Jeff Sandquist

With AI adoption and deeper integration into enterprise products, the world moves toward being collectively intelligent. First movers will have a distinct advantage. Fully backward-integrated AI companies are best positioned to win on economics.

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Salesforce's VP Gary Lerhaupt, VP Product Architecture weighs in on Google's A2A protocol in enabling intelligent communication and collaboration between AI agents.

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If you are anywhere near Fullerton, California this week, join us for the next "I Build with AI" meetup, where Mark Warrick, AI Strategist and App Developer, will be presenting and running a workshop on how to Turn Your Domain Expertise into a Mobile App with AI! Instead of focusing on code or technical implementation details, the session centers on understanding intent, decision-making, and real-world judgment, and how AI can help translate that thinking into software. To make the concepts concrete, Mark will walk through a real example called Trail Palette, a mobile weather app designed for outdoor photographers. Drawing on his experience spending countless hours in mountainous terrain as both a guide and photographer, Mark brings an intuitive understanding of how weather, light, and landscape interact, and how that domain knowledge shapes the logic and behavior of an app. This session is designed for knowledge workers and domain experts who are non-developers that want a clear mental model of what it takes to build a mobile app using AI. Along with a hands-on workshop, you’ll gain an understanding of the tools involved and inspiration to see how your own domain expertise could become the foundation for software. RSVP for the event at: https://lnkd.in/gCj-GTD5

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The simultaneous emergence of Claude Cowork and Clawdbot (now rebranded as Moltbot) is a clear signal of where consumer AI is headed this year: genuine end-user productivity through semi-autonomous agents. The days of purely prompt-based, one-shot results are giving way to systems that can handle real, ongoing work based on high-level requests. These tools are starting simple, but their trajectory is unmistakable: they will likely evolve quickly into capable digital assistants running 24/7. If recent AI trends hold, most of us will soon dedicate at least one (if not several) always-on machines or cloud instances to handling our routine personal and professional chores. This shift could free up significant time and mental energy, allowing us to focus on higher-leverage activities with far greater returns. Security, however, remains the single biggest hurdle. Granting AI agents access to local files, email, calendars, or messaging apps introduces real risks such as prompt injection, data leakage, unintended deletions, or worse. While tools like Cowork use sandboxes and careful permissions, and self-hosted options like Moltbot keep data local, vulnerabilities persist and demand vigilance. For now, the wisest path is cautious experimentation: start small, isolate sensitive data, monitor closely, and contribute feedback to help shape safer, more reliable agents. These early steps are opening exciting new chapters for everyday users. AI, it appears, won't just answer questions, but will truly work alongside us.

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Part 2 of my ongoing series From Vibe Code to Production-Ready is live! This one dives into Claude, Gemini, and MCPs (Zen + Serena) -- not just as shiny tools, but as a framework for moving from quick prototypes into reliable systems: Command patterns, context, and the practices that make "vibe coding" scale. Part 1 explored why vibe coding eventually breaks down, and Part 2 is the bridge: where experimentation starts becoming engineering. Part 3 will explore agent command patterns: Compacting, Context, and Claude Code Ops Read here: https://lnkd.in/e8-dnaYC #AI #MCP #ClaudeAI #Gemini #AgenticAI #Engineering #ProductDevelopment #VibeCoding #FromPrototypeToProduction #TechLeadership

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For enterprise leaders watching their AI pilots struggle to scale, Element offers a crucial lesson. Success isn’t about choosing the right model or vendor, it’s about building the organizational capability to turn AI potential into a consistent operational reality at scale. Walmart has demonstrated what’s possible when enterprises stop thinking of AI as software to install, and start thinking of it as a capability to create. The enterprises that understand this distinction will define the next decade. https://lnkd.in/eU2FT-Qf

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Tokens per watt isn’t just a slide in a deck—it’s how you know your AI factory is actually efficient. At #GTC26, Emily Potyraj will walk through how we use Performance Benchmarking Recipes to squeeze more tokens per watt out of any AI infrastructure, and turn “it seems fast” into hard numbers you can act on. If you care about GPU utilization, energy bills, or cost per million tokens, this session is for you. https://bit.ly/4s5YBIB

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