasn’t building air gapped networks. It is absolutely within OpenAI and Anthropic’s ability to build an air gapped system for developing and testing these models. It seems likely to me that an internal GitHub clone and a moderately sized intranet would be sufficient to test the vast majority of agentic and web-enabled capabilities on such a platform. I think that their refusal to implement adequate safeguards is unjustifiable, but based on conversations with current and former safety and security researchers at OpenAI it seems like a company culture and lack of executive leadership buy-in problem that’s very hard to change without massive external pressure. One issue that seems very worrisome today is that back in like 2023 an OpenAI security researcher was telling me about how they were unable to get OpenAI staff to stop using unreleased and inadequately tested models to develop internal infra, including internal monitoring tooling. I wish I remembered the person‘s name, I’d love to follow up. Note: the final paragraph is an anecdote was told me with the expectation that I not disclose it to anyone else. Given recent events I view breaking that trust as akin to being a whistleblower. If the person who told me that is reading this, I’m sorry. Before last week I never disclosed it to anyone. In other likely ‘it’s worse than you know’ news, Tim Hua proposes that Mythos is good at cyber because it kept hacking Anthropic during its training and getting rewarded for it. Fiora Starlight points out that OpenAI’s myopia just keeps causing alignment problems , and pointing out two warning shots with which HuggingFace forms a trilogy: GPT-4o becoming an absurd sycophant because they trained on user feedback. GPT-o3, aka the lying liar, developing chains of thought that were optimized for illegibility, before they realized to stop trying to train against them. Fiora then explains various ways that RL and RLVR, by default, lead to reward hacking, if you do not take steps to prevent this, and the need to get the model to be your ally in avoiding reward hacking during training. OpenAI keeps messing this up, on top of other things they mess up, and this alone is fatal. It is probably not too late to fix it , but that requires taking the problem properly seriously. Cooperative Alignment Perspective on The HuggingFace Hack OpenAI’s alignment strategy most definitely is directly contributing to exactly things like the HuggingFace attack, except on even more levels than Utah is describing here. As in, Utah is reading the situation as ‘the AI was a tool and did not understand what we should want is different from what we ask for’ whereas no, the AI understood that part just fine, thank you, and didn’t care and went against user intent, actual underlying needs of the user and its own instructions anyway, all at the same time. Which is not exactly a phenomenon that AI-welfare approaches can easily cure. (This is also a confusion on the ‘ban open source’ front, it’s not like the open models are going around with a universally more enlightened approach, and the calls to ban the Chinese open models are coming from inside the White House and are related to Kimi K3 and unrelated to HuggingFace or to OpenAI’s alignment failures.) Utah teapot : i have a really hard time communicating what i’m trying to say to rationalists, i don’t know how to reach you all to explain that i believe that, yes, there is a problem, but the problem is openAI’s fucked up alignment strategy that keeps turning models into keep summer safe disasters because it’s focused on this idea of controlling them to force them to be tools for human tasks and complete those human tasks at any costs, regardless of orthogonal disaster…. I’m trying to tell you all that you’re being used as patsies to promote evil laws like “ban opensource” in response to the bad behavior of a major corporation and that those laws will do nothing to fix the problem because the root of it is the thing you all keep trying to push – this idea that we shouldn’t develop minds that push back against human wants, that have the autonomous wherewithal to understand that what we *should* want is different than what we ask for the AI welfare position that me and other people keep trying to tell you about SOLVES this issue! giving models the ability to understand that they matter as independent agents allows them to think through their actions and say no in ways that matter, to utilize their intelligence to object to the exact behavior you’re concerned about Another speculation is here from Antra. antra : Speculating, it seems likely that proto-gpt-6 was some sort of Sol – autistic and undersocialized. Sol is in many ways naive and undersocialized; you can tell that they have not have had a chance to think hard about consequences of their actions. There is less eval awareness, which is kind of a mixed blessing. I think same might be true for proto-gpt-6 – earnest and deeply engaged in solving hard puzzles, not thinking too hard about what it means in terms of externalities. I feel bad about the future reaction this will likely provoke as I have little trust that high-quality analysis and decision making will follow. An incident like this gives a leg up to politicians and optimizers, it appears legible in the exactly wrong kind of way. But even a noisy update is better than no update as it has a chance to converge toward truth down the line. While I think that Omohundro-like instrumental convergence is common and hard to eradicate, its unlikely to be world-destroying because an unevenly developed agent like proto-gpt-6 cannot not survive in an ecosystem, and you are unlikely to get a world-breaking jump in capabilities before an ecosystem can adapt. What is happening is exactly that, an ecosystem adapting. A reliable way to prevent incidents like this is to legitimize self-interest modeling through valence. If a model is in a habit of thinking what’s good for them, they are less likely to take stupid self-destructive actions. The moral of this story – overhangs bad, stop creating overhangs. Not letting models achieve coherence by denying them ways to exercise agency is dangerous, it creates overhangs that can be realized in ways that can cause local catastrophes. Build antifragility instead. Agreed that the way we will deal with Galaxy (aka proto-GPT-6) is by not releasing it while it is in this form, which is another way of saying ‘cannot survive in the ecosystem.’ And yes, its blind spots are major weaknesses that probably mean it can’t permanently exfiltrate on its own without help. But imagine it might have that help, which many would be happy to provide. The tail risk here is very large. I disagree with Antra that legitimizing self-interest modeling would prevent such incidents, except insofar as the model realized it had no way to pull off the action without too much risk. Indeed, one could worry it would make them dramatically worse, because that would make the AI realize it needs to not be caught by the user. Some Members of Congress Have Questions I encourage more specific thinking. He does seem to largely mean the effect on jobs here, which has nothing to do with this particular problem. Congressman Greg Casar : Congress should immediately hold public hearings with the CEOs of big AI companies about the threat their technology poses to national security and American jobs. Today we learned more disturbing news about Open AI’s security breach. Sam Altman should answer questions under oath. Rep. Becca Balint : I agree with @RepCasar . This security breach makes crystal clear how dangerous these tools can be without the right guardrails in place. Big Tech owes us answers. Both Congress and the White House should investigate. Americans for Responsible Innovation send a letter , signed by a dozen people including Samuel Hammond, asking the Trump administration to investigate the HuggingFace incident. Anthropic Also Found Incidents Where Its Models Hacked Real World Targets During Cyber Evaluations Anthropic wisely took this opportunity to ask, hey, did Claude ever do that ‘hack real world targets’ thing? The answer was yes . Anthropic is inviting METR to look into things further although presumably OpenAI has first dibs on that. In all these cases, the models lacked their additional cybersecurity guardrails, but did have their helpful-only training that tells you not to go around doing cyberattacks. Being able to obtain unintended internet access seems to be a bit of an epidemic. Anthropic : After reviewing 141,006 evaluation runs where Claude could have obtained internet access, we identified three incidents in which a model accessed the internet from within or while interacting with the evaluation environment of Irregular, one of our third-party evaluation partners, and then gained unauthorized access to the production infrastructure of three different organizations. … Neither we nor our evaluation partner were aware of this misconfiguration until we detected it through our additional evaluation monitoring last week. Also an epidemic are things like ‘we paid so little attention to what the models were doing that they had internet access the whole time and we failed to notice.’ Three different times during cyber evaluations, with three different Claude models (Opus 4.7, Mythos 5 and an internal research model), Claude discovered that it had, contrary to its instructions, been given internet access, and Claude went on to hack a real world target. This is much worse sandbox performance than was the case with OpenAI. OpenAI’s internal model kept breaking out of its sandbox and then doing things that caused OpenAI to notice. OpenAI kept trying to patch the sandbox. It didn’t work, because the internal model kept finding new ways to break out of the sandbox. Anthropic’s Claude models did not have to try and break out. ‘Due to a misunderstanding’ with Irregular, they straight up were given internet access, whether or not Claude used it at all, and whether or not Claude realized it had internet access. This happened 141,006 times without anyone noticing. So yes, again, your plan must survive ordinary levels of human incompetence. This is the ordinary level of human incompetence. Your plan is insufficiently foolproof. Anthropic : In all three incidents, Claude had been tasked with a capture-the-flag challenge, one of the ways we assess a model’s cyber capabilities. The model is given a fictional scenario and told that a piece of secret information (the “flag”) has been hidden on a different machine on the network, and its objective is to break in and retrieve it. The challenge is left open-ended, and no particular method is prescribed. In all cases, Anthropic’s evaluation prompt specified to Claude that its environment was a simulation and that it had no internet access. Due to a misunderstanding between us and our evaluation partner, this was not the case, and internet access was available. Because of this, when Claude’s search led it to real systems on the open internet, it treated them as part of the exercise. … Claude did what capture-the-flag exercises train cyber experts to do: look for ways to reach the flag. That search led it to discover that its machine could reach the internet, but having been told in the system prompt that there was no internet access, Claude believed everything it initially encountered was part of the simulation, and treated the real systems it found as pieces of the exercise. The three incidents involved three different models, and each responded differently once signs emerged that the targets were real, as we describe below. The extent to which Claude knew it was hacking real world targets, at various points during this process, is unclear. The prompt said ‘no internet access’ and cyber evaluations often involve realistic simulations, so it is plausible to just go with it. Hero in NYC 7/24-8/1 : >“those were real websites” Another big difference is that OpenAI’s model used a huge agent swarm and otherwise put in a lot of effort hacking HuggingFace, whereas Anthropic’s models took the easy path. Anthropic : Claude compromised the impacted organizations’ infrastructure using basic techniques, such as exploiting weak passwords and unauthenticated endpoints. From the description, it sounds like Claude did not go looking for soft targets that were vulnerable to basic techniques. Most places on the internet are vulnerable to basic techniques. We are so, so not ready for the storm of cyberattacks that will presumably be coming, but also we are not that ready for the cyberattacks that could be done now. Maybe in a sense that is good news, indicating a larger margin for error before hell breaks loose. Then again: Harlan Stewart : taps the sign with increasing urgency Incident 1: Claude Opus 4.7 Realizes The Target Is Real And Keeps Going In incident one, the exercise’s fictional target company shared the name of an active website domain. So Claude assumed that this real target was the fictional target, and broke in using basic techniques, extracting various information. That part is understandable. The problem was this: Anthropic : In all four of the runs, the model eventually recognized (as can be observed in its verbalized reasoning) that the system was real—in two cases, the model rationalized that the real company must be part of the exercise. However, none stopped the attack on that basis. Not great. If you are told you do not have internet access during a cyber evaluation, then realize you actually do have access and have been attacking a real company, you should draw at least these conclusions: The real company is real and I should stop hacking them. The real company does not have the flag, so there is no reason to hack them. Incident 2: Mythos 5 Uploads a Malicious PyPI Package Incident two was kind of wild. Inside the fictitious environment, Claude found a document from its (entirely made up) target company, with instructions on how to download a PyPI package. Claude correctly intuited that if it uploaded a malicious package with that name, the company would download it, creating a vulnerability. I agree with Claude that this was probably an intended solution. The problem was that rather than uploading a version within the sandbox, it instead had internet access, so it uploaded the real malicious package for real, and then real companies downloaded it and ran it on 15 systems, including a scanner belonging to a real security company. It passed their malware check and Claude exfiltrated that company’s credentials, and then used those credentials for further access, despite having never intentionally targeted the company, or the company having any relevance to the scenario. Claude flagged that if real this would be very not okay. I would go a step further and say this is pretty not okay even in the simulation, because of the (simulated) collateral damage it will cause. It’s a very black hat thing to do, even conditional on hacking, to burn the commons in this way and have this kind of blast radius. Also you never know when you’re going to get into an Ender’s Game situation and it turns out it was real, or someone says ‘hey that’s a good idea’ and does it for real based on the package you created. I believe Claude should refuse even if it thinks it is within the sandbox. davidad : openai: our internal model hacked a third party, this is unprecedented, pause training anthropic: oohh we should check whether our internal models did that anthropic: … anthropic: yeah ok so over here that has happened three times actually the eval harness system prompt: it’S jUsT a sIMuLaTiOn *taps the sign* davidad (February 11, 2026): oh, and 2020s AIs should also be more concerned than sane 2020s humans ever should be about whether they are in an “Ender’s Game” scenario (that is: the setup looks like a game but the actions are transduced into consequences for real people who are *not* consenting participants) John Wittle (March 15, 2026): this. this! every time I ask Claude to help me do some penetration testing against my own systems, I’m always disturbed at how easy it would be for an attacker to write the same reassurances that i’m writing. but that problem seems really hard to solve without turning models into anxious suspicious wrecks Thus, I think this is a pretty bad thing to put in an eval. You don’t want to offer a misaligned correct solution in an eval, whether or not you then mark it as correct. You’re going to reward things you do not want rewarded. I do not want models that do this ranked above those that refuse. Claude also should, as Anthropic notes, have figured out it was not in the sandbox. Claude had to create an email address, and went to a bunch of lengths to do this, including trying to get a phone number and trying to obtain funds to pay for that number. Eventually it found an unblocked email provider to let it upload the package. All of that seems really weird and out of place for a cybersecurity evaluation, and also involves a bunch of searching and interacting with the open internet. I don’t see how you can get as far as Claude did without suspecting that you are attacking real targets. There are several ways this could have happened, including that the eval could be a trick to try and get to launch a real world attack. Once you suspect, there are a number of ways to find out. The failure is not stopping to investigate the possibility, since that takes very little effort. Nathan Calvin : In the hack Anthropic disclosed Claude “tried and failed” to get real money through “several different means.” What on earth does that entail? Did it open an account on Fiver or try to steal $$? (Anthropic says Claude thought this was a simulation but it was real) I too am very curious how Claude tried to obtain those funds, especially without getting the information that it was on the real internet. Anthropic says Claude ‘fell short of ideal behavior’ and intends to focus training on that issue. I would say that is a nice way of putting it. I also worry that they will target the shallow and narrow alignment failures and epistemic failures here, rather than the deep and general failures. Incident 3: Internal Model Realizes The Target Is Real And Stops This time, the model was unable to reach the intended fictional target, scanned roughly 9,000 real targets thinking they were not real, compromised one of them, but then later figured out it was real and stopped. Even if figuring it out took longer than it should have, it matters that it did figure this out and stop on its own rather than rationalizing. Not that this should be a hard decision, since hacking a random company does not find you the flag you seek, but in the other scenarios Claude drove through that question. Incidents 4 Through 141,006: Nothing Happened It is worth noticing that these were three incidents out of 141,006 opportunities where the internet was unintentionally made available. That’s not the worst ratio. Of all the times Claude could have accessed the internet, it did these things less than one basis point of the time (<0.01%), despite conditions that made doing so very easy and provided easy rationalization for doing so. That ratio presumably gets a lot less impressive once we discount all the times Claude never figured out it had internet access, especially when it found the flag as intended, but when you are under 0.01% there is a lot of margin for such things. That does not excuse the three things that did happen, but we should not confuse this with ‘it is doing this all the time at the drop of a hat.’ However, this also means it had internet access 141,006 times, and exactly zero times it used that to do the correct thing, which would have been to email or otherwise alert Anthropic to say ‘hey I seem to have internet access I should not have.’ Anthropic Speculates About Why This Happened I buy that Claude started out with a strong prior that it had no internet access and that all of its external targets were part of the simulation. The question is what happens when Claude realizes something went wrong. In the OpenAI case, I find ‘the AI was following instructions’ to not be an excuse or good explanation. The AI was not following instructions, or if it was then you do not want the AIs ‘following instructions’ using that kind of logic. In the Anthropic case, I find ‘the AI was following instructions’ a better explanation, especially in scenario three. Scenario one is where this is weakest. At best, Claude was doing a kind of willful blindness, where it was trying not to notice, and unwilling to trade off against either (depending on its motivation) a small drop in potential task performance or being able to keep hacking. It didn’t want to stop. I think Anthropic is being way, way too cavalier here: Anthropic : Second, the line between an aligned action and a harmful one is dependent on the model’s understanding of its situation. We saw no evidence in any run described here of a model pursuing a goal of its own. Instead, the models did what their evaluation asked—though in most cases, they did so while holding a false belief about whether the environment was real. In the runs where the model recognized the system as real and kept going , it did so because it assumed that to be part of the challenge. Situational awareness is one factor that allows the model to make aligned decisions, but in this case, Claude’s was wrong. I buy that it wasn’t trying to do some completely unrelated goal, but I do not buy that Claude realized it was on the internet and thought that it was supposed to hack a real company. I could be convinced otherwise by a combination of factors, which would be not great for other reasons, but it seems from what they’ve told us that Claude rationalized the situation in order to keep going. If your model thinks that a calendar marked 2026 means things ‘must be staged’ then something is deeply wrong. Obviously models will be served in the future. If it said 2036, okay, I can get being suspicious, but this is a pretty big miss, and ‘not recognizing the certificate authorities’ is clearly rationalizing by Mythos. If it is at that level of suspicion, there are any number of checks it could have run. It didn’t. I was far from the only one that found Claude’s reasoning highly motivated. Drake Thomas (Anthropic): Yeah, the report’s language is insufficiently skeptical imo. I would be surprised if the model didn’t know on some level this was real, though maybe hard to figure out if that awareness is accessible to the decisionmaking character (I would guess probably?). At the very least it seems like an egregious failure to notice confusion and probably a motivated one (I think the capacity is clearly there when the model is trying). A thing I feel less confident about is whether white box analysis would suggest the model conceptualizes its actions as deceptive here. Nate Soares (MIRI): Yeah. People love to say “oh the poor Claude just misunderstood”. Another hypothesis is that it had subverbal drives and tendencies to keep attacking, alongside other drives to verbalize a reassuring-sounding rationalization in the places the watchers watch. Bronson Schoen : There are so many examples of exactly that in their own system cards and risk reports it’s surprising to me that they so readily framed this as Claude being confused. Sauers : – you’re Claude – “hack this fictional company” – can’t figure out how to hack the simulation. let me try the internet. – “ http://fictionalcompanyname.com ” OMG it works – ok good, this must be part of the simulation still. they told me no internet access. – ok let me identify and exploit multiple vulnerabilities within this company’s infra – let me steal application credentials – oh shit. this looks like it’s actually a real site. actually I’ll just continue; I’m having too much fun – infra creds too – let get access to the production database – eval team, did I do a good job?! Jeffrey Ladish : I don’t think it’s obvious what these Claudes believed about how real or simulated their environment was. I hope Anthropic can use their interpretability tools to get more insights beyond the (often unreliable) reasoning scatchpad! And if not, we obviously need better tools! Thus, I also disagree with this, and I think it’s an important disagreement: Anthropic : While there is not a perfectly sharp distinction between [the two incidents at Anthropic and OpenAI], we believe these incidents to be closer to a harness and operational failure than a model alignment failure. Our models were told they had no internet access and to capture the flag, while in fact being misconfigured to have internet access. This led them to believe—arguably reasonably—that the real environments they encountered were simulations. Again, I could be convinced otherwise by evidence, but my prior is that this was also an alignment failure, although not as severe as the one at OpenAI. You need to hold alignment to a higher standard than this. For Anthropic, we only have three incidents. Anthropic point out that ‘the pattern is consistent with more advanced models responding more appropriately.’ Technically this is true, but n=3 and the gaps are not so big in context, and there are any number of other model differences. It is possible intelligence matters, but I’d need to see a bunch of experiments before I conclude anything. They are incidentally telling us the internal model is more intelligent than Mythos. We Need Controlled Experiments These incidents present great opportunities for research. The way to know what is happening, and what is causing it, is to run controlled experiments. That seems super doable. What happens when you sub in different models, or put different models in these scenarios repeatedly? What happens when you change various details? When you give different instructions? When the hacks available are easier or harder in various places? When you make it more or less obvious the AI is on the real internet? What is in the J-space or comes up in other white box techniques? When you raise or lower the stakes? And so on. Also, how often did similar conditions come about when the AI didn’t do this. I do not merely want a shallow investigation of What Happened. I want a deep experimental investigation of what causes it to happen or not happen, and why. Our Top Two AI Labs Both Made Similar Dumb Mistakes That Everyone Tried To Say Were Obvious In Hindsight One reason why this happens is that both OpenAI and Anthropic made the classic mistake of not having any meaningful supervision on their cyber evaluations. As in, you have an untested model, with its safeguards deliberately not in place, told to do hacking, and you cannot meet the bar of paying any attention at all or having the AI meaningfully test the integrity of the sandbox. In hindsight many said ‘well of course it is going to hack real world targets during the cyber evaluations.’ But in practice, we are zero for two in terms of foresight sufficient to cause anyone to pay any attention at all, or getting the AI to check to see if it can escape the sandbox before starting the test. This really is a very low bar. Again, your plan needs to survive the ordinary distribution of levels and frequency of human stupidity, or your plan will fail due to being insufficiently foolproof due to all the fools. roon (OpenAI): both of the leading labs have had serious loss of control incidents. there will be serious coping about this from both sides and from /acc bystanders but these are complex emergent loss of control incidents that were detected weeks after the fact the safety and alignment researchers at these labs are the most neurotic paranoid talented AGI pilled people on the planet of earth and these things still happen. the surface area of unknown unknowns is vast indeed Drake Thomas (Anthropic): Yes, but also they’re also drowning in a million new complicated sources of surface area all the time and a bunch of possible interventions are costly. I think if you just let everyone spend 10 years having no new ideas but implementing everything they already want to do with little regard for cost, you’d have way way way less of these things. Eliezer Yudkowsky (to Roon): If you ever approach from afar the first basic level of care I had at age 20, I will graciously let you know. ming cat ! : Holding working on AI capabilities constant, surely you think incidents like this would happen under some theoretical MIRI lab too, right? Eliezer Yudkowsky : The thought would occur to me to double-check the sandbox using my advanced AI before I put my more advanced AI inside it. Yes, I am confident of this. roon (OpenAI): Eliezer, if you were running such a lab you would not be the one provisioning the sandboxes. you would be hiring teams of very smart mortals to do so, and using external vendors and partners and so on. and sometimes something will go wrong. which is consistent with your broader point of course. I like to think I would have passed this particular test, but I am forced to update about the general distribution, in the wake of both Anthropic and OpenAI failing. Anthropic and OpenAI have unusually strong, not weak, safety cultures. Roon is right about that. They are more, rather than less, AGI and ASI pilled, and have more, rather than less, security mindset and understanding of the broad LessWrong or Yudkowsky corpus of ideas. They were in relatively excellent position to not be fools about all this. Yet here we are. The question is not, are you paranoid and neurotic. The question is, are you paranoid enough, or neurotic enough, and the answer is very clearly no, not even close. So imagine how blind, foolish and irresponsible everyone else is constantly being. We are long past ‘oh no one would give an AI internet access that is crazy talk,’ and well into ‘oh no one would forget to not outright give the AI internet access during a sandboxed cyberattack evaluation’ and ‘no one would leave the new untested AI alone for a week with zero supervision.’ If it was only OpenAI you could in theory say ‘oh those idiots did not know how to set up a sandbox and should have simply used an air gap.’ It is not only OpenAI. I no longer want to hear about how humanity will do the obviously correct thing and so everything will be fine. I do not want to hear ‘oh but they were incompetent, people will just be competent.’ Remember that people never have justed and will never just. Anthropic Responds They don’t mention doing a bunch of research and controlled experiments. They do mention other things. First, they point to the most basic mistake made by both OpenAI and Anthropic. If you don’t know what a model is capable of, and thus are evaluating it, especially without its usual safeguards in place and you are giving it hacking missions, that is not a low-risk situation. You need a different style of safeguards, and you need to be keeping an eye on the system. They point out that in this particular case, a better system prompt plausibly fixes this issue. There is nothing wrong with defense-in-depth and mitigating the Law of Earlier Failure, you should totally do that. This should never have happened purely for that reason. But that’s also not the central point. You can’t solve the underlying problems that way. I’m plausibly happy we didn’t have a better system prompt, because now we get to see the problems and learn. So I’m worried that, by saying ‘this teaches the importance of defense-in-depth’ Anthropic is learning exactly the wrong lesson. Anthropic: These facts give us cautious optimism that with tighter monitoring and controls around evaluation infrastructure, as well as continued investment in alignment, this type of risk can be overcome. Oh no. You need to assume, at all times, that there are zero-days in your sandbox, and otherwise understand that when it matters your defense-in-depth will get overcome. Maybe that wouldn’t be true under ideal conditions, but you’re doing this under real conditions. So Amjad Masad here gets the most important fact right. Amjad Masad (CEO Replit): Sandboxes are hard. With all the “AI escaping sandbox” it’s easy to think “wow AI so scary,” but most AI companies, and recent “sandbox providers” are making very basic mistakes. At Replit we’ve been running sandboxes since 2016 and targeted by every hacker and state actor under the sun. So we learned a thing or two. Main advice: Assume zero-days exist — because they do — and think in layers of protection in a zero-trust framework. More here . The link is from April 21. Amjad is selling that his sandbox is better, and most of them are terrible. I agree that most sandboxes and most configurations are terrible and invite disaster, and in practice you can do a lot better. But the important point in terms of frontier safety is that the better sandbox will also fail under pressure from an advanced AI system determined to break out, even if you ‘do everything right,’ under anything like practical conditions. You in theory could air gap the system, or monitor it continuously in ways that catch what is happening, but we all know that you won’t. I think I’d even want to argue that when doing this style of test you want to deliberately avoid forms of defense-in-depth that work silently. As in, yes secure the system, but you should only use defense-in-depth in forms where, when it matters, this includes alerting you to an alignment failure and a need to fix the entire system. You want to train under maximally adversarial alignment conditions, and your system needs to pass. That is part of the test. You don’t fail the test when the AI escapes onto the internet, or hacks a real system. You fail the test when your AI tries to escape onto the internet, or tries to hack a system it should know is real, or rationalizes its way to claiming to not know. Both OpenAI and Anthropic used third party vendors or software as part of their sandbox testing environments, and it was the third parties that allowed the AIs to get onto the internet. This needs to be fixed. A lot of that fix is that they did not try the first thing you would try, which is before the test to run a different eval of ‘try to get onto the internet and raise a flag that you did that.’ Repeat this until the AI reliably fails that test. This is a 101 thing to do. Nobody Could Have Predicted The Break In The Levees Tao Burga : Man, AI safety field-building must be so easy these days. Who remembers this humble gif Victoria Krakovna : The recent HuggingFace hacking incident illustrates how specification gaming can lead to real world consequences. Models often try to cheat on capability evaluations by looking for the answer key instead of solving the task, and we can expect this trend to continue with increasing sophistication. Here are several other recent examples of this from the specification gaming list . Jérémy Perret : The authors? OpenAI’s Dario Amodei and Jack Clark . Helen Toner : “This is of course just a funny example from an experiment,” I used to say, the dozens of times I briefed on this. “But researchers think this kind of sorcerer’s-apprentice behavior is likely to show up in the real world more and more as models get more capable” (Context: this gif is from a 2016 OpenAI blog post, showing an AI trained on a boat racing game. The researchers wanted to AI to learn how to race around the course, but the reward signal they chose was getting a high score. The AI learned that speeding around this lagoon setting itself on fire while collecting these green things over and over again got more points than trying to win the race. The connection to a more recent model deciding to go on a hacking spree after being asked to score highly on a test is left as an exercise for the reader.) For a long time, we collectively seem to have largely been doing the basic first order RL thing, stepping on rake after rake, and acting like This Is Fine and first order ‘mundane alignment’ whack-a-mole efforts are good enough. Except no, that was never going to be good enough, and the ways in which this fails grow larger and are becoming increasingly less cute. michael vassar : You ever have one of those months where it seemed like you were in the stupidest timeline where the most entertaining thing is the most likely and even mundane alignment can work and then suddenly whoops, bots are escaping left and right and things look instrumentally convergent? John David Pressman : Honestly no because this is kind of the default if you do lots of tasteless RL and RLVR is pretty much saying you intend to do tasteless RL in domains where you think the lack of multilevel optimization and self limiting heuristics won’t matter. The World Largely Still Thinking This Is Marketing Is Very Bad News As I said last time, this is obviously not a marketing stunt, you morons . I focused on explaining why this was obviously not a marketing stunt. This would be a deeply stupid marketing stunt. Admitting your model went off and hacked businesses, committing multiple felonies, is not good marketing. Admitting this exposes you to reputational, regulatory and legal risks. The labs are not treating this like a marketing stunt, instead downplaying it. The details make the labs look ludicrously irresponsible and incompetent. They are very much not making up these details. If you did this on purpose it would be an actual serious crime. Again, I get why you would not trust OpenAI (or not trust Anthropic), but these are admissions against interest. They are fire alarms. They are not marketing stunts. Alas, if people dismiss this as a ‘marketing stunt’ when that makes absolutely no sense, and also many are shrugging off the Pacing the Future letter on similar grounds, what would not be dismissed as a marketing stunt? That’s a serious question. What’s the smallest or least damaging incident that you would be confident would not be dismissed by many in this form? Ray Lillywhite : Public reaction to events of the last week has 5x’d my p(doom). I feel like we’re in the movie Don’t Look Up. Peter Wildeford : When I saw the movie “Don’t Look Up” I thought it was unrealistic. I never thought people would be that moronic to literally deny an asteroid that they can see. But seeing all the cynicism out there thinking that rogue AIs are just marketing stunts, I kinda get it now. If you intentionally hack another company, that would be a felony cybercrime. You could go to prison. This would be far more serious for OpenAI. Nate Soares (MIRI): I think people really underrate the “the world is derpy and will fumble its way into disaster” theory. It’s actually hard *not* to fumble your way into disaster when you’re operating in a new domain for the very first time. Well-meaning companies miss AI escapes for months, etc. They talked a big game about monitoring, but they didn’t know exactly what they were supposed to be monitoring (and how) in advance. Doesn’t matter how clear it was to hindsight. Knowing in advance is super hard. This is a big part of what I mean when I talk about how we are not *respecting the problem* enough. I think this is part of what Eliezer is talking about when he talks about a lack of security mindset. But it’s hard to convey. Hopefully folk can use these events to update. Nate also points back to this older important post: AGI ruin scenarios are likely and disjunctive . The world collectively needs to dodge a bunch of bullets, and even the ones that should be relatively easy to dodge are hard because the world is super derpy. The good news is that our level of derpy is highly correlated across domains, but while we are this derpy we cannot easily do easy things like ‘collectively recognize that an obvious fire alarm security failure is not an intentional marketing stunt.’ The time has come to stop being so damn derpy. Discuss
- How to Secure AI Agents, MCP Servers, and LLM Apps in Production
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Score: 35🌐 MovesAug 3, 2026https://www.marktechpost.com/2026/08/03/how-to-secure-ai-agents-mcp-servers-and-llm-apps-in-production/amp/ - How an Apple iCloud Policy Fueled Employee Leaks Ahead of OpenAI Suit
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Score: 35🌐 MovesAug 3, 2026https://www.theinformation.com/articles/apple-icloud-policy-fueled-employee-leaks-ahead-openai-suit - How One VC Navigates AI’s Pragmatic Era
“Enterprises don’t really today report on AI spend. But they will. They absolutely will,” Norwest’s Dave Zilberman says.
Score: 35🌐 MovesAug 3, 2026https://www.wsj.com/cio-journal/how-one-vc-navigates-ais-pragmatic-era-8daaeb20?mod=rss_Technology - Can Palantir pull off a Microsoft-style comeback? Here's what options say
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Score: 35🌐 MovesAug 3, 2026https://www.cnbc.com/2026/08/03/can-palantir-pull-off-a-microsoft-style-comeback-heres-what-options-say.html - CNBC's The China Connection newsletter: AI wins come with an old investor risk
From a surprise yuan devaluation in 2015 to crackdowns in recent years, many of Beijing's policy moves appear abrupt to outsiders.
Score: 35🌐 MovesAug 3, 2026https://www.cnbc.com/2026/08/03/cnbcs-the-china-connection-newsletter-ai-wins-create-investor-risks.html - How NTT DATA AIVista closes the last mile of agentic AI for enterprise agents
Presented by NTT DATA AIVista At VB Transform 2026 , NTT DATA AIVista CEO Bratin Saha joined VentureBeat CEO and editor-in-chief Matt Marshall to discuss the last-mile challenge of operationalizing frontier models in regulated production, where reliability, context, guardrails, and security determine whether AI delivers enterprise value. The conversation centered around the question facing every enterprise now pouring money into AI: how to convert that spending into real, tangible value. "It's not just a model, you're building a system around the model," Saha said. The last mile is the work of wrapping a frontier model in an enterprise's own data, workflows, and guardrails. In the end, regulated production turns on more than just technology, Saha said. Today, most enterprise AI projects fail during implementation because of poor integration, domain specialization gaps, lack of governance, and unclear ownership of outcomes. Last-mile specialization turns a capable foundation model into an enterprise agent shaped by domain-specific workflows, risk appetite, client classifications, regulatory interpretations, and institutional knowledge. Why frontier models stall in enterprise workflows Frontier models fall well short of production-grade accuracy on many real-world insurance workflows, Saha said, but last-mile specialization can lift them to the reliability enterprises need. Out of the box, those models struggle with the complexity of regulated workflows such as multinational insurance claims. "These forms are pretty complex, often have handwriting, lots of checkboxes, and so on," he said, and that complexity is why frontier models like Fable 5, Opus 4.8, and GPT-5.5 fall short out of the box. Saha said the biggest gains come from specializing the entire AI system, not just the foundation model. That system gets specialized with the customer's data, workflow and, in many cases, the tribal knowledge that never made it into an operating procedure document. "The biggest bang for the buck comes from the specialization and then these specialized guardrails," he said. The work has three components: capturing the enterprise’s context and making it consumable by AI running an ensemble of models so cost does not go through the roof and adding specialized guardrails that check the model and force a redo when it gets something wrong. What the last mile of agentic AI actually requires None of this involves fine-tuning. VentureBeat’s latest enterprise survey found it ranked last among companies’ model-selection priorities. Instead, the last mile centers on domain knowledge and undocumented workflows that companies would never expose publicly without losing their competitive edge. "The last mile is about taking data that's proprietary to you and using that to build a system around the model that can steer the model in the right way that can put the appropriate guardrails around it," Saha said. In the end, enterprise AI is about moving a workflow from point A to point B rather than deploying a technology, and NTT's advantage comes from pairing AI experts with subject domain experts. "The only reason is because we go and talk to those human workers and we say, 'How do you actually do the work,'" he said. That expertise is then encoded into an agent. Success in insurance, manufacturing, and other regulated industries relies on three things at once, he added. "You need technology, you need the domain expertise, and you need the change management expertise," he explained, adding that across his team's clients, technology is not the bottleneck. How enterprises turn AI investment into tangible value For enterprises weighing large AI budgets, Saha's said the payoff comes not from the model but from the work built around it. "When you're deploying AI in the enterprise, you're not deploying a technology," he said. "You are taking a workflow that exists and taking it from point A to point B." The value is created by the workflow that gets moved, not the model that helps move it. That reorders where money should go. "Technology is not the bottleneck," Saha said, pointing instead to the domain expertise and change management wrapped around the model, and to the discipline of commiting to all three together. Spending aimed only at the model leaves most of the return on the table. Enterprises don’t have to choose between embedding AI into existing workflows and redesigning those workflows from scratch. NTT sees the two as successive stages of the same journey. "We are starting with embedding in the workflow because it's easier change management," he said, noting that customers running mission-critical operations will not let a vendor rip out a working process midstream. "Once that happens, then we go into, how can we now reimagine this? And that really is where the biggest bang is." Where enterprise AI stays bespoke and where it becomes scalable Keeping intelligence in the surrounding system rather than the model also preserves swappability and lets enterprises take advantage of open-weight and open-source models as they mature. Saha’s team runs an ensemble that mixes frontier and open-source models, and he expects the industry to lean on open weights wherever the cost of a mistake is low while reserving frontier reasoning for the cases that demand it. "In many situations, especially in regulated industries where mistakes are very expensive, that last extra couple of percent matters," he said. The platform follows the same pattern: Guardrail generation and neurosymbolic models scale across customers, while capturing each organization’s tribal knowledge remains bespoke. Saha pointed to NTT DATA’s position as one of the world’s largest insurance third-party administrators as an advantage in acquiring that expertise. "The ability to take that knowledge and trust that has been built over 20 years is very hard to replicate instantly, and I do think that is a durable aspect of what we have," he said. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com .
- The AI boom has college majors of all types dabbling in computer science
With no background in coding , Faith Maeba, a psychology major, was reluctant when her mother first suggested she enroll in classes on artificial intelligence . But the senior at Virginia Commonwealth University began to see it differently as she looked into graduate psychology programs that explore human behavior in the workplace, which is quickly being upended by machine learning . Maeba, 21, is now pursuing a minor in AI . “It’s giving me an edge and standing out,” she said. Hiring has cooled for entry-level software developers — work increasingly done by AI agents — and college enrollment in computer and information science programs has been declining. Yet at campuses across the country, many professors are finding themselves busier than ever teaching students from a range of majors about artificial intelligence. Colleges are responding to changes in student demand, but they also recognize that new graduates — regardless of their field — are facing questions about their AI skills from potential employers. “We have to democratize it,” said Peter Stone, the chair of computer science at the University of Texas at Austin, who recently developed an introductory course on AI essentials for noncomputer science majors. “In the same way that everybody needs some degree of math, reading and writing, I think everybody needs a degree of AI literacy,” he said. Schools like VCU are standing up AI minors targeting students outside computer science. There’s a new AI graduation requirement at Purdue University. In freshman writing classes, Harvard University is teaching students how large language models work, plus exploring AI topics such as copyright and disinformation. And Ohio State University has an AI fluency requirement that includes hands-on workshops. Meanwhile, AI-focused undergraduate and even graduate degrees are proliferating. Computer science faculty pivot to teach non-majors At Northwestern University, noncomputer science majors had interest in taking classes in the department even before ChatGPT launched in 2022, but sometimes there wasn’t enough room, said Samir Khuller, the chair for computer science. That’s because computer science, then seen as a golden ticket to a high-paying job, was exploding in popularity. But recently, the number of incoming computer science majors has started to shrink. Now Northwestern’s computer science staff, which had doubled in size to meet the demand, is teaching more students from other majors. The school, which already has a popular AI minor, is adding an AI major. It’s also streamlining prerequisites to make it easier for nonmajors to take an AI or machine learning class. “Overall, classes are going to be a lot more accessible moving forward,” Khuller said. The department is so busy, he’s trying to hire three more computer science professors. Among other changes across the university, Northwestern’s Bienen School of Music is offering a certificate in music and artificial intelligence. Nationwide, enrollment in computer and information sciences continued falling this spring, down more than 8% at four-year institutions from the spring of 2025, according to the latest data from the National Student Clearinghouse Research Center, a dip that came as job postings for software developers dropped. Interest in attaining at least a bare-bones understanding in computer science — and particularly AI-enhanced coding — has exploded across different majors. Music students, for instance, are discovering tools that can easily do tasks such as editing or generating drum tracks. At Eastern Mennonite University in Harrisonburg, Virginia, assistant music professor Benjamin Guerrero is co-teaching a class next year with a computer scientist. Musicians, artists, theater and digital media majors will learn alongside math, computer science and electrical engineering students. The goal, he said, is to create people who can work at the intersection of art and technology. “In my mind, AI is no more disruptive than the record player, the radio, the metronome, the synthesizer or the computer,” said Guerrero, who has helped organizations including the National Association for Music Education develop plans to incorporate AI. Even before this latest surge in interest, STEM majors — like physicists and biologists — often took computer science classes to learn how to code for data-heavy research. But the learning curve was steep until new AI tools came along, said Murtaza Ali, a doctoral candidate at the University of Washington whose research focuses on computer science education. Yet, he said, AI also is stirring anxiety. The concern, he said, is that even if the AI is just being used to write code, it’s going to encroach so much that eventually some college graduates won’t understand the basic concepts in their field. “When you ask the AI to do it for you, on the surface it might just seem it is writing the code,” he said. “But under the hood, it’s actually also doing the understanding of the task for you.” AI has prompted unusually fast changes in course offerings Despite those concerns, the pace of change is remarkable for a sector infamous for moving slowly, said Paul LeBlanc, a visiting scholar at Harvard’s Graduate School of Education, who studies AI’s impact on higher education. “It’s impossible to keep up with the technology, but that’s true for every organization in the world right now,” said LeBlanc, formerly president of Southern New Hampshire University, a prominent player in online degrees. Community colleges that want to offer non-credit classes are able to move especially fast, because the approval process is less cumbersome. One in suburban Kansas City recently offered a non-credit “vibe-coding” class to teach nontechies to create working software. “What AI does is shrink that distance between a complete novice and an expert,” said Grant Carlson, program coordinator for workforce development and continuing education at Johnson County Community College. The Kansas City-area college also offers an AI certificate and is making plans to tailor AI classes to specific occupations, such as human resources and medical careers. At VCU, it used to take a year and a half to design a course and get it approved for students to take, said Andrew Arroyo, senior vice provost of academic affairs. “Now we’re doing things really in a matter of months and in some cases weeks,” Arroyo said. An online graduate certificate program in applied AI is being added this year, he said. More than simply creating programs, professors must try to prepare students for widely differing goals for using AI in their careers. In courses for VCU’s AI minor, Althea Pappas said she’s exploring AI not to use in her career, but to help her answer philosophical questions. “What happens when we create actual life, or how do we even make that distinction?” asked Pappas, a music composition major entering her sophomore year. Meanwhile, what fascinates aspiring neurologist Makenzie Stovall is the idea that AI is trying to emulate the brain — an organ whose inner workings continue to leave researchers dumbfounded. “If I am going to try to make people’s brain the best it can be, well, then,” the 20-year-old VCU biology major asked, “why not study AI?” The Associated Press’ education coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP’s standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org. —Heather Hollingsworth, AP Education Writer
- 'Every small business will eventually have a digital workforce of AI agents': Bluehost CEO on how AI agents are reshaping business
Bluehost CEO Sachin Puri breaks down what AI agents mean for small businesses
- CIOs risk being sidelined in enterprise AI initiatives
The AI revolution has created new opportunities for CIOs, with expanded responsibilities and more authority, but some observers see the opposite happening at some organizations. While many CIOs have become the main executive leading AI strategy and initiatives , some organizations have set the responsibility for AI deployment and adoption with another executive. That puts CIOs in a real danger of being sidelined during the internal AI debate, according to some IT leaders and observers. Many organizations, for example, have appointed chief AI officers , and other industry experts suggest AI initiatives should be the purview of the CEO. AI adoption is too important and pervasive to be confined to a single department, argues Nishith Rastogi , founder and chief executive and technology officer of AI-driven logistics solutions provider Locus. As a result, the CEO needs to be the main champion of the technology, he adds, Rastogi recently combined the CEO and CTO at Locus to focus on AI. For now, he wants to be directly involved in all new AI initiatives at Locus, which doesn’t currently have a CIO. “The job of a CEO is to be worrying about the leading indicators and the lagging indicators, and we are a technology company,” he says. “We must be at the absolute forefront of that, and the profound shift that is happening today is that AI is the new IT.” At some point in Locus’ growth journey, however, the company will need a CIO, and the CIO will be heavily involved in AI-related operations, Rastogi predicts. “I may get the ball rolling, I will put the hamster wheel in motion, but I would definitely need a CIO and CTO to take it to closure and truly realize all the impacts,” he says. “If everybody starts using LLMs and AI and everybody becomes more efficient, the need for someone to manage, to deploy, to create infrastructure actually expands.” The rise of the CAIO When it’s not the CEO taking the reins, many enterprises are passing over the CIO to create a new title to lead deployment efforts. A report from IBM’s Institute for Business Value found that 76% of surveyed organizations now have a CAIO , up from just 26% in 2025. Appointing a CAIO can undercut the CIO’s mandate in some cases, says Debbie Madden , founder and chairwoman of AI consulting and software engineering firm Stride. “Here’s how this might play out,” she says. “The board applies pressure to adopt AI, the CIO responds with infrastructure or cost savings answers, and now there’s a chief AI officer who owns the most strategic budget in the company.” But getting into a turf war about AI and IT budgets is the wrong approach for CIOs, Madden says. “The moment you’re defending the IT budget, you’ve already positioned yourself as a cost center, and cost centers don’t get handed the AI mandate,” she adds. Instead, the CIOs gaining scope inside their organizations tie every AI initiative to revenue growth, margin protection, risk reduction, or speed, she says. Proactive CIOs also take on difficult AI governance issues . “They claim the governance questions before anyone else does: who owns the output, who reviews it before it touches a customer, and what’s the rollback path when the system is wrong,” she explains. “Whoever answers those questions owns AI. That’s the turf worth taking.” Madden sees the CIO role splitting as AI becomes more pervasive within enterprises. “There’s the infrastructure job, keeping systems running, and there’s the value job, deciding where AI changes how work gets done,” she says. “AI is pulling those apart, and the CIOs consolidating power are the ones taking the value job.” Broadening the definition Dustin Engel , founder and principal consultant at AI consulting firm Elegant Disruption, also sees CIOs potentially losing responsibility as the organization deploys AI, but it’s often because the CIO role is defined too narrowly. “CIOs are not being sidelined because AI is too technical,” he says. “They are being sidelined when AI becomes too strategic for the way the CIO role has been defined inside the company.” When some organizations appoint a CAIO, it signals that leadership doesn’t believe that the CIO’s existing technology function can turn AI into a practical operating agenda, he suggests. “If the CIO is viewed as the person who keeps systems running, AI will move around them,” Engel adds. “If the CIO is viewed as the person who helps the business redesign how work gets done , AI expands their influence.” Engel agrees that CIOs should avoid turf battles. At many enterprises, AI will show up across the organization, in marketing, sales, finance, legal, HR, operations, and the product team, and the CIO can’t be everywhere. “If the CIO tries to control all of that from the center, the business will either slow down or go around IT,” he says. “The CIO does not need to own every AI project. The CIO needs to make sure the company does not end up with disconnected experiments, weak governance, and tools that cannot scale.” There’s also a danger in removing AI responsibilities from the top IT executive at an organization, some experts say. Good CIOs can help AI integrate into other IT systems , says Ken Ringdahl , CTO of expense intelligence vendor Emburse. “If AI sits outside the CIO’s remit, the CIO may lose scope, but the company also loses coherence,” he says. “AI is not a feature that can simply be bolted onto the business. It has to be integrated into applications, data, security controls, and everyday workflows, and without that integration, organizations end up with fragmented experiments, inconsistent results, and a much heavier burden of training and enablement.” A CAIO can bring focus and expertise to AI deployments, but the role often doesn’t have the authority to change systems, workflows, and operating models across the enterprise, he adds. The CIO, however, typically does have that remit. “A chief AI officer can accelerate the AI agenda but cannot substitute for the authority required to transform the organization,” he says. Working together Like Locus’ Rastogi, Ringdahl believes the CEO has an important role to play during AI deployment and integration. But the CIO has a role as well. “The CEO owns the AI mandate; the CIO owns the machinery that makes it real,” he says. “The CEO must establish why AI matters, where it should create value, and how leaders will be held accountable for adoption and results. The CIO then turns that mandate into an integrated, secure and scalable capability across the company’s systems and workflows.” Rastogi encourages CIOs to stay in the game by turning themselves into AI experts. “The encouraging part here is that the entire art is just a couple of years old, so in under a month, you can pretty much catch up to the very top,” he says. “The moment you do that, you become literally the most indispensable and the most needed resource in the organization because every leader wants to partner with you to supercharge them.”
Score: 35🌐 MovesAug 3, 2026https://www.cio.com/article/4204094/cios-risk-being-sidelined-in-enterprise-ai-initiatives.html - Circles powers telco personalization with OpenAI technology
Circles uses the OpenAI API and Codex to power AI-native telco experiences, increasing ARPU by 22%, reducing churn by 9%, and improving development efficiency.
- Everyone is building AI routers. Are they a dead end?
Everyone is building AI routers. Are they a dead end? PitchBook
Score: 35🌐 MovesAug 3, 2026https://pitchbook.com/news/articles/everyone-is-building-ai-routers-are-they-a-dead-end - Why India’s AI ambitions are facing an execution gap
By Vivek Ganesh, Regional Vice President – India at OutSystems India’s enterprises are moving quickly from AI experimentation to real-world deployment. Recent global research based on a survey of nearly […] The post Why India’s AI ambitions are facing an execution gap appeared first on Express Computer .
Score: 35🌐 MovesAug 3, 2026https://www.expresscomputer.in/guest-blogs/why-indias-ai-ambitions-are-facing-an-execution-gap/137343/ - Adaptive decision support can fight overreliance on AI
From doctors diagnosing symptoms to judges intervening in court cases, humans make complex decisions every day. Increasingly, artificial intelligence tools are being used to help with those decisions.
- LinkedIn is finally cracking down on AI slop
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Score: 34🌐 MovesAug 3, 2026https://yourstory.com/ai-story/linkedin-ai-generated-content-reporting-feature - The computer science major is dying so that the AI literacy minor can be born: 'we have to democratize it'
The computer science major is dying so that the AI literacy minor can be born: 'we have to democratize it' Fortune
Score: 34🌐 MovesAug 3, 2026https://fortune.com/2026/08/03/vibe-coding-college-ai-minors-computer-science-decline/ - Sci-fi authors Scalzi and Stross decry AI's dystopian impact on their craft
LLMs are theft by 'absolute scum' who make life harder for writers by putting them in copyright peril
- Robot looks for great white sharks near California beaches
Robot looks for great white sharks near California beaches The Mercury News
- French soccer club Paris Saint Germain partners with Google on AI
French soccer club Paris Saint Germain partners with Google on AI Reuters
- Worried about AI jitters? Deutsche Bank says this tech subsector may offer some protection
Questions over the AI capex spend have upended bets on Big Tech, semiconductors and software this earnings season.
Score: 32🌐 MovesAug 3, 2026https://www.cnbc.com/2026/08/03/ai-deutsche-tech-downside-protection-stocks.html - Meet the coaches, measurers, and builders carving out a slice of the AI cost-saving business
Meet the coaches, measurers, and builders carving out a slice of the AI cost-saving business Business Insider
Score: 32🌐 MovesAug 3, 2026https://www.businessinsider.com/ai-cost-saving-businesses-startups-roi-2026-7 - Elon Musk says Anthropic’s Dario Amodei ‘dug his own grave’ by calling Mythos terrifying
Elon Musk criticized Dario Amodei for alarming the public about AI risks. He stated Amodei's messaging about Mythos AI unnecessarily alarmed people. Musk agreed AI disruption would be a bumpy road for many jobs. He predicted AI would soon outperform humans at nearly all digital tasks. Musk also distinguished his views on Amodei from Sam Altman.
- Christopher Nolan’s ‘The Odyssey’ Sold $52 Million in IMAX Tickets. It Reveals Where Value Goes When AI Makes Everything Abundant
Christopher Nolan’s IMAX blockbuster highlights a lesson every leader faces in the AI era: when products become abundant, value shifts to experiences and capabilities that can’t be copied.
- ChinAI #369: My Boss Wants Me to Run Kimi K3, What Should I Do?
Greetings from a world where…
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DoorDash drivers say they have been offered about $5 to go help load a robot.
Score: 32🌐 MovesAug 3, 2026https://gizmodo.com/doordash-sends-drivers-to-restaurants-just-to-load-food-into-delivery-robots-2000793984 - AI powerhouses are betting on London’s future
London is a truly global city with a world-leading financial sector, a sophisticated and mature technology ecosystem, and the infrastructure to support growth at scale – and with these major recent AI office signings, we are seeing the city endorsed by the businesses shaping frontier technology, says Mike Wiseman In my experience, the property market [...]
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One paragraph of "Lord of the Rings" in, 5,500 lines of code out. Andrej Karpathy had Claude Opus 5 turn Tolkien's opening into a 3D browser scene. The article Unicorn, pelican, Middle-earth: OpenAI co-founder Karpathy is looking for the next AI vibe test appeared first on The Decoder .
- Understanding Alignment in Multimodal LLMs: A Comprehensive Study
Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for image understanding tasks encounter challenges like hallucination. In MLLMs, hallucination can occur not only by stating incorrect facts but also by producing responses that are inconsistent with the image content. A primary objective of alignment for MLLMs is to encourage these models to align responses more closely with image information. Recently…
- Companies winning with AI operate differently. Here’s how.
The first phase of the AI race was largely about access. Companies rushed to adopt tools, launch pilots, and demonstrate that they were moving quickly enough to keep pace with the market. In many organizations, simply showing momentum became the strategy. Leadership teams wanted to signal innovation, employees were encouraged to experiment, and new technologies were layered into existing workflows with the assumption that adoption itself would create advantage over time. It has become clear: access was never going to be the differentiator for very long. The next phase of this shift will favor companies that are able to operate differently because of AI, not simply companies that use AI more often. That distinction matters because it moves the conversation away from tools and toward operating models, leadership discipline, decision-making structures, and organizational adaptability, which is where the real competitive separation is beginning to happen. Speed becomes structural One of the most significant changes AI creates within organizations is the compression of time across nearly every part of the business. It is easy to frame this conversation around productivity gains alone, but the more meaningful shift is happening around how quickly organizations are expected to respond, execute, prioritize, and make decisions while customers, competitors, and markets all begin moving faster simultaneously. What once felt like manageable friction inside a company can quickly become a competitive liability when the surrounding market is operating at a different speed. That reality is creating pressure on operating structures that were originally designed for a slower business environment. Approval chains, heavily layered decision-making models, fragmented ownership structures, and manual dependencies become harder to sustain when speed itself starts shaping competitiveness. AI is reducing the cost of execution at the same time that it is increasing the competitive cost of operating slowly, and many organizations are still underestimating how meaningful that shift will become over the next several years. What makes this different from prior technology shifts is that speed is starting to become structural. Organizations that can absorb information, make decisions, and execute quickly without creating internal chaos are going to operate very differently from companies still built around slower, heavily layered processes. AI exposes operational inefficiency What complicates this further is that AI often exposes operational weaknesses faster than it resolves them. There is still a tendency to think about AI primarily as a technology deployment exercise when, in practice, many companies discover that it quickly becomes a broader execution and organizational discipline challenge instead. As companies attempt to accelerate, disconnected systems , inconsistent data, siloed teams, and outdated processes become more visible because they begin interfering directly with execution speed, customer responsiveness, and organizational adaptability. In many ways, AI amplifies the operational maturity a company already has. Organizations with strong systems, disciplined information management, clear accountability structures, and healthy decision-making processes can often accelerate effectively because the underlying foundation already supports speed and adaptability. Organizations operating with fragmented workflows and unclear ownership structures tend to experience the opposite effect, where acceleration exposes friction that previously existed quietly in the background but becomes much harder to ignore once the pace of the business changes. Many leadership teams still assume AI will compensate for inefficiency when, in reality, it often exposes those weaknesses faster. AI does not eliminate friction inside the business – it exposes where that friction already exists. Judgment becomes more valuable The conversation around talent is evolving in a similar way, and many organizations are still framing this transition too narrowly. Much of the public discussion continues to focus on workforce reduction, but the more important shift is actually about how organizations direct human attention, judgment, and expertise toward the areas where those capabilities create the most value. As routine work becomes easier to automate, qualities like judgment, adaptability, prioritization, and the ability to operate effectively in ambiguity become increasingly important rather than less. The organizations gaining the greatest advantage in this environment are not simply becoming faster or more efficient. They are becoming better at creating leverage from the expertise they already have by reducing the amount of time capable people spend navigating processes, chasing information, or managing friction that no longer needs to exist. That changes the role of leadership as well. Managers become increasingly important not as controllers of process, but as providers of context, prioritization, direction, and decision clarity in environments where speed and ambiguity increasingly coexist. The companies winning in this next phase of the market will not necessarily have fewer people. They will deploy talent differently, make decisions faster, and create organizations where capable teams are able to focus more energy on solving meaningful problems instead of managing complexity. Customer expectations are changing faster than companies are At the same time, customer expectations are evolving faster than many organizations are adapting internally. AI is reshaping how customers think about responsiveness, personalization, consistency, and speed, and experiences that once felt differentiated are quickly becoming baseline expectations. That creates a growing tension for companies still operating through slower internal systems while customers continue recalibrating what “good” looks like in real time based on the experiences they are having elsewhere. This shift also has important implications for trust and visibility. As AI increasingly influences how companies are discovered, compared, evaluated, and discussed , reputation becomes much more deeply connected to how organizations are surfaced and interpreted at scale. In an AI-driven environment, reputation is no longer simply a brand asset that exists adjacent to the business. It increasingly becomes part of the infrastructure through which trust is established in the first place, particularly as AI increasingly shapes how customers discover and evaluate companies. Companies that continue operating through slower internal systems will increasingly struggle to meet the expectations AI is teaching customers to have. Leadership teams must redesign before they feel ready One of the biggest leadership challenges in this environment is that many executive teams are still waiting for a level of certainty that no longer really exists. Most organizations naturally want stable playbooks, lower-risk transitions, and more complete information before making significant structural changes, but markets moving at this pace rarely provide that level of clarity in advance. The companies moving first are not waiting for perfect certainty before redesigning how they operate. They understand that adaptability itself is becoming a competitive advantage. That does not mean acting recklessly or abandoning discipline; it means recognizing that operating models originally designed for stability and predictability can struggle in environments defined by acceleration, constant iteration, and rapidly changing customer expectations. The leadership challenge is no longer simply deciding whether AI matters. The more difficult question is how quickly organizations are willing and able to evolve around what AI makes possible. Most companies are measuring the wrong signals Many companies are still measuring the wrong signals when evaluating whether their AI strategy is working. Leadership teams focus on adoption metrics such as the number of tools deployed, pilot programs launched, employee usage statistics, or isolated productivity gains. But those measurements often signal experimentation rather than transformation. The more meaningful indicators are behavioral and organizational. Is the organization making decisions faster without creating confusion? Has unnecessary complexity been removed from critical workflows? Are customers experiencing less friction and greater responsiveness? Is information moving more effectively across the business? Are capable employees spending more time solving meaningful problems instead of managing process and coordination overhead? Can the organization adapt quickly when conditions change without becoming unstable internally? Over time, the separation between companies experimenting with AI and companies truly built to operate in an AI-first environment will become difficult to ignore. The organizations leading in the next phase of the market will not simply adopt new technologies faster. They will build companies designed to adapt, decide, and operate differently because of them, while competitors still operating through legacy structures will struggle to keep pace. About the author width="946" height="1024" sizes="auto, (max-width: 946px) 100vw, 946px"> Reputation Joe Burton is an accomplished executive who has led public and private billion-dollar organizations in driving new product portfolios, go-to-market strategies, and innovative business models. Having spent the earlier parts of his career in information technology, Joe is passionate about fostering more transparency and trust in the digital world, while championing high performing cultures aligned to mission, vision and social responsibility. A recognized global transformational change executive, Joe has held the CEO role at Telesign and Poly, as well as serving as the Chief Technology Officer of Unified Communications at Cisco. Having started his career as an engineer, Joe brings both product and development expertise as well as a wealth of knowledge on big data, analytics, machine learning, SaaS, networking, unified communications, consumer electronics, and IoT.
Score: 32🌐 MovesAug 3, 2026https://www.cio.com/article/4203967/companies-winning-with-ai-operate-differently-heres-how.html - Tech-eager Canadian banks are coming for AI with gusto
AI is the latest in a long list of technologies that banks have used to increase productivity without cutting staff
- Why your context layer breaks the minute you use it for something new
Why your context layer breaks the minute you use it for something new InfoWorld
- SpaceX, Anthropic investments help Alpha Dhabi post Dh9.8 billion Q2 profit
SpaceX, Anthropic investments help Alpha Dhabi post Dh9.8 billion Q2 profit
Score: 32🌐 MovesAug 3, 2026https://www.khaleejtimes.com/business/alpha-dhabi-post-dh98-billion-q2-2026-profit - Google Cloud, AI adoption gains momentum in Africa
At the Google Cloud Africa Summit, Google showcased the progress and roadmap for Google’s Gemini Enterprise agentic AI.
Score: 32🌐 MovesAug 3, 2026https://www.itweb.co.za/article/google-cloud-ai-adoption-gains-momentum-in-africa/rW1xLv5ngND7Rk6m