AGI

Huawei introduced SuperPoD at HUAWEI CONNECT 2025, a new AI infrastructure architecture that aggregates thousands of AI chips into a unified resource using UnifiedBus (UB). This creates a “supercomputer” from distributed servers, designed to address the limitations of traditional architectures. The Atlas 950 SuperPoD utilizes up to 8,192 Ascend 950DT chips, with future plans for the larger Atlas 960. Beyond AI, TaiShan 950 SuperPoD targets general-purpose computing. Huawei’s open-source approach with UnifiedBus 2.0 aims to accelerate innovation and foster broad industry participation in AI infrastructure development.

Netskope reports near-universal (95%) generative AI adoption in retail, up sharply from 73% last year, driven by competitive pressures. While usage of company-approved AI tools rises (from 21% to 52%), security risks escalate, with source code (47%) and regulated data (39%) commonly exposed. Companies are banning risky apps like ZeroGPT, and increasingly using enterprise platforms like OpenAI via Azure and Amazon Bedrock (16% each). Concerns extend to API connections (63%) and broader cloud security vulnerabilities, including malware via OneDrive and GitHub. Strict data protection and visibility are crucial.

OpenAI and Nvidia are reportedly discussing a potential $100 billion partnership, with Nvidia supplying at least 10 gigawatts of hardware and investing significantly in OpenAI. This collaboration aims to bolster OpenAI’s AI infrastructure for advanced model training, utilizing Nvidia’s Vera Rubin platform starting in 2026. The deal raises concerns about competition, potentially solidifying Nvidia and OpenAI’s dominance. OpenAI seeks to secure computational resources crucial for AI development, while also exploring custom chip solutions. The partnership is under scrutiny for potential circular funding and antitrust implications.

Agentic AI, intelligent systems acting as autonomous agents, is rapidly integrating into business, yet raises significant risks. Organizations deploying it must address potential deviations from business rules, regulatory mandates, and ethical standards. Low-code platforms offer a solution by embedding governance and compliance into the development process, unifying app and agent development within a single environment and enabling seamless integration with existing systems. This approach fosters transparency, control, and scalability, ensuring AI-driven processes align with strategic goals while mitigating risks.

AI implementation often stalls due to poor data quality. Snowflake’s Martin Frederik emphasizes that a robust data strategy is crucial; AI is only as good as the data it uses. Successful AI projects require clear business alignment, addressing data challenges from the start, and viewing AI as an enabler, not the end goal. Key factors include accessible, governed, and centralized data platforms and breaking down data silos. The future lies in AI agents capable of reasoning across diverse data, empowering users and freeing data scientists for strategic tasks.

A recent study reveals a significant lack of public trust in AI, hindering its widespread adoption despite government efforts. This skepticism stems from unfamiliarity and concerns about ethical considerations like data privacy and potential misuse. Trust correlates with usage, as those familiar with AI are less likely to perceive it as a risk. The report emphasizes the need for targeted communication highlighting tangible benefits, demonstrable effectiveness in public services, and robust regulations to ensure ethical and responsible development. Building trust requires transparency and a collaborative approach.

Agentic AI promises to unlock generative AI’s potential, addressing the current disconnect between adoption and bottom-line impact. AI agent orchestration is emerging as crucial, with platforms like BMC’s Control-M evolving to manage autonomous agent deployments across diverse systems. BMC envisions Control-M as an “orchestrator of orchestrators,” connecting various tools and facilitating AI agent coordination. As companies like Salesforce develop “digital labor platforms,” demand for robust orchestration layers is growing. Effective orchestration is vital for operationalizing AI, mitigating risks, and ensuring compliance, thereby maximizing business outcomes and ROI.

TechEx Europe 2025 in Amsterdam will host over 8,000 attendees and 250+ speakers across AI, cybersecurity, IoT, digital transformation, and data center expos. Focused on AI operations, particularly agentic AI, the conference addresses governance, trust, and infrastructure needs for scaling AI. Sessions feature leaders from Deutsche Bank, Mastercard, Reddit, NVIDIA, and NATO, discussing responsible scaling, monitoring frameworks, and infrastructure readiness. Attendees will gain practical insights and network with industry peers to navigate the evolving AI landscape.

Dell Technologies is focusing on helping enterprises scale AI projects into production with its AI Factory, AI Data Platform, and Data Lakehouse. Collaborations with NVIDIA and others provide infrastructure and data management for seamless AI integration. Key features include an unstructured data engine (powered by Elastic and GPU-accelerated PowerEdge servers), addressing data gravity with federated queries, and prioritizing on-premise solutions for data-sensitive industries. Dell emphasizes governance, security, and a unified ecosystem to accelerate AI adoption across various environments, including personal devices.

At Huawei Connect 2025, Huawei revealed its Ascend chip roadmap, including the 950, 960, and 970 series for AI and HPC, challenging NVIDIA’s dominance. Despite semiconductor manufacturing challenges, Huawei focuses on domestic design, proprietary tech, and open-source strategies. New Ascend chips promise performance leaps with enhanced interconnects. Huawei’s SuperPoD and SuperCluster strategy, powered by UnifiedBus 2.0 (an open protocol), aims to provide scalable, high-performance computing, expanding into general-purpose computing with Kunpeng 950 processors and TaiShan SuperPod. Huawei claims significant performance advantages over competitors.