Electrical Equipment and Power Systems

General Electric Company

AI Fuels Small Business Growth in Competitive Markets

StartupHub

AI Fuels Small Business Growth in Competitive Markets

AI is reshaping the workforce by transforming job roles and HR practices. A CNBC poll reveals varied AI adoption, with 50% of organizations seeing AI impact fewer than half of jobs, while 17% report half or more affected. HR leaders expect increased AI integration, with 45% predicting AI will impact half or more jobs in the next year. AI is seen as enhancing efficiency and innovation, with a shift towards skill-based hiring. The consensus is that AI will augment, not replace, human capabilities, emphasizing strategic integration and adaptation.

- The discussion on CNBC highlighted the transformative impact of AI on the workforce, with a poll revealing that 50% of HR leaders see AI affecting fewer than half of jobs in their organizations.  
- Looking ahead, 45% of HR leaders expect AI to impact half or more of jobs within the next year, indicating a growing integration of AI technologies.  
- The conversation emphasized the need for strategic AI adoption in hiring practices, moving towards skill-based assessments, and underscored AI's role as a tool for augmenting human capabilities rather than replacing them.  

C3.AI

How AI is reshaping work

DeepMind has unveiled SIMA 2, an advanced AI agent powered by Gemini AI, capable of operating in 3D virtual worlds with human-like reasoning and independence. It can interpret complex instructions, adapt to unfamiliar environments, and learn autonomously, marking a significant step towards Artificial General Intelligence (AGI) and future robotics.

CoinLive

Alphabet - C

Alphabet

DeepMind’s SIMA 2 Uses Gemini AI to Play Games, Navigate Complex Virtual Worlds, and Handle Multi-Step Tasks Like a Human Player

A survey by Omdia reveals mixed results in AI adoption among firms, with 31% experiencing low success rates in AI PoC projects. The main issue is not the AI technology itself but the lack of preparation and understanding of AI deployment complexities. Larger firms with more resources tend to manage more PoCs successfully. Despite challenges, some companies report AI deployments exceeding productivity expectations, though proving ROI remains difficult.

The Register

AI pilots keep crashing, mostly because firms skip the prep, survey finds

The CNCF launched the Certified Kubernetes AI Conformance Program to ensure AI workload standards, but analysts question its maturity. The program, modeled after the Kubernetes Conformance Program, includes major vendors like AWS and Google Cloud. CNCF aims to simplify AI/ML on Kubernetes and accelerate adoption. Nvidia's role in the program is under scrutiny, despite its significant market presence. CNCF leaders foresee a standard open-source framework for AI agents emerging in the next few years.

TechTarget News

CNCF Kubernetes AI program faces scrutiny from IT analysts

In 2025, fintech transitioned from disruption to durable transformation, driven by AI, digital assets, embedded finance, and data analytics. Major banks increased AI personnel by over 25%, enhancing product development and governance. Tokenization became a foundational market layer, surpassing $30 billion, while embedded finance emerged as a core growth strategy. Data analytics evolved into a critical asset, enabling real-time insights for better decision-making. To thrive, fintech must integrate these elements into cohesive strategies, focusing on innovation, interoperability, and regulatory compliance for future success.

- In 2025, fintech has transitioned from disruption to durable transformation, focusing on scalability and measurable value creation.  
- Key forces shaping the industry include artificial intelligence, digital assets and tokenization, embedded finance, and data analytics, which require strategic integration for sustainable competitive advantage.  
- To thrive, firms must treat AI as a backbone, adopt interoperable systems, embed financial services into platforms, and leverage data for real-time insights, ensuring they remain at the forefront of innovation.

Forbes

The Top 4 Fintech Takeaways Of 2025

The recent Security Intelligence podcast emphasized that human error, not sentient AI, drives modern cyber threats. Experts discussed vulnerabilities in operational technology (OT) systems, which lag in patching compared to IT systems, creating exploitable gaps. Cyberattacks are increasingly affecting physical infrastructure, with hackers targeting freight companies and embedding dormant logic bombs in software. The conversation highlighted the need for proactive security measures and debunked myths about AI-driven malware, asserting that the real risks stem from human behavior and systemic weaknesses.

Human Error, Not Sentient AI, Drives the Modern Cyber Threat Landscape

Jack Hidary, CEO of SandboxAQ, emphasizes the need to broaden the focus of AI beyond Large Language Models (LLMs) to include its applications in the physical world. He highlights inefficiencies in energy consumption and the potential for AI to optimize resource management. Hidary advocates for a hybrid approach combining AI with quantum computing to tackle complex problems in sectors like materials science and drug discovery. He warns against an over-reliance on LLMs, suggesting it could lead to an unsustainable investment bubble, and stresses the importance of addressing real-world challenges for sustainable growth in AI.

- Jack Hidary, CEO of SandboxAQ, argues that the focus on Large Language Models (LLMs) in AI is too narrow, neglecting broader applications.  
- He emphasizes the need for advanced computational power to address inefficiencies in energy consumption and to optimize resource management in various industries.  
- Hidary advocates for a hybrid approach to AI, integrating quantum computing to tackle complex real-world problems, which he believes will drive sustainable growth and innovation.

SandboxAQ CEO: People are too focused on LLMs, need to think about AI in bigger...

DTST

In a recent IBM presentation, Prabira Acharya highlighted the importance of data storage observability in modern hybrid environments. He discussed how AI and AIOps are transforming observability from a reactive task to a proactive capability essential for enterprise efficiency. Acharya outlined seven pillars of observability: Availability, Performance, Capacity, Security, Inventory, Cost, and Sustainability. These pillars help organizations manage storage effectively, optimize performance, and ensure security. AI enhances observability through anomaly detection, predictive analytics, and intelligent automation, ultimately leading to data-driven decision-making and improved operational resilience.

Data Storage

The Intelligent Dashboard: AI and Agentic AIOps Transform Data Storage Observability

A report on Supply Chain Management Software has been added to ResearchAndMarkets.com's offerings. The global market for this software was valued at US$19.0 billion in 2024 and is expected to reach US$22.9 billion by 2030, with a projected growth rate of 3.2% from 2024 to 2030. The report offers in-depth insights into market trends, drivers, and forecasts to assist in making informed business decisions regarding the supply chain.

Unusual Whales

Global SCM software market valued at $19.0B in 2024, projected to reach $22.9B by 2030 with 3.2% CAGR growth. Increased demand for end-to-end visibility and real-time data analytics driving growth.

The article discusses the growing significance of AI inference, highlighting its impact on power consumption, costs, and user experience. With millions of users and billions of tokens processed, optimizing inference is crucial for performance. Key strategies include model compression, adaptive compute, and system optimization. As AI applications evolve, the demand for inference increases, leading to recursive chains of requests. Despite decreasing per-call costs, the intensity of usage in agentic AI systems raises overall expenses. The shift towards multimodal AI further complicates the landscape, necessitating efficient management of resources.

- The article discusses the increasing operational costs and complexities associated with AI inference, highlighting the significant power and financial resources required as usage scales.  
- It outlines strategies for optimizing inference, such as model compression, adaptive compute, and efficient system design, while noting the shift towards smaller, task-specific models that can operate closer to users.  
- The piece concludes by emphasizing the need for careful planning and management of resources, projecting a future where AI's compute demands could reach substantial levels, necessitating a utility-like approach to infrastructure.