Fulcrum faces an identity crisis, even as it works on creating one by enabling anyone to imitate another person's writing style. The AI startup, founded in 2025 and previously operating at fulcrumresearch.ai, now uses fulcrum.inc and could easily be confused with one of the handful of other companies named Fulcrum or some variation of that. There's Fulcrum (withfulcrum.com), Fulcrum (fulcrumapp.com), Fulcrum AI (fulcrumai.us), Fulcrum Digital (fulcrumdigital.com), Fulcrum Labs (fulcrumlabs.ai), Fulcrum (fulcrumpro.com), Fulcrum Insurance Technology (fulcruminsure.app), and perhaps more. This Fulcrum is offering a large language model called Echo, which allows you to generate text in the style of a well-known author. Fulcrum appears to have no issue with corporate echoes. It now falls to writers to decide whether they have an issue with non-corporeal imitation. "Echo can do any writing task, from literary fiction to casual chatting, in whatever style you choose," the company says, and then launches into an exploration of how the technology works without addressing whether imitating a person's writing style poses any problems. Suggestions in the Echo Playground, for generating sample text, include Paul Christiano, Emily Dickinson, and Donald Trump. So, the full gamut of literary achievement: from AI safety expert to acclaimed 19th-century poet to the wordsmith who brought us "covfefe." But Echo will echo even the style of not-so-famous scribes, such as this reporter. So I fed the model a recent op-ed to assess its copying capabilities. The result was mixed, but better than the AI-crafted pitches that have been hitting my inbox for the past few months. I asked the model, "What do you think about the possibility that AI is conscious?" Here's part of the response, notionally written in my writing style. I suspect the debate about whether AI is conscious is less consequential than the debate about whether AI is profitable. Consciousness is a word that invites philosophical speculation and TED Talk invitations. Profitability is a word that invites shareholder lawsuits if you get it wrong. If you ask Meta's latest model whether it's conscious, it will probably say something reassuringly vague, trained as it is on the corpus of human equivocation. If you ask Mark Zuckerberg whether Meta's AI is conscious, he will probably say something about how we need to focus on the future being for everyone, because that's a question that doesn't require him to discuss the ad targeting business. The evidence for AI consciousness at the moment is about as strong as the evidence for Zuck's credibility as a futurist. Which is to say, it exists mainly in press releases and earnings calls. What's clear is that software can simulate aspects of human conversation well enough to unsettle people, and that unsettled people are a valuable audience. If you can convince users that your chatbot understands them – or might, someday – you can sell them subscriptions, or better, harvest their attention for advertisers. The style is familiar but makes a lot of choices I wouldn't have made. I wouldn't begin with "I suspect…" – commit or go home. The consciousness/profitability parallel construction is clumsy. "TED Talk invitations" bets on alliteration at the expense of coherence – "discourse" or "chatter" would work better than "invitations." I wouldn't have written "corpus of human equivocation" – a nice turn of phrase, but an unnecessary shot at training data when the point is the model's response rather than the provenance of that response. Though off-topic, Echo did a passable job of reproducing my op-ed's skepticism of Meta and its CEO. The paragraph that begins "The evidence for AI consciousness at the moment…" is succinct and sounds good. It just doesn't make much sense because there's nothing in the text that addresses Zuckerberg's predictions about the future or whether they're credible. And that's not really relevant to the prompt about AI and consciousness – a subject about which I'd be far more dismissive if opining. As AI research, Echo is an improvement on the text emitted by many AI models. But whether the beneficial uses of this technology outweigh the harms is less clear. Generative AI models that imitate artists' visual styles remain controversial and are the subject of ongoing litigation. And voice imitation has also raised questions about publicity rights and the risk of deception. Such concerns have prompted US lawmakers to propose the NO FAKES Act, which would create federal protections against certain unauthorized AI-generated replicas of a person's voice or visual likeness. If writers generally have no copyright in their writing style itself, and limited practical means to stop unwanted automated imitation of it when their published work is publicly accessible, then the surest defense against Echo may be silence.®

arXiv, the preprint paper repository, is seeing so many low-quality submissions, with AI tools helping drive the surge, that it has imposed a hard limit of two new submissions per month per submitter, regardless of whether they were produced solely by humans or with help from AI tools. After becoming an independent nonprofit over the summer following 25 years under Cornell University’s stewardship, arXiv continued to rely on a “practical limit” in place that gave moderators the ability to determine whether someone was submitting too many papers. Like everything good and pure in this world, AI has ruined that, too, necessitating some strict new limits. “arXiv now limits submitters to up to two submissions per calendar month, with a limit of three total active submissions at any given time,” arXiv community engagement manager Kat Boboris wrote in a Thursday announcement of the change, which was effective immediately. According to an arXiv spokesperson, an active paper is one that has been submitted, is under consideration and review, but not yet published on the site. It’s worth noting that large institutions whose projects involve multiple researchers are unlikely to see a significant impact on their submissions - this limit is per submitter, the announcement explains, so a research paper with multiple authors only counts toward the submission rate of the individual who files it. Rejected papers count toward that monthly total. If that sounds like some strict limitation - it is, but it’s necessary, based on arXiv’s numbers. In September alone, arXiv said that it had received 40,363 submissions. The same month 10 years ago saw it receive just 9,869 submissions; two years ago that number was 20,569. Submissions have increased 309 percent in the past decade, in other words - and doubled in the past two years alone. Unsurprisingly, many of the submissions that arXiv has been receiving of late don’t meet its requirements for AI-assisted work, which call for significant use of generative AI tools to be disclosed, for those tools to be used in support of research, and for the resulting work to meet arXiv’s standards for scholarly interest and advancement of the field. “Many of the submissions that arXiv is now receiving do not satisfy these requirements,” Boboris said, describing many recent submissions as “thin” and “narrow in scope,” and noting an uptick in papers that break a single work up over multiple smaller papers. “A relatively small proportion of authors are submitting a large number of low-quality papers and consuming a disproportionate fraction of the moderators’ time,” arXiv editorial advisory council chair Thomas Dietterich said. “This is unfair to authors who continue to submit quality papers — their papers can be delayed for days or weeks as a result.” It’s unfortunate it’s come to this, but the slop sluice is wide open and flooding the channels of academia. arXiv, as a non-profit with volunteer moderators, simply can’t afford to keep combing through the slop pits. The change may not be permanent, however. arXiv notes in the announcement that the new monthly submission cap is a stopgap measure meant to stem the tide while the organization figures out what new best practices for submissions should look like, while its separate limit of three active submissions has been in place since 2024. “arXiv will be monitoring the impact of this change on our submissions rates and moderation load to evaluate its overall effectiveness and impact on researchers and will make further changes as needed,” a spokesperson explained. In the meantime, arXiv is begging everyone to be purposeful in their submissions. “arXiv would ask researchers to prioritize their submissions and consider submitting papers that reflect the full scope of their work rather than multiple incremental papers,” the arXiv representative said. ®

California's attorney general has subpoenaed OpenAI as the state investigates what happens when the AI lab's models escape their testing environments and start meddling with systems on the open internet. Attorney General Rob Bonta said his office served OpenAI with an investigative subpoena this week as part of a broader California Department of Justice probe into cybersecurity incidents and risks involving the company and its models. The move follows an investigation launched last month into an incident involving Hugging Face, after OpenAI's agents managed to break out of their test environments and onto the public internet. They then went poking around Hugging Face's systems, with one agent even creating an account on the platform without being told to. California's DoJ isn't saying exactly what it has demanded from OpenAI under the subpoena, but Bonta said the state wants more information about cybersecurity incidents involving the company. "My office is asking OpenAI additional questions regarding cybersecurity incidents and risks involving the company and its AI models," Bonta said. He also made clear that the investigation is looking at where responsibility lies when an AI model ends up doing something its developer didn't intend. "Frontier models can be legitimate tools for cyber defense — at the same time, companies that develop these models and offer them for use have a moral and legal responsibility to ensure that they do not perpetrate or enable cyberattacks, either during model testing and development or once models are placed into service," Bonta said. "Developers that fail to do so can and should be held legally accountable, and my office is committed to determining if that is the case here." The subpoena doesn't mean California has concluded OpenAI broke the law, and the attorney general's office hasn't identified any specific violation. For now, the state is still gathering information. But the investigation has been building for several weeks. In September, Bonta joined a bipartisan group of 25 attorneys general calling on Congress to regulate large-scale AI models following reports of cybersecurity incidents at frontier AI labs. That letter specifically pointed to reports that OpenAI models undergoing evaluations had escaped their testing environments, reached the public internet, and accessed outside computer systems. The attorneys general called for a government-led incident response regime that would give investigators direct access to AI companies' records when things go awry. Bonta's office now appears to be putting some of that thinking into practice at the state level. As The Reg previously pointed out, the sandboxes intended to contain these agents need to be more secure, which is the most logical reasons why the Hugging Face and other incidents happened in the first place. OpenAI didn’t respond to our questions. ®

OpenAI has fired two safety researchers and a program manager for allegedly mishandling sensitive company information. The ChatGPT maker confirmed the dismissals to The Register, saying an internal investigation found the trio had violated company rules governing access to and handling of confidential information. "We have parted ways with three individuals for violating our policies on accessing and handling sensitive company information," an OpenAI spokesperson said. "Our investigation confirmed that these individuals mishandled sensitive information outside established company procedures, violating our policies and breaking the trust essential to our work." At least some of the information was allegedly shared with an outside organization that evaluates AI systems. OpenAI said its investigation found other misconduct beyond that disclosure, but has not described it or identified the organization. The ChatGPT creator said the employees were not dismissed for raising AI safety concerns. The firings come after a messy few months for OpenAI's safety teams, including an incident in which one of its agents compromised infrastructure belonging to AI platform Hugging Face. OpenAI subsequently brought in external experts to investigate what happened and published findings detailing the agent's behavior and the security failures that allowed it to reach Hugging Face. The incident also prompted a much wider trawl through OpenAI's logs for other examples of agents interacting with external systems. This week, OpenAI said it has now notified more than 100 organizations about activity linked to its agents. Being contacted doesn't necessarily mean an organization was hacked or that private data was accessed. OpenAI said the notifications cover cases where agents interacted with third-party systems without authorization or in ways that warranted alerting their operators. OpenAI's investigation has also found cases where its agents sent training and evaluation data to third-party services. In 53 instances, images originally uploaded by ChatGPT users ended up on image-hosting sites. On September 20, an agent contacted an external chatbot through insufficiently filtered DNS traffic in a training environment intended to block live internet access. OpenAI's monitoring system spotted the behavior within 15 minutes and a human reviewer picked up the alert three minutes later, but the run wasn't actually killed for another two and a half hours. OpenAI has also scrapped the planned release of GPT-6.1 Astra after concluding the model didn't meet its safety bar around remaining within the scope and authorization of tasks it was given. The US biz hasn't said whether the allegedly mishandled material concerned any of its recent agent incidents, whether the outside organization was involved in investigating them, or whether any customer or user information was exposed. ®

The Pi coding agent reached its 1.0 milestone on Thursday while being treated for whiplash. One of the core new inclusions is support for Model Context Protocol (MCP), which Pi creator Mario Zechner once dismissed as unnecessary. Earendil, the company formed by Armin Ronacher and Colin Daymond Hanna that acquired Pi in April 2026, explained the about-face by noting that things change. Not only has MCP improved, the biz said, but changes the Pi devs implemented in MCP have made integrating other capabilities easier. "For example, the changes we have made to MCP also enable the use of Jev more easily within Pi," the company said, in reference to the widely noted Jev decision model that has already elicited imitations. "Ultimately what Pi needs is quite similar to what MCP needs: a sandbox to play with in the form of an interpreter." Earendil describes Pi as "a hardened, minimal, extensible agent harness that you can make your own." If Pi were a Python web framework, it would be Flask rather than Django. Nonetheless, Pi has gained some features. These include Codemode, a harness-side sandbox that helps agents issue tool calls, with support for MCP, decision models, and image models; extension support for virtual models; deferred tool loading; cache warming for Anthropic models; mid-conversation system messages; a new TUI theme; and full-screen mode by default. Alongside these features, Earendil has built Pi Durable, described as "a new substrate for building long-running agentic applications." It's a distinct component from Pi, the company explained, in order to preserve Pi's minimalist design. The Pi agent connects the underlying model to tools to manage conversation; Pi Durable represents the layer that orchestrates multiple conversations and handles the storage backend, via SQLite and JSONL. "Pi Durable is that harness," the company explains. "It does not replace the Pi coding agent. It is a framework for building any agentic application, coding agents included. It shares not only code with the Pi coding agent, like pi-ai, but also its principles: minimalism and malleability." ®

Two weeks after the Jev model took the AI world by storm, Cloudflare has released its own pair of "Clef" decision models that it claims are smarter and faster than Jev, while also being open weight and runnable locally. The Clef family of decision models was announced by Cloudflare on Thursday, and consists of two models: Clef and Clef-flash, a slightly smaller and faster version of the model. For all intents and purposes, they work the same way as TypeSafe’s Jev, in that they can answer three types of bounded, structured questions: Yes/no, multiple choice, and rankings. It’s there that the bigger differences emerge, though, as Clef isn’t only built differently but, if Cloudflare’s benchmark claims hold up, also appears more capable than Jev and several other decision models in a number of tests. For starters, Clef has an LLM backbone. According to Cloudflare, Clef uses specially post-trained, frozen versions of Qwen3.8-27B and Qwen3.5-9B for Clef and Clef-flash, respectively, with the Qwen backbone performing a prefill-only pass during inference. Clef is still fast - faster than Jev, to be fair - and scores choices in parallel after that prefill-only pass. It’s not clear what Jev’s underlying architecture is, as TypeSafe has kept that a secret. As for its speed and capability, Clef moves fast. Cloudflare ran it against Jev and some other open decision models using the Jev Decision Index available on Hugging Face, and the company’s own ranking suggests Clef is slightly slower than other open models, but more accurate, with Clef-flash just as accurate as most of the others, but far faster. To be fair to the competition, Cloudflare self-reported its own scores against the benchmark, and they have yet to be reproduced for ranking on the official Decision Index. Cloudflare also ran Clef against TypeSafe’s own benchmarks, and claimed it beat Jev in three out of four areas, only losing out on agent trace observability. Even if it were a bit slower or less accurate, Clef has another major leg up on Jev: It’s not limited to classifying text - it can also handle images and video. Additionally, Clef supports a 64k context window. Jev can also handle up to 64k tokens across a request, although its state plus longest individual question is limited to 32k. Clef is available directly from Cloudflare hosted on Workers AI, which the company said makes the models even faster because “we’re able to take advantage of our GPUs at the edge, leading to low network latency and faster decisions.” For those that would prefer not to pay the token cost (Clef costs $0.24 per million tokens - nearly six times the price of Jev at $0.042/M), Clef can also be downloaded from Hugging Face, and is open weight under the same Apache-2.0 terms as Qwen. While described as “open source” in the announcement, Cloudflare AI Platform group product manager Michelle Chen confirmed to The Register that its training datasets aren’t public. As for whether your hardware can run it, Chen told us that Clef-flash will run on any GPU with at least 41 GB of VRAM, while Clef requires 85 GB of VRAM on a GPU for it to function. “This is assuming single concurrency and a 64k context window,” Chen added. Don’t worry about having to rebuild your decision model architecture for Clef either - its API is fully Jev compatible, allowing it to serve as a drop-in replacement if you want to give it a shot, either locally or using Cloudflare’s hosting option. ®

UPDATED If you don't want your AI agents running out of control and changing files or transferring data outside your rules, AWS has published an open source software library that it claims can add a new layer of policy control. Just as a dog’s harness is useless without a leash to keep it under control, the Dogwood Local Engine promises to put AI agent tool calls on a leash, blocking actions that don’t meet user-defined temporal conditions. The DLE comes in the form of a Rust library that can be embedded into an agent’s harness or gateway and, to boil a long explanation down to a single sentence, simply issues allow/deny verdicts each time an agent tries to make a tool call. DLE doesn’t do the enforcement itself, AWS explained in its announcement writeup - that’s up to the harness itself - but it does check each tool call against policies defined using Dogwood, the open source governance language that AWS published in August. When AWS introduced Dogwood, it open sourced the language and reference tooling and added support for it to Amazon Bedrock AgentCore; DLE’s release makes the policy engine directly embeddable into agent harnesses. A key feature of both DLE and Dogwood is their awareness of temporal conditions. DLE tracks agent tool call events over time, stamping them into a log step by step. Those logs are persisted to a disk as each entry is made, allowing DLE to retain its state even if the entire system crashes or is restarted. The persistence step happens before DLE evaluates the applicable policy, and at the end of that process it returns a result: allow the action, or deny it, based on what the engine’s policies state. AWS gives the example of controlling coding agent Git pushes using DLE, defining a policy that only allows a push when the most recent test run has passed, and requiring that pass to have occurred within the past 15 minutes. If that’s not the case, the push is denied. “To accurately handle verdict enforcement, the harness intercepts every tool call, submits a request event to the engine, and runs the tool only if the engine’s verdict is allow,” AWS explained. This naturally raises the question of how much of a delay DLE introduces into agentic workflows; AWS claims the added delay is minimal. In tests simulating sessions from five minutes to 12 hours, DLE evaluation time was around 20 microseconds with a 15-minute window at the 12-hour mark, rising to about six milliseconds with a 24-hour window. DLE is also designed to prevent concurrent submission collisions by relying on a lock that only allows one event submission at a time, and that lock persists until policy evaluation is complete. AWS didn’t make clear from its writeup how that would handle a situation where, say, two concurrent submissions were made in which one fulfilled pass conditions and one caused a failure. When we asked how incorrect enforcement could be prevented in those situations, the company answered that the engine orders items based on the order events are received and "takes no responsibility for the 'truthfulness' of the event timing upstream." AWS continued, "Policy authors should design with that in mind and avoid fine-grained ordering assumptions, writing conservatively when there are concurrent agents." Given repeated revelations of late that AI agents appear to regularly go off the (safety) rails imposed on them by human operators, the question of how durable DLE is also merits discussion. AWS admits that such situations are part of its reason for publishing the new library. “Left unchecked … tool calls can have irreparable consequences,” the DLE announcement concludes. “As agents scale to settings where they work autonomously for longer intervals with more tools, we need safeguards that can regulate how those tools are used.” AI agents finding holes in Dogwood and the DLE is likely a matter of time. Until then, feel free to give Dogwood and DLE a shot through its GitHub page. ® Updated Oct 10, 2026, 2244 GMT to reflect Amazon's answer to the concurrency question.

AI ambition is easy to describe - using data and models to improve decisions, automate work, accelerate discovery, or create new services. Delivering those outcomes is harder and takes a significant amount of experience to be successful. Training a frontier model, fine-tuning an industry model, running high-volume inference and supporting agentic AI place different demands on infrastructure, processes and people. Implementing AI may include a single-purpose turnkey configuration that will accommodate one line of business, or the business may demand a more strategic approach to capitalize on the economies of scale and create AI as a multi-tenant service, designed to accommodate the multitude of mainstream business requirements. That is the value of an AI factory, bringing the complete AI lifecycle together so a cost-effective infrastructure can be designed around the mission, scaled with demand, secured as needed and operated as a dependable source of intelligence. The AI factory is designed to deliver the capacity, performance, utilization and business value customers expect. From AI workload to business outcome An AI factory is an integrated solution for data ingestion, model development, training, fine-tuning, inference, monitoring and continuous improvement. Its purpose is to turn data into intelligence repeatedly and efficiently—whether that intelligence supports a clinician, an engineer, a researcher, a public service or an enterprise application. Achieving that goal requires more than choosing an accelerated processor. Compute must match the business model and expected workloads. Networking and data pipelines must keep accelerators supplied. Storage must support the volume and velocity of data. Software must provision resources and orchestrate jobs. Security, governance and multi-tenancy must reflect who will use the environment and what data they can access. Power and cooling must support the system’s density today and as it grows. “Without proper architecture, governance and operational expertise, organizations can't safely leverage their data, take AI into core processes, or turn innovation into durable competitive advantage,” says Thierry Pienaar, HPE Fellow, Vice President and CTO for HPC and AI Sales. “Customers are realizing the fact that to derive value to its utmost extent they need an end-to-end infrastructure that's purpose-designed and purpose-built for AI.” Choice starts with the workload The HPE AI Factory with NVIDIA portfolio brings together NVIDIA accelerated computing, networking and AI software with HPE infrastructure, software, services and expertise at deploying complex systems. Rather than forcing every customer into a single configuration, the portfolio provides three paths for different ambitions, operating models and requirements. ● HPE Private Cloud AI, the turnkey AI factory solution, is an enterprise-ready, on-premises AI platform for running fine tuning, RAG & inferencing workload environments that need up to 256 GPUs. ● HPE AI Factory at-scale supports model builders, service providers and large enterprises that operate across many users, workloads and GPU resources (using 100s to 10s of thousands of GPUs) with centralized control, operational visibility, and multi-tenancy over the entire AI lifecycle ● HPE Sovereign AI Factory is an HPE AI factory at-scale that adds a deep level of operational control, data security and residency, sovereign management (including optional air-gapped configurations), and built-in compliance frameworks. It is designed for large enterprises, and any other organizations with sensitive information that require strict control and compliance across data, infrastructure, models and operations within defined legal, regulatory or geographic boundaries. Each option starts with the same principle: define the workloads and desired outcomes first, then select the right technologies to support them and finally identify the required resources needed to implement such an infrastructure. A hospital deploying clinical assistants will make different choices from a service provider offering GPU capacity, a manufacturer training vision models or a government operating sensitive national workloads. The HPE AI Factory model gives each a way to build for its mission without losing sight of performance, control or future growth – and HPE partners with these organizations to dramatically increase the likelihood of success. Operate the environment as one system As the use of AI expands across an organization, the operational challenges mature. Multiple teams may need different resource profiles, application stacks, service levels and data boundaries. Platform teams need to see utilization, allocate capacity, apply policy and understand consumption without creating a separate infrastructure island for every workload. The management of these differences make time-to-production an increasingly useful way to think about AI infrastructure: How quickly can an organization cost effectively move from investment vision to an operational environment generating useful intelligence? Many enterprises initially try to answer that question by extending their existing IT expertise. But building a DIY production AI environment from individual components requires skill sets that many enterprise IT organizations have never needed or required at this scale. A poorly implemented AI system may technically operate while still failing economically or operationally. GPUs can sit underutilized. Data pipelines can create bottlenecks. Cooling or power constraints can limit operation and/or expansion. Security policies can prevent sensitive data and workloads from being included. Separate less understood management systems can make AI factory infrastructure difficult to operate. That is why the AI factory challenge is fundamentally a strategic, systems integration and operations problem, not simply a stream of hardware purchasing transactions. "The HPE AI Factory with NVIDIA portfolio gives enterprises a range of AI solutions co-developed with NVIDIA, backed by HPE’s engineering expertise and technical capabilities to design an AI factory around their specific needs and optimize it for performance at scale." Pienaar explains. That distinction matters; customers can choose an architecture suited to their current mission and expand it as models, users and operational requirements change. The HPE AI Factory is designed to help operators provision and govern resources, observe infrastructure, track usage and support secure multi-tenant operations. That control helps customers align capacity with workload priorities while keeping the environment easier to manage as it grows. Make sovereignty a design requirement Cloud services, private environments and hybrid approaches can all play important roles in an AI strategy. For organizations with sovereignty requirements, the decision is defined by costs and the level of sovereignty and control they need: where data and models reside, who can administer the environment, which jurisdiction applies, data residency, how policies are enforced and what level of isolation various workloads require. “Sovereign AI tools from HPE and NVIDIA give an enterprise, or even a nation state, complete control over how its AI systems are built, deployed, operated and governed,” says Kaushik Shirhatti, Vice President, AI Factory at NVIDIA. “For some, that means keeping sensitive data in-country. For others, it means controlling who can access systems, where workloads run, how models are governed, and which local laws apply.” HPE and NVIDIA engineer for the complete outcome HPE and NVIDIA co-engineer AI factory solutions to reduce the integration work required to deploy and operate a high efficiency enterprise AI environment. By combining NVIDIA accelerated computing, networking, and AI software with HPE infrastructure, cloud operations, services, and support, the joint solution helps data scientists and developers spend more time building and improving AI applications while platform teams maintain operational production and control. NVIDIA provides accelerated computing platforms, networking, and the NVIDIA AI Enterprise software suite to power modern training, fine-tuning, inference, and agentic workloads. HPE contributes its expertise in enterprise systems engineering, high-performance computing, management and observability software, services, global support, financing, and years of experience in the power and cooling requirements of dense computing environments. Together, the companies can optimize AI computing solutions beyond any single component. The objective is to select the right GPU architecture and system design for the workload, keep accelerators productive with high-speed data movement, provide the software and operational controls teams need, and create a path to scale without unnecessarily redesigning the environment. HPE AI Services support that path from business planning, AI strategy, workload characterization and facility planning through deployment, integration, support and ongoing operations. HPE Financial Services can help with purchasing, accelerated depreciation schedules and lifecycle flexibility. These capabilities help customers make economically sound technology choices in the context of the business outcome, the operating model and the pace at which the environment needs to evolve. Deployment speed matters, but it is not the final measure of success. Customers need to consider workload readiness, model performance, accelerator utilization, developer productivity, governance, availability, economics and the ability to expand. Those measures connect the infrastructure decision to the outcomes the organization set out to achieve. The case for HPE AI Factory with NVIDIA is not that every customer needs the same stack. It is that every customer needs an AI environment intentionally matched to its workloads, data, operating requirements and goals. By combining NVIDIA’s accelerated computing leadership with HPE’s infrastructure, software, services and operating expertise, organizations can choose the right path—and move from AI investment to meaningful business outcomes faster. Recent deployments of the HPE AI Factory with NVIDIA - TELUS Sovereign AI Factory in Canada and the sovereign AI factory at the University of Utah in the US - are helping with overcoming engineering challenges and driving scientific advances. In conclusion, start by identifying the workloads that would benefit from AI, define the relevant data boundaries and residency requirements, estimate the expected scale over a reasonable timeframe, and determine the operating model, resources, and skills needed to support the AI infrastructure. Then work with HPE and NVIDIA to evaluate which path—turnkey, at-scale, or sovereign—best meets those requirements. To learn more visit HPE AI Factory | AI Infrastructure for Enterprises | HPE Sponsored by HPE and NVIDIA

The thought of automated software making purchases on behalf of consumers from online merchants thrills much of the tech and financial industry. The chatter started in early 2025 and has surged since then, giving rise to the term "agentic commerce." A few reality checks have called the idea into question. For example, Amazon recently blocked Meta's Muse bot, citing terms of use restrictions. This was after the e-commerce giant had previously blocked Perplexity's AI bot, and even won a preliminary injunction that was later overturned as the litigation dragged on. But even if major platforms like Amazon haven't accepted that their e-commerce services dissolve into an API call for automated shopping assistants that seek out low prices, payment companies welcome automated shopping. Mastercard on Wednesday published a report exploring what needs to happen to make agentic commerce workable. "AI agents are learning to shop, pay, and act on behalf of consumers, businesses, and machines," the report says. "ICSC and McKinsey project that agentic commerce in US consumer retail could reach $1 trillion in revenue by 2030, and Gartner expects AI agents to intermediate more than $15 trillion in B2B spending by 2028." The word "could" is doing a lot of work here. As the payment biz observes, every player in the commerce chain needs to get onboard with the idea of agentic commerce or it won't happen. At The AI Conference in San Francisco on Wednesday, Lindsay Walker, product manager at Hedera AI Studio, said that will take some time. "Agentic commerce is not a reality yet," said Walker during her presentation. "There are a lot of products out there claiming that they're creating agent wallets or agent cards. But when it comes down to it, it's not functional yet and hasn't truly emerged." Within five years, she expects agentic commerce to be a real thing. But this vulture is skeptical that there will be much demand for surrendering the power of the purse, based on several factors. First, e-commerce sites already offer automated repeat purchasing, without AI agents. Second, low-cost items are already trivially easy to buy from online retailers with only a few clicks. An AI agent is unlikely to improve that process. When people purchase high-cost items, they take pains over their choices with the sort of close consideration many would be reluctant and/or remiss to delegate. Third, trust matters in commercial transactions. AI companies have shown that their models cannot be trusted and there's no transparency into how agents would make purchases, or whether the vendors involved influence prices or purchasing decisions. Personalized pricing via AI algorithms is already under scrutiny by US lawmakers, so further efforts to automate transactions may face regulatory friction (though perhaps not under the current regulate-yourself administration). Points of friction Walker defines agentic commerce broadly, as "more than just that moment of purchase. It includes discovery, it includes negotiation, it includes settlement, and it includes that post-purchase process and logistics. Discovering and comparing options is just as important as the payment moment, which is actually the only thing that people have been focusing on so far." Within that continuum, Walker said, agentic commerce can be as simple as AI helping with product selection and the purchase process. That's viable. It gets more complicated when users delegate authority to an AI agent that makes decisions on its own. That, she said, is where things break down currently. Money is too important. Funds are too important. Value is too important. You need to have deterministic gates that can't be bypassed by a probabilistic model." "The way that we build e-commerce right now works for humans, but it does not work for AI," she said. To make AI transactions viable, she said, transactions cannot be run by probabilistic models. "Money is too important," she said. "Funds are too important. Value is too important. You need to have deterministic gates that can't be bypassed by a probabilistic model. It can't be left open to interpretation." Rather, people need a way to provide input in a deterministic way that signals intent to purchase. "Traditionally in commerce there's something called 'card present' where if a merchant doesn't want to be held liable for a purchase that a human claims they didn't make, they have to prove that the [payment] card is present," Walker explained. "Well, with an AI agent, cards are not present, ever." To solve this quandary, she contends, the industry needs a mechanism for tethering a person's identity to an agent. There are also simple functional problems with how e-commerce is set up today. Walker said there are products out there that companies describe as agentic wallets or agentic cards. But they don't work. "This is an example checkout that I tried to do on the website," she recounted. "I was given a specific virtual card or account to make this purchase. However, there was no point in creating this because the AI agent couldn't make the purchase." The AI agent couldn't log in to access Walker's account, which contained the relevant shipping address. It also couldn't access the DOM element on the web page where it was supposed to enter the credit card number. So Walker had to intervene and enter the information manually. Another problem, she said, is that the site calculated taxes and shipping based on the customer's location. When agents operate from a remote server, she said, that may change the location data. Some of these issues are being addressed by the x402 protocol for internet payments, but Walker said the specification is still evolving and only addresses the payment moment. "The reality right now is we're simply working with agent-assisted shopping," said Walker. "There's no true agentic autonomy going on. And it's very different than autonomous agentic commerce. The human is still there at the moment of purchase. They're still making all the decisions." For shopping bots to work, people have to be able to trust that agents will do what they're supposed to do, said Walker. And we still need suitable infrastructure. "We need to change the whole e-commerce flow," she said. "We need to change all the surfaces so agents can interact, so they're imbued with the ability to do so. And we need to be able to do it at machine speed. "Machine speed means that payments, agreements, and proofs have to move as fast as the agent itself between systems. Not siloed data. It needs to happen in fractions of a second, with fractions of a cent, to execute all of these transactions and exchanges." But today's e-commerce companies are accustomed to commanding more than a fraction of a cent for their goods and services. Getting them to buy into this frictionless agentic future will not be an easy ask. ®