AI News Archive: July 13, 2026 — Part 3
Sourced from 500+ daily AI sources, scored by relevance.
- Is India Reconsidering Anthropic, OpenAI Use in Cybersecurity? (Updated)
Ever since the Trump administration blocked the global release of Claude Mythos and GPT-5.6 for a review, global governments have questioned attempts by the United States to build an AI hegemony. Now it looks like India may be having second thoughts over using Anthropic and OpenAI solutions for its cybersecurity drive. Prime Minister Narendra Modi […] The post Is India Reconsidering Anthropic, OpenAI Use in Cybersecurity? (Updated) appeared first on CXOToday.com .
- Near-memory Dequantization Architecture In Custom HBM for LLM inference (SK hynix)
Researchers from SK hynix published a technical paper titled “StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration.” The paper proposes StreamDQ for “a lightweight architectural enhancement that enables on-the-fly dequantization in the memory subsystem for high-throughput, large-batch LLM inference,” and reports “up to 7.08× speedup and 90.23% lower energy” for mixed-precision... » read more The post Near-memory Dequantization Architecture In Custom HBM for LLM inference (SK hynix) appeared first on Semiconductor Engineering .
Score: 67🌐 MovesJul 13, 2026https://semiengineering.com/near-memory-dequantization-architecture-in-custom-hbm-for-llm-inference-sk-hynix/ - Infrastructure for the agentic era: A new conversation layer for the Twilio Platform
A new era of customer engagement is taking shape. AI agents are quickly becoming integral to the way businesses serve, support, and sell to customers — able to respond, reason, and take action in ways that go far beyond scripted automation. Many customer journeys, however, are still built on systems that don’t talk to each other. Customer data lives in one place, channel history in another, and AI agents often operate with only part of the picture. Customers feel the pain when they switch between channels like voice and messaging, get transferred, and have to repeat themselves yet again. It doesn’t matter that they’ve been loyal to a brand for years, every interaction feels like a cold start. That is the conversation gap. It’s clear that AI isn’t the problem, infrastructure is. Closing the gap requires new building blocks that focus on continuity, so context can carry forward across systems, channels, human agents, and AI agents. To bridge the gap, at SIGNAL 2026 , we are introducing a new conversation layer for the Twilio Platform. Twilio Conversation Orchestrator, Twilio Conversation Memory, and Twilio Conversation Intelligence are now generally available. Together, they help businesses coordinate interactions, preserve context, and connect human and AI agents so every conversation is more continuous and useful. In addition to the new Conversations layer, we’re also announcing platform updates that make it easier to build, manage, and scale customer engagement on Twilio — from a reimagined Twilio Console to expanded channels and new voice AI capabilities. New building blocks for connected conversations The conversation gap does more than create inconsistent customer experiences. It hurts conversion and retention, increases operational costs, adds integration complexity, and makes agents less productive. The new platform capabilities we’re introducing are designed to fix that by coordinating interactions, maintaining context, and surfacing signals as conversations happen. Conversation Orchestrator Conversation Orchestrator helps businesses coordinate interactions across Twilio channels without complex custom logic. Teams can configure it in Console or configure their implementation with the API. It connects interactions into a single thread and manages handoffs between human agents and automated systems. Conversation Memory Conversation Memory creates a living, identity-resolved profile by connecting customer data with conversation history and customer traits. That means each interaction starts with the right context. It’s built specifically for LLMs to reduce latency and token usage by surfacing the most relevant details when they matter. A new Enterprise Knowledge API (now generally available) also allows teams to deliver more relevant experiences and ground interactions in trusted business knowledge such as FAQs, policies, and product documentation. Conversation Intelligence Conversation Intelligence provides real-time understanding of live interactions. Using prebuilt and custom LLM-based operators, it can detect changes in sentiment, flag potential escalations, and trigger action during a conversation, not only after it ends. That gives teams the ability to respond sooner, support agents more effectively, and improve customer outcomes while the conversation is still in progress. Together, these products help businesses create customer experiences that feel more connected across channels. Open by design Twilio remains neutral by design. We start with the premise that you know your business. We aren’t here to prescribe a model, framework, or data strategy. We provide the infrastructure that helps you build customer engagement in the way that works best for your business. You pick the model and agent runtime. You own the data. That doesn’t mean you need to start from scratch, either. We partnered with Microsoft, AWS, and others to create blueprints that support faster development. We are also introducing an open-source developer toolkit, Twilio Agent Connect (now generally available), that lets your teams connect agents built on any LLM or framework directly to Twilio’s infrastructure. For developers, this means more flexibility. For businesses, it means less lock-in and the ability to get value from existing investments. For partners, it means more ways to build with Twilio. A new front door We are also introducing a reimagined Twilio Console , because as customer engagement grows more complex, managing the infrastructure behind it should feel effortless. The new Console is a single mission control center that brings your communications, identity, and data into one experience: one login, consistent logs across every surface, an intelligent Console Assistant, transparent billing insights, and streamlined compliance workflows that no longer slow you down. Over the coming months, we’ll roll out this new Console experience to customers automatically. You can also opt in to gain early access. More channels, more control, smarter conversations In addition to these launches, we are announcing several updates that expand customer reach, support enterprise requirements, and make it simpler to build on Twilio. Apple Messages for Business (Private beta) and Twilio Email (GA) give teams new ways to reach customers on the channels they already use. Data Residency for SMS (EU) (Public beta) enables teams to manage personal data locally to support regional data requirements. Conversation Relay enhancements add PCI compliance, HIPAA eligibility, Insights, and support for Deepgram Flux for smarter turn detection — helping AI agents better understand when a person has finished speaking. Stripe Projects integration enables developers and AI agents to seamlessly provision Twilio within Stripe Projects in a single, programmable CLI workflow. Built with our customers Bringing these new products to life required a close partnership with many beta customers and partners. This helped us understand real-world signals and needs to help make the capabilities robust from the start. Among dozens of others, Centerfield, Constellation Dealerships, Car Finance 247, and Meera.ai leveraged Twilio to solve their own customer engagement challenges. These teams showed what is possible when businesses carry context forward, act on live conversation signals, and connect AI agents with human teams in the moments that matter. Car Finance 247 , a leading UK online car finance broker, is using Twilio to help recover stalled loan applications. When customers miss a field, need to correct information, or still need to confirm terms and conditions, AI-powered outreach across voice, SMS, and RCS, Conversation Memory tracks the application state. Conversation Orchestrator manages the outreach journey, and Flex helps bring in a human agent as needed. As Reg Rix, Co-Founder and CEO, shared: “Because the platform remembers where each customer left off, we can pick up right where they stopped, helping them cross the finish line in a way that is modern, responsive, and genuinely helpful.” Centerfield , a technology company powering AI-driven commerce, helps brands connect with consumers across digital and phone-based journeys. With Twilio, the team is connecting real-time conversation data with customer context to guide agents and AI systems in the moment, standardise what works, and improve performance at scale. As Aniketh Parmar, Chief Technology Officer, said: “Performance comes down to how well every interaction moves a customer forward. We’re capturing each conversation in real time and applying what we already know about the customer to guide our agents and AI systems in the moment. With the Twilio Platform, including Conversation Orchestrator, Conversation Memory, and Conversation Intelligence, we can see what’s driving conversations so we can standardise what works, eliminate what doesn’t, and continuously improve outcomes at scale.” Constellation Dealerships is using Twilio’s agent infrastructure to accelerate AI-powered engagement across its dealer network, moving from evaluation to measurable outcomes in days. As Richard Pineault, Director of R&D, shared: “The value of this partnership is evident—our team progressed from evaluating Twilio’s agent infrastructure to realising measurable outcomes within days. This rapid speed-to-value exemplifies the agility and innovation required to propel the dealership industry into the future.” Meera.ai is building on Twilio to modernise outbound engagement, replacing repeated manual follow-ups with always-on conversations across voice, SMS, and messaging. Vivek Zaveri, Chief Executive Officer, said: “Meera.ai has partnered with Twilio since our inception to champion a conversation-first future for commerce. As the industry shifts toward real-time LLM-enabled interactions, Twilio’s Platform and the new Conversations products will help us reach customers in the moment.” Together, these customers and partners show that the Twilio Platform can help businesses recover stalled journeys, improve live interactions, accelerate time to value, and create more connected experiences across AI agents, human teams, and every customer channel. The next era of customer engagement starts here As AI agents own more of customer engagement, businesses need infrastructure that keeps conversations connected across channels, systems, and teams. That means preserving context, coordinating handoffs, and acting on what is happening in real time. That is what we are building with this next generation of the Twilio Platform: a new layer that connects channels, context, intelligence, and human and AI agents, helping businesses make every digital interaction more connected, more useful, and more amazing. For 17 years, Twilio has helped builders create better ways for businesses to connect with their customers. In this next era, that connection matters more than ever. Explore the new Conversations layer , try the products, and let’s build what comes next, together.
- Analysis of 380 trillion AI tokens reveals how the technology is transforming financial markets
Financial markets are rewarding companies that are well-positioned to benefit from widespread adoption of artificial intelligence (AI) with higher returns, according to a new Yale-led analysis of 380 trillion AI tokens, one of the largest datasets of real-world AI consumption ever studied.
Score: 67🌐 MovesJul 13, 2026https://techxplore.com/news/2026-07-analysis-trillion-ai-tokens-reveals.html - AI agents are becoming the enterprise's most privileged users. And most organisations don't know it yet
AI agents are outpacing identity governance.
- Russia says it seizes Western-made, AI-powered drones prepared for attacks in Urals, Far East
Russia says it seizes Western-made, AI-powered drones prepared for attacks in Urals, Far East Reuters
- 'Yellow Teams' Are Defining the Future of AI Security
In some companies, engineers are building defense and attack tools to test the potential of artificial intelligence for cybersecurity — and its threat.
Score: 66🌐 MovesJul 13, 2026https://www.darkreading.com/cybersecurity-operations/yellow-teams-defining-future-ai-security - This new AI model thinks in images, not just words
For almost a decade, AI researchers have been obsessed with one kind of artificial intelligence —large language models (LLMs), which they train to write, talk, and reason using words. The tech industry is making an enormous bet that making such models bigger and smarter is the path to superintelligence. But in 2026 some very high-profile AI researchers doubt that this word-centric approach is the whole answer. Among them is Andrew Dai, a veteran AI researcher who left Google DeepMind to build models designed from the ground up to understand, reason about, and generate images . His startup company, Elorian AI, came out of stealth in April. Dai believes that the visual image is as fundamental to model intelligence as language—if not more so—and that frontier language models have hit a ceiling because they still can’t reason effectively about the physical world, not to mention being “incredibly unstable,” as he puts its. A model that can’t count the number of cups on a table or judge spatial relationships falls short of general intelligence, no matter how well it writes or codes, Dai says. Elorian, which Dai cofounded with the former Apple machine-learning researcher Yinfei Yang, is developing models that give visual data equal play with language tokens within their architecture. “We’re betting on these visual representations for things like spatial problems and navigation and everything else,” he says. When recent multimodal language models like Google’s Gemini process imagery, what they really do is create a detailed description of an image using words, which they arrange in a giant internal word map based on their meanings. They then reason about the words to make observations, judgements, and even suggestions about the image. For instance, if shown a schematic of a motorcycle engine design, a multimodal LLM might observe that when motorcycle engines heat up, an aluminum alloy piston crown can expand, which might decrease the clearance between it and the cylinder wall. By contrast, Elorian is building visual reasoning models that will “think” about images directly. Instead of creating a word map, they’ll form a detailed 3D internal map of an image, the way humans imagine things. Because the model has an understanding of physics, it can make much more detailed and accurate observations. In the motorcycle engine example, the model could show how the dimensions of piston crown and cylinder wall, and the clearance between the two, would change over time as the motorcycle engine revs up. Then it could suggest changes to the design to make the engine more durable at high rpm operation. Elorian’s models will reason and simulate, while an LLM can only describe and reference. So Elorian’s visual reasoning models could find natural applications in industries that require a deep physical understanding of imagery. Elorian is targeting what Dai calls the physical economy, an $80 trillion category that includes video understanding and mechanical engineering. In the latter, Dai said engineers currently spend hundreds of hours manually drawing components in CAD software. Elorian’s models can run what Dai calls an edit-simulate-correction loop; they generate a design, then test it within a physics simulation, then identify design flaws, then revise the design accordingly, automatically. Dai spent 14 years at Google Brain and DeepMind. There he coauthored research that provided technical groundwork for the GPT series models, and led data work on Gemini. He left with a team of researchers to start Elorian, headquartered in Palo Alto. Dai says one of the reasons he left Google was that the company is now concentrating its compute resources on a few distinct areas, including code generation, and allotting less compute to research on visual reasoning. Elorian’s specialized models already outperform Gemini 3 Pro on certain visual reasoning benchmarks, Dai says, though he declined to name them, saying disclosure would let competitors optimize against them. (Gemini is still fundamentally a transformer model that projects vision into a shared latent space, but DeepMind is also investing in research on models that reason over visual images themselves rather than words.) Elorian has raised $55 million in seed funding at a $300 million valuation. Investors include Nvidia and Menlo Ventures, as well as Dai’s former boss, the legendary Google chief scientist Jeff Dean. PitchBook also lists 49 Palms Ventures, 500 Global, and Altimeter Capital Management, among others, as investors. Elorian’s $300 million seed valuation is modest next to other startups building AI for the physical world. Physical Intelligence, a robotics foundation model company founded in 2024 by the former Google DeepMind researcher Karol Hausman and the Stanford professor Sergey Levine, was valued at $5.6 billion in a $600 million funding round in November. World Labs, a spatial-intelligence startup founded by the Stanford professor and AI researcher Fei-Fei Li, works on a related problem: AI systems that reason about three-dimensional space rather than 2D images. The company’s product, Marble, generates navigable 3D environments from text, images, or video. World Labs raised $1 billion in a February round led by Autodesk along with Nvidia, AMD, and Andreessen Horowitz. Moonvalley, which was acquired by Reka this year, built the licensed-footage video model Marey, then moved into world models. Reka said the combined team will develop AI that can simulate motion and physics to help robots reason about the consequences of their actions. But Dai’s company is young and is still developing its models and fitting them to its first addressable markets. Elorian plans a general Application Programming Interface release by the end of the year, which will allow developers to build apps on top of its visual reasoning models.
- Pixel 11’s Tensor G6 chip beats the iPhone 18 to TSMC’s 2nm process
While Google’s next Tensor chipset in the Pixel 11 series isn’t expecting to be a gamechanger by any means, it sounds like it will be right on the bleeding edge, as a new report claims Tensor G6 will be the first to adopt TSMC’s 2nm process, beating even Apple to the punch.
- ByteDance explores autonomous driving for unmanned logistics
ByteDance is exploring autonomous driving technology for unmanned logistics through the world model team under Seed, its AI research unit, according to Chinese media reports. The early-stage project is reportedly tied to Volcengine’s automotive industry line. ByteDance said its large model research includes early exploration in physical AI, but added that it has no plan […]
Score: 66🌐 MovesJul 13, 2026https://technode.com/2026/07/13/bytedance-explores-autonomous-driving-for-unmanned-logistics/ - Forget typosquatting; slopsquatting is the software supply chain threat created by AI coding tools
Slopsquatting represents an emerging supply chain threat made possible by AI hallucinations. As developers increasingly rely on AI coding assistants, they unknowingly grant cybercriminals access to their software from day one. Understanding what slopsquatting is Slopsquatting is a new type of supply chain attack that uses large language model (LLM) hallucinations to inject malicious code into development workflows. The term combines "AI slop" and "typosquatting," a deceptive practice where attackers register misspelled or lookalike versions of popular domains to prey on users who enter URLs incorrectly. This novel attack vector exploits LLMs' tendency to generate fictitious software package names, which threat actors can then register and populate with malicious code. During AI-assisted coding, the model may generate fake open-source packages — bundled collections of files, programs and installation tools. This alone is not necessarily harmful. However, if an attacker registers that fake package name, they can inject malware that gets incorporated directly into a developer's codebase. How AI creates a supply chain risk Traditionally, AI safety risks stem from hallucinations , which can adversely affect users who treat misinformation as valid. However, those same hallucinations have evolved into exploitable security vulnerabilities. Typosquatting is a deceptive practice where a cybercriminal registers a mispelled version of a popular package to trick developers. It has existed for decades, so registries have built protections against it. However, AI has changed the threat model . It recommends fictitious packages that sound plausible rather than making simple misspellings. Once attackers learn which hallucinated packages models tend to invent, they can register malware-filled packages under those names. Since the hallucinated packages are not simply typoed versions of popular libraries, there are no protections against this practice at scale. For example, the registry protects against an attacker publishing "crossenv," a squat of the popular "cross-env" package. However, it would not identify "mpn install cross-env file" or "cross-env-extended" as threats. Hallucinations are persistent and severe Even if many LLMs recommend the same hallucinated package, widespread compromise is still possible. Malicious packages could remain undetected in production for months or even years, allowing threat actors to passively inject malware across countless environments. One research team analyzed 31,267 vulnerabilities belonging to 14,675 packages across 10 programming languages. They discovered that reported vulnerabilities are increasing at an annual rate of 98%, faster growth than the 25% annual increase in the number of open-source software packages. The team also observed an 85% increase in the average lifespan of vulnerabilities, indicating a decline in security. Real-world dangers of AI hallucinations Malicious actors can create open-access packages under the same name as commonly hallucinated libraries. Instead of standard code, they are filled with malware. The models believe they are referring to existing packages, so they often repeat the same hallucinated names. Since the hallucinations are not random, attackers could theoretically register packages that trick tens of thousands of developers. These packages appear legitimate. String similarity to real libraries makes them recognizable. One-character typos suggest simple mistakes rather than malicious intent. Even fully fabricated names remain believable when the AI presents them in proper context. Detection is challenging, as developers trust their coding assistants to recommend valid dependencies. Why are LLMs hallucinating packages? LLMs generate the statistically most likely answer rather than prioritizing accuracy. Hallucinations are relatively common as a result. One study found hallucination rates range from 50% to 82% , depending on the model and prompting method. Even GPT-4o, the best-performing model, goes no lower than 23%, even with prompt-based mitigation. Adversarial hallucination attacks could worsen this problem. Threat actors can leverage token-level manipulation or retrieval poisoning to force models to hallucinate in ways they want, increasing the likelihood that models recommend their malicious packages. Which LLMs are prone to slopsquatting? While all LLMs are prone to slopsquatting, some are more vulnerable than others. The likelihood of producing hallucinated packages during code generation depends on the model. Proprietary models are four times less likely to generate hallucinated packages than open-source models. One research group proved this by conducting 30 tests across 30 different systems. Out of the 576,000 code samples and 2.23 million packages it produced, 19.7% were hallucinations. GPT-4.0 Turbo had a hallucination rate of 3.59%, while DeepSeek 1B, the best-performing open-source model, reached 13.63%. This research suggests that organizations relying on open-source AI tools for code generation are roughly four times more exposed to slopsquatting attacks. That doesn’t necessarily mean proprietary tools will always remain safer, though. Once attackers realize this disparity, they may manipulate proprietary LLMs to take advantage of perceived safety. Vibe coding contributes to the problem Software developers who use AI tools estimate that over 40 percent of the code they commit includes AI assistance. They expect that percentage will increase considerably within the next few years. Already, 72% of those who have tried AI use it daily. The uptick in vibe coding and AI-assisted coding amplifies the threat surface. As more developers integrate AI tools into their workflows without implementing proper verification processes, the attack surface for slopsquatting continues to expand. For those using AI to assist with coding, double-checking output is essential. Verifying that recommended packages actually exist in official repositories before incorporating them into projects reduces risk. Navigating AI-assisted development Implementing automated checks that validate package names against known registries can help catch hallucinated packages before they enter production code. Security teams should also monitor for unusual package installations and maintain up-to-date threat intelligence on known slopsquatting campaigns. Zac Amos is the Features Editor at ReHack .
- Carbon storage could curb more than 90% of AI data center emissions, study finds
As artificial intelligence accelerates demand for computing power across the U.S., a new study co-authored by Hon Chung Lau, adjunct professor in the Department of Chemical and Biomolecular Engineering at Rice University and founder of Low Carbon Energies LLC, has found that carbon capture and storage could play a major role in limiting the climate impact of data centers.
- GitHub Copilot introduces upgrade canvas for modernizing .NET applications
GitHub Copilot introduces upgrade canvas for modernizing .NET applications InfoWorld
- From Bombmaking To Motorcycle Tweaks: How Nigerian Jihadists Use AI
From Bombmaking To Motorcycle Tweaks: How Nigerian Jihadists Use AI Barron's
Score: 65🌐 MovesJul 13, 2026https://www.barrons.com/articles/from-bombmaking-to-motorcycle-tweaks-how-nigerian-jihadists-use-ai-56bbc305 - Kenya says AI works can’t be copyrighted
On Techpoint Digest, we discuss Kenya's ruling that AI works cannot be copyrighted, Disney+'s plans to launch a new R49 app in South Africa in September, and Klump's introduction of Jumia instalment payments.
- ‘They don’t need people’: the workers left behind by China’s robot drive
‘They don’t need people’: the workers left behind by China’s robot drive The Japan Times
Score: 65🌐 MovesJul 13, 2026https://www.japantimes.co.jp/news/2026/07/13/asia-pacific/workers-left-china-robot-drive/ - Alipay Launches AI Open Platform: The Agent Commerce Infrastructure Behind Ant Group AI Strategy
Alipay AI open platform lets merchants package services as plug-ins for AI agents across phones, cars, and terminals, completing Ant Group three-month AI commerce infrastructure buildout.
- Chinese internet firms sign AI agent data protection pact
The China Internet Association released a self-regulatory pact on personal information protection for AI agents at a forum in Beijing, with Baidu, Tencent, Alibaba, Volcengine, and 27 other internet companies among the first signatories. The pact is aimed at standardizing how AI agents collect, process, and use personal data as agent-based services spread across internet […]
Score: 65🌐 MovesJul 13, 2026https://technode.com/2026/07/13/chinese-internet-firms-sign-ai-agent-data-protection-pact/ - Ex-Palantir executive to build ‘sovereign’ British AI tech
Ex-Palantir executive to build ‘sovereign’ British AI tech The Telegraph
Score: 65🌐 MovesJul 13, 2026https://www.telegraph.co.uk/business/2026/07/13/ex-palantir-executive-to-build-sovereign-british-ai-tech/ - Cyber body Crest launches AI security charter
Cyber security trade body Crest has launched an artificial intelligence (AI) charter and set of principles, backed by a founding cohort of 60 signatory organisations from all over the world, setting out a set of guidelines and principles for adoption of AI-powered cyber services. According to Crest, the AI Charter was developed in response to a growing need for confidence – especially among buyers – in emerging AI capabilities. It said that as AI becomes increasingly embedded in security practices, end-user organisations need reassurance that AI is used responsibly, transparently, and with human oversight. In areas such as penetration testing, for example, data compiled by Crest purport to show that 47% of organisations now use AI for vulnerability reporting and 44% for scanning and enumeration – while three quarters of security services providers have increased their AI use over the past 12 months. “Since the advent of AI, the cyber security industry has faced a turbulent mix of immediate threats and a rapidly shifting landscape, while bracing for future disruptions to its workforce and stability,” said Crest CEO Nick Benson. “The support of our founding signatory organisations reflects industry recognition that responsible AI adoption requires more than technology alone. It requires shared expectations, professional standards, and practical approaches to assurance.” Privilege with responsibility Crest said that since managed security services providers (MSSPs) and their ilk need highly privileged access to sensitive systems, networks and data, they need to be able to demonstrate that they are using AI responsibly to maintain customer trust, improve accountability, and reduce risk. As such the AI Charter sets out clear expectations for how the cyber industry should responsibly adopt AI, reflecting the practical experience of Crest’s community members and wider thinking on the issue. It is also underpinned by nine core principles : That the scope and purpose of AI-enabled cyber services and how they may affect service delivery, outcomes, data handling and risk, are defined, and that oversight, testing and governance controls proportionate to the nature, scale and risk of AI use are applied; That customers are fully informed of AI use in cyber tools, methodologies and automations, including internal and third-party solutions where appropriate, and that the use of AI is explained, covering aspects such as benefits, limitations, and risks; That AI use is documented, traceable and reviewable, with relevant records retained for assurance purposes; That competent personnel retain oversight of all AI-enabled activity, reviewing outputs, challenging decisions, and taking control if needed, with controls in place to stop rogue usage; That customers are informed of how AI may use their data, such as for model training, and whether data may be transferred outside their organisation or jurisdiction, subject to agreed legal, regulatory and contractual safeguards; That customer data, prompts, outputs and other AI-generated artefacts are protected; That AI cyber tools are developed using secure development, integration and assurance best practices; That there is visibility into the AI supply chain, with material third-party AIs or providers known and properly assessed, and appropriate governance and risk management controls applied. That material AI dependencies in service delivery are identified and their impacts assessed should systems crash or become otherwise unavailable. “Artificial intelligence presents significant opportunities for the cyber security profession,” said Crest chief product officer Sebastian Madden. “It has the potential to improve efficiency, accelerate analysis, strengthen defensive capabilities and help organisations respond more effectively to emerging threats. But at the same time, we must urgently address the practical challenges of governing, securing, and validating these capabilities.” Further to its charter, Crest is also working on the development of a set of standards to cover AI security and the use of AI in security services through an ongoing research programme and various working groups. Read more about AI-enabled cyber tools Agentic AI is touted as a helpful tool for managing tasks, and cyber criminals are already taking advantage. Should information security teams look to AI agents to keep up? At Google Cloud Next, Wiz co-founder Yinon Costica called on security defenders to use AI to steal a march on threat actors, and launched agentic capabilities for cyber teams. TXOne Networks CEO Terence Liu says AI is transforming both industrial operations and cyber threats, forcing GCC energy operators to prioritise visibility, operational control and resilience.
Score: 65🌐 MovesJul 13, 2026https://www.computerweekly.com/news/366645629/Cyber-body-Crest-launches-AI-security-charter - AI Chip Giants TSMC and SK Hynix Pull in Different Directions
TSMC soared while SK Hynix tumbled — why are are the two essential AI chip firms pulling in different directions? Tom Mackenzie explains. (Source: Bloomberg)
Score: 65🌐 MovesJul 13, 2026https://www.bloomberg.com/news/videos/2026-07-13/tsmc-and-sk-hynix-pull-in-different-directions-video - XPENG Aims to Go Head to Head with Tesla in Europe with Self-Driving Tech
XPENG has been developing self-driving tech in a very similar way as Tesla for several years. In fact, while pretty much everyone else has decided radar and/or lidar sensors are needed in addition to cameras, XPENG has stuck with Elon Musk’s argument that cameras are the only sensors needed. Our ... [continued] The post XPENG Aims to Go Head to Head with Tesla in Europe with Self-Driving Tech appeared first on CleanTechnica .
Score: 64🌐 MovesJul 13, 2026https://cleantechnica.com/2026/07/13/xpeng-aims-to-go-head-to-head-with-tesla-in-europe-with-self-driving-tech/ - Neolix at Forefront as Autonomous Logistics Scales to the Middle East and Beyond
Neolix at Forefront as Autonomous Logistics Scales to the Middle East and Beyond The Straits Times
- 3 Ideas To Efficiently Solve AI’s Emerging Energy Problem
Advancing AI tools need increasingly immense power to work, putting unprecedented demand on U.S. energy infrastructure. This executive leader at Schneider Electric has a plan to solve the critical emerging energy challenge.
- TCS and ABB sign multi-million, multi-year deal to transform global network operations with AI
Tata Consultancy Services announced an expanded collaboration with ABB to transform its global network operations. The engagement marks the next phase of a trusted 20-year partnership. As part of this multi-million, multi-year deal, TCS will scale its role from managing infrastructure and applications to delivering end-to-end global network operations, through an integrated network-as-a-service model. The post TCS and ABB sign multi-million, multi-year deal to transform global network operations with AI appeared first on Express Computer .
- Has Tencent AI Finally Turned the Corner? Inside Hunyuan Hy3, the Model That Got Tencent Back in the Race
Tencent Hunyuan Hy3, with 295B MoE parameters and strong Agent performance, climbs to top-8 on OpenRouter and demonstrates a rebooted approach to building AI models by rethinking the training pipeline.
- Zoom will let you add an AI receptionist at work, as 'businesses shouldn’t have to replace their phone system to benefit from AI'
Zoom has made its AI receptionist tool available to all businesses, regardless of which business phone provider they use.
- AI is changing older workers' careers, research finds — here's how
AI may either prompt some older workers to leave their jobs or help make their roles more efficient, research finds. Here's which careers may be most affected.
- Google Will Now Tell You If That Ad Was Made With AI
Google is adding AI disclosure labels to ads on Search, YouTube, and Discover, but third-party AI use still depends on advertiser reporting. The post Google Will Now Tell You If That Ad Was Made With AI appeared first on TechRepublic .
Score: 63🌐 MovesJul 13, 2026https://www.techrepublic.com/article/news-google-ai-ad-disclosure-labels/ - Augmodo raises $21M to push its spatial AI beyond just retail toward the broader physical workforce
Customer demand is pulling the Seattle startup's technology into warehouses, facility maintenance, delivery, and other parts of the physical workforce. Read More
- Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment MarkTechPost
Score: 62🌐 MovesJul 13, 2026https://www.marktechpost.com/2026/07/13/stanford-researchers-introduce-trace/ - TCS to take on larger share of AI model and inference costs for project deployment
While customers initially bore most of the AI model and inference costs, the company is now making upfront investments through partnerships, Chief Financial Officer Samir Seksaria has said.
- Exclusive: 34 CEOs on what thrills and terrifies them about agentic AI
When businesses leaders think about AI right now, they’re thinking about how agentic tools will change the very nature of their work. “We’ve crossed a line,” says Varun Krishna, CEO of the fintech giant Rocket Companies . “AI is no longer just creating. It is thinking, deciding and acting. That changes everything, from client interaction to security.” As agentic tools get more sophisticated, they “expose how many organizations are still constrained by siloed data, fragmented ownership, and legacy ways of working,” says Ndidi Oteh, who leads Accenture Song , the professional services and creativity arm of the IT company Accenture. “Companies that don’t rewire how work gets done will slow themselves down at exactly the moment speed matters most.” Even those skeptical of how real agentic tools are recognize that they can wholly transform business. “Most of what people are calling agentic is just automation with better marketing ,” says Meng Ru Kuok, CEO of Caldecott Music Group , owner of BandLab , a social music creation platform with AI-assisted features. “The genuinely autonomous systems—ones that negotiate, transact, and make judgment calls without a human in the loop—those aren’t here yet for most businesses. That said, the potential for agentic AI to empower people is absolutely real.” This spring, Fast Company asked almost three dozen CEOs—in industries ranging from healthcare to marketing and including the leaders of Abridge, American Express, Anduril, Sephora, and the ad agency Johannes Leonardo —how they’re thinking about agentic AI: their strategies, the tools they’re building, what they’re excited about, and what scares them. Among the clearest themes that emerged from CEOs’ responses: Nobody is ready for how agentic AI is going to transform business, because companies are still figuring that out. But as uncertain as the agentic future’s exact contours are, it’s equally clear that companies should already be sketching them out. As Charles Yang, cofounder and CEO of the AI workspace company Vibe , puts it, “I don’t think readiness is the right frame. The right frame is: Are you in the water? Because you can’t learn to swim from the shore.” Agentic AI will reshape productivity Agentic AI may be growing, but it’s still in its early stages. Despite that, executives are already extrapolating productivity gains that agentic tools might bring based on how AI tools are already changing the way their organizations work. Steve Squeri, CEO of A merican Express , notes that with AI-assisted development tools, the company has scaled them to the point that coding cycle times are down 30%, speeding up the software development process. “Agentic AI takes that a step further,” Squeri says, “moving from tools that support developers to systems that can increasingly take on full ownership of designing, testing, and deploying new capabilities. Over time, that has the potential to fundamentally change the speed and scale at which we innovate.” Squeri says he can see a future where AI agents handle entire trips for cardholders—booking airfare and lodging, and even creating a personalized itinerary with reservations through Amex’s subsidiary Resy . “If we do this right, the best agentic AI experiences won’t feel like AI at all,” he says. Customer-facing autonomous tools aren’t entirely conjecture—at least not for Arintra , an Austin-based AI medical coding company. Its CEO, Nitesh Shroff , says the company has built an agentic AI coding engine that sits within a provider’s electronic health record that can turn clinical documentation into billing codes using insurance-specific rules. At the health system Mercy Health, Shroff says Arintra’s tools autonomously process 50,000-plus charts a month, helping boost Mercy’s revenue 5.1%. “What we’ve seen is that agentic AI doesn’t replace the expertise of human coders,” he says. “It gives them the space to do the more complex work that they were trained for, instead of the routine high-volume work they can’t keep up with.” Efficiency is great, but there are downsides Helping teams of any size work faster is a big advantage that companies see as they explore agentic tools. But the advantages can be quickly undone. On the one hand, there’s what Nick Deveau, cofounder and CEO of the AI-based leasing tool company Grotto AI , calls “the mirage of productivity” being driven by AI. “It’s easier than ever for me—within the span of 10 minutes—to work on optimizing my Google ads campaign, dive into data analysis, submit some new code to our codebase, and spitball a new strategy. You feel like you’re on fire,” he says. “But is the output quality good? Can you truly produce high-quality results with so much competition for your focus? We can move extremely quickly now, but it’s easier than ever to move in the wrong direction even faster.” Beyond quality of work accompanying increased efficiency, there’s also the fundamental challenge of autonomous tools acting on their own. “When an agent makes a bad procurement decision or a flawed design call, who is responsible?” says Nathan Silvernail, CEO of Plantd , which builds structural building panels out of grass rather than wood. “Most companies don’t have answers to that yet.” One of the biggest areas of agentic potential is in marketing, where executives are excited about the speed that it can bring to organizations of all sizes. Helen Andrews, CEO of the advertising agency Johannes Leonardo , says agentic tools mark a big shift from other tech that has become standard across industries. “The past 20 years of technology mostly added work,” she says. “More platforms, more formats, more reporting, more coordination. Most of it shallow. Agentic AI is the first thing in a long time that could actually subtract from that pile. Hand the false busyness to agents. Give humans back the hours that produce the real stuff: the unexpected insight, the relationship, the idea that no one saw coming. Creativity.” Even as agentic AI frees up creatives to do more visionary work, Jason Harris, cofounder and CEO of the creative agency Mekanism , says that the discourse around agentic AI and its ability to replace workers is a big concern, not because it will replace workers, but because “clients convince themselves it already has.” The result, he says, is “the commoditization of ‘good enough.’ Agentic AI can spin up briefs, generate concepts and optimize media buys without a single moment of instinct or risk-taking. A procurement team looking at a spreadsheet can’t tell the difference between work that’s efficient and work that’s alive.” It’s sentiment that’s shared by another agency executive Sean McDonald , Rethink ’s global chief strategy officer and partner . “My biggest concern with regards to AI is where it might lower the bar and tempt marketers with ease and speed over quality and impact,” he says. “We have seen a litany of technology-driven hype cycles come and go, promising significant cost and efficiency gains while glossing over the negative impact on quality.” John Elder, CEO of the Supergood agency , feels similarly. “The real risk is that every brand gets access to the same off-the-shelf agents and the work becomes indistinguishable,” he says. “If everyone’s prompting the same models with the same data, we all produce the same mediocre middle. That’s why we invest 74% of our stack in proprietary data. AI will be evenly distributed. Data won’t.” Speaking of data . . . Data will be your advantage—so will good governance “There is a data quality problem sitting underneath all of this,” says Taryn Crouthers, CEO of the digital media firm Big Spaceship . “Agents are only as good as what they’re fed, and most organizations don’t have the clean, structured, accessible data that agentic systems require. This is going to be a silent bottleneck for a lot of brands. Some organizations are deploying agentic AI on top of org charts and data infrastructure that were never designed for it.” In industries like healthcare, where Abridge deploys its AI clinical documentation tools, data will be the differentiator, but it’s up to systems to make sure data can travel between places where it’s needed. “Agentic systems are only as powerful as the data they can access and act on. If clinical, financial, and administrative data remain siloed, fragmented, or delayed, it constrains what agents can actually do—no matter how advanced the models are,” says Shiv Rao , CEO and cofounder of Abridge . “At the same time, if data does become highly portable and standardized, it raises a different risk: Value shifts quickly to whoever can orchestrate that data into action in the safe and reliable way that healthcare requires.” Rao’s assessment might be optimistic. Ali Diab, cofounder and CEO of the employer health plan administration company Collective Health , doesn’t see much of healthcare adopting agentic tools anytime soon. “We are still a country where fax machines are a primary mode of communication and ‘proprietary data’ is used as a weapon to prevent competition,” he says. “Agentic AI requires high-fidelity, real-time data to function safely. If the underlying data is garbage—or worse, intentionally obscured—the AI will fail.” Many of the same challenges that plague healthcare span industries. Mariam Hakobyan, CEO of Softr , which enables businesses to create enterprise apps with no code using AI, says the groundwork of data and governance is still being laid. “Readiness isn’t just about willingness. It comes down to infrastructure, trust, and understanding,” she says. “Most companies still lack clean data, well-defined workflows, and strong governance. The shift is clearly coming, but for many traditional industries, adoption will take time.” Better data isn’t a magic bullet, though. According to the CEO of a leading marketing and advertising firm, who opted for the anonymity we offered to speak candidly, thinking that having data is sufficient to change how things are done is giving up the game before it starts. “[Companies are] still shaped by the Old World-notion of a company provides a product or service and the consumer buys it. Then they say AI and data will help that become more personal. That is a very narrow lens,” the CEO says. “Better data doesn’t just mean that I know now if you’re into this or not. You are going to have an ongoing relationship with your customers, and then you have to think about what that means.” Every agent can be a security vulnerability—or a hacker Healthcare isn’t the only industry thinking about how data can be secured. Agentic AI brings with it various security threats, both internal and external. It was the biggest concern identified by many CEOs surveyed, and Logan Brown, the Harvard Law-educated founder and CEO of the legal AI company Soxton , puts it plainly: “The increase in capabilities of AI is going to lead to an unprecedented level of malware, hacks, and security issues. I am concerned about the risk that agents interacting on the web will pose to business and humans.” That fear exists across industries. John Levy, who leads the quantum computing company SeeQC , says security needs to be on everyone’s mind. “Most organizations are still figuring out how to deploy even basic AI tools safely. Agentic systems, which can take autonomous action, raise the stakes significantly,” he says. “A significant portion of our value resides in unpublished research and prepatent innovation. Agentic AI can accelerate workflows like prior art searches and patent drafting. But it also creates real risk if sensitive information is exposed through external models or insecure environments.” Agents don’t just create risk when deployed by businesses. Their ease of use also makes them easily enlisted by malicious actors to supercharge scams and malware. “The latest AI models give both defenders and attackers access to powerful tools,” says Navrina Singh, CEO of the AI governance company Credo AI . “That creates a new class of ‘unknown unknowns.’ We don’t yet fully understand the attack surface these systems introduce, and that uncertainty is deeply concerning.” Ben Colman, who leads the deepfake detection company Reality Defender , actually knows a lot of those unknowns. His company knows that AI agents are helping scammers—even small operations—to the next level. “Bad actors are using AI voice agents at scale to make calls, start social engineering campaigns, and generally wreak havoc: Think 10,000 calls being made to a call center to waste their resources or seek the PII [personally identifiable information] of a person (or people) who have an account with a specific company,” Colman says. “As the least secure person is a company’s greatest security vulnerability, it just takes one of those calls to part with information it should not have for such attacks to be a success.” He adds that the ability for agents to run cheaply in parallel, and generate synthetic voices in real time, means that “it’s impossible for the average or even the most astute user to determine whether or not they’re speaking with a real person or an AI agent, which is something that our clients and industry peers are concerned about.” Autonomous tools still need people at the center Whether internal or customer-facing, agentic tools are still tools. CEOs who participated in this survey largely recognize that, regardless of how this technology fits into their business. From the consumer side, Sephora ‘s global president and CEO Guillaume Motte says that the retailer’s efforts to deploy agentic tools to undergird customer experiences isn’t coming at the expense of shopping’s interpersonal elements. “Beauty is built on trust and connection. Technology must strengthen and complement—not replace—those fundamentals,” Motte says. “For Sephora, our interest is to use it well to enhance the consumer journey, like navigating complexity, understanding products, and making better-informed choices. At the same time, beauty will remain a client-focused business and our beauty advisors will always be essential to interpretation, empathy, and reassurance.” Maintaining the human element amid increased use of AI in the creative sector is also critical, says Jennifer Gonzalez, CEO of the Studio & Marie agency . “Human friction and struggle are genuinely productive for us,” she says. “Chance interactions during our days often yield as much as a landscape deep dive, design ‘mistakes’ become new ways into concepts, and rethinking templates keeps us challenging our own assumptions. Our edge has always been an instinct for pairing the right creative voice with the right problem and that’s something tools can’t replicate.” Your workers can probably stick around—but might need some coaching “It is likely true that “employees face more threat than businesses; the majority of businesses will find it much easier and cheaper to implement major operational efficiencies due to agentic AI,” write Ramy Khorshed and Hussein El Kheshen, CEO and CTO respectively of Sakneen , an Egyptian property tech company. But responses from a large swath of business leaders we surveyed show that many executives are more circumspect than simply equating efficiency with an opportunity to cut headcount. At best, Jarek Kutylowski, CEO of the AI translation company DeepL , sees an autonomous future of workers whose job is managing outcomes rather than solving problems. “In many ways, it is forcing every employee to stop being a builder and start being a CEO,” he says. “A CEO’s role is about overseeing a system that is far too vast to probe in every minute detail. Instead, you have to decide what to trust versus where to manage risk. With agentic, we’re essentially asking people to move from knowing it all, to the discipline of high-level oversight. This is a massive opportunity for the workforce to prove their judgment in the same way a leader does. Helping people redefine their professional expertise and interests to account for this shift is also a challenge.” Robin Forbes, CEO of the creative agency R/GA , sees the opportunity for workers—especially creatives—to change how they view their jobs and how they end up billing for work. “With agentic AI enabling creative service businesses to work more efficiently, time can no longer be viewed as the main unit of value. Value needs to be measured by the quality of the output and impact on business outcomes,” he says. “By anchoring success in quality and business outcomes rather than hours, we also validate the strategic value of our talent. This ensures our people are recognized as the architects of impact rather than being relegated to machine operators in the name of efficiency.” Janet Sherlock, CEO of the organization design firm Org.works , says that a transformation of workers’ roles needs to be accompanied by concurrent changes in how leaders define roles. “Most companies haven’t answered the basics: who decides where agents are used, who owns the outcomes, and what platforms they’re standardized on,” she says. “When decision rights and accountability aren’t clear, agentic AI doesn’t create efficiency; it amplifies confusion.” When thinking about challenges as companies experiment with agentic processes, two CEOs used calculators as an example of what widespread agentic tools could do to the workforce. Stephen Smith, founder and CEO of the mental health platform NOCD and its parent software company Noto , says: “The biggest risk I see is that our team will over-leverage AI to the point where their foundational skills as operators erode. Reflect on using a calculator. Do you still know how to do long division, or have you forgotten due to your reliance on using a calculator? If you overutilize AI for problem solving and writing, what will happen to your core problem solving and writing skills? They might erode, unless we intentionally keep growing.” The need for continued growth is an area where Bijal Shah, CEO of the workforce education benefits company Guild , sees an opportunity for companies to invest in upskilling their employees. “The businesses that are preparing [for the agentic future] are not necessarily the ones with the most advanced technology. They are the ones creating the conditions for learning, giving people time to experiment, sharing use cases, and normalizing the friction that comes with learning something new,” she says. “Every major technology shift has followed this pattern. The difference now is the pace. Therefore, being ready means investing in people as intentionally as we invest in technology.” The potential for investment in agentic AI to go hand-in-hand with workforce transformation is a prospect fueling the defense tech startup Anduril , whose CEO, Brian Schimpf , views agentic tools as a path to a resurgence in U.S. economic strength in making physical products, not just knowledge work. “The singularly most underdiscussed part of the AI transformation is how we can use the technology to increase industrial resilience and expansion—not threaten the future of work,” Schimpf says. “So far, early economic reporting and predictive analysis alike predict steady and ready growth in the job market. Especially in physical industries, agentic AI is likely to be additive to output and increase demand, rather than to shrink either. At Anduril, we view the technology as a way to rebuild military power and bolster the U.S. industrial base at the same time, rather than subscribing to the view that AI is a harbinger of economic mass extinction.” Nobody feels ready. Start anyway Our survey concluded with the question: “Are businesses and consumers ready for the sort of sweeping change agentic AI may bring? Why or why not?” Nobody responded with an unqualified yes. Most said no. Some said consumers are further along than businesses. Part of that is the sheer speed at which AI and agentic tools have proliferated. “Never before have so many fundamentals been in flux simultaneously: models, agents, interfaces, distribution, measurement, and the economics of the open web,” says Judith Carr-Rodriguez, partner and CEO at the independent creative agency Fig . “Previous shifts gave you time. Broadcast had decades before cable fragmented it. Digital had years before mobile reshaped it. You could adapt. What’s different now isn’t that change is happening; it’s the number of things changing at once and the speed each one is moving.” But respondents also agreed that they should start identifying the competitive advantages of agentic tools as soon as possible. Vikram Bhaskaran, CEO of the physician-only knowledge network Roon , says the way consumers interacted with AI in healthcare is instructive of how business can take the tools that exist and just start experimenting. “For years, patients turned to ‘Dr. Google’ to fill that gap. Now many are turning to tools like ChatGPT for faster, more contextual answers,” he says. “That shift isn’t happening because people think AI is perfect; it’s happening because access to human expertise is constrained. Agentic AI is just accelerating a behavior that’s already underway.” And though first movers may be at an advantage, another anonymous marketing and advertising exec advises business leaders that with a technology as nascent as agentic AI, they should be gearing up for a marathon, not a sprint. “This might be nerdy, but I think the German sociologist Niklas Luhmann is instructive here: People tend to underestimate system inertia, i.e. people are not ready and they will slow down the development. That’s good and healthy,” this exec says. “Not everything that is possible today will happen at scale tomorrow. It’s going to be a long and winding road.”
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OpenClaw’s announcement that it has become a nonprofit foundation is generating IT excitement because of the potential for governance and development consistency that the popular platform has thus far lacked. Still, some worry about the risks created by the move. “Our ambition is for OpenClaw to be the Switzerland of AI. Neutral ground where every model and every lab can plug into the technology and collaborate on standards in the era of agents,” OpenClaw said in a post . “That work is already underway in Foundation-convened councils on agent identity, agent profiles, evals, and enterprise deployment.” The statement, co-authored by OpenClaw creator Peter Steinberger , pointed out, “the great open source projects of our time — Linux, Apache, Mozilla — endure because a neutral steward stands behind them. That is the role we are taking on to keep OpenClaw MIT licensed, open, and independent so that everyone building on it can trust it will be here for the long term.” But it reassured users that the original OpenClaw leadership is still in charge. “Peter built this thing and Peter keeps making the calls, especially the technical ones. Since joining OpenAI earlier this year, he has continued to steward OpenClaw as an open and independent project, and OpenAI has made a commitment to keep it that way,” the post said. “The foundation is here to serve: good governance, stable funding, and paying the people who keep the claws alive.” However, some analysts and consultants were skeptical about how much true independence Steinberger would have, given his salaried role with OpenAI. Neutrality claim in question “The Switzerland of AI neutrality claim collapses under its own announcement,” said Noah Kenney , principal consultant at Digital 520. “OpenAI runs a team [at OpenAI] called Claw Labs that Peter leads and OpenAI is a major donor to OpenClaw. The ‘neutral steward’s’ chief technical decision maker is employed by one of the competing labs it is supposed to be neutral with.” To OpenAI, he said, OpenClaw is closer to a tax-exempt nonprofit subsidiary than it is to a neutral ‘Switzerland of AI.’ He pointed out that, in addition, Microsoft is shipping the enterprise version of OpenClaw, and Nvidia is shipping the hardware bundle. “This is being called the Switzerland of AI, but Switzerland does not have its central bank run by France,” he observed. Kenney said that what the new OpenClaw has actually built is “a shared dependency that several competitors fund, staff, and steer, wrapped in a nonprofit structure. Enterprise IT should understand that structure, because treating OpenClaw as neutral is a mistake,” adding that CIOs need to look at this development devoid of the emotional component. “There is a strategic irony here that CIOs should sit with,” Kenney said. “If OpenClaw succeeds at becoming the universal agent substrate, then every model plugs into the same identity layer, the same profiles, and the same deployment plumbing. The thing every vendor is racing to own becomes a commodity that nobody owns.” He pointed out that, in the short term, that is genuinely good news for buyers because it means less lock-in and more portability. “But,” he said, “when the connective tissue is free and natural, the only labs that benefit are the ones with the best models and the deepest distribution. Commoditize the layer below you and you compete on the layer where you are already strongest. The foundation is not a charity. It is the biggest players agreeing to stop fighting over the plumbing so they can fight over the water, and the enterprise is the one paying the water bill either way.” Good news, bad news Jason Andersen , principal analyst at Moor Insights & Strategy, liked the potential consistency that could emerge from the structural change, given the complexity of agent development today. “We are seeing a lot of OpenClaw variants hit the market, such as those from Nvidia as well as competing products from cloud and SaaS vendors. A common base helps solidify the common parts,” Andersen noted. “That said, a common challenge is the sustainability of these open source foundations over time. In addition to releasing code, these foundations need funding to evolve and grow. And that funding needs to come from continued momentum to incentivize existing members to increase investment and recruit new members to join.” Andersen stressed that IT buyers need to keep an eye on the roadmap for any OpenClaw variant they choose to deploy, “as that will directly impact the foundation, and the momentum of the foundation and common base. If the common base loses momentum, it can lead to forks, or just a loss of innovation. When that happens, members tend to back away, which puts customers in limbo.” But not everyone sees the promised structure as entirely good for IT. Ishraq Khan , CEO at coding productivity tool vendor Kodezi, said, “most CIOs do not want to bet their future entirely on a single model vendor. They want Claude for some workloads, GPT for others, open models for sensitive environments, and potentially internally fine-tuned systems for specific use cases. The problem is that every vendor currently brings its own identity system, tool interfaces, permissions model, and operational assumptions. That fragmentation does not scale.” He said, “the risk if standards fail is straightforward: every vendor builds its own closed ecosystem, enterprises become locked into individual stacks, and security becomes dramatically harder. The opportunity if OpenClaw succeeds is equally significant: enterprises get portable agents, common identity standards, interoperable tooling, and a healthier competitive market around models rather than ecosystems.” Will it remain a nonprofit? However, said Justin Greis , CEO of consulting firm Acceligence, one of the key details that IT executives will want to keep in mind is that OpenAI also began as a nonprofit, but it was quickly seen as not adhering to nonprofit objectives . “OpenAI’s transition from a nonprofit research organization into a more complex structure highlighted the challenge of maintaining mission alignment while scaling technology, capital, partnerships, and commercial operations,” Greis said. “OpenClaw has the opportunity to address some of those governance questions earlier by establishing clear principles around neutrality, transparency, and decision-making before the ecosystem becomes even larger and more valuable.” He noted, “we have seen this pattern before with technologies like Linux and Kubernetes. The strongest open ecosystems succeeded because they created trusted foundations that enterprises could build upon. The technology was important, but the governance model that underpinned it was equally critical.” Risks are ‘squarely in IT’s lap’ Consultant Brian Levine , executive director of FormerGov, echoed Greis’ concerns. “CIOs shouldn’t assume that this nonprofit will always be a nonprofit, or confuse being a nonprofit with actually being neutral or unbiased,” he said. “The risks are squarely in IT’s lap: autonomous agents ‘with their own identity’ acting on a user’s behalf blow straight through traditional IAM assumptions. Issues, such as agent identity, auditability, secret handling. Identity boundaries have not yet been reliably solved. Until they are, enterprises should treat OpenClaw agents like privileged service accounts, not like a browser plugin.” Independent cybersecurity and risk advisor Steven Eric Fisher pointed to another IT exposure that might come from this OpenClaw transition: Cost. “OpenClaw currently has a very high token burn rate in usage, which presents a significant cost consideration for large-scale enterprise adoption,” he said. “The skills marketplace introduces a new supply chain threat that enterprises will need to manage. Threat management, and specifically handling external marketplace elements , can be highly challenging for open-source operations. Ultimately, at scale, enterprise adoption could become a difficult balancing act between managing high operational costs and securing an expanded security surface.”
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