Render Networks has made further expansion of its footprint as a system of execution for critical infrastructure with the ClearWay platform.

As infrastructure investment accelerates across fibre broadband, electric grid modernisation, distributed energy and AI-driven datacentre expansion, capital discipline has emerged as a defining concern, according to the company.

Render has stated that traditional methods of data analysis and manual decision-making often hamper progress, with deployment risk now consequentially translating directly into capital risk. It added that operators, utilities and builders must reduce variance, accelerate cash conversion and establish audit-grade accountability across increasingly complex, multi-asset deployments.

Originally establishing itself in telecommunications, Render now supports electric utilities and multi-utility environments where construction accuracy is a prerequisite for operational reliability. Built for infrastructure environments where governance is “non-negotiable”, ClearWay is claimed to advance automation without eroding engineering authority.

The new platform is built to transform design data into live scopes of work, to capture verified field progress in real-time and to econcile workflows to maintain financial integrity. This is seen as producing defensible as-built records that flow “seamlessly” into operations. Rather than a collection of isolated AI features, ClearWay is said to operate across a federated system of specialised agents designed to operate autonomously in identity, policy and audit controls.

Each agent operates with a uniquely defined degree of autonomy, managed identity and least-privilege access. As additional ClearWay agents are introduced, the system is built to support progressively higher levels of autonomy, bounded by deterministic guardrails derived from user-defined operational policies. The result is that decision-making choices are underpinned by controlled, auditable automation that preserves first-order accountability while also enabling meaningful scale.

The first release of ClearWay scheduled for release in the second quarter of 2026 and is scheduled to introduce field assurance and work approval capabilities across telecom and electric deployments with an assurance agent and approval agent.

The former is said to validate field-captured evidence against planned work in real-time, ensuring accuracy before crews leave the site. By contrast, the latter approves work autonomously based on a correlation of work type, planned vs. actual units, photos, and test results. When predefined criteria are met, the agent processes the approval and escalates exceptions only when human review is required.

By ensuring work is correct and defensible at the point of execution, Render is confident that ClearWay can accelerate design to build lead times, reduce construction rework, accelerate closeout and improve working capital velocity. This will be “particularly vital” in broadband and grid modernisation environments, where construction accuracy directly affects serviceability, network reliability and regulatory compliance.

“We have always focused on ensuring that work in the field becomes verified operational truth. The next step is ensuring that truth drives disciplined, governed and rapid action across the lifecycle,” said Stephen Rose, CEO of Render Networks.

“As capital efficiency becomes central to telecom and electric infrastructure, automation must ensure rapid decisions are made well to reinforce control and accountability. ClearWay is designed to do exactly that.”

Render will introduce additional specialised agents in the ClearWay architecture, spanning lifecycle management and financial reconciliation, service activation and operational monitoring, and predictive maintenance and sustainability governance.

Advanced Machine Intelligence (AMI), a new Paris-based startup cofounded by Meta’s former chief AI scientist Yann LeCun, announced Monday it has raised more than $1 billion to develop AI world models.

LeCun argues that most human reasoning is grounded in the physical world, not language, and that AI world models are necessary to develop true human-level intelligence. “The idea that you’re going to extend the capabilities of LLMs [large language models] to the point that they’re going to have human-level intelligence is complete nonsense,” he said in an interview with WIRED.

The financing, which values the startup at $3.5 billion, was co-led by investors such as Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions. Other notable backers include Mark Cuban, former Google CEO Eric Schmidt, and French billionaire and telecommunications executive Xavier Niel.

AMI (pronounced like the French word for friend) aims to build “a new breed of AI systems that understand the world, have persistent memory, can reason and plan, and are controllable and safe,” the company says in a press release. The startup says it will be global from day one, with offices in Paris, Montreal, Singapore, and New York, where LeCun will continue working as a New York University professor in addition to leading the startup. AMI will be the first commercial endeavor for LeCun since his departure from Meta in November 2025.

LeCun’s startup represents a bet against many of the world’s biggest AI labs like OpenAI, Anthropic, and even his former workplace, Meta, which believe that scaling up LLMs will eventually deliver AI systems with human-level intelligence or even superintelligence. LLMs have powered viral products such as ChatGPT and Claude Code, but LeCun has been one of the AI industry’s most prominent researchers speaking out about the limitations of these AI models. LeCun is well known for being outspoken, but as a pioneer of modern AI that won a Turing award back in 2018, his skepticism carries weight.

LeCun says AMI aims to work with companies in manufacturing, biomedical, robotics, and other industries that have lots of data. For example, he says AMI could build a realistic world model of an aircraft engine and work with the manufacturer to help them optimize for efficiency, minimize emissions, or ensure reliability.

AMI was cofounded by LeCun and several leaders he worked with at Meta, including the company’s former director of research science, Michael Rabbat; former vice president of Europe, Laurent Solly; and former senior director of AI research, Pascale Fung. Other cofounders include Alexandre LeBrun, former CEO of the AI health care startup Nabla, who will serve as AMI’s CEO, and Saining Xie, a former Google DeepMind researcher who will be the startup’s chief science officer.

The Case for World Models

LeCun does not dismiss the overall utility of LLMs. Rather, in his view, these AI models are simply the tech industry’s latest promising trend, and their success has created a “kind of delusion” among the people who build them. “It’s true that [LLMs] are becoming really good at generating code, and it’s true that they are probably going to become even more useful in a wide area of applications where code generation can help,” says LeCun. “That’s a lot of applications, but it’s not going to lead to human-level intelligence at all.”

LeCun has been working on world models for years inside of Meta, where he founded the company’s Fundamental AI Research lab, FAIR. But he’s now convinced his research is best done outside the social media giant. He says it’s become clear to him that the strongest applications of world models will be selling them to other enterprises, which doesn’t fit neatly into Meta’s core consumer business.

As AI world models like Meta’s Joint-Embedding Predictive Architecture (JEPA) became more sophisticated, “there was a reorientation of Meta’s strategy where it had to basically catch up with the industry on LLMs and kind of do the same thing that other LLM companies are doing, which is not my interest,” says LeCun. “So sometime in November, I went to see Mark Zuckerberg and told him. He’s always been very supportive of [world model research], but I told him I can do this faster, cheaper, and better outside of Meta. I can share the cost of development with other companies … His answer was, OK, we can work together.”

Nvidia is planning to launch an open-source platform for AI agents, people familiar with the company’s plans tell WIRED.

The chipmaker has been pitching the product, referred to as NemoClaw, to enterprise software companies. The platform will allow these companies to dispatch AI agents to perform tasks for their own workforces. Companies will be able to access the platform regardless of whether their products run on Nvidia’s chips, sources say.

The move comes as Nvidia prepares for its annual developer conference in San Jose next week. Ahead of the conference, Nvidia has reached out to companies including Salesforce, Cisco, Google, Adobe, and CrowdStrike to forge partnerships for the agent platform. It’s unclear whether these conversations have resulted in official partnerships. Since the platform is open source, it’s likely that partners would get free, early access in exchange for contributing to the project, sources say. Nvidia plans to offer security and privacy tools as part of this new open-source agent platform.

Nvidia did not respond to a request for comment. Representatives from Cisco, Google, Adobe, and CrowdStrike also did not respond to requests for comment. Salesforce did not provide a statement prior to publication.

Nvidia’s interest in agents comes as people are embracing “claws,” or open-source AI tools that run locally on a user’s machine and perform sequential tasks. Claws are often described as self-learning, in that they’re supposed to automatically improve over time. Earlier this year, an AI agent known as OpenClaw—which was first called Clawdbot, then Moltbot—captivated Silicon Valley due to its ability to run autonomously on personal computers and complete work tasks for users. OpenAI ended up acquiring the project and hiring the creator behind it.

OpenAI and Anthropic have made significant improvements in model reliability in recent years, but their chatbots still require hand-holding. Purpose-built AI agents or claws, on the other hand, are designed to execute multiple steps without as much human supervision.

The usage of claws within enterprise environments is controversial. WIRED previously reported that some tech companies, including Meta, have asked employees to refrain from using OpenClaw on their work computers, due to the unpredictability of the agents and potential security risks. Last month a Meta employee who oversees safety and alignment for the company’s AI lab publicly shared a story about an AI agent going rogue on her machine and mass deleting her emails.

For Nvidia, NemoClaw appears to be part of an effort to court enterprise software companies by offering additional layers of security for AI agents. It’s also another step in the company’s embrace of open-source AI models, part of a broader strategy to maintain its dominance in AI infrastructure at a time when leading AI labs are building their own custom chips. Nvidia’s software strategy until now has been heavily reliant on its CUDA platform, a famously proprietary system that locks developers into building software for Nvidia’s GPUs and has created a crucial “moat” for the company.

Last month The Wall Street Journal reported that Nvidia also plans to reveal a new chip system for inference computing at its developer conference. The system will incorporate a chip designed by the startup Groq, which Nvidia entered into a multibillion-dollar licensing agreement with late last year.

Paresh Dave and Maxwell Zeff contributed to this report.