Understanding Technological Evolution

This week MIT dropped a stat engineered to go viral: 95% of enterprise GenAI pilots are failing. Markets, predictably, had a minor existential crisis. Pundits whispered the B-word (“bubble”), traders rotated into defensive stocks, and your colleague forwarded you a link with “is AI overhyped???” in the subject line. Let’s be clear: the 95% failure rate isn’t a caution against AI. It’s a mirror held up to how deeply ossified enterprises are. Two truths can coexist: (1) The tech is very real. (2) Most companies are hilariously bad at deploying it. If you’re a startup, AI feels like a superpower. No legacy systems. No 17-step approval chains. No legal team asking whether ChatGPT has been “SOC2-audited.” You ship. You iterate. You win. If you’re an enterprise, your org chart looks like a game of Twister and your workflows were last updated when Friendswas still airing. You don’t need a better model - you need a cultural lobotomy. This isn’t an “AI bubble” popping. It’s the adoption lag every platform shift goes through. - Cloud in the 2010s: Endless proofs of concept before actual transformation. - Mobile in the 2000s: Enterprises thought an iPhone app was strategy. Spoiler: it wasn’t. - Internet in the 90s: Half of Fortune 500 CEOs declared “this is just a fad.” Some of those companies no longer exist. History rhymes. The lag isn’t a bug; it’s the default setting. Buried beneath the viral 95% headline are 3 lessons enterprises can actually use: ▪️ Back-office > front-office. The biggest ROI comes from back-office automation - finance ops, procurement, claims processing - yet over half of AI dollars go into sales and marketing. The treasure’s just buried in a different part of the org chart. ▪️Buy > build. Success rates hit ~67% when companies buy or partner with vendors. DIY attempts succeed a third as often. Unless it’s literally your full-time job to stay current on model architecture, you’ll fall behind. Your engineers don’t need to reinvent an LLM-powered wheel; they need to build where you’re actually differentiated. ▪️Integration > innovation. Pilots flop not because AI “doesn’t work,” but because enterprises don’t know how to weave it into workflows. The “learning gap” is the real killer. Spend as much energy on change management, process design, and user training as you do on the tool itself. Without redesigning processes, “AI adoption” is just a Peloton bought in January and used as a coat rack by March. You didn’t fail at fitness; you failed at follow-through. In five years, GenAI will be as invisible - and indispensable - as cloud is today. The difference between the winners and the laggards won’t be access to models, but the courage to rip up processes and rebuild them. The “95% failure” stat doesn’t mean AI is snake oil. It means enterprises are in Year 1 of a 10-year adoption curve. The market just confused growing pains for terminal illness.

Let’s clear something up. Not every Industry 4.0 project is a transformation. And that’s OK—as long as you know what game you’re playing. In fact, most aren’t. But we call them that anyway. That’s a problem. Why? Because how you frame an initiative determines: • What you measure • How you lead • And whether it even survives Here’s the real breakdown: 𝐌𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 is when you upgrade. New systems. New infrastructure. Old pain, gone. It’s not sexy—but it’s necessary. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 is when you squeeze value. You’ve got tools. Now get smarter with them. Faster. Leaner. Tighter. Better. 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 is when you redraw the map. You rethink how the business works—maybe even what business you’re in. These aren’t stages. They’re strategic choices. Treating a modernization project like a transformation? You’ll overhype and underdeliver. Expecting optimization-level ROI from a transformation? You’ll kill it before it starts. So before you greenlight another “digital transformation” project, ask yourself: 𝐀𝐫𝐞 𝐰𝐞 𝐦𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐢𝐧𝐠, 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠, 𝐨𝐫 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠? Because if you don’t know which path you’re on, any outcome will look like failure. 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐦𝐨𝐫𝐞? https://lnkd.in/eNSBCkVz ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.    Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

Had to share the one prompt that has transformed how I approach AI research. 📌 Save this post. Don’t just ask for point-in-time data like a junior PM. Instead, build in more temporal context through systematic data collection over time. Use this prompt to become a superforecaster with the help of AI. Great for product ideation, competitive research, finance, investing, etc. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰ TIME MACHINE PROMPT: Execute longitudinal analysis on [TOPIC]. First, establish baseline parameters: define the standard refresh interval for this domain based on market dynamics (enterprise adoption cycles, regulatory changes, technology maturity curves). For example, AI refresh cycle may be two weeks, clothing may be 3 months, construction may be 2 years. Calculate n=3 data points spanning 2 full cycles. For each time period, collect: (1) quantitative metrics (adoption rates, market share, pricing models), (2) qualitative factors (user sentiment, competitive positioning, external catalysts), (3) ecosystem dependencies (infrastructure requirements, complementary products, capital climate, regulatory environment). Structure output as: Current State Analysis → T-1 Comparative Analysis → T-2 Historical Baseline → Delta Analysis with statistical significance → Trajectory Modeling with confidence intervals across each prediction. Include data sources. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰

When we think about batteries, we usually picture the ones in our phones or laptops. But the real transformation is happening at a very different scale. Around the world, utility-scale battery projects are being built with hundreds of millions of times the capacity of a smartphone battery. These “mega batteries” are rapidly becoming a cornerstone of modern power systems. Key trends: - Battery storage project costs have fallen by around 40% since 2024. - Utility-scale battery power capacity in 2024 was more than 12 times higher than in 2020. That is extraordinary growth in just four years. Large-scale batteries are now providing critical short-term flexibility. They store electricity when supply is abundant and dispatch it when it is needed most - for example at night when solar generation drops, during extreme weather events, or when unexpected outages disrupt supply. As highlighted in the IEA’s Electricity 2026 report, battery storage is shifting from a niche technology to a system-level asset. The pace of deployment shows how quickly power systems can evolve when technology costs fall and policy frameworks provide clarity and direction.

A snapshot of 10 years 👇 Last year - when the IEA investment report was released - this chart provided a clear evidence of evolution on the global energy system A key point for investment is that those are a sort of glimpse into the #future, as what is invested today shapes the system of tomorrow. It is more complex than that – but definitely it is an indication of where the overall system is going. This chart offered a clear view in the major events that the global energy sector has experienced throughout the last decade. Some were "exogenous" with the pandemic and Russia's invasion of Ukraine being probably the most relevant. Others were "indigenous", mainly through shale revolution and solar ☀️boom that have reshaped dynamics in the energy sector. Above all, the topic of transition, the multi-years policy push for a shift in the way we produce and use energy, the rise of #innovation and concerns on climate change have made the #energy sector experiencing multiple transformations. This is mirrored by #investment trends, with a general push for electrification⚡ across the board. While the spending on traditional fuels has moderated amidst potential concerns on future demand levels and a renewed capital spending discipline, their importance in meeting global energy needs remains central. The latest data available showed all of that: - electricity grows much more than energy  - All fuels (traditional and new) contributes to the growth - New technologies enjoys highest growth rates and more  (for more - look at IEA's Global Energy Review 2026 with all latest data available) When commenting this chart last year about 12 months ago, I thought that “there are 2 main ‘wild cards’ for the energy system” 👉 One is #innovation, with technology progress - especially in the era of AI - that could provide surprises. 👉 The other is the “external factor” – namely #geopolitics and other decisions that might create points of flex in the overall trend; It is pretty evident how those are in strong motion (and to a certain extent each having spill over effect on the other)… A complex and fascinating journey continues  

Payments have evolved from paper and plastic to APIs and orchestration - giving rise to a new breed of players that simplify the complexity and connect the dots behind the scenes. Here's how we got here. 𝟭. 𝗜𝗻 𝘁𝗵𝗲 𝗽𝗿𝗲-𝟭𝟵𝟵𝟬𝘀 𝗲𝗿𝗮, banks owned the entire payments value chain -acquiring, processing, settlement. Merchant onboarding was complex, and domestic clearing systems ruled. 𝟮. 𝗧𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 in the late 1990s changed everything. Players like PayPal and Authorize made online payments possible, while banks began exiting the acquiring space or partnering with processors to keep up with demand. 𝟯. 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝟮𝟬𝟬𝟬 𝗮𝗻𝗱 𝟮𝟬𝟭𝟬, specialized gateways and regional wallets began to scale, offering merchants greater flexibility and control. The launch of SEPA in Europe marked a push toward payment harmonization, while non-bank players started building infrastructure that bypassed traditional acquiring models altogether. 𝟰. 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗔𝗣𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 transformed payments from siloed systems into modular, developer-friendly tools. Merchant onboarding became faster, integrations simpler, and innovation more scalable. Open Banking regulations enabled direct access to bank data, while new credit models redefined consumer behavior. Payments evolved into a flexible, programmable layer of the digital economy. 𝟱. 𝗧𝗼𝗱𝗮𝘆, we’re in the age of seamless integration. Payments are embedded in everything - from ride-hailing apps to SuperApps. Real-time rails like SEPA Instant, UPI and PIX are live. CBDCs are in pilot. However, as payment ecosystems grow more fragmented - with new methods, regional schemes, compliance layers, and fraud risks -complexity has become a major bottleneck for merchants, fintechs, and even banks. Integrating multiple providers, maintaining uptime across systems, and ensuring regulatory compliance isn't just costly - it's unsustainable without the right foundation. This is where a new breed of infrastructure players like 𝗔𝗸𝘂𝗿𝗮𝘁𝗲𝗰𝗼 fit in - offering the tools to simplify complexity and still retain control. • 𝗪𝗵𝗶𝘁𝗲-𝗹𝗮𝗯𝗲𝗹 𝗽𝗮𝘆𝗺𝗲𝗻𝘁 𝗴𝗮𝘁𝗲𝘄𝗮𝘆𝘀 let banks, PSPs, and fintechs launch their own branded platforms fast - without building from scratch. • 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 enables merchants to route transactions dynamically across multiple acquirers, reducing costs and failed payments while improving UX. • 𝗕𝗮𝗻𝗸𝘀 can embed API-driven acquiring services into their offerings without the burden of a full-scale tech overhaul. In a world where growth brings fragmentation, the real challenge isn’t enabling payments - it’s managing them. The advantage will lie with infrastructure that can unify complexity, adapt in real time, and scale across borders without adding friction. Opinions: my own, Graphic source: Akurateco Payment Hub Subscribe to my newsletter: https://lnkd.in/dkqhnxdg

A group of small public power utilities in Virginia is doing something deceptively simple—and potentially disruptive: deploying multiple 5 MW, distribution-connected batteries instead of pursuing a traditional transmission upgrade. At first glance, this is just smart economics. Small batteries can be deployed faster, with far less upfront cost, and can charge off-peak and discharge during system peaks. But the bigger story is what this signals about the future of “capacity.” For decades, grid economics have revolved around peak demand. Build enough generation and transmission to meet a handful of critical hours each year, and recover those costs through demand-based constructs like coincident peak pricing. Storage breaks that model. If a utility can shave just a few peak hours, it can avoid a disproportionate share of system costs. Peak demand is no longer something you simply serve—it’s something you can shape. That has real implications: • Capacity value becomes less about static MW and more about flexibility • Pricing models built around a few peak hours become increasingly fragile • Distribution-level solutions begin to substitute for large, centralized infrastructure This doesn’t mean capacity goes away. It means it evolves. Instead of a blunt, peak-hour construct, capacity becomes more granular, more locational, and more time-dependent. Value shifts toward resources that can respond precisely when and where the system is stressed. And yes—storage accelerates all of this. But moves like this in Virginia show where things are heading: Smaller, faster, more flexible assets—deployed closer to load—beginning to reshape both infrastructure planning and market design. The interesting question isn’t whether storage changes the system. It’s how quickly the rules will change in response. https://lnkd.in/ebXaC3PB

I’m coming out of #HRTechConf with one big realization, we are in trouble, my friends. I could write a hype post about the shiny things, but what’s really on my mind is the gap I saw in us as analysts, buyers, and users. We used to understand tech. We spent years mastering integrations, if/then logic, and workflows. But those aren’t today’s rails. The ground has shifted and most of us haven’t learned how to move with it. If your mental model is pre-AI, you’re thinking in legacy code. The rails have been rebuilt. Data flows, system connections, and decision-making inside the machine are fundamentally different. Keep buying and building like it’s 2019 SaaS, and you’re laying train tracks for a world that now flies. How the tech rails have changed 1. Streaming-First Data Architecture The old way: nightly ETL batches. The new way: event-driven streaming using tools like Kafka or Pulsar feeding real-time feature stores and triggering AI inference in milliseconds. 2. Embeddings Are the New Index Forget tables and joins. Modern systems embed everything into high-dimensional vectors stored in vector databases such as Pinecone or Weaviate. This enables semantic search, personalization, and context retrieval at scale. 3. Retrieval-Augmented Generation Is the Default Applications no longer rely on static data calls. They dynamically retrieve relevant knowledge, inject it into prompts, and let LLMs reason in context. 4. Agents and Orchestrators Run the Show Hardcoded workflows are out. Multi-agent frameworks like LangGraph or CrewAI coordinate tasks in adaptive loops: plan, retrieve, act, evaluate, re-plan. 5. Microservices Meet Model Endpoints Traditional APIs are being joined by model endpoints that generate outcomes, not just retrieve data. Latency, token budgets, and model choice are now architectural decisions. 6. Continuous Feedback Loops Models are never “finished.” Fine-tuning, RLHF, and automated monitoring are constant. Your software stack evolves week by week. 7. Semantic Layers Beat Point Integrations Instead of brittle point-to-point API wiring, systems rely on shared semantic layers or knowledge graphs so data flows with meaning, not just schema. 8. Inference Moves to the Edge Smaller, quantized models run on devices and edge nodes for faster response times, improved privacy, and lower cost. This changes infrastructure from central to hybrid. 9. Security and Governance Go Deep Beyond firewalls, you now need prompt injection defenses, output filters, audit logs for model calls, and bias and drift monitoring dashboards. 10. Time to Deployment Is Hours, Not Quarters Containers spin up in minutes, pipelines auto-configure, and models hot-swap. A six-month rollout is no longer competitive. If you do not understand these rails, you cannot: •Ask vendors the right questions. •Connect systems to deliver compounding intelligence. •Govern for risk in a world where models shift under your feet.

Autonomous driving is no longer just a transportation trend — it’s becoming a large-scale AI system deployed in the physical world. Would you travel like this? We’re now seeing real production scale: 🚗 Robotaxi fleets have completed millions of autonomous rides, with some systems logging 10M+ miles/month across real and simulated environments. 🚚 Long-haul trucking is emerging as a major use case, driven by a shortage of ~3.5M truck drivers in the US alone. 🚜 Agriculture autonomy is already improving efficiency by 10–20% in large-scale deployments through precision AI. 🚆 Fully automated metro systems operate today with 99.9%+ reliability in multiple global cities. ⸻ 🧠 The real shift is AI, not vehicles Modern autonomy is powered by: * Multimodal AI (vision + radar + LiDAR fusion) * Transformer-based prediction models * Self-supervised learning from billions of driving frames * Reinforcement learning in simulation environments A single autonomous vehicle can generate up to 4–6 TB of sensor data per day, feeding the next generation of models. ⸻ 🖥️ Compute is the new battleground Autonomy is becoming one of the most compute-intensive AI applications: * Training uses massive distributed GPU clusters * Simulation generates hundreds of millions of scenarios daily * On-vehicle inference requires sub-50ms decision latency * Modern stacks reach 1,000+ TOPS per vehicle platform ⸻ 🔮 What’s next We are moving toward transportation systems that are: * AI-native and continuously learning * Optimized via digital twins of entire cities * Operating 24/7 with near-zero human intervention in select domains * Increasingly cheaper per mile than human-driven systems The future of transportation is not just electric. It is autonomous, AI-driven, and software-defined. #AI #AutonomousDriving #MachineLearning #Robotics #FutureOfMobility #EdgeAI #HPC #DigitalTwin #Innovation