Meta describes how an AI agent can be designed to capture the logic and expertise of domain experts, rather than simply storing documents or retrieving relevant information. The system, dubbed an "organizational second brain", was built for a specialized compliance domain, but Meta argues the architecture generalizes to areas like security, finance, engineering, and procurement. By Sergio De Simone
Meta describes how an AI agent can be designed to capture the logic and expertise of domain experts, rather than simply storing documents or retrieving relevant information. The system, dubbed an "organizational second brain", was built for a specialized compliance domain, but Meta argues the architecture generalizes to areas like security, finance, engineering, and procurement.
Samsung has partnered with Mistral AI to deploy on-premises models across its semiconductor manufacturing and engineering operations.
The agreement was announced during the bilateral state summit held in Paris between South Korea and France. Samsung will integrate Mistral’s software suite – including its flagship Mistral Large model – into internal semiconductor facilities to build customised models for intelligence-driven factory infrastructure.
On-premises AI models for semiconductor fab
Samsung has partnered with Mistral AI to deploy on-premises models across its semiconductor manufacturing and engineering operations.
The agreement was announced during the bilateral state summit held in Paris between South Korea and France. Samsung will integrate Mistral’s software suite – including its flagship Mistral Large model – into internal semiconductor facilities to build customised models for intelligence-driven factory infrastructure.
On-premises AI models for semiconductor fab infrastructure
The deployment relies on private enterprise installations to process sensitive engineering and operational records within Samsung’s computing perimeter. This architecture keeps proprietary technical data contained within company infrastructure, avoiding external cloud exposure while maintaining control over operational assets.
“Increasing complexities involved in AI chip design and manufacturing requires continuous innovation in semiconductor technologies,” says Young Hyun Jun, Vice Chairman and CEO of the Device Solutions (DS) Division at Samsung Electronics.
Mistral will provide Samsung with a specialised stack of software tools to assist in how processors are designed and produced.
“AI is reshaping how we build complex technologies, from silicon to software,” says Arthur Mensch, co-founder and CEO of Mistral.
“We are proud to support Samsung Electronics with our expertise in electronics and semiconductors, helping to improve how chips are designed and manufactured, and to accelerate technical progress across the global semiconductor and AI value chain.”
Defect detection and yield stabilisation
Samsung plans to deploy the targeted models directly to automated defect detection and fab machinery tuning. As semiconductor production processes advance, rapid data analysis inside the fab becomes necessary to maintain factory throughput.
The company expects targeted AI models to accelerate development cycles, improve manufacturing precision, and stabilise production yields across advanced memory and logic chips. The operational scope covers Samsung’s memory division, logic design units, and contract foundry business.
Samsung also led Mistral AI’s Series D funding round, securing a strategic equity stake to support long-term technical cooperation.
The lead investment expands cross-industry collaboration between silicon manufacturers and AI developers across advanced memory, logic, and foundry operations.
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Zhou Yu discusses why AI agents stall in demo phase and shares how simulation-driven testing solves compliance and reliability bottlenecks. Learn how Columbia and Arklex AI use synthetic user personas, trajectory entropy, and automated CI/CD pipelines to evaluate multi-turn agents, catch edge cases before deployment, and scale self-learning workflows in production. By Zhou Yu
Zhou Yu discusses why AI agents stall in demo phase and shares how simulation-driven testing solves compliance and reliability bottlenecks. Learn how Columbia and Arklex AI use synthetic user personas, trajectory entropy, and automated CI/CD pipelines to evaluate multi-turn agents, catch edge cases before deployment, and scale self-learning workflows in production.
M&T Bank has deployed AI copilots to more than 15,000 employees as the US regional bank applies AI to internal operations, customer service, software development, and risk management.
The bank uses AI to analyse call-centre conversations, draft reports, generate code, identify customer needs, and flag portfolio risks, according to Fast Company. M&T is also examining agentic AI applications in cybersecurity and fraud detection.
American Banker reported in September 2025 that 16,000
M&T Bank has deployed AI copilots to more than 15,000 employees as the US regional bank applies AI to internal operations, customer service, software development, and risk management.
The bank uses AI to analyse call-centre conversations, draft reports, generate code, identify customer needs, and flag portfolio risks, according to Fast Company. M&T is also examining agentic AI applications in cybersecurity and fraud detection.
American Banker reported in September 2025 that 16,000 of M&T’s roughly 22,000 employees were already using Microsoft Copilot for tasks including drafting emails and reports and summarising call-centre conversations.
Before the wider rollout, M&T initially restricted employee access to public large language models. Chief data officer Andrew Foster told American Banker that the bank blocked the tools because employees could potentially enter sensitive company information into public-facing services.
M&T later evaluated enterprise providers and selected Microsoft Copilot, starting with a pilot involving about 800 employees before expanding access across the organisation.
Foster said using generative AI to summarise call-centre conversations saves about six minutes per call. Software developers at the bank also use GitLab tools to generate code, while employees remain responsible for reviewing AI-generated work.
M&T’s human-review requirement is also reflected in its 2026 Code of Business Conduct and Ethics. The policy requires employees to use approved AI tools and prohibits confidential, proprietary, customer, employee, or regulated information from being entered into unapproved systems. Employees remain responsible for the accuracy and appropriateness of AI-assisted work.
Building the technology and data foundation
M&T’s AI deployment follows a technology overhaul that began in 2018. The bank said more than half of its technology specialists were external workers at the time, compared with an 80% in-house technology workforce today.
M&T now has about 2,000 technologists working across more than 300 agile teams and has hired more than 1,000 technology specialists during the programme.
The bank has also replaced dozens of older platforms. M&T said technology outages have fallen by more than 80% since 2018, while the number of system upgrades completed annually has increased by 300%.
Technology spending exceeded $1.2 billion in 2025, nearly three times its 2017 level. Wisler told Forbes in August 2026 that annual technology releases increased from about 15,000 in 2018 to 65,000 in 2025.
Wisler joined M&T as chief information officer in 2018 before becoming senior executive vice-president for technology and operations in 2025. His current remit covers both technology and operational functions across the bank.
M&T’s data programme developed alongside the broader technology overhaul. Foster, who joined the bank in 2023, began building a data-lineage programme to track where information originates, how it is used, and how it moves between systems.
Foster told American Banker that the data-lineage work was not created in response to generative AI. He described it as a core capability for understanding M&T’s data estate.
The bank also established a Data Academy focused on data governance and data skills, with around 2,000 employees participating in the programme.
M&T has created an internal repository called Edison containing authoritative documents and information on bank policies. The bank also uses data-lineage software from Solidatus and Monte Carlo to trace information as it passes through databases, applications, and business-intelligence systems.
The lineage work gives M&T visibility into the source, meaning, quality, and governance of individual data elements, according to Foster. He said one application for that governed data is the bank’s use of Copilot.
M&T also uses retrieval-augmented generation with internal, governed data, according to American Banker.
Scaling AI into daily banking operations
Wisler told Forbes that M&T is pursuing generative AI through three routes: general employee use, AI capabilities embedded in existing applications, and proprietary systems built around the bank’s own data and processes.
M&T operates more than 1,800 applications, many supplied by third-party vendors. Wisler said one of the bank’s AI pathways is identifying useful AI capabilities already embedded within those applications.
M&T’s third pathway involves proprietary AI development around the bank’s own data and processes. Forbes reported that early applications include repetitive operational work, software development, fraud prevention, and cyber defence.
Fast Company’s September report also said M&T continues to assess both internally developed AI systems and external tools, including general enterprise software and technology designed specifically for banks.
Earlier workforce use cases centred on drafting, summarisation, call-centre work, and software development. Fast Company reported that newer applications include identifying customer needs and flagging portfolio risks.
Other large US banks have also expanded generative AI across employee workflows.
JPMorganChase launched its internal LLM Suite platform to more than 200,000 employees in 2024. By 2025, more than 65,000 employees in its Corporate and Investment Bank were actively using the platform, while more than 90% of its engineers were using AI coding assistants.
The bank also said AI-based transaction screening allowed it to review more than twice the previous transaction volume while reducing manual operator checks by half.
Bank of America is using a generative AI-enabled system called EricaAssist with more than 18,000 customer service employees. The tool summarises why a customer is calling, retrieves relevant information, and recommends possible next steps while keeping the employee responsible for the interaction.
Bank of America said in July 2026 that EricaAssist can deliver contextual guidance in under three seconds and has reduced average call times by nearly one minute. The bank plans to extend the system to additional servicing scenarios and business lines later in 2026.
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NVIDIA has agreed to acquire Hugging Face for $12.93 billion to scale the open-source model repository’s platform and infrastructure.
The transaction targets platform growth and infrastructure investment, aiming to expand AI access for enterprise developers, software engineers, and research institutions globally.
Built over the past decade by Clem Delangue, Julien Chaumond, Thomas Wolf, and their engineering team, Hugging Face serves as the primary home for the open model developer communi
NVIDIA has agreed to acquire Hugging Face for $12.93 billion to scale the open-source model repository’s platform and infrastructure.
The transaction targets platform growth and infrastructure investment, aiming to expand AI access for enterprise developers, software engineers, and research institutions globally.
Built over the past decade by Clem Delangue, Julien Chaumond, Thomas Wolf, and their engineering team, Hugging Face serves as the primary home for the open model developer community.
Platform metrics show more than 18 million developers, researchers, and creators share more than three million models, 500,000 datasets, and one million applications. Commercial adoption includes more than 200,000 companies using the environment to discover, evaluate, customise, and deploy AI models.
Hardware neutrality and multi-cloud commitments
NVIDIA stated that Hugging Face will remain an open platform for the entire AI sector. Developers will retain full control over their selection of models, software frameworks, cloud providers, inference services, and computing hardware.
NVIDIA hardware will not be mandatory to build on or deploy software through the platform. The service will maintain operational support for alternative accelerators, multi-cloud architectures, and open-weight models from all third-party builders.
Jensen Huang, Founder and CEO of NVIDIA, said: “Hugging Face will remain an open platform for the entire AI ecosystem. Developers will choose the models they want, the frameworks they want, the clouds and inference service providers they want, and the computing platforms they want.
“NVIDIA compute will not be required to build on or deploy through Hugging Face.”
Open-weight models and distributed development
Huang noted a recent open letter he co-authored regarding the role of open weights in the AI economy. The position argued that open weights broaden AI access and ensure technical leadership remains distributed across companies, academic institutions, and developer communities.
Under this operational model, commercial businesses, startups, universities, and public bodies can build on advanced capabilities without the expense of training baseline models from scratch.
The approach allows organisations to match specific models to operational tasks across factories, hospitals, farms, classrooms, and commercial businesses, while addressing cybersecurity and data sovereignty requirements.
Julien Chaumond, Co-Founder and CEO of Hugging Face, commented: “AI is at an inflection point. Open-source AI can become less relevant in the coming years if the big closed labs run away with it, or it can become the foundational fabric of the next phase of human civilisation.
“Those are vastly different outcomes, and we need the critical mass to ensure we give our collective best shot to the second outcome. Given Jensen Huang’s stance on open source AI and how he stepped up to defend it when it was under threat earlier in the summer, NVIDIA was the only partner we truly considered.”
Infrastructure expansion and brand preservation
NVIDIA stands as the largest contributor of open models and data to Hugging Face, with a portfolio of more than 500 open models and more than 250 open datasets. The company builds its libraries, tools, and models openly to allow external engineers to inspect, modify, and build atop the software.
Technical integration will focus on applying NVIDIA infrastructure and engineering resources to improve repository reliability, safety controls, model evaluation tooling, inference execution, and deployment pipelines.
“I am honored that Clem came to me as he considered the next chapter of Hugging Face and believed NVIDIA would be a great home for the company, its community and the future of open models,” Huang stated.
Hugging Face will retain its independent brand identity following the completion of the transaction, with the existing team continuing operations across multi-cloud and multi-accelerator environments.
“This gives fuel to our long-term vision and mission of unlocking the community’s progress to ensure that AI, which is the greatest breakthrough of our lifetime, is accessible to as many people as possible,” Chaumond concludes.
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
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