#IBM

Meta Lifts AI Spend to $145B, Tesla and IBM Beat

Meta lifted 2026 capex to $135B-$145B and added non-voting shares while Tesla beat on profit and IBM said slipped mainframe deals are returning.

Sam Altman wrong about AI: $700B trap

Eli the Computer Guy says OpenAI's bigger-is-better bet locked in $700B in contracts as enterprises shift to bespoke, fine-tuned models.

IBM and Arm Forge Dual-Architecture Processor for Enterprise

IBM's AI App Enhances US Open Tennis Experience

IBM's new AI-powered app for the US Open provides advanced data analytics for players and fans, including 'serve quality' and key moment insights.

AI's Impact on Wages and the USMCA Under Pressure

Former IBM CEO Sam Palmisano discusses AI implementation plans, while data shows AI slowing wage growth. Also, USMCA trade and the Colorado River crisis are examined.

CoreWeave Named Gartner Cloud AI Visionary

CoreWeave named a Visionary in 2026 Gartner Magic Quadrant for Cloud AI Infrastructure, highlighting its specialized platform for demanding AI workloads.

Nadella, A16z, IBM Push for Open AI Models

Microsoft's Satya Nadella, alongside tech leaders from Andreessen Horowitz and IBM, have signed a letter advocating for open-weight AI models, citing competitive threats from China and regulatory concerns.

Tesla's AI Bets Drive Spending, Mobileye CEO Departs

Tesla's AI spending, Alphabet's increased CapEx, and Mobileye's CEO transition dominate tech news.

Big Tech Earnings: AI Spend, China, and Chip Stocks in Focus

Nathan Hager and Bailey Lipshultz discuss upcoming Big Tech earnings from Alphabet, IBM, Intel, and Tesla, focusing on AI spending, US-China competition, and market trends.

SpaceX IPO, Google's AI Push, World Cup Prices

Wall Street Week covers the U.S. government's quantum computing investments, Google's AI search overhaul, and World Cup ticket price concerns.

Multi-Agent AI: Why One Brain Isn't Enough

IBM AI Engineer Bri Kopecki explains why multi-agent AI systems are crucial for high-stakes decisions, drawing parallels to human collaboration and the Apollo 11 mission.

CAG vs. Long Context: AI's Memory Explained

IBM's Martin Keen explains how AI models use Long Context and Cache Augmented Generation (CAG) to process information, highlighting the trade-offs and efficiency gains of each approach.

Project Glasswing: AI's First Findings Revealed

Project Glasswing's first findings reveal the power of specialized AI agents in vulnerability discovery, stressing practical, layered approaches over monolithic models.

The 4 Types of AI Agent Memory Explained

IBM Master Inventor Martin Keen details the four essential memory types AI agents need: working, semantic, procedural, and episodic.

5 AI Risks That Can Get You Fired

IBM Master Inventor Martin Keen reveals 5 ways AI use can get employees fired, including shadow AI, data leakage, and prompt injection.

AI at Graduations & Claude's Blackmail Tactics

IBM experts discuss AI's evolving role, from college graduations to ethical dilemmas like LLM data corruption and potential 'blackmail' scenarios.

AI Security: Daybreak vs. Mythos & LLM Vulnerabilities

OpenAI's Daybreak and Mistral's Mythos are discussed as AI cybersecurity tools, alongside the challenges and opportunities of AI in threat detection and defense.

Agentic AI Transforms Maintenance and Asset Decisions

IBM's Michael Dawson explains how agentic AI is transforming asset maintenance from reactive to proactive, enabling intelligent systems of action.

AI Agents: MCP vs. ADK for Interoperability

IBM's Anna Gutowska and Red Hat's Cedric Clyburn explain MCP and ADK, detailing how AI agents connect and work together through standardized protocols and flexible development frameworks.

IBM's Tejas Kumar on 'AI Harnesses'

IBM's Tejas Kumar explains the concept of AI harnesses, detailing their types (Eval and Agent) and key components like tools, models, context management, and guardrails.

AI Agents Break Zero Trust at the Last Mile

IBM's Grant Miller explains how AI agents break Zero Trust at the 'last mile' and outlines strategies to secure these complex integrations.

Agentic AI Fails: Loops, Planning & Unsafe Tool Use

An IBM Advisory AI Engineer breaks down why agentic AI systems fail, focusing on infinite loops, planning errors, and unsafe tool use, and offers mitigation strategies.

LLMjacking: Hackers Steal AI API Keys, Cause Bill Shock

Hackers are increasingly targeting AI API keys through 'LLMjacking' to incur massive charges on victims' accounts, as highlighted in a recent Security Intelligence podcast.

AI Agents Need an OS, Says IBM Engineer

IBM AI Engineer Bri Kopecki explains why AI agents need an operating system to manage their tasks, memory, tools, and identities for reliable and safe operation.

Predictive vs. Generative AI: Key Differences Explained

IBM's Martin Keen clarifies the distinction between predictive AI (forecasting outcomes) and generative AI (creating new content), outlining their core mechanics and use cases.

AI Agents Need Consent: IBM Explains Agentic Consent

IBM's Grant Miller explains agentic consent, detailing how AI agents' autonomous actions necessitate dynamic, transparent, and revocable consent mechanisms.

The Rise of Crypto Agility: Preparing for the Quantum Era

IBM Distinguished Engineer Jeff Crume discusses the critical need for 'crypto agility' to prepare for the quantum computing era, highlighting historical examples of obsolete cryptography and a roadmap for organizations.

AI Security's Y2K Moment: Anthropic, OpenAI & Beyond

Experts discuss Anthropic's new security beta, OpenAI's AI safety plan, and how AI is creating a 'Y2K moment' for cybersecurity.

RAG's Evolution: From Keywords to Agentic AI

Explore the evolution of Retrieval Augmented Generation (RAG) from basic keyword search to sophisticated agentic AI systems.

IBM Vice Chairman Gary Cohn on AI's Financial Transformation

IBM Vice Chairman Gary Cohn discusses AI's impact on finance, the importance of hybrid cloud, and the need for workforce adaptation in the face of technological change.

AI Agents: CLI vs. MCP for Tool Selection

IBM's Martin Keen explains how AI agents use CLI commands or the more descriptive Model Context Protocol (MCP) to select and utilize tools, highlighting the benefits of structured data for AI.

IBM's Downie on Synthetic Monitoring for AI

Amanda Downie of IBM explains how synthetic monitoring proactively identifies issues in user journeys like login and checkout, enabling teams to ensure reliability and performance.

IBM Master Inventor on AI's Contextual Bottleneck

IBM Master Inventor Martin Keen discusses how context is the key bottleneck for AI models, outlining four pillars of context engineering: connected access, knowledge layer, precision retrieval, and runtime governance.

IBM Experts on AI Training: Efficiency vs. Scale

IBM's Marina Danilevsky and Gabe Goodhart discuss the company's new 'Bob' and 'Granite' AI models, highlighting the shift towards specialized, efficient training and the challenges of distributed AI infrastructure.

IBM Expert: Quantum Computing Threat to Crypto is Real

IBM Distinguished Engineer Jeff Crume warns that quantum computers will break current encryption by 2030-2035, urging immediate migration to quantum-resistant cryptography.

Open Source AI: Boon or Bane for Security?

IBM's Martin Keen and Gabe Goodhart discuss the security implications of open-source AI, balancing innovation with risk.

Why GPUs are Key to Generative AI

IBM's Lauren McHugh explains how GPUs, originally for gaming, became vital for generative AI's computational demands, highlighting their parallel processing advantages.

IBM Experts on Building With AI Agents

IBM experts Katie McDonald and Brianne Zavala discuss the strategic choices for implementing AI agents: build, reuse, or hybrid. They stress the importance of orchestration.

Apple's AI Ambitions and the Race for Compute

Apple is reportedly exploring significant cloud partnerships, including with AWS, to bolster its AI capabilities, indicating a strategic shift in its compute strategy.

AI Pen Testing: Open Source AI Finds 23 Flaws in Mock Network

IBM security experts discussed an experiment where the AI agent OpenClaw found 23 vulnerabilities in a mock network, highlighting AI's potential and challenges in cybersecurity.

IBM's AI Agents: Building a Team for Complex Tasks

Lauren McHugh Olende from IBM discusses the importance of building collaborative AI agent teams, detailing key roles and strategies like prompting and model selection for optimal performance.

IBM's Katie McDonald on AI: ADK vs. RAG

IBM's Katie McDonald explains the core differences between AI Agent Development Kits (ADK) and Retrieval Augmented Generation (RAG) and when to use each.

AI Agents Need Skills: Martin Keen on LLM Tooling

Martin Keen of IBM explains how AI agent skills, defined in structured files, are essential for LLMs to perform tasks, detailing the "skill file" format and different knowledge types.

IBM's Dan Wiegand on AI and Mainframe Augmentation

IBM's Dan Wiegand discusses how AI, including RAG and agents, is transforming daily productivity and enhancing mainframe operations.

AI Adoption in the Workplace: What Experts Say

IBM experts discuss the rise of AI in the workplace, highlighting improvements in models like Claude 4.7, Apple's AI hardware ambitions, and future trends in AI adoption.

Jeff Crume on IBM's Quantum-Safe Crypto

IBM Distinguished Engineer Jeff Crume discusses the urgent need for quantum-safe cryptography and how lattice-based algorithms are key to securing data against future quantum computers.

AI in Cybersecurity: Threats and Opportunities

IBM experts discuss the dual role of AI in cybersecurity, from finding vulnerabilities to enabling sophisticated scams, and the rise of insider recruitment.

7 Skills for Effective Agent Engineering

IBM AI Engineer Bri Kopecki outlines 7 key skills for building effective AI agents, emphasizing system design, tool integration, and reliability beyond basic prompt engineering.

IBM's Martin Keen on Physical AI Training

IBM Master Inventor Martin Keen discusses the advancement of Physical AI, focusing on how robotic agents learn through simulation and real-world data, and the role of compute efficiency.

IBM CTO on AI Agent Identity and Access Management

IBM CTO Grant Miller presents a four-step model for maturing AI agent identity and access management, from 'ad hoc' to 'adaptive' controls.