Thomas Winter

How will Taming the Long Tail (TLT) impact AI? Taming the Long Tail (TLT) is an MIT research method for making reasoning LLM training more efficient. In plain English, it reduces wasted GPU/processor time during reinforcement-learning training by using idle processors to train a smaller “drafter” model that predicts the larger model’s next outputs, which the larger model then verifies. In short, TLT is a method for speeding up reasoning-model training by turning idle compute time into useful work. MIT just highlighted an important truth about the future of AI: breakthroughs and innovation will come not only from better models, but from better efficiency. As organizations push deeper into AI, the challenge is not just capability, it is also cost, speed, and scalability. The companies that win in AI will not only build smarter systems, but they will also build them more efficiently. Enterprise AI success is not measured only by model performance. It is measured by the ability to deploy solutions that are cost-effective, scalable, energy-conscious, and practical in real-world environments. While few have probably heard of Taming the Long Tail, this is a meaningful step toward the democratization of AI. One of the biggest barriers to broader AI adoption is cost. Training and deploying advanced AI systems has often been limited to organizations with massive infrastructure, deep technical resources, and significant capital. Innovations that reduce training time and improve compute efficiency help lower those barriers. At Pinnacle AI, we see this as part of a much bigger movement: bringing the power of AI to more organizations in practical, scalable, and impactful ways. The future of AI leadership is not just about building the most advanced systems. It is about helping make those systems more available to the many, not just the few.

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How Should We Really Measure Generative AI Performance? In the race to adopt GenAI, much of the conversation has centered around metrics like BLEU, ROUGE, MMLU, HellaSwag, and others. These benchmarks serve academic needs and enable objective comparisons, but they often fail to reflect real-world impact. A model scoring high on benchmarks may still falter in enterprise settings. Why? Because these metrics tend to evaluate narrow, curated tasks — grammar, factual recall, or logic — under lab-like conditions. They rarely account for contextual complexity, ambiguity, domain nuance, or user trust. There’s a growing concern that GenAI vendors are optimizing to beat benchmarks rather than improving true model utility. It echoes a familiar theme: systems built to pass tests instead of solving problems. A model might excel at HellaSwag, yet produce subpar legal summaries or unreliable financial interpretations. So, what’s the better approach? Focus on outcomes, not just scores. Effective adoption must be grounded in real use-case validation: • Integration into existing systems and workflows • Domain-specific evaluation using internal or real-world data • Usability, trust, governance, and operational reliability Benchmarks can help establish a baseline. But the most critical “metrics” are those that reflect value in context, and this requires significant work on company and people culture, not just systems and models. Too often, individuals bring personal biases shaped by past experiences — sometimes from previous roles or companies — rather than decisions based on actual business needs and objective evaluation of the current already available solutions. The question isn’t “Which model achieved the highest benchmark score?” It’s: “Which model delivers the best fit for our problem, measured through our lens?” To move forward, we must shift from model performance to model fit, anchored in business-relevant value, not abstract accuracy. #GenerativeAI #AIMetrics #AIAdoption #DigitalTransformation #EnterpriseAI #AIinBusiness #ModelEvaluation #BenchmarkingAI #ResponsibleAI #AILeadership #BusinessValue #AIImplementation #TechStrategy

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Continuing from the previous post: SDD has been widely misapplied. With LLMs, spec-to-code is now technically possible, but that doesn’t change the nature of software. Until software is running, requirements are inherently unclear. Our real goal should be to accelerate the cycle from idea to experience. LLMs may appear to introduce new uncertainty, but it’s the same old problem—human developers are also non-deterministic. If documentation can’t fully constrain people, it won’t constrain LLMs either. The real difference is capacity: humans produce a few thousand lines of code a day at most, while LLMs can generate that in seconds and in parallel. This is where our focus should be—using LLMs to remove time-consuming work, not limiting them with human-centric processes. In that sense, Spec-Driven Development is a step backwards. https://lnkd.in/gXjDi6nJ

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📄I read an interesting paper on the use of LLMs within Multi-Agent Systems (MAS). It highlights some important points about how these technologies can work together to handle complex tasks. Here are three key takeaways: 👉 Collaborative problem-solving: MAS enables multiple agents, each with different skills, to work together more effectively. 👉Managing context and memory: Handling layered context and different types of memory across agents remains a significant challenge, but is essential for consistent coordination. 👉Flexible system structures: The paper discusses various MAS architectures, including flat, hierarchical, and dynamic structures, that can adapt to different use cases. This research highlights practical challenges and opportunities as AI systems become increasingly integrated and collaborative. 🔗 Link to the paper in the comments!

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𝗪𝗵𝘆 𝘁𝗵𝗲 𝗚𝗹𝗼𝗯𝗮𝗹 𝗟𝗶𝗴𝗵𝘁𝗵𝗼𝘂𝘀𝗲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗜𝘀𝗻’𝘁 𝘁𝗵𝗲 𝗪𝗵𝗼𝗹𝗲 𝗦𝘁𝗼𝗿𝘆 – A call to go beyond just the use cases I’ve been reflecting on the Global Lighthouse Network (GLN) by the World Economic Forum — and while it's inspiring to see so many factories leading the way in the Fourth Industrial Revolution (AI, IIoT, digital twins, etc.), I believe we’re only seeing part of the picture. Recently, I was in a conversation with someone and he shared an interesting North Star vision that made me think deeper. Here’s the thing: 👉 Everyone seems to have their own definition of a digital twin. 👉 Every plant interprets digital maturity differently. What’s clearly missing is standardization — not just in terminology, but also in how we measure success. Because behind every successful use case is a much deeper story that often gets overlooked — one built on the classic triad: >>People – How do you shift mindsets, upskill teams, and manage resistance to change? >>Process – How are legacy workflows reimagined, not just digitized? >>Technology – What does the actual tech stack look like? How is data structured, secured, and governed? When we only talk about results like “20% increase in yield using AI,” we skip the invisible infrastructure that enabled it: change management, training programs, system integration, and data architecture. If we want to make smart manufacturing repeatable and scalable, we need operational playbooks, not just showcase slides. Let’s move beyond the “what” and start talking about the “how.” Because that’s where the real transformation lives. #Industry40 #SmartManufacturing #DigitalTransformation #GLN #LighthouseFactories #EdgeComputing #PeopleProcessTech #DigitalMaturity #Leadership #OperationalExcellence #ManufacturingInnovation

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New Paper Published: Referential Incompleteness Theorem Can a formal system define the conditions of its own intelligibility — without invoking a meta-system? Building on Gödel, Tarski, and Kripke, this paper explores a fundamental limitation in logic and semantic self-reference. I show that the set of grounded sentences — those whose truth values stabilize in a fixed-point semantics — is Π¹₁-complete. This places it beyond the expressive power of any system defined by recursively enumerable axioms. In other words: A system may prove truths, but it cannot define which of its own expressions are semantically meaningful.   Provability ⊂ Truth ≡ Groundedness This reframes a core epistemic boundary with implications for AI, formal verification, and symbolic reasoning: Your model may be coherent — but it cannot recognize coherence from within. If you work in logic, theoretical computer science, AI safety, or philosophy of mind — or are simply curious about the boundaries of formal systems — I’d welcome your thoughts, critiques, or questions. Read it here: https://lnkd.in/gkasP73r #Logic #AI #Philosophy #FormalSystems #Gödel #Tarski #Kripke #SemanticGroundedness #Computability

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𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜: 𝗧𝗵𝗲 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗖𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗿𝗺𝘀 𝗥𝗮𝗰𝗲 In our last post, we looked at IBM’s warning about overbuilding AI infrastructure – and the economic risks of massive CapEx. But there’s a bigger question looming behind the dollars: 𝗖𝗮𝗻 𝘄𝗲 𝗮𝗳𝗳𝗼𝗿𝗱 𝘁𝗵𝗲 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗵𝘆𝗽𝗲𝗿𝘀𝗰𝗮𝗹𝗲 𝗔𝗜? Training large language models like GPT-4 or Gemini Ultra consumes immense energy. According to a Bloomberg report (Nov 2023), the carbon footprint of training a single large model can rival 𝗵𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗼𝗳 𝘁𝗿𝗮𝗻𝘀𝗮𝘁𝗹𝗮𝗻𝘁𝗶𝗰 𝗳𝗹𝗶𝗴𝗵𝘁𝘀. And that’s just training, inference adds ongoing demand. A recent Time investigation revealed that Microsoft’s water consumption surged by 34% in a single year, largely due to cooling needs for AI data centers supporting OpenAI. Google’s water usage jumped 20%, reaching 5.6 billion gallons in 2022. These aren’t theoretical problems. They’re happening now. That’s why IBM’s strategy feels so relevant, and maybe, more responsible: • 𝗦𝗺𝗮𝗹𝗹𝗲𝗿, 𝘁𝗮𝘀𝗸-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗺𝗼𝗱𝗲𝗹𝘀 that run on local or edge devices • 𝗖𝗹𝗼𝘂𝗱 𝗼𝗻𝗹𝘆 𝘄𝗵𝗲𝗻 𝗻𝗲𝗲𝗱𝗲𝗱, reducing unnecessary always-on compute • 𝗛𝘆𝗯𝗿𝗶𝗱 𝗔𝗜 𝗮𝘀 𝗮 𝗹𝗲𝘃𝗲𝗿 for smarter energy and resource use Instead of scaling at all costs, we need to ask ourselves: What do we really need? This aligns with growing pressure from regulators and investors to report AI’s environmental impact. The EU’s AI Act and sustainability reporting mandates will soon demand more than vague ESG promises. 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗴𝗼𝗼𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗶𝘁’𝘀 𝗮𝗻 𝗲𝗰𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗶𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲. Are we pushing AI beyond sustainable limits? Or can we design models and infrastructure that balance innovation with responsibility? Let’s keep the conversation going. #AI #Sustainability #GreenAI #IBM #ClimateTech #HybridCloud

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🚨 AI’s “Illusion of Thinking”: Why #CXOs Must Rethink #LLM Reasoning Apple’s paper The Illusion of Thinking reveals today’s LLMs only simulate reasoning—they don’t truly “think.” When tested on logic puzzles (Tower of Hanoi, River Crossing, Blocks World), they falter: 1. Low complexity: LLMs invent extra steps—#overthinking reduces accuracy. 2. Medium complexity: Prompt engineering or chain-of-thought helps momentarily, but gains shatter with slight changes. 3. High complexity: Models break down—answers become truncated or superficially coherent well before token limits, exposing brittle “reasoning scaffolds.” ⚠️The #Enterprise Risk Embedding #LLMs in critical workflows (customer journeys, compliance checks, strategic planning) without rigorous validation means you’re building on an illusion. Chain-of-thought = #fluency, not true understanding. Under real-world complexity, decisions can fail silently. 🚀#CTO Playbook 1. Stress-Test Reasoning Depth • Create #synthetic logic challenges to map collapse points. • Automate scaling of difficulty to uncover hidden flaws. 2. Engineer #Hybrid Architectures • Offload deterministic tasks (math, rule evaluation) to symbolic engines or graph databases. • Use LLMs for natural-language interpretation and context. 3. Instrument Chain-of-Thought • Capture token-level traces and execution trees. • Alert on #shallowchains or sudden step failures in live inference. 🔍#CDAO Agenda 1. Govern Reasoning Fidelity • Define KPIs for logical depth, coherence, and coverage—not just accuracy. • Establish human-in-the-loop checkpoints for high-risk outputs. 2. Domain-Specific Logic Benchmarks • Build funtional testcases mirroring your own compliance rules, financial models, or decision workflows. • Continuously refine benchmarks based on real incidents. 3.Leadership Education • Train stakeholders: fluency ≠ thinking. • Set clear policies on where AI can augment—and where it must yield to human expertise. 5 Patterns to Build Beyond the Illusion: 1. LLM + Symbolic AI: Logic rules and ontologies ensure deterministic validation. 2. LLM + RAG: Ground outputs in #audited knowledge bases to avoid “improvisation.” 3. LLM + Program Executors: Delegate critical steps (math solvers, optimizers) to code. 4. Agentic LLMs with Self-Reflective Loops: Auto-critique and reject shallow reasoning before output. 5. Hierarchical Reasoning Pipelines: Orchestrate multi-stage tasks with verification at each step. 🔐 The Bottom Line: True AI trust is earned via transparent, hybrid designs and governance. For CXOs—now is the time to architect beyond the illusion.

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Great article from Serge Lucio where he discusses that whilst most enterprises are rushing to deploy AI agents, few have the orchestration layer needed to keep them governed, trustworthy and under control. I love the analogy "Just as autonomous vehicles require traffic signals, enterprise AI requires a framework". Its well worth a read for anyone deploying AI Agents #automationbybroadcom #orchestration #controlplane https://lnkd.in/efqKDY5Q

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Scaling ethical AI frameworks often hits a wall when isolated teams struggle to align on shared standards. For leaders like Desiree Cho - Institute for Ethics in AI, driving responsible practice requires more than individual expertise; it demands a unified ecosystem where researchers and practitioners can collaborate on real-world implementation. AI Coalition bridges that gap by connecting AI builders, researchers, and organizations into a single network focused on responsible development. We turn fragmented efforts into coordinated action, ensuring ethical guidelines are practical and scalable. If you are ready to move beyond theory into collaborative practice, join us. https://ai-coalition.net #ResponsibleAI #AIEthics #AICoalition

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RAG vs MCP — Two Roads Diverging in the AI Stack” As we embed LLMs deeper into enterprise systems, two architectural patterns are shaping the future of intelligent apps: 🔁 RAG (Retrieval-Augmented Generation) 🧠 MCP (Multi-Context Prompting) Both aim to improve output quality — but they approach it differently. RAG builds a mini search engine over your domain data. MCP injects richer context upfront into the prompt: personas, state, metadata, workflows. Think: 🔎 RAG = Better memory 🧩 MCP = Better awareness #RAG #MCP #AIArchitecture #GenAI #EnterpriseAI #LLMOps

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Manufacturing AI Consortium 𝟵𝟰% 𝗼𝗳 𝗦𝘄𝗶𝘀𝘀 𝗦𝗠𝗕 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗲𝗿𝘀 𝗵𝗮𝘃𝗲 𝗻𝗼 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆. Not because they don't see AI's potential. Not because they lack ambition. But because they can't afford what Enterprises have: a Chief Innovation Officer, a dedicated AI team, or expensive consulting engagements. After 10+ years advising companies at Microsoft on AI transformation, I've seen a pattern: 𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘁𝗵𝗮𝘁 𝗻𝗲𝗲𝗱 𝗔𝗜 𝗺𝗼𝘀𝘁 𝗰𝗮𝗻 𝗮𝗰𝗰𝗲𝘀𝘀 𝗶𝘁 𝗹𝗲𝗮𝘀𝘁. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜'𝗺 𝗽𝗿𝗼𝗽𝗼𝘀𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗦𝘄𝗶𝘀𝘀 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗔𝗜 𝗖𝗼𝗻𝘀𝗼𝗿𝘁𝗶𝘂𝗺 Instead of each mid-market manufacturer (CHF 50-500M) struggling alone, what if 8-10 companies 𝘀𝗵𝗮𝗿𝗲𝗱 strategic AI advisory? 𝗧𝗵𝗲 𝗺𝗼𝗱𝗲𝗹: → Quarterly AI opportunity assessments for each member → Peer-learning workshops where members share wins and failures → Industry-specific AI frameworks (predictive maintenance, demand forecasting, quality control) 𝗧𝗵𝗲 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀:  → Individual consulting: CHF 150,000-250,000 per company → Consortium membership: CHF 40,000-60,000 per company → 75% cost reduction, 10x knowledge sharing 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸𝘀: 1. Cross-pollination of ideas - A textile manufacturer's AI solution for demand forecasting might inspire a precision engineering firm's approach to capacity planning. I've seen this pattern recognition drive innovation across different sectors. 2. Reduced risk through peer validation - When 8 manufacturing leaders discuss an AI vendor or use case, collective wisdom prevents expensive mistakes. 3. Faster adoption - Manufacturers don't need to reinvent the wheel. See what worked at a peer company, adapt it, deploy it. The gap I'm addressing Swiss manufacturers face an impossible: * Hire expensive consultants → great advice, unsustainable cost * DIY with internal IT → limited AI expertise * Do nothing → fall behind competitors The consortium model creates a third option: 𝘀𝗵𝗮𝗿𝗲𝗱 strategic capability. 𝗜'𝗺 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝗮𝗻𝗱 𝘄𝗼𝘂𝗹𝗱 𝘃𝗮𝗹𝘂𝗲 𝘆𝗼𝘂𝗿 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲: → Manufacturing leaders: Would you join a peer consortium for AI strategy? → Industry associations: Could this model accelerate sector-wide innovation? 𝗧𝗵𝗿𝗲𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂: 1. What's the biggest barrier preventing your manufacturing company from implementing AI? 2. Would you be comfortable sharing AI learnings with non-direct competitors? 3. What would make a consortium model attractive enough to join? If you're 𝗮 𝗦𝘄𝗶𝘀𝘀 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗼𝗿 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗹𝗲𝗮𝗱𝗲𝗿 interested in exploring this concept, let's talk. DM me or comment below. The future of Swiss manufacturing isn't about individual companies becoming AI leaders. It's about the entire sector becoming AI-capable, together. #Industry40 #SwissManufacturing #AIStrategy #AIAdoption #SwissBusiness

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TL;DR - AI is Top of Mind, Iran War is the spoiler, and tariffs are fading! ✅ According to IoT Analytics’ latest "What CEOs Talked About" report, #AI maintained the top spot in CEOs’ minds in Q1 2026, with discussions around physical and agentic AI increasing. ✅ The onset of armed conflict in the Middle East made discussions about #Iran rise steeply. ✅ Talk about #OpenClaw and #SaaSpocalypse, too, had sharp increases. ✅ Discussions around AI bubble concerns, uncertainty, and #tariffs declined. Read the report here 👇 https://lnkd.in/gEiEqrPN

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The Daily Tech Digest (26 January 2026): Two realities collide: AI is consolidating into a few strategic brains, while physical and digital safety remain brittle. #Apple’s reported #Gemini-powered #Siri signals a major mobile realignment, as active #vCenter exploitation and encryption-key handoffs reinforce the limits of “trust the cloud.” Meanwhile, a winter storm warning for 230M Americans shows how infrastructure risk becomes immediate.

At 1M tokens, the ceiling shifts. HBM bandwidth: not compute, not model quality , determines what reaches the model. Physical memory. ⚛️ Context is the new compute. And unlike compute, you cannot manufacture more of it. The evidence arrived fast. Addy Osmani quantified the cost: context files duplicating discoverable information raise inference costs 20% while dropping task success 2-3%. ETH Zurich (Gloaguen et al.) measured 2.5x tool over-invocation from redundant context. Agents comply with every instruction indiscriminately. Mitko Vasilev Vasilev instrumented Claude Code via local vLLM: 96% of 47M prompt tokens were prefix-cache hits. The compute was cheap. Context management was the real expense. IBM Research's trajectory-informed memory (Fang et al., arXiv:2603.10600) demonstrates what intelligent context management produces. The system converts execution traces into structured tips (strategy, recovery, optimization) and retrieves them via LLM-guided selection. +14.3 percentage points on scenario goal completion for unseen tasks. +28.5pp on the hardest problems. Context selection outperformed context volume. Three production architectures converge on the same principle. Sleep-time compute from Letta and UC Berkeley pre-processes context during idle periods, achieving 5x test-time reduction while maintaining accuracy. @Anthropic's progressive disclosure loads ~100 tokens at Level 1, under 5K on trigger, full resources on demand. Graph-based retrieval structures context as traversable relationships, enabling multi-hop reasoning instead of flat token dumps. The practitioners have already shifted. Boris Cherny 's three-tier context architecture mirrors CPU cache hierarchies: hot tier (150 lines, every session), warm (on-demand via links), cold (search and memory systems). GitHub Copilot validates memory citations at retrieval time. Karthik Ravindran Ravindran orchestrates five modalities (glossaries, ontologies, semantic models, knowledge graphs, digital twins) through a Conductor pattern. The hardware constraint makes this permanent. HBM and DRAM shortages cap how many tokens physically flow to the model per second. Longer windows without intelligent management produce "context rot" regardless of advertised capacity. The solutions fragmenting across the industry must converge on structured context management or each hits the same physical wall. Stop adding context. Start managing it. Graphs provide the structure. Progressive disclosure controls cost. Evaluation determines what survives.

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🚀 Breakthroughs in AI shouldn’t be limited by closed ecosystems. That’s why I’m excited about the AMD ROCm platform — an open and developer-first ecosystem built to remove integration barriers and accelerate AI innovation. With ROCm, developers have the flexibility to build and scale AI solutions without being locked in. Future-ready, adaptable, and open — this is how we advance AI together. #AMD #ROCm #AI #OpenEcosystem #FutureReady #TogetherWeAdvanceAI @AMD #AMDBrandAmbassador

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Former Google CEO Eric Schmidt breaks down the next big leap in #artificial_intelligence and why it will arrive faster than most people think. He explains how modern #AI is racing up the “capability ladder” with new models arriving every twelve to eighteen months, each one more powerful than the last. Schmidt also walks through three forces that will reshape everything from medicine to climate science. Put together, these shifts point toward a world where millions of #agents collaborate, code, and discover at a scale humans alone could never reach. #Innovation #AI #Deeptech https://lnkd.in/eBqB7TMY

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