David Linthicum

On "The AI Revolution in Medicine, Revisited" podcast @MSFTResearch, guest @CALonghurst talks about AI "empathy." E.g., a worried patient might send "nasty" message to the doctor, but AI has infinite ability to respond with in a supportive and useful way. https://lnkd.in/gAZYhDnE

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I thoroughly enjoyed moderating a panel on quantum computing for executive leaders at SIBF this week in Palo Alto with Amit Bhattacharyya (UC Berkeley), Charles Chung (IBM), and Rajesh Ramanujam (Applied Ventures). We talked about a broad future "Beyond AI" and explore the various ways quantum is advancing. A few key takeaways: 1 - Chuck shared that IBM anticipates demonstrating "Quantum Advantage" next year, with a fault tolerant computer by 2029. 2 - Rajesh shared how companies concerned about data security should explore "Post Quantum Cryptography" 3 - Amit expounded upon how AI is accelerating quantum research, and that no matter how helpful AI may be today, students still must develop core critical thinking skills.

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Special Breaking Analysis: IBM’s Technology-Arbitrage Play—Turning Agentic AI & Durable Platforms into Enterprise Outcomes We believe IBM is executing one of the more underappreciated pivots in enterprise AI: shifting from labor arbitrage to technology arbitrage—packaging agents, durable platforms, and domain IP into repeatable workflow outcomes for regulated, complex environments. Why it matters IBM is marching toward ~50% software mix bringing margin tailwinds, while consulting is being rewired for “human + digital labor.” IBM’s “Client Zero” program mapped 490 workflows, instrumented ~70 with digital workers, and claims $3.5B in productivity gains - one proof point that outcomes, not GPUs or models, are what enterprises value. The hybrid stack (Red Hat + OpenShift AI + open-weight models) keeps latency low and inference close to data, policy, and governance - important for regulated enterprises. Partnerships (Anthropic for lifecycle governance; Groq for ultra-low-latency inference) strengthen agent reliability and cost/perf in production. Would like to see stronger ecosystem momentum. What’s working IBM claims it's seeing Agentic tractioin in customer care, back-office, and IT/SDLC and shows measurable cycle-time compression and operating leverage. Durable platforms (Z, Power, GPFS/Fusion) are being reframed as AI infrastructure to modernize, not rip-and-replace. What’s missing In our view, IBM still needs a clearer platform unifier across data, systems, and software - e.g. a WebSphere-for-AI-era - plus a stronger semantic layer to harmonize silos and feed agents with governed, actionable context. Investor angle If IBM can consistently move clients from weeks → days with packaged agent blueprints - and price on value/outcomes - our view is its momentum continues and is undervalued. We assign ~70% probability to this technology-arbitrage thesis for IBM to deliver above-market growth over the next 12–24 months. This research note based on day long analyst AMAs with Arvind Krishna Rob Thomas Ritika Gunnar Matt Hicks Mohamad Ali Ric Lewis Josephine Schweiloch Jay Gambetta & perspectives from several analysts including Matt Eastwood Crawford Del Prete Anurag Agrawal Anthony J. Bradley Rick Villars - Thanks Elizabeth Bamonte for hosting... #AI #AgenticAI #HybridCloud #EnterpriseSoftware #RedHat #Watsonx #Quantum #TechArbitrage Full post here: https://lnkd.in/ebv3VDv3

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It is not just rethinking how we work — it’s rethinking who/what we work with. The rise of agentic AI — autonomous systems that take initiative, make decisions, and collaborate — is fundamentally reshaping enterprise solutions. As highlighted in this recent article by Chad Wilson and SiliconANGLE & theCUBE’s coverage at Dell Technologies World, it is clear that we are entering an era where AI is not a tool, it is a potential collaborator. This evolution has important implications for identity and access management. If an AI agent can initiate actions, interface with systems, and process data: What level of trust do we assign to this non-human actor? How do we govern access, ensure accountability, and preserve compliance if digital “employees" make autonomous decisions? From Saviynt's perspective, there is an opportunity to apply core identity management principles to this new type of identity. However, Agentic AI will require an evolved model of identity governance because AI entities have unique characteristics that must be considered before incorporation in our zero trust architectures. Finally, as O’Donnell and Milanesi note, organizations must empower their people — not to just use AI as a tool, but to incorporate it into their business processes. The future of work will require us harnessing this new form of technology (as we did during the industrial revolution and computer revolution) to deliver human priorities; it will be a hybrid environment that requires both smart systems and smart policies.   https://lnkd.in/gJEJq4BQ

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The lasting value of LLMs and the road ahead The lasting value of LLMs will be the ability to create computer interfaces based on natural language. Humans who previously were unable to perform certain tasks because they were lacking domain-specific knowledge or technical skills will be able to invoke actions that cross several computational systems and obtain information which they previously could not access. LLMs will retain desirable characteristics in terms of natural language processing while their size will continue to shrink, ultimately finding their way onto every device. Beyond the execution of NLP tasks (primarily as the Input/Output interface of natural language for computing systems), LLMs will be integrated with other software components to which they will delegate various actions. At a high-level, that could be achieved by augmenting the design of applications that are LLM-centric today through appropriate extensions, for example, the development of protocols such as the Model Context Protocol (MCP) point in that direction. LLMs are not the way to "general artificial intelligence” (human-level AI) but instead a powerful foundation for computer interfaces based on natural language. A longer version of this thesis can be found here: https://lnkd.in/dQHkjDsa #LLM #AI

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Gemini 3 is here! It was incredible reading about Google's new model. It's feels far more than a simple performance bump—it signifies a real shift where AI moves into being a genuine “thought partner”. Few features that stood out: • State-of-the-Art Reasoning: I'm excited that this model is built to genuinely grasp depth and nuance, which means it's phenomenal at "reading the room" for complex problems and is setting a new standard for intelligence. • Advanced Agentic Power: What really caught my attention is Google's claim that this is their most powerful agentic and vibe-coding model yet. It enables planning and executing complex, multi-step workflows, which is a massive leap forward for productivity. • Broad Multimodality: I was amazed to read it's claimed as the best model in the world for multimodal understanding. It can seamlessly synthesize information—vision, code, and text—helping us learn, build, and plan complex tasks in totally new ways. Looking at these features, we are entering a new era of intelligence. I absolutely cannot wait to see what everyone builds with this! #Gemini3 #GoogleAI #AIInnovation #FutureOfWork #LLM #AI https://lnkd.in/guMDTxAU

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Reinforcement Fine-Tuning (RFT) is a technique that adapts Large Language Models (LLMs) to intricate reasoning tasks. Dive deep into a comprehensive exploration of RFT with GRPO in this course. Learn how to construct reward functions crucial for steering LLMs towards enhanced performance on complex, multi-step reasoning tasks during the GRPO training process.

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Organizations need a reliable way to measure the trustworthiness of #GenAI output. So Egen developed a new confidence score approach. Learn how our framework helps organizations safely automate high-value workflows, unlocking efficiency while maintaining rigorous standards for responsibility and risk management: https://lnkd.in/d7wjhE4j #CreateNewPossibilities #AI #innovation 

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John Snow Labs Releases #GenerativeAI #Lab 7.0 to Help Domain Experts Evaluate and Improve #LLM Applications and Conduct #HCCCoding Reviews. This is a #NoCode Tool to let your domain experts validate, fine-tune, and provide specific feedback on #AI models. Available now on all the major cloud marketplace or for on-premise deployment. https://lnkd.in/g9UD8mQS

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The explosion of unstructured data is reshaping the AI infrastructure landscape. As MinIO's Co-founder & Co-CEO Garima Kapoor Ph.D shared at Trace3's Evolve 2025, IDC predicts 221 exabytes of data by 2026, with 90% of it unstructured: text, images, audio, video… everything modern AI needs to learn. At MinIO, we’re already seeing customers starting at a 5+ exabyte scale. Watch Garima's full talk here: https://lnkd.in/gPzyfZqc #AI #ExascaleAI #DataInfrastructure #Evolve25

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When it hosts the next international AI summit, Tech Policy Press contributing editor Amber Sinha expects the Indian government to focus on innovation, investments, and access issues while evading scrutiny of its low-rights legal environment, which is ripe for untested technological experiments. https://lnkd.in/ey487FvF

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Preview of the #AI OS (operating system) of the future? The emerging "vibe coding" tools, such as #ClaudeCode, are becoming general-purpose personal productivity improvement tools, applicable to more than just programmers/coders. The video below provides clear, brief examples of novel ways to create #agents to support personal productivity tasks with or without custom coding. I encourage my American University - Kogod School of Business ITEC-617 MBA students to experiment with these tools to stay ahead of the curve in applying #AI to their education and work. https://lnkd.in/eSw2REB9

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