David Carmona

Andrew Ng reported from Davos, that AI deployment for transformative impact will require workflow redesign. It is "theory of constraints" all over again (remember Eli Goldratt's "The Goal"?), we have to understand that real business benefits come only from working across silos, from analyzing flow bottlenecks, from understanding that optimizing non-bottlenecks creates waste. As Ng noted, letting a thousand (AI) flowers bloom has failed to lead to significant payoffs. Obviously! We in the silicon industry are no strangers to this. Workflows have been stagnant for decades. Technologies for high level specification, high level synthesis, and formal verification are not yet integrated into the full silicon development flow. They are often done piece-meal, and enthusiasts' pilots are dropped at the hint of a failure or delay. People say they want formal verification in their flow, but want it yesterday and do not have the time to invest in training engineers for its rigorous usage. Ashish Darbari, responding to a question on "if formal verification is so powerful then how come it is not widely used", noted that the problem is in the name - the actual task to be done is formal analysis while the bug detection is only a corollary, but the industry puts the cart before the horse. Based on my experience with industrial practice of high level specifications and formal verification, I tend to agree with his succinct comment. Jan Rabaey, in a DAC keynote speech, said that someday he expects to see a comic book series entitled "adventures in formal verification". AI deployment faces the same problem, if not actively worked on by business leaders. To be fair, deployments of all disruptive technologies face this mental barrier, we are so used to thinking in terms of the old flow (using the old technology) that thinking of a new flow literally means us having to begin again from first principles - few leaders have the courage and the time to do that. Goldratt mentioned that he himself had faced the same mindset problem when deploying the early ERP solutions and was surprised why he wasn't getting the expected large benefits, and he said it took him many years before he could identify the core problem and define solutions for it, which he named (rather academically) as "theory of constraints". AI leaders would do well to rework the conventional workflows in a holistic manner in order to get the real benefits from this new technology. As Henry Ford had said, "If you always do what you've always done, you'll always get what you've always got".

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Great to see the momentum coming out of NVIDIA GTC this week. Microsoft’s announcements across Microsoft Foundry, Azure AI infrastructure, and Physical AI reflect how deeply we’re working with NVIDIA to help partners and customers build and run AI systems that operate reliably inside real business environments. Together, we’re enabling AI agents and physical AI solutions that can reason, act, and scale—across cloud, edge, and the physical world—while meeting enterprise expectations for security and trust. #NVIDIAGTC #Azure #AI #AINatives #Softwaredevelopmentcompanies

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AI Leadership I was impressed by Microsoft Vice Chair Brad Smith's recent testimony before the Senate Commerce Committee on AI. His strategic vision outlines three critical imperatives for American leadership: 1) Win the AI innovation race by building world-class infrastructure Brad highlights the urgent need for "massive datacenters and AI infrastructure" supported by modernized electrical grids. This physical foundation, combined with recruiting specialized skilled labor like electricians and pipefitters, creates the backbone for innovation. As organizations plan their AI strategy, this infrastructure consideration must be central. 2) Accelerate AI diffusion through comprehensive workforce development His emphasis on "AI skilling of the American workforce" resonated deeply with me. Brad advocates for AI fluency beginning in schools, specialized training in community colleges, and advanced education in university programs. This multi-tiered approach ensures we're developing talent at all levels—from basic AI literacy to sophisticated technical expertise. 3) Build strategic alliances by exporting AI responsibly Brad wisely notes that "no company or country is so powerful that it can master the future of AI without friends." This collaborative mindset is essential as we balance security concerns with the need for international partnership. I've seen firsthand how his foresight translates into meaningful action.  For leaders navigating the AI revolution, his framework provides an invaluable roadmap. Read his complete testimony here: https://lnkd.in/enKtEZpV #AILeadership #DigitalTransformation #Infrastructure #WorkforceDevelopment

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Terence Tao - discusses many tools and techniques solving “Erdos” problems leveraging AI as an assistant. Paul Erdős was actually a tutor to Terence as a child. “Machine assistance and the future of research mathematics” - 29 minute YouTube video Terence Tao, Institute for Pure & Applied Mathematics, UCLA, 10 February 2026 Link: https://lnkd.in/eCtzmTs3 #math #science #verifiable

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NVIDIA's latest release, Orchestrator-8B, is a reinforcement learning-trained controller that intelligently selects the optimal tools and models for each step in a task. This moves beyond the limitations of monolithic large language models by orchestrating a diverse set of specialized components, resulting in more efficient and flexible AI systems. In practice, this means improved performance in automated workflows, where the system dynamically chooses the best resource, reducing computational overhead and enhancing adaptability. Key insights from the release: First, reinforcement learning allows the controller to learn from trial and error, making smarter decisions over time for complex, multi-step processes. Second, by integrating heterogeneous tools, it addresses inefficiencies in traditional setups, potentially cutting costs in business applications like data analysis or customer service automation. Third, this approach paves the way for scalable AI architectures that can evolve with new tools without constant retraining of a single model. For businesses leveraging AI, this could streamline operations and boost productivity in an era where efficiency is key. What are your thoughts on using RL for AI orchestration in your workflows? Have you encountered bottlenecks with single-model approaches? Share below. Read more: https://lnkd.in/gJwNewkh #ArtificialIntelligence #AIAutomation #ReinforcementLearning #BusinessEfficiency

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