Innovation

Grid bottlenecks are a feature — not a bug — of the energy transition. For years, we viewed economics as the main hurdle to scaling clean energy. High costs for wind, solar, heat pumps, and storage dominated the conversation. But the world has changed. Thanks to extraordinary innovation and dramatic cost reductions in renewables and electrification technologies, the bottlenecks we face today are different. They’re no longer about whether clean energy is affordable — it is. Instead, the challenge is whether our energy systems can evolve quickly enough to integrate it. A recent Financial Times piece highlights this clearly: across Europe, the rapid build-out of renewable generation now outpaces the ability of grids to move electricity to where it’s needed. Curtailment, congestion, and long queues for grid connections already cost billions annually — and without decisive action, these costs will grow. This isn’t a sign of failure. It’s a sign of success. It means the transition is happening faster than the infrastructure built for the fossil era can handle. The rise of decentralised, variable renewables and electrified heating and transport requires a fundamentally different approach to planning — one that anticipates growth rather than reacts to it. The EU’s move toward more coordinated, top-down scenario building and cross-border grid planning recognises exactly this. Better alignment between countries and system operators, faster permitting, and prioritisation of critical projects are essential steps to unlock the full value of cheap clean energy. Because every euro lost to bottlenecks is not a cost of climate action — it’s a cost of not modernising our grids fast enough. The more successful we are in deploying renewables and electrification, the more urgently we must upgrade and expand our grids. Grid constraints are not a reason to slow down. They’re a reason to speed up the transformation of an energy system that was never designed for the technologies now powering our transition.

A new 20-year analysis of satellite data shows that the Old Continent’s freshwater reserves are shrinking, silently and steadily. Satellites that weigh the Earth by tracking gravitational changes reveal 👉 Northern Europe is getting wetter. 👉 Southern and central Europe are drying fast. And what’s disappearing fastest is the water we don’t see — groundwater, the strategic reserve that keeps our taps running, our crops alive, and our economies functioning. This is #climatechange in real time. No models, no projections — observations from space. Researchers warn that Europe is barreling toward a 2°C world, and the consequences are already here: • Heavier downpours but longer, harsher dry spells • Winter recharge seasons shrinking • More runoff, less infiltration • Deep aquifers declining across the EU • Increasing pressure on public water supply and agriculture Groundwater is the backbone of Europe’s resilience. In 2022 alone: 🔹 62% of all public water supply came from groundwater 🔹 33% of agricultural demand relied on it 🔹 Groundwater abstractions increased by 6% despite lower overall water use Farmers across southern Europe are watching reservoirs drop while fruit and vegetable yields continue to fall. These are the same dynamics long documented across the Global South, now hitting Europe with unprecedented force. The old assumptions no longer hold. Europe is not water-secure. Infrastructure alone will not save us. New reservoirs arriving in 20 years are not a solution for a crisis happening today. We need: ✅ Radical efficiency — cutting leakage, modernising networks, accelerating water-smart design ✅ Water reuse at scale — separating drinking water systems from non-potable recycled streams ✅ Nature-based solutions — restoring wetlands, aquifers, and natural recharge ✅ Smarter climate-informed water governance — using the best science to guide every decision ✅ A mindset shift — rainwater harvesting, circular water systems, and demand-side management must become standard, not exceptional read the article in The Guardian 👇 https://lnkd.in/eeTsyMve

Real consumer insight does not sit in market reports. It lives in everyday behaviour. I have always believed that if you want to understand the Indian consumer, you must walk the aisles, visit the kirana stores, and spend time in homes. The questions are simple: why did they choose this brand, what made them switch, what are their latest unsatisfied needs, what habit stopped them from trying something new. The answers are rarely written down. They are observed in the pauses, the hesitations, the way a hand reaches for one pack over another. India is a mosaic of markets. What sells in Chennai might fail in Chandigarh. A message that resonates in Delhi could fall flat in a tier-three town. Income, culture, and even climate shape choices. Unless you immerse yourself in these realities, your strategy risks being built on assumptions. The sharper your consumer insight, the stronger your competitive edge. Do not delegate consumer understanding to agencies or reports. Make it a personal discipline. Sit with retailers, shadow buyers, watch the trade. The real breakthroughs are found not in a meeting agenda, but in how people actually live, shop, and decide. #leadership #entrepreneurship #consumer #mindset

Meta just hit Command + Zuck on its AI strategy - shredding the open-source playbook and replacing it with one that reads: Compute. Talent. Secrecy. The vibe is no longer “open source for all.” It’s “closed doors, infinite compute, elite team, existential stakes.” Let's break it down: (1) Compute: Zuck’s Manhattan Project Meta is building gigascale AI clusters. Prometheus comes online with 1 GW in 2026; Hyperion scales to 5 GW soon after. For context, Iceland’s total electricity consumption is ~2.4 GW, Cambodia is at ~4 GW. Meta’s Hyperion cluster alone could out-consume entire nations. These clusters are for training frontier models - GPT-4-class and beyond. In this new regime, FLOPS per researcher is the KPI, and Meta is going from GPU-starved to GPU-dripping. Each researcher now has more compute to play with than entire labs elsewhere. That’s not just good for performance, it's a hell of a recruiting pitch. (2) Secrecy: From Open Arms to Closed Labs Meta won developer love by open-sourcing its LLaMA models. But it also accidentally became the free R&D department for its own competitors. DeepSeek AI, for example, built on Meta's models and vaulted ahead. Now Meta is reportedly shelving its most powerful open model, Behemoth, due to both internal underperformance and external regret and shifting toward a closed frontier model, aligning more with OpenAI and Google. This is a massive philosophical reversal from “open wins” (as Yann LeCun would say) to “closed dominates.” (3) Talent: Just Buy Everyone Comp packages reportedly range from $200 million to $1 billion for AI leads. All AI efforts are now housed under a new unit, Superintelligence Labs, run by Alexandr Wang (ex-Scale AI). This elite team is small, only ~12 engineers, working in a separate, high-security building next to Zuckerberg himself. Forget beanbags and 10xers. This is a DARPA-style moonshot with a trillion-dollar company behind it. Zuckerberg has said, basically, “Look, we make a lot of money. We don’t need to ask anyone’s permission to spend it.” He’s not wrong. While OpenAI, Anthropic, and xAI rely on outside capital to fund their ambitions, Meta runs on a $165B/year ad engine. And unlike Google and Microsoft - who have boards, activist investors, and share classes that allow for dissent - Zuckerberg controls Meta, structurally and operationally. Meta’s unique dual-class share structure gives Zuckerberg over 50% of the voting power, even though he owns less than 15% of the company. He doesn’t need anyone’s approval, he can build whatever he wants. This makes Meta less like a public company and more like a founder-led sovereign AI lab - with Big Tech cash and startup flexibility. That governance structure is a strategic weapon, letting them place bold, long-term bets at breathtaking speed. Meta’s open-source era is over. This is the closed, compute-soaked, capital-fueled empire play. Less GitHub, more Los Alamos.

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.

Ever heard of the Lippitt-Knoster Model for Managing Complex Change? It's a classic in the change management world, laying out the essential pieces needed to navigate big transformations. Taking a cue from that, I've adapted it to fit the world of digital transformation. There are seven key elements you can't afford to miss: Vision, Strategy, Objectives, Capabilities, Architecture, Roadmap, and Projects & Programs. Skip any one of these, and you're asking for trouble. Here’s why each one matters: • 𝐕𝐢𝐬𝐢𝐨𝐧: This is the 'what' of your transformation. A clear vision gives everyone a target to aim for, aligning all efforts and keeping the team focused. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Think of this as the 'why' and 'how.' A solid strategy explains the logic behind your vision, showing how you plan to get there and why it's the best route. It’s designed to guide everyone in the company on how to make decisions that support the vision, aligning all efforts and keeping the team focused. • 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: These are your milestones. Clear, specific objectives make it easy to measure success and ensure everyone knows what's important. Without them, you can easily veer off course and waste resources. • 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: These are what your company will now be able to do that it wasn't able to before in order to achieve the objectives. These can be organizational capabilities (like improved decision-making), technical capabilities (such as real-time operational visibility), or other types like enhanced customer engagement or streamlined processes. • 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: A robust architecture ensures all your tech works together smoothly, preventing inefficiencies and costly headaches. This includes various types of architecture such as data architecture, IT infrastructure architecture, enterprise architecture, and functional architecture. Effective architecture is central to reducing technical debt and aligning software with broader business transformation goals. • 𝐑𝐨𝐚𝐝𝐦𝐚𝐩: Your roadmap is the game plan. It lays out the sequence of actions, helping you avoid uncertainty and missteps. It's your guide to getting things done right. • 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬: These are where the rubber meets the road. Actionable projects and programs turn your strategy into reality, making sure your plans lead to real, tangible outcomes. From my experience, I think '𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬' and '𝐑𝐨𝐚𝐝𝐦𝐚𝐩' are the two most overlooked. What do you think? ******************************************* • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

I think AI agentic workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it. Today, we mostly use LLMs in zero-shot mode, prompting a model to generate final output token by token without revising its work. This is akin to asking someone to compose an essay from start to finish, typing straight through with no backspacing allowed, and expecting a high-quality result. Despite the difficulty, LLMs do amazingly well at this task! With an agentic workflow, however, we can ask the LLM to iterate over a document many times. For example, it might take a sequence of steps such as: - Plan an outline. - Decide what, if any, web searches are needed to gather more information. - Write a first draft. - Read over the first draft to spot unjustified arguments or extraneous information. - Revise the draft taking into account any weaknesses spotted. - And so on. This iterative process is critical for most human writers to write good text. With AI, such an iterative workflow yields much better results than writing in a single pass. Devin’s splashy demo recently received a lot of social media buzz. My team has been closely following the evolution of AI that writes code. We analyzed results from a number of research teams, focusing on an algorithm’s ability to do well on the widely used HumanEval coding benchmark. You can see our findings in the diagram below. GPT-3.5 (zero shot) was 48.1% correct. GPT-4 (zero shot) does better at 67.0%. However, the improvement from GPT-3.5 to GPT-4 is dwarfed by incorporating an iterative agent workflow. Indeed, wrapped in an agent loop, GPT-3.5 achieves up to 95.1%. Open source agent tools and the academic literature on agents are proliferating, making this an exciting time but also a confusing one. To help put this work into perspective, I’d like to share a framework for categorizing design patterns for building agents. My team AI Fund is successfully using these patterns in many applications, and I hope you find them useful. - Reflection: The LLM examines its own work to come up with ways to improve it. - Tool use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data. - Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on). - Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would. I’ll elaborate on these design patterns and offer suggested readings for each next week. [Original text: https://lnkd.in/gSFBby4q ]

We’ve called efficiency the unsung hero of the energy transition in the past. While the energy transition will happen first through the transition of energy usages, like the shift with transport, from internal combustion engines to electric vehicles, or from fuel or gas boilers to heat pumps, we cannot ignore the utmost priority of the energy transition: efficiency. Efficiency is the greatest path to reduce our energy use, our impact on the world’s climate through CO2 emission reduction, and very importantly, the best way to make solid and practical savings. In its most historical form, energy efficiency is about better insulation, to reduce heating (or cooling) loss in buildings like family homes, warehouses, office high rises, and shopping malls. This is useful, but expensive and tedious to realize on existing installations. Digitizing home, buildings, industries and infrastructure brings similar benefits at a much lower cost and a much higher economic return. The combination of IoT, big data, software and AI can significantly reduce energy use and waste by detecting leaky valves, or automatically adjusting heating, lighting, processes and other systems to the number of people present at any given time, using real-time data analysis. It also allows owners to measure precisely progress, report automatically on their energy and sustainability parameters, and benefit from new services through smart grid interaction. And this is just the energy benefit. Automation and digital tools also optimize the processes, safety, reliability, and uptime leading to greater productivity and performance.

WIPO’s global report on IP filings is out and records are being broken. 2024 saw the highest ever patent filings – 3.7 million worldwide. Design filings also peaked at a record 1.6 mln, while trademark filings stabilized after two years of decline. But within this rich trove of data from nearly 150 IP offices, a few deeper insights stand out. First, emerging and developing countries continue to embrace IP-driven growth and transformation, whether driven by the need to diversify engines of growth, support increasing aspirations of local innovators and entrepreneurs, create more attractive investment environments, or simply seek new sources of growth. For the sixth consecutive year, India posts double-digit growth in patent filings, with Türkiye also up some 15%. Among the top 20 countries of origin, 12 saw increases in trademark filings, led by Argentina, Brazil and Indonesia, and with strong growth in upper middle-income economies like Colombia, South Africa, Thailand and Viet Nam. Design filings tell a similar story, with the fastest growth in India, Morocco and Indonesia. What this means is that many emerging economies are following the path of the world’s established innovation powerhouses in using IP as a strategic lever for economic growth, diversification, development and resilience. The next challenge is commercializing more of these filings, so they become real-world products and services. Second, we’re seeing more domestic, or “resident” filings. In areas like trademarks and designs, resident filings have traditionally made up the vast majority (+70%) as local businesses often register IP to protect brands and designs serving domestic markets. Now, we’re seeing the same dynamics in patents. Resident patent filings grew almost 7% last year, the fastest rise since 2016, to 72% of the total. This growth in domestic filings suggests that innovation ecosystems are maturing (even for high-tech discoveries, inventors typically file at home first before expanding abroad). It may also reflect shifts in global trade flows, with some industries becoming more localized. Third, many of the major trends in recent years continue to accelerate. Just as AI and digital innovation dominate the headlines, computer technology remains the top field for patent activity, with its growth outpacing all others. The gender balance in innovation is also improving. The proportion of women inventors in international patent applications has increased from 11.6% in 2010 to 18% last year. Beyond the individual data points, the value of this report lies in what it reveals about the global state of innovation and the direction it’s heading. This year’s WIPI shows that people everywhere continue to believe in the power of IP to protect ideas and incentivize innovation, and it gives WIPO the energy to continue strengthening IP ecosystems everywhere to give these innovators and creators the tools to protect and commercialize their ideas. 🔗 https://ow.ly/gub150XqnE7

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas