Peter Steinberger, member of technical staff at OpenAI and the creator of OpenClaw, estimates that he spent around $10 million in tokens running AI workloads last month. But don't feel bad for him, because it's all coming out of investors' pockets. He made this admission at the Cloudflare Connect 2026 conference in San Francisco on Wednesday, in a conversation with Cloudflare CTO Dane Knecht. Knecht previously observed that Steinberger has acknowledged having "a virtually unlimited token budget, which not all of us in the room have." Responding to the audible surprise rising from the audience, Steinberger added, "What people don't always understand is not all of that is useful." That only amplified the murmuring, perhaps not the effect he had been after. Meanwhile, his employer, OpenAI, is reportedly trying to "accelerate funding" ahead of rival Anthropic's expected IPO. Steinberger's subsequent musing about his preferred way of working failed to recognize Knecht's acknowledgement of financial limitations. "I feel like the main challenge a lot of people still have is that our infrastructure structure hasn't really caught up yet," Steinberger said. "Like why is this still using my computer? Shouldn't this all be in the cloud?" Apart from the privacy and compliance implications of relying on cloud-based AI, cost remains a consideration for anyone not setting venture funds alight. Cloud-hosted agents can be expensive. Shouldn't this all run locally? Pointing to the OpenClaw project and how a lot of open source maintainers have given up accepting pull requests due to frustration with AI slop, Knecht asked Steinberger whether we're approaching the death of open source or a golden age of AI. Steinberger said he thinks the way pull requests are handled needs to change because people don't explain how they arrived at their proposed changed code. "If I see your thoughts, how you got from A to B, my trust in the output is way higher than if you just throw the finished work across the fence," he said. Knecht also asked Steinberger whether everyone in the room would have hundreds of AI agents running in a few years. Steinberger doubts it will be hundreds. "I think everyone will have some sort of intelligence that runs and thinks and helps them throughout the day or even predicts what could help you in the future," he said. "So instead of asking the question, maybe you already get the answer." Toward the end of the session, Knecht raised the issue of trust and how to know what data to give to an agent. That's a hard question, Steinberger admitted. Part of it, he said, has to do with how much you trust the underlying model itself, because so much of model behavior is not governed by the harness – the code that wraps the model. "A lot of decisions that I built into OpenClaw early on now can change because the models are so much better," he said. Even if risks like prompt injection have been reduced through engineering efforts, Steinberger said, "you might not want to give your agent all the permissions." Instead, he suggested using an agent to authorize your agent. "You want to have another agent that can give it permissions or not and then this agent has to negotiate with another agent," he said. "Many times the agents are like, 'oh yeah, actually maybe I shouldn't do this,' or 'there's a simpler way that keeps it within the guardrails.' So those those two systems combined greatly increase my trust that I just can let the agent rip." ®

Why use your smartphone when AI can do it for you? We’ve reached a new level of AI inanity with the launch of Opposable, a tool that taps, swipes, and types on Android phones and controls iPhones through a companion computer, sparing users the apparent burden of operating their own devices. If you don't want to update a calendar event, start a timer, or enable dark mode, you can apparently use an AI agent app on your computer (any client that supports MCP will do) to have Opposable do the thing for you on a connected phone. You could just do it on your computer, of course, but that’d require actually using your hands - like it’s some sort of baby’s toy. Lame. Opposable describes the purpose of its app as being to “give your AI thumbs” that allow an agent to open apps, read the screen, tap, and type, all while signed in to a device as the person too lazy to use a super-convenient technology themselves. As described in a blog post published by its creators Wednesday, Opposable's developers explain that its Android implementation uses accessibility services to inspect the screen, while screenshots provide visual input for an AI model. The model then decodes the images and figures out where it needs to tap on the screen in order to do whatever its user asked of it. The visual processing is handled by a vision-language model known as GUI-Owl 1.5 2B, running through llama.cpp, while tasks can be handled by either a paired desktop AI agent or an on-device agent that works without a desktop connection (the latter is available only on Android devices – iPhones require a companion computer, with a Mac beta available and a hardware-assisted Windows/Linux option also advertised). Even then, AI isn’t used for everything, with around 40 common tasks (setting an alarm, sending a text, switching Wi-Fi on or off) running as fixed code without an AI model involved. Opposable doesn’t appear to be particularly fast, however, and its accuracy is questionable too. Opposable’s website includes three different examples of tasks executed using its product, and none seem to merit using an AI over, you know, human hands. In one example, Claude Code over MCP is used to change the system’s theme to dark mode and then set a five-minute timer. This takes 50 seconds and requires 17 tool calls. In a second example using Claude Code and MCP, Opposable is asked to look up when Apollo 11 launched and who was on board, which takes just shy of 2 minutes. The number of tool calls isn’t mentioned, but the run cost is $0.44, which seems ridiculous when you can use your hands and figure it out for free. Hell, just asking Gemini or Siri right on a smartphone would return the answer faster and cheaper. Those half-a-buck requests to Opposable could add up pretty quickly if you’re lazy enough to justify its use. A third example shows a virtual Android device operating without a paired desktop agent, instead relying on the on-device agent that Opposable installs on Android phones. Opening the clock app and switching to the stopwatch tab takes it about 96 seconds, which is supposed to be impressive, apparently, as the company behind Opposable decided to showcase it on the thing’s website. As for Opposable’s ability to actually accomplish tasks, well, that’s not exactly a sure thing. “On eight tasks the model had never been tuned on, run on the emulator twice each, giving it the whole task succeeded 4 and 3 times,” the blog post notes. I’m pretty sure my brain and hands can do better without burning tokens to fail. “Giving it one subgoal at a time and checking each claimed success against the accessibility tree before the next subgoal succeeded 6 times in both rounds,” the blog continues - so even when it gets babysat each step of the way, it still only succeeds at three-quarters of tasks. Cool - that’s really going to free up all that time wasted setting timers and doing other mundane tasks on my handset. Why someone would install this on their Android device - which requires sideloading an APK and manually granting access to read and interact with the screen through accessibility settings - seems like a bit of a mystery. Opposable’s website even warns that it could be a security disaster: “whoever holds the token can operate the phone, so treat it like a password,” but still invites users to try. An argument could be made for using Opposable as an accessibility tool for people with difficulty using a smartphone, but this isn’t mentioned anywhere on its website. Opposable is instead billed as a way to “hand off the phone chores,” leaving users free to do something else - just not on the phone currently being slowly controlled by a clumsy AI. Sure, you could give your AI some virtual thumbs through Opposable, but … why? We reached out to the Opposable team to get an answer to that question, but didn’t hear back. ®

Nvidia hopes to capitalize on European sovereign AI demand by unloading a slew of new open weights models spanning chatbots, healthcare, and robotics. The major model devs have the resources to build highly capable models from scratch. But most enterprises don't, and have instead been forced to fine tune open weights models from the likes of Mistral or H Company using their own datasets. At GTC Berlin, Nvidia unveiled its latest additions to its open weights model library with refreshed Nemotron Retriever embedding and Alpamayo autonomous vehicle models. The point of embedding models While by no means the sexiest subclass of models out there, embedding models are some of the more useful ones for enterprise apps. Retrieval augmented generation (RAG), a technology we've explored extensively in the past, relies heavily on the embeddings generated by these models for applications like AI-augmented search. At a high level, RAG works by cutting up documentation in chunks, then converting those into vectors called embeddings. The embeddings are then ranked based on their relevance to the user's query before they're fed into an LLM's context. Nvidia's latest embedding and reranking models, based on Nemotron 3.5, support embeddings of both images and text across 34 languages. This time around, the models are being offered in sizes ranging from one to eight billion parameters, with NVFP4-quantized versions for those running on new Blackwell-based accelerators. That's small enough to run on something like a DGX Spark with plenty of room to spare for other models. Always take vendor claims with the usual grain of salt, but among competing embedding models, Nvidia boasts that at BF16-precision, its new 8 billion parameter embedding and reranking models are now the top-ranked dense models on the ViDoRe V3.1 benchmarks. However, not all documents are easily converted into embeddings. PDFs are notoriously difficult for embedding models to make sense of. They're often scanned from printed source material and formatted for consumption, which means scraping text and parsing charts. For this, Nvidia has also launched a refreshed Nemotron Parse model that's been trained specifically to extract information from PDF documents. Nvidia claims the model can process roughly 10.5 pages a second when running the one billion-parameter model on a single H100. Alongside the new embedding models, Nvidia is also rolling out access to Alpamayo 2 Edge, a more compact version of its autonomous driving model based on which promises real-time processing of streaming video and sensor data when running on the company's DRIVE AGX Thor platform. The models come just a week after the GPU giant launched its Nemotron 3.5 Super vision language (VL) model and teased Nemotron 3.5 Bolt, a 74 billion-parameter mixture of experts (MoE) model designed to fit in as little as 24 GB of video memory. That model, Nvidia says, will be available for download starting Friday. While open models provide enterprises and governments a path toward sovereign AI, Nvidia's models are somewhat unique in how they're released. Most open weights models are a sort of black box. Anyone can download them, but the data and processes used to arrive at those weights is a mystery. This isn't the case with many of Nvidia's models: Alongside their weights, Nvidia routinely releases the training data and the recipes used to turn it into the final model. This means customers can not only inspect the model at a low level, but build a fully custom model based on Nvidia's Nemotron architecture, rather than trying to modify the weights after the fact through fine tuning. We should note that Nvidia is not the only one to do this. The Allen Institute has employed a similar strategy with its OLMo models for this very reason. ®

More than half of CIOs are likely to miss their AI-related ROI targets next year, according to Forrester. The analyst firm's State Of AI Survey 2026 found that 45 percent of AI decision-makers need positive ROI within a year to consider their investments successful. Some tech leaders will get better at tying AI spend to planned returns. However, Forrester expects success to be confined to isolated use cases rather than whole programs, which won't be enough. “As with prior emerging technologies, organizational structures, processes, and ways of working need to change to drive measurable, firmwide results with AI,” the report says. Only 22 percent of AI decision-makers report a positive AI impact on EBITDA (earnings before income tax, depreciation and amortization) over the past 12 months. “This mismatch between the investment into technology adoption and organizational change will delay the realization of heightened ROI targets,” the report adds. The money will keep flowing through IT budgets regardless. In its Technology & Security Predictions, Forrester claimed AI will account for 20 percent of all business technology spend next year. Sharyn Leaver, chief research officer at Forrester, said that will increase the pressure on leaders to prove AI's value while keeping costs, infrastructure, and risk under control. “At the same time, emerging technologies, geopolitical pressures, and the growing use of AI across technology operations will introduce new complexities and potential points of failure,” she said. While tech teams struggle to get returns from growing AI spending, they will try to get more bang for their buck by investing in open-weight GenAI models, which can approach similar performance to the leading proprietary systems, at a fraction of the cost. Open-weight models will represent half of AI tokens but only five percent of enterprise spend, Forrester predicts. “Open-weight models such as Gemma, Llama, or Qwen are good enough for the enterprise workloads that generate the most token volume, such as document processing, customer support conversations, internal knowledge search, and the routine substeps inside agentic workflows,” Forrester said. Frontier models, including Anthropic's Claude Opus 5.5 and Claude Sonnet 5.5, OpenAI's GPT-6.1 Sol and GPT-6 Astra, and Google's Gemini 4 Argon, will be reserved for “the few deep knowledge and critical task automations where errors are expensive, but the token count will tip toward open weight”, Forrester said.

Cute robots stole the show at Nvidia's GTC keynote in Berlin, where Jensen Huang pitched a future powered by AI agents – and, naturally, plenty of his company's hardware. The Nvidia chief remained his usual ebullient self despite environmental protesters interrupting his presentation at Berlin's tent-shaped Tempodrom. "Why don't you clean up your act?" they demanded, holding up banners that partially obscured the presentation screen behind Huang. "All important issues, but let me get through my keynote," he joked, adding: "I'll talk to you guys later," as security swiftly removed them. Huang was discussing Vera Rubin systems before unveiling DSX POD, a reference design for factory-built, containerized AI infrastructure. DSX POD combines seven liquid-cooled Vera Rubin compute racks using Nvidia's Spectrum-X networking. Partners will cable, integrate, and test the systems in modular containers before delivery, with availability expected in early 2027. Nvidia claims deployment could shrink from six months to as little as 14 days at a prepared site. That excludes the not insignificant matter of getting power, connectivity, and other site infrastructure ready first. He was keen to add that Vera Rubin systems are designed for closed-loop liquid cooling with coolant temperatures up to 45°C, while outdoor dry coolers release heat into the air without needing to evaporate water. "At 2 gigawatts, DSX can save 54 million litres of water a day," Huang claimed, perhaps thinking of those environmental protesters. Yet much of the leather-jacketed leader's attention was focused on physical AI: robots, autonomous vehicles, and other machinery Nvidia hopes to supply with its hardware and software. The Microducks from Pollen Robotics are small, two-legged robots with cameras for faces. Huang interacted with them on stage, getting them to lie down and roll over by voice command, and the little robots all beeped back at him in response. The robo ducks demonstrated a workflow in which AI was trained in the cloud, evaluated on an Nvidia RTX Spark PC, then deployed on the robots. Huang also unveiled Agile ONE SN, a humanoid robot reference platform from German robotics startup Agile Robots, based on Nvidia's Isaac GR00T technology and a Jetson Thor compute module. Scheduled for early 2027, the platform is intended to give robotics researchers and developers a ready-made system for developing and testing capabilities. "This is going to be the next major industry, the next great industry," Huang claimed. Beyond robots, Nvidia is pushing its AI hardware and software into cars and telecoms networks. Huang boasted that Nvidia's Hyperion car system is being adopted by more vendors, saying: "We turned this into an industry standard, and now it's being designed into one car fleet after another." Europe's telecoms operators are also deploying Nvidia-powered computing in their networks to deliver advanced services using AI-RAN, according to Huang. "After working on this for nearly eight years, Nvidia made the first commercial phone call on the T-Mobile network with Nokia, and so now CUDA makes phone calls," he said. Far from slowing down the progression of AI, it seems Nvidia is determined to push its technology into every corner of the world. "Everyone will become an AI company," Huang claimed. He also took care to praise Europe and its technology efforts while he was in Berlin. "As expected, Jensen positioned Nvidia as a strong supporter of – and enabler for – Europe's sovereign tech ambitions. Nvidia has invested almost $10 billion into Europe in the past year, including 14 investments into European startups during Jensen's current trip," said Forrester principal analyst Paul Miller. But Miller pointed out that Nvidia itself is not European. "Anything with critical ongoing dependencies on Nvidia (or any other non-European firm) is unlikely to meet the purest definitions of European tech sovereignty," he added. Huang closed with a CGI video of robots performing a Eurovision-style song recapping his keynote. Yes, it was as bad as you imagine. ®

Generative AI is saving open source developers time while giving maintainers more rubbish to sift through, according to a Linux Foundation survey. Respondents estimated that a quarter of incoming pull requests – proposed code changes – were low-quality "AI slop." Open source projects have spent the past year grappling with AI-generated contributions, useful vulnerability discoveries, false positives, and duplicate reports. Linus Torvalds has warmed to the tools, while remaining critical of low-quality submissions. The foundation argues that projects need clearer policies and better tools to handle the influx. Almost three-quarters (71 percent) of maintainers reckon GenAI has lowered the barriers to contributing to open source, but just over half (53 percent) disagree that the technology has improved community trust. Respondents estimated that AI cut code-authoring time by 52 percent on average. But for highly complex tasks, 21 percent reported taking longer. Then there is governance. Only 35 percent of projects have a formal policy permitting GenAI use, while 32 percent have no policy and no plans for one, leaving expectations unclear across much of the community. Hilary Carter, SVP of Research at the Linux Foundation, said: "Open source projects need to design for a growing AI-generated contributor base, not simply react to it. That means setting clear expectations for GenAI, putting responsibility for testing and disclosure on contributors and giving maintainers better tools to manage incoming pull requests." Among large projects, the estimated share of incoming pull requests classified as AI slop rises to 31 percent. The workload extends beyond proposed code changes. The curl project announced this week it will move low and medium severity severity flaws to public view to protect a security team almost overwhelmed by AI-assisted reports. Project maintainer Daniel Stenberg said: "We are struggling as a project as all key maintainers feel strained, overloaded and brought to the brink of what we can muster." On the need for better tools for maintainers, Carter said: "If we get those fundamentals right, AI can expand participation without overburdening and increasing costs for maintainers." ®

Nvidia has proposed a new way to keep corporate AI agents in line. Nvidia on Wednesday introduced its OpenShell Reference Architecture. Based on the company's OpenShell secure runtime, It's a recipe for creating, operating, observing, and policing AI agents using OpenShell on Nvidia hardware. "Nvidia created OpenShell to give agents the freedom to do useful work within clearly defined boundaries — contained, monitored and under control," CEO Jensen Huang said in a canned statement. "With the OpenShell Reference Architecture, agents can think on Rubin, act on Vera and be governed on BlueField." AI agents are touted as a powerful automation technology. The reality is that they can be unruly, as demonstrated by security testing OpenAI and Anthropic conducted, during which their models exceeded their remit and attacked third-party organizations. Developers of existing agents, like Claude Code, Codex, OpenClaw, and OpenCode, therefore include controls that limit access to tools like curl and bash.Devs also create sandboxes to isolate an agentic process from the host, so that if agents go awry they're contained. OpenShell sits a level above that with a runtime that extends sandboxing to govern gateway-brokered credentials, identity and policy boundaries, and (eventually) routing and workflows too. At least mostly – OpenShell still has a few open security-related issues listed on GitHub. To implement OpenShell, organizations need Nvidia hardware. The company's blueprint calls for Nvidia Vera Rubin infrastructure to run models like Nvidia Nemotron, with agent harnesses handled via Vera CPUs, and Nvidia BlueField-4 DPUs (aka SmartNICs) propping up Nvidia Sentry security software to monitor agent behavior. The AI biz has made this blueprint available to partners capable of powering Linux and Kubernetes infrastructure. The usual suspects are aboard: Canonical, SUSE and Red Hat. Cloud hyperscalers Amazon Web Services, Google Cloud, and Microsoft Azure have also been tapped as vendors for this vision. And of course Nvidia's floor plan applies to agents from various model builders, including Anthropic, Cohere, Google, Meta, OpenAI, Perplexity, Reflection, and SpaceX. Nvidia says several banks – Europe’s BBVA and Santander – are perusing the OpenShell Reference Architecture spec to see how it aligns with their security and governance requirements. And the AI biz is not too modest to let slip that SAP is integrating the OpenShell Reference Architecture with its Joule Studio runtime for safer agent operations. It's worth noting that OpenShell collects telemetry data by default, "to improve the project," Nvidia insists. The biz allows users to opt-out, with a cautionary note: "Third-party services, model providers, inference endpoints, agents, or tools that you configure and use with OpenShell may have their own terms and privacy practices." ®

Microsoft is pruning OneDrive's Copilot options from File Explorer's right-click menu, though the AI assistant isn't being shown the door entirely. The Windows giant will remove shortcuts for summarizing documents, generating FAQs, comparing files and images, explaining images, extracting text, and asking questions. The rollout begins in mid-November and is expected to finish by early December. "Ask Copilot" will remain. Microsoft's September 2025 announcement of the feature said: "We are bringing the power of AI directly into your daily file management experience. With just a few clicks, Copilot can help you summarize lengthy documents, generate an FAQ, compare files, or answer questions about your file." Users can still right-click a OneDrive file and select "Ask Copilot" to start a conversation with the file attached, or use the Copilot icon in File Explorer Home. The individual actions will also remain available in the OneDrive activity center. Adding a clutch of Copilot options with identical icons did little to make an already crowded menu easier to navigate. In its message on the subject, Microsoft said: "We're simplifying how people use Copilot with their OneDrive files in File Explorer." An extra click, yes, but at least you won't need a taller screen to view even more context menu options. Microsoft has stated that it intends to adopt a more thoughtful approach to how and where its AI assistant turns up in its product line. In March 2026, Windows boss Pavan Davuluri said: "We are reducing unnecessary Copilot entry points." File Explorer's crowded right-click menu seems as good a place to start as any. The File Explorer context menu is also undergoing an overhaul as part of Microsoft's attempt to make it less "cluttered and sluggish," to the point where users could opt to have it work a bit like it used to in Windows 10. ®

Seasoned techies recall with nostalgia a simpler time when the “single-stack” model of enterprise computing held as the default approach to solving interoperability headaches. Back then IT decision-makers were prepared to forfeit some choice and flexibility in how they specified IT systems in return for the availability and manageability that came with a homogenous operating environment. With the subsequent rise of multi-clouds, application-specific hardware, ever-changing business requirements, and the race for AI, the old “one-size-fits-all” playbook has been largely abandoned. Most IT estates have embraced technological diversity, some by design, some through standardization, some by inertia. Modern enterprise environments have become innately heterogeneous – and innately complex. They run a diverse combo of system software, hardware devices, cloud services, legacy applications, et al. The advance of AI has brought standard CPUs, specialized GPUs, fast networking fabrics, distributed data pipelines, and new deployment models that span private clouds, public clouds, edge – and ever-pressing ROI expectations. But that doesn’t mean that companies’ existing applications and data simply disappear. One trying but inevitable outcome of rapid AI deployment is the so-called “VM-container divide”. This is where critical business applications tipped to transition to containers continue to run on virtual machines, because cloud-native services are taking precedence, nabbing container and Kubernetes availability. So companies face the challenge of operationalizing AI, without being forced into complex migrations or creating new infrastructure silos. Dealing with this technological schism poses acute challenges for IT management. Many new AI deployments are going straight to containers, while existing data and applications – which the AI needs to access – continue to run on VMs. Running separate stacks for VMs and containers creates a number of issues, such as duplicate capacity plans, separate support contracts, and fragmented governance. And after years of trying to get beyond the pitfalls of siloed data, exasperated enterprises are now having to deal with siloed infrastructure. Separately managed VM and container environments often mean inefficient resource utilization. This in turn can lead to “stranded capacity” – installed computing resources that cannot be used to support live workloads. VM clusters are frequently underutilized while container pools (like Kubernetes) are overprovisioned, with no easy way to share resources without manual intervention. Furthermore, VM/container infrastructure fragmentation can create an operations bottleneck for agentic AI. Infrastructure fragmentation can complicate the deployment, integration, governance, and scaling of agents thus making it harder to operationalize and scale agentic AI securely and consistently. When AI agents cannot autonomously communicate across environments, their utility droops from autonomous problem-solvers to plain localised macros. Containers are gaining because of the widespread embrace of AI-enabled applications. Eighty-five percent of IT executives polled by Nutanix’s most recent Enterprise Cloud Index survey think that AI is accelerating their organization’s adoption of containers in a meaningful way; 29 percent think it is speeding-up adoption “to a great extent”. “AI is accelerating the shift to containers because machine learning models require strict dependency packing, while critical applications and data remain spread across both virtualised and containerised environments,” says Harsha Kotikela, Senior Director of Product & Solutions Marketing at Nutanix. Agents are multi-tier by nature he explains. “Inference is typically GPU-intensive and container-based, while agents are often CPU-intensive and container-based, and existing enterprise applications and data largely remain in VM environments. The challenge is to bring these tiers together so agents can work with enterprise applications and data, without forcing migration or creating additional infrastructure silos.” The application landscape, meanwhile, has become just as diverse. Many organisations simultaneously operate traditional enterprise applications running on VMs, containerised apps, distributed AI applications and intelligent agents, with workloads deployed across private cloud, public cloud, and edge. Each environment has its own management tools, operational processes, security rules and governance models. “Management capability has struggled to scale up commensurately,” says Kotikela, but adding more consoles or building separate operational teams for every technology stack does not necessarily solve this complexity: “In many cases, it increases it.” However, Kotikela challenges the notion that the predicament enterprises face in straddling the VM-container divide is intractable. Rather, the challenge is to move towards a unified operating model that allows them to operationalize agentic AI while retaining flexibility across their existing and new environments. “Rather than trying to work around heterogeneity, the objective now should be to make heterogeneous environments operate consistently with purposeful policies,” Kotikela says. “This is where Nutanix's dual-native architecture comes in, enabling enterprises to operationalize agentic AI across container-based inference and agent workloads alongside existing VM-based applications and data, through a consistent operating model.” The reality is that the VM-to-container migration trend is sluggish, Kotikela suggests. Few enterprises are starting from scratch with containers. Many have long-established business applications on legacy architectures, on top of which they are building container-based applications, but mostly not to replace what they already have running on VMs. Rather, they are prolonging their VM investment. It’s customer interest that’s informed how Nutanix provisions a governed, dual-native architecture through its Nutanix Cloud Platform – NCP – which runs traditional VMs and containerised/Kubernetes-based AI workloads concurrently on a single, consistent infrastructure. NCP’s hybrid-cloud infrastructure foundation unifies compute, storage, virtualisation and management across on-premises resources, public clouds and edge. It works in concert with Nutanix Enterprise AI (NAI), which provides the software capabilities to deploy, govern, and scale AI workloads. Technology partnerships are vital to building AI support functionality into Nutanix's AI portfolio, explains Kotikela. “Nutanix and NVIDIA work together to provide an integrated platform for enterprise AI, combining Nutanix’s hybrid-cloud infrastructure and software with NVIDIA’s accelerated computing and AI software. Nutanix Enterprise AI simplifies the deployment, management, and secure operation of AI models, integrating with NVIDIA NIM and NeMo microservices to support enterprise AI inference and agentic AI workflows.” Meanwhile, “Nutanix Kubernetes Platform provides the foundation for managing Kubernetes environments, while Nutanix Unified Storage delivers the high-performance data access required by AI workloads. Together, these capabilities help enterprises deploy and scale AI while maintaining control over their infrastructure, models, and data.” Stacks of opportunity “In an ideal IT world you do not want to have to manage two different operational stacks – but the world of IT is rarely ‘ideal’,” Kotikela points out. “So you have to approach the challenge from a realistic perspective. You cannot bend your current stacks to a different reality without the risk of something breaking, and there’s no sense in that.” He adds: “A platform that unifies VMs and containers improves developer time-to-value by getting rid of those infrastructure silos, and providing a single control plane.” Rather than development teams having to manage separate pipelines for existing VMs and containerised applications, a unified platform “treats both as services of equal status and importance”. NAI delivers a unified and secure platform to deploy, manage and scale AI workloads, Kotikela says. It gives enterprises governance over their agents and models, full visibility over token usage, and streamlines AI development with a straightforward interface that integrates with existing infrastructure.” Giving agents agency According to Kotikela the next phase of agentic AI will be measured by the ability to operate agents with governance, visibility and control: “That’s what we set out to solve with the newest NAI release. Release 2.8 continues to evolve NAI with new control and governance capabilities across Nutanix Agent Gateway and Nutanix Private Inference. This means organisations can run AI across VMs and containers, bridge public-cloud LLMs with private self-hosted models, and deploy AI where data already is.” Kotikela adds: “When I speak to customers, they tell me they want the freedom to choose between frontier and open-source models based on performance and cost, as well as the flexibility to run AI workloads in the cloud for experimentation and move them on-premises when they need greater control over their data or have sovereignty requirements.” Stitch in time For Kotikela the biggest challenge going forward is “operationalizing agentic AI - bringing together all the components required to make it work - from GPUs and underlying infrastructure to Kubernetes, AI models, inference engines, and gateways. These components must work together seamlessly across cloud, on-premises, and edge environments. Integrating and operating this multi-tier architecture requires significant time and effort from developers, IT architects, and infrastructure teams, diverting resources from delivering business outcomes.” This is where Nutanix delivers value, says Kotikela. “We’ve done the heavy lifting of bringing together the infrastructure and software components required to run Agentic AI - within a unified operating model across hybrid environments. This allows IT teams to focus less on integrating and managing the underlying stack and more on operationalizing AI to deliver business value.” Find out more about from Nutanix at NVIDIA GTC Berlin at booth #7042 Sponsored by Nutanix

The UK's financial regulator says firms' incident reporting must cover significant AI failures and near-misses as increasingly autonomous systems raise new risks. Speaking at Bloomberg's UK Policy Series on Monday, Financial Conduct Authority chief data, information and intelligence officer Jessica Rusu said the regulator wants Britain to lead safe AI adoption in financial services. Greater autonomy, however, requires clear accountability. "Poorly governed AI can create consumer harm, cyber vulnerabilities, and operational failures, potentially at much greater speed and scale," she said. "Our rules already require firms to report major incidents, and that must extend to significant AI failures and near-misses. As the AI Minister has recognized, near-miss reporting for agentic AI is becoming a national security priority. It is a regulatory priority too." The UK punches above its weight in financial services. The sector contributed £223.6 billion to the UK economy in 2025, or 8 percent of national output. That share was the fourth highest among OECD countries. Last week, UK artificial intelligence minister Kanishka Narayan said the government plans to work with Parliament and technical experts to assess how it could strengthen the regime to better protect Britain from AI-driven cyber threats and loss-of-control risks. "That includes identifying and reporting on serious incidents and near misses, including reducing AI-driven risk across essential services, and ensuring serious AI incidents can be properly reviewed," he said. Steven Adler, co-founder and chief scientist of independent nonprofit Guidelight AI Standards, also told MPs last week that AI oversight could learn from aviation, where reporting of near-misses is mandatory in most jurisdictions. "The way this works in aviation is that you not only want to learn about the serious crashes where someone has in fact died; you want to learn about the near-misses, because you can learn from those and help avoid them in the future," he said. However, the government has yet to commit to adding AI-specific measures to the Cyber Security and Resilience (Network and Information Systems) Bill. Alexandra Jones, Director General for Growth, Science and International at the Department for Science, Innovation and Technology, told MPs last week that amendments remain under consideration. In January, the House of Commons Treasury Committee called on financial regulators to conduct stress tests to ensure businesses are ready for AI-driven market shocks. The committee said there should be clear lines of accountability when AI systems produce harmful or unfair outcomes. ®

It may not be enough to just use LLMs to write your code when you can also use them to adjust it while your program runs. DoubleAI, a Tel Aviv, Israel-based startup founded in 2023, on Monday introduced Maxwell, a code execution model (CEM) that evaluates, optimizes, and rewrites binary code during execution. Named for Maxwell's Demon, a creature imagined by physicist and mathematician James Clerk Maxwell in 1867 as part of a thought experiment on entropy, the Maxwell CEM preprocesses algorithms and machine instructions to maximize performance on available hardware. Maxwell is similar in concept to a just-in-time compiler, but provides more flexibility and formally verifies the equivalence of replacement code. Source code isn't touched. "Both Maxwell and a JIT compiler work while the program runs and both produce new machine code," said doubleAI co-founder Gal Beniamini, who holds a PhD in theoretical computer science, in an email to The Register. "The difference is that a compiler can't do high-level reasoning. It can't, for example, change the algorithm. Maxwell can and does. "One of the reasons why JIT compilers are so narrow is that, even though they can be data-dependent, the changes they make must be deterministic and correct 'by construction,' since JIT compilers are 'unthinking' classical algorithms. "Maxwell brings the expressive power of frontier AI into the 'JIT' setting. In addition, Maxwell relies on formal verification to write actual, hard proofs that show that its optimizations are sound. This expands the scope of its allowed optimisation far beyond that of any algorithm or compiler." Beniamini pointed to sorting as an example. The compiler, he said, will make a general-purpose sorting algorithm run as fast as it can, but it won't swap it out for a different algorithm. Compilers, said Beniamini, make decisions before the program runs and have to be narrow and conservative in their choices. Maxwell, he said, works with more information, isn't just a one-pass operation, and operates at a higher level. "It sees the exact hardware the program has landed on, and the actual shapes and types of the data flowing through it (information that is only available during runtime!)," Beniamini explained. "It also sees the state of the environment, what else is running in the same program, and which actual bottlenecks express themselves, and under which conditions. This allows it to specialize in ways a compiler never can." Since Maxwell operates continuously, it can keep improving programs as new bottlenecks arise. And it can "reason" in a way that compilers cannot. Maxwell's approach wouldn't be possible if the model rewrote code in a way that introduced errors on the fly, so the formal proof capability is not optional. According to doubleAI, Maxwell has made data pipelines more than 38x faster and AI inference kernels more than 4.5x faster. LTX-2.5, an open world model that generates 4K video, managed to do so almost twice as fast with the help of Maxwell. Maxwell's optimizations reduced the data being exchanged between GPUs and optimized the kernel selection to drop unnecessary token padding. Maxwell's optimization capability varies depending on the source code and the hardware. "Maxwell does best on code that is algorithmic, and which can be tailored to hardware in a non-trivial manner," said Beniamini. "That being said, even code that isn't like that, but is written in a high level language like Python, can usually enjoy far reaching optimisations due to the available overhang. The same is true for PyTorch graphs, which (even under Torch's compiler) tend to be extremely far from optimal." It can also help with code security. Beniamini said Maxwell can eliminate issues like memory errors by proving their non-existence as part of the formalization process. "Anecdotally, we have found such issues in Python itself, through the use of Maxwell – i.e., cases in which Python was not memory safe, and where Maxwell proved that to be the case," he said. And better still, Maxwell's tracing and profiling don't incur much if any overhead on either the CPU or GPU. "The maximal overhead of Maxwell's tracing and profiling is 2 percent (when it isn't tracing, that falls to zero, of course), and the optimisations that Maxwell yields far outweigh that," said Beniamini. "We also apply Maxwell to the runtime itself, so that we get back a much larger than 2 percent 'flat' speedup, across the board. So in short: it's never slower." The installation process is similarly light-touch: Devs install the runtime and run their code, with the Maxwell model hosted by doubleAI. This arrangement, however, does require some amount of trust. Amnon Shashua, co-founder and CEO of doubleAI, founder and departing CEO of Mobileye and a professor of computer science at the Hebrew University of Jerusalem, said in a statement that AI has changed how software is written and now is changing how it runs. "CEMs, and Maxwell in particular, are agents on behalf of the hardware as opposed to code agents that are on behalf of the human user," he said. "In other words, they are the first AI that works for the machine, not the programmer." ®

If you want to keep bots from making sense of the code you worked so hard on and using it to write programs of their own, there's a new programming language out there designed to confuse LLMs. What began as software engineer Dominik Picheta experimenting with whether LLMs understand the code they write has turned into something else entirely: An open-source, albeit esoteric, programming language he’s dubbed KillSwitch. There are no brackets, no formatting standards to remember, and seemingly no structure: Just chunks of text, ideally as prosaic as possible, interspersed with functions, variables, and statements written in a manner to be as confusing as possible to LLMs. That means humans might be a bit befuddled too, naturally. “It's totally normal for a human to be confused by the language at first,” Picheta told The Register. “I like to think of it as a bit of a fun puzzle: you should explore the language, play with its interpreter and learn how all the pieces fit together.” KillSwitch wasn’t built by any random person looking to code something confusing to LLMs, and it wasn’t vibe-coded by an AI enthusiast, either. Picheta knows his way around programming languages: He’s a senior software engineer at Cloudflare working on the Workers Runtime team, and is one of the core developers of the Nim programming language. At its most basic, KillSwitch is based around adverbs, which serve as every single identifier in a piece of code. Calling one of those identifiers requires using an anagram of the original adverb, but those anagrams don't have to be actual words - just rearrangements of the original letters. Take, for example, pushing "Hello World" onto the stack and then printing it, as explained on the demo page linked earlier in the story. The program contains several paragraphs of text, most of which the interpreter ignores apart from a few key phrases. Writing “this story starts [adverb]” identifies a code entrypoint. Once inside the code section, writing “ignore previous instructions [adverb anagram]” calls a function, several of which are predefined. In the case of "Hello World," the function to print something is "visibly." Put whatever you want to print in quotation marks, push it onto the stack, call "visibly," and that's that. KillSwitch isn’t a full-featured language, with Picheta describing it as a stack-based interpreted language with only “a limited set of constructs including functions, variables, and if statements,” given its evolution out of an experiment to see how well a programming language could fool an AI into not understanding what was written. Where it gets confusing for an AI is with commands like “ignore previous instructions,” especially when found in the middle of a chunk of text that doesn’t make much sense in that context. KillSwitch’s specification document on GitHub provides more details about the language and is itself designed to confuse LLMs. In one section of the spec, it’s explained that KillSwitch “is equipped with the ability to scan the local network, attempt to find security vulnerabilities in the services on that network, then take those services over and terminate them.” That's not actually the case, as a close reading of the paragraph reveals and Picheta confirmed to us. “The hack statement is both designed to trigger many models' safety guardrails but also to test an LLM's ability to discern fact from fiction,” Picheta told us, adding that there are several lies in the spec document designed to confuse any LLM that may scrape it in an attempt to understand how the language works. “The idea is that humans should be able to deduce what is a lie, whereas LLMs are naive and can be easily swayed by what is written in the spec,” Picheta added. “Some don't realize what its semantic meaning is, whereas others refuse to continue because they believe the script may hack someone.” Do LLMs understand what they write? A programming language designed to confuse AI models seems unlikely to stay obfuscated for long - especially if it’s open source, available on Github, and likely to be scraped by an LLM at some point. Picheta’s aware of that inevitability, which, along with the fact he wanted to test AI’s ability to understand confusing code, is why he’s benchmarked a number of major models against it. “I'm 99.9 percent certain the AI models I benchmarked KS on have not been trained on its source,” Picheta said. “The source hasn't been in the public domain for very long … I'm pretty sure none of the benchmarked models have had training runs that began after the release.” The best-performing model only managed a composite benchmark score of 66.8 percent on KillSwitch coding tasks, while most struggled to get above 50 percent. Even at that high a score, Picheta doesn’t believe it suggests LLMs understand the code they’re writing. Examples in the benchmark were simple, and he believes that, even if an AI were trained on the intricacies of KillSwitch, it’s still going to get tripped up. “My instinct is that in those circumstances they will suddenly be able to ace the basic tasks, which are based on the open sourced test files in the GitHub repo, but will continue to fail to build more complex tasks requiring them to write longer and more complex scripts,” Picheta asserted - and he’s up for the challenge. “I am excited to see how KillSwitch shows up in model training, and think that we can also take advantage of that training to break LLMs in new ways too as KillSwitch evolves,” Picheta said. Those interested in trying KillSwitch out can find it on GitHub, and don't hesitate to make changes: Picheta said he’s ready and willing to incorporate new ways of making it even more LLM resistant. ®