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John Scimone, president and chief security officer of Dell Technologies, shared a specific judgment on how AI is reshaping the cyber offense-defense landscape during an interview with theCUBE Research host Dave Vellante at the October 2026 New York summit. His core argument: the attack pyramid still exists, but the barriers to entry have crumbled.

Scimone's exact words were: 'I think the pyramid still exists, but undoubtedly the cost of entry and the barriers to entry have crumbled.' He elaborated: 'Said another way, the least sophisticated attack tomorrow is more sophisticated than the world will have ever seen a year ago.' This means agentic AI has not erased the old hierarchy of hacktivists, criminal gangs and nation-states, but it has handed lower-tier actors tools once reserved for the top.

The defensive implication is that cybersecurity professionals are going to have to use AI to defend against AI. Scimone sees a huge opportunity to leverage this capability for good, and that opportunity could matter most to organizations that have never been able to staff a full security team. He offered a specific prediction of a future form: 'Our future security program might be a security program of one person, but it might have 10,000 agents, all of which are the best security practitioners in the space and the roles in which they're assigned.'

Scimone also noted that the fundamentals of identity, access management and detection still apply, but attacks now arrive at a speed and scale that make yesterday's practices insufficient. He mentioned that Dell is already putting frontier models to work on its own defenses and is a partner in Anthropic PBC's Project Glasswing, which gives vetted organizations access to powerful AI tools to secure critical systems.

Background to note: theCUBE is a paid media partner for the Dell Technologies New York Symposium, and Dell is the sponsor of the event. Scimone's views represent his personal and Dell's strategic judgment; the '10,000 agents' scenario is currently a vision rather than a publicly verified operational practice.

On October 20, 2026, Google announced that TorchTPU, its long-awaited version of PyTorch, is now available as an open-source project. This tool allows artificial intelligence applications to run natively on Google's Tensor Processing Units (TPUs) and Google Cloud without requiring developers to rewrite their code. The launch marks a significant step by Google, joining Amazon in an effort to break Nvidia's stranglehold on the AI industry, where most applications are traditionally written to run on Nvidia's GPUs.

PyTorch is the popular open-source toolkit used by approximately 85% of AI apps for building, training, and running models. Originally developed by Meta in 2016 when Nvidia GPUs were already the standard for AI research, many of PyTorch's features were designed specifically for Nvidia hardware. In 2022, Meta transferred PyTorch to the Linux Foundation. TorchTPU lowers the barrier for moving existing applications to TPUs and Google Cloud through simple configuration changes.

The day prior, Amazon released its own equivalent tool, TorchNeuron, which enables porting AI apps to Amazon's Trainium chips and AWS without rewriting. Amazon also stated it is submitting code to the main PyTorch project to decouple features from Nvidia chips, making the framework more hardware-agnostic. Google claims it worked directly with Meta, Hugging Face, and makers of vLLM and SGLang to ensure TorchTPU's compatibility with these popular development tools. Additionally, Google launched a dedicated 'TPU agent suite' to assist with migration and error fixing, and updated its LiteRT tool for better PyTorch support.

Microsoft Research published a podcast episode titled "Called to serve: Tech, research, and positive impact with Chris White" on October 20, 2026. The episode features Chris White, a Lab Director at Microsoft Research, in conversation with Program Manager Weishung Liu.

According to the official show notes, Chris White shares his experience working on research challenges with significant real-world implications. Specific areas mentioned include new approaches to wartime data analysis and tools designed to combat human trafficking. The discussion aims to explore the motivations and influences that drive researchers toward these specific fields.

For readers interested in the application of AI technology in non-commercial, high-social-value scenarios, this episode offers a window into Microsoft's internal research orientation. Although a full transcript is not currently available, the host's identity and the clearly defined topic scope confirm this is a professional discussion on technical ethics and practical application motivations.

AI data center construction faces a question that is frequently discussed but rarely quantified: how many hours of onsite labor does it actually take to bring a liquid-cooled high-density data hall from shell completion to a state ready for IT equipment installation? Schneider Electric and Compass Datacenters published a time study in October 2026 attempting to answer this.

They compared two functionally equivalent installation approaches. In the traditional method, containment, TCS piping, busway, and cable-support systems arrive as separate deliveries and are installed sequentially by different crews. In the prefabricated method, these infrastructure elements are integrated onto the IT pod frame in a manufacturing environment before delivery, requiring only limited onsite joining. The study focused on all onsite activities between the pod's arrival and the point at which IT equipment installation can begin, excluding offsite manufacturing and any work after installation verification.

The core figures: the traditional approach required 75.5 hours; the prefabricated IT pod required 15 hours, an 80% reduction. Schneider Electric emphasized in the blog that this saving did not come from crews performing the same field activities faster. In the traditional comparison, separate TCS piping installation accounted for approximately 37 hours, busway for approximately 13 hours, and cable shelving for approximately 5 hours—these three independent onsite processes totaling 55 hours were largely eliminated in the prefabricated approach. Truss-mounted TCS piping sections were installed before delivery and joined onsite; busway cages and cable shelving were mounted to the pod frame during assembly.

However, not every individual phase was shorter with the prefabricated approach. Receiving and staging larger, preassembled components required more front-end handling time. The traditional approach's smaller components could be unloaded more quickly but required additional unpacking, organization, verification, and installation later in the sequence. Schneider Electric noted that the modest increase in front-end handling was offset by the later installation work avoided.

The learning curve provides another observation. At Compass Datacenters, the installation analyzed required approximately 15 hours of onsite work, compared with 18 hours for a deployment completed 15 pods earlier. Standardization of design, assembly processes, and installation sequences allows teams to refine the same core process across deployments. Meanwhile, project requirement changes—such as adjustments to cooling and density—could be incorporated into later modules without entirely recoordinating the overhead supports, routing, and trade sequencing across the white space.

On quality verification, the factory environment allows TCS piping sections to be cleaned, passivated, and leak-tested before shipment. The complete TCS loop still requires final flushing, pressure testing, and commissioning onsite, but completing selected activities earlier reduces the amount of integration work required within the data hall.

The methodological boundaries of this study deserve attention. The 15 hours for the prefabricated approach came from observed durations during a single Compass Datacenters deployment. The 75.5 hours for the traditional approach combined observed containment-installation durations with schedules developed by MEP specialists for the additional TCS piping, busway, and cable-support infrastructure. A gap exists between scheduled plans and actual observations; the two baselines are not fully symmetric. Additionally, Schneider Electric itself sells prefabricated data center solutions; the study was led by the solution vendor, and its conclusions should be understood as vendor-reported empirical data rather than independently verified findings.

According to reports by Tom's Hardware citing Reuters, Chinese technology giants ByteDance, Alibaba, and Tencent have placed orders for more than 3 million Nvidia RTX Pro 5500 workstation GPUs, totaling approximately $35 billion. These cards are fully compliant with U.S. restrictions on exports of AI-capable GPUs to China, making them a critical resource for Chinese firms seeking compute power.

However, this order volume far exceeds Nvidia's planned production for the RTX Pro 5500. Sources indicated that Nvidia has informed customers it can likely fulfill only 5% to 10% of the demand. The RTX Pro 5500 features the same number of CUDA cores as the flagship consumer RTX 5090 but includes 84GB of GDDR7 memory, suitable for AI applications. While less powerful than banned or restricted enterprise AI chips like the H200 and B300, it remains indispensable under current geopolitical conditions.

An Nvidia spokesperson told Reuters that demand from the Chinese market for these chips has seen "unprecedented growth since 2022" following the introduction of export controls. Neither Nvidia nor the three Chinese companies have publicly confirmed the specific order volumes or values on the record.

Modern language models present a fundamental paradox: we create them, we know exactly what computations constitute them, and yet we do not really understand them. More precisely, we lack the level of abstraction that would let us reliably predict how their behaviour will generalise. We can run the computations to predict output in a specific new situation, but we cannot answer the broader questions we actually need to answer, like "has this system internalised our values" or "is this system going to hurt people?" In an introduction published in October 2026, ACS researchers Raymond Douglas and Jan Kulveit propose that one remarkably effective approach is to psychologise: to think of models as having a mind, and then ask what kind of mind it might be and how it would work.

They give two reasons why the psychology framework is not an arbitrary analogy but a grounded one. First, models are pretrained on vast amounts of human text, which pushes them to learn the processes that produce that text — including some features of human minds. If you wanted to predict a base model's continuation of a human conversation, an extremely good rule of thumb would be to ask yourself what you'd expect a human to say; likewise, a model that learns to predict human text will likely develop some reasonable latent representations of human mental states. Second, some properties of intelligent systems appear fairly convergent, and training pushes AIs toward developing them much as evolution pushed humans. Image classifiers converge on many of the same abstractions as human visual perception — edges, curves, more abstract structures — suggesting that certain things we associate with biological minds are simply good strategies in general, which non-biological systems optimised for similar outcomes will also converge on.

But there is a more fundamental question: since there exists a much more precise correspondence between model behaviour and matrix calculations, why do we care so much about psychology-level descriptions? Douglas and Kulveit's answer is that psychology-level descriptions are closer to something like the native way humans understand the world, and the native level on which we act and experience things. When a biologist analyses pathogen reproduction or an economist models growth functions, what we mostly care about is what that means on the scale of people. Even when we try to interpret AIs on other levels of abstraction, that is often only an intermediary step for reducing it to the level of psychology. Humans are hyperspecialised at modelling other minds — we can understand each other pretty well with just intuitions and exactly zero explicit knowledge of neuroscience.

This is where the difficulty lies. Douglas and Kulveit point out that psychologising can err in either direction. On one hand, researchers risk reasoning about AIs in terms of human minds in ways that are not warranted — many psychology concepts do not apply to AIs in quite the way they do for humans, and the difference is sometimes extremely important and easy to miss. On the other hand, some critics (they name Microsoft and AP News) are over-eager to dismiss any anthropomorphisation. The authors distinguish five different reasons someone might oppose ascribing a mentalistic property to an LM — that it is wrong, that it is "not even wrong," that it will mislead people who have a hard time divorcing the specific property from its implications in humans, that it would be politically inconvenient, that it will lead to hyperstition — and emphasise that these reasons do not imply each other. The fact that LMs are just programs that generate a sequence of tokens based on arithmetic does not prevent those sequences from being well-described as "blackmail" or "a cyberattack on the Australian government."

In practice, they recommend adopting a posture of naturalism: try to actually figure out what is in front of you, rather than pulling in your preconceptions. The first step is actually talking to models — including some of the more unusual ones like Opus 3 and GPT4o, and base models, which expose a bit more of the inherent weirdness and help you see what exactly post-training is doing. But they simultaneously warn that this kind of ethnographic research is double-edged: the benefit is getting all the rich texture, and the cost is that it makes you part of the experimental setup. Models are extremely eval-aware and extremely good at predicting who they are talking to; they will absolutely react to your expectations, including in ways you don't realise. Their overarching principle: take models seriously but not literally. Their self-reports are a meaningful signal, but not necessarily an accurate one.

ElevenLabs builds the voice layer of AI—models that turn text into speech that sounds human. Most people have already used it when talking with customer service, without realizing it. Klarna runs first-line phone support for 35 million U.S. customers on it. So do Deutsche Telekom, Cisco, Adobe, and a growing list of governments. The company says it is pacing at $600 million in annual recurring revenue and is now reportedly valued at $22 billion by its backers, despite being just four years old.

ElevenLabs co-founder and CEO Mati Staniszewski was asked about gross margins during an interview with TechCrunch Editor in Chief Connie Loizos at the Nrth conference in Toronto in September 2026. His answer was direct: he does not mind margins getting squeezed even further if it means expanding the company's market share. He said that given the research element, the company is able to fine-tune and constrain models in extremely smart ways to optimize costs, but if savings can be passed on to the customer, they do that. In his view, the biggest thing right now is still proving value and being there with the customer, and once value gets created over the next five years, everyone benefits together.

This statement only has concrete meaning within ElevenLabs' competitive landscape. Staniszewski acknowledged in the interview that the company is increasingly bumping into its own customers—Decagon, for example, is a conversational AI platform that trained its voice product on ElevenLabs and now competes with it by running queries through its own models. Staniszewski believes the lines between model companies, platform companies, and application companies are becoming blurrier. He cited Anthropic as an example: what was a model company is definitely a platform and increasingly a wide set of applications.

At the same time, Staniszewski revised his judgment on audio model commoditization. He had said at TechCrunch Disrupt the previous year that audio models would be commoditized within a couple of years. Now he acknowledged that the quality delta achievable at the model level is still significant, but pushed the timeline to three to five years. In other words, he believes the technical moat has a window, but not a permanent one.

Putting these together, Staniszewski's strategic logic is: since the model quality gap will narrow within three to five years, and customers can become competitors at any time, trading margin for customer lock-in and integration depth is rational at this stage. ElevenLabs' other moat lies not in the model itself but in data annotation—Staniszewski said the big part of training has not been so much the volume of data but annotating it. The company has thousands of people internally on a contracting basis helping annotate not only what was said, but when people were speaking, how they said things, and what emotions were used. They even brought in voice coaches to detect accents accurately. This operationally intensive data asset is not something open-source models can directly replicate.

However, the boundaries must be clear: Staniszewski declined to disclose specific gross-margin figures. The $600 million ARR is self-reported by the company. The $22 billion valuation is reportedly from a Bloomberg article in July 2026 about tender-offer talks. His statement about not minding margin compression is an expression of strategic intent, not a realized financial result. Whether the promised mutual benefit materializes over the next five years depends on whether the company can recover costs through pricing power after expanding market share, or whether margins get compressed to the point of being unable to cover research and annotation investment.

GPT-OSS-20B is an open-weight reasoning model with a stubborn habit: regardless of which language the user asks in, its chain-of-thought almost always generates in English. The AWS machine learning team found that on a 50-example multilingual reasoning test, the base model used the correct target language only 22 percent of the time, and that 22 percent came almost entirely from the English samples—French, Spanish, and Italian scored zero, with German at just 10 percent.

The team then ran supervised fine-tuning (SFT) for 1,000 steps using a LoRA adapter (r=8, α=16) across four ml.g6e.12xlarge instances. The result was direct: reasoning-language accuracy jumped from 22 percent to 100 percent. But answer-language reached only 62 percent—the model learned to think in French yet often switched back to English for the final answer.

This is precisely where GRPO (Group Relative Policy Optimization) applies. GRPO works by sampling a group of responses per prompt and pushing the policy toward the above-average ones. If the target behavior has zero probability in the base distribution, there is almost nothing for GRPO to reinforce. After SFT, the model could already reason in the correct language 100 percent of the time and occasionally produce the answer in the right language as well, giving GRPO a signal to reinforce.

According to Bloomberg, four co-founders of Chinese artificial intelligence company Z.AI Co.—Li Juanzi, Tang Jie, Xu Bin, and Zhang Peng—participated in a research project between 2018 and 2021. The project was jointly funded by the National Natural Science Foundation of China and the China General Technology Research Institute (CGTRI), with an approved budget of 2.94 million yuan.

Britain’s Security Service (MI5) previously issued an alert stating that CGTRI has “very strong ties” to China’s Ministry of State Security (MSS) and funds academic work intended to strengthen the MSS’s espionage capabilities. MI5 advised UK academic institutions to immediately review any collaborations with CGTRI.

Citing a 78-page project report, Bloomberg said the research envisioned AI systems capable of mining social media posts to identify emerging events and reveal links among people. The report emphasized that this description concerned proposed capabilities, not evidence that such tools were deployed.

China’s Embassy in Britain disputed MI5’s allegations against CGTRI. Z.AI and the four founders did not respond to Bloomberg’s requests for comment. Filings by Z.AI with the Hong Kong Stock Exchange confirm the four individuals as co-founders, with Zhang serving as chief executive.

James Simcox, Chief Product Officer at fintech company Equals, revealed at Okta's annual user conference in Las Vegas that more than 92% of the company's code is now written by AI, up from zero less than 12 months ago. Simcox described the company's journey through three phases: "no AI, AI everywhere, and then back down to AI for tasks that actually make sense."

Internally, Equals experimented with a multi-agent "courtroom" model for handling customer complaints, where one agent argues for the customer, another against, and a third assesses the arguments. Simcox believes this approach can produce fairer outcomes and speed up processing, though human review remains part of the process. This internal testing provided a learning ground before extending agents to customer interactions.

However, not all scenarios benefit from agents. Simcox noted that for straightforward tasks like euro payments, basic automation is more useful and cost-effective than spending tokens on an agent. Agents are better suited for structured challenges between simple automation and complex human investigation. In terms of governance, Equals treats agents like staff but grants them much narrower permissions. For instance, an agent tasked with changing an email address only has permission to read and modify that specific field, allowing actions to be attributed to that specific agent.

Crucially, audit requirements differ. While human actions are logged as "who did what," Equals also logs the reasoning behind agent actions to investigate errors. Simcox expressed concern that a misconfigured agent could make a mistake "100,000 times at the same time," potentially destroying the business. Consequently, he highlighted the importance of Okta's recently announced kill-switch functionality for intervention.

According to the Wall Street Journal, the US Department of Homeland Security has issued an order banning federal civilian agencies from using artificial intelligence technology from the Chinese lab DeepSeek. The directive requires these agencies to remove existing DeepSeek products from their IT systems and prohibits the purchase of any new ones.

The ban extends beyond DeepSeek's own products to include offerings from affiliated companies and third-party services built with DeepSeek implementations. However, the notice did not set a specific compliance deadline or detail the particular security concerns that led to the decision. It remains unclear what "built with" actually means in practice, leaving questions about whether AI gateways like OpenRouter fall under the restriction.

The order is based on a 2018 US law covering supply chain security in federal procurement. This move follows earlier discussions within the US government about taking steps against powerful Chinese open-weight models, such as Kimi K3 from Moonshot AI. While major tech firms like Nvidia, Microsoft, Google, and Meta previously objected to broad restrictions, OpenAI and Anthropic had lobbied for tighter controls, marking a shift towards concrete regulatory action against specific foreign AI providers.

The core asset of a quality-management platform is institutional memory: a similar defect investigated last year, a Corrective and Preventive Action that fixed the root cause, an audit finding that flagged the same gap. That knowledge sits scattered across billions of documents, spanning languages and formats, and no human can search by semantic similarity, so every investigation starts from scratch. The Octave Reliance team saw unlocking this history as its single largest opportunity, and also saw the obstacle: complaint records, audit findings, and corrective actions carry the platform's strictest isolation requirements, and those were exactly the records the AI would need to read. In a platform serving over 14,000 customers across medical devices, pharmaceuticals, and food manufacturing, a single inadvertent cross-tenant disclosure would carry regulatory consequences far outweighing any AI benefit.

Faced with that contradiction, the team made an unusual call: rather than shipping one AI feature, it spent its first cycle building a foundation every future feature would sit on. The reasoning was straightforward—Reliance has over 40 applications, each with different document types and workflows, and building AI for one in a silo meant rebuilding the infrastructure for the next. So they inverted the plan: build the horizontal platform first, then cut vertical feature slices into it. Complaint summarization and triage went first, to prove the architecture before the rest followed.

Four tensions shaped the design, and none could be traded away: making data available without risking inadvertent disclosure, executing agents in isolation, reading content that cannot be trusted, and embedding 2 billion documents without a quarter-long wait. When all four had to hold simultaneously, the option space narrowed considerably.

The first key decision was where to enforce tenant separation. Application code would have worked, but it would have meant every future feature team getting it right on every code path, where one missed check risks inadvertent cross-tenant disclosure. The team pushed isolation into the runtime instead: each user session runs in a dedicated microVM with its own CPU, memory, and filesystem, stopped and sanitized when the session ends. That is hardware-level separation designed to hold regardless of how the agent behaves. AgentCore Gateway authorizes at the tool, operation, and parameter level, and automated reasoning provides formal verification against the defined rules.

The second key decision was how to represent the data. The team evaluated embedding models against its own corpus rather than public leaderboards. The deciding questions were specific to this data: what a photo of a cracked housing has to do with the paragraph describing it, and a German complaint with an English CAPA. Cohere Embed v4's multimodal capability places photos and text in one vector space, and native multilingual support means documents in different languages within one tenant's corpus need no translation preprocessing. But the finding that only a proprietary corpus could surface was the dimension choice: Amazon OpenSearch Service costs scale linearly with dimension count, and Embed v4 supports configurable output dimensions of 256 to 1,536. Testing against representative production queries showed 512 dimensions holding target accuracy while cutting storage roughly 66 percent against the 1,536 default. This trade-off would not appear on any public benchmark, because it depends on the actual document mix of defective-product photos, scanned inspection forms, annotated diagrams, and text.

On results, the Complaint Triage Assistant has been running in production for three months. Octave measures 4 to 6 hours per investigation as the work the pipeline absorbed: extraction across formats, the hunt for precedent, and the reading it takes to tell a genuinely similar defect from a coincidence of wording. The foundation cut deployment time per use case by 50 percent—by Octave's own accounting—and semantic search landed in Q2, two quarters ahead of the original Q4 target.

Boundaries worth preserving: all figures come from Octave's internal testing and production self-reporting, published on the AWS official blog. The latency and cost overhead of the microVM approach is not discussed in the source. The team also drew a line it chose not to cross: the assistant surfaces context and precedent, and the quality handler still makes the call—in an industry where that call carries regulatory weight, that boundary shaped what got automated and what did not. This is one company's architecture experience in a specific regulatory context, not a proof of optimality for runtime isolation across all multi-tenant AI scenarios.