Innovation

Wind and solar generation is scaling faster than any other electricity sources in history. This chart looks at the time taken for different technologies to grow from 100 TWh to 1,000 TWh of annual electricity generation. ✅ Solar took just 8 years ✅ Wind took 12 years ✅ By comparison, gas took 28 years, coal 32 years and hydro 39 years Nuclear also reached the milestone in 12 years, but then its growth slowed sooner than wind. Importantly, this chart is not measuring market share. It is measuring how quickly different technologies scaled once they reached meaningful levels of deployment. And the story today is also bigger than generation alone. Batteries are increasingly extending solar generation into the evening peak, while electrification is creating new demand for clean electricity in transport, heating and industry. Much of this growth is being driven by improving economics, with wind, solar and batteries becoming increasingly competitive across a growing range of applications. That combination is beginning to reshape energy systems around the world. Renewables now generate more than one third of global electricity, and wind and solar continue to account for the majority of new power capacity added each year. The pace of deployment matters because energy transitions are ultimately about scale. And by that measure, wind and solar are growing faster than anything that came before them.

We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We discovered a near-perfect log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and this loss directly predicts real-robot success rate. Humanoid robots will be the end game, because they are the practical form factor with minimal embodiment gap from humans. Call it the Bitter Lesson of robot hardware: the kinematic similarity lets us simply retarget human finger motion onto dexterous robot hand joints. No learned embeddings, no fancy transfer algorithms needed. Relative wrist motion + retargeted 22-DoF finger actions serve as a unified action space that carries through from pre-training to robot execution. Our recipe is called "EgoScale": - Pre-train GR00T N1.5 on 20K hours of human video, mid-train with only 4 hours (!) of robot play data with Sharpa hands. 54% gains over training from scratch across 5 highly dexterous tasks. - Most surprising result: a *single* teleop demo is sufficient to learn a never-before-seen task. Our recipe enables extreme data efficiency. - Although we pre-train in 22-DoF hand joint space, the policy transfers to a Unitree G1 with 7-DoF tri-finger hands. 30%+ gains over training on G1 data alone. The scalable path to robot dexterity was never more robots. It was always us. - Website: https://lnkd.in/gxzgeP-2 - Paper: https://lnkd.in/g7PJdz_8

The rise of AI agents is not a sudden breakthrough, but a steady evolution through multiple layers of capability: LLM Processing Flow – Basic input-to-output transformation. LLM with Document Processing – Expanding scope to handle structured and unstructured documents. LLM with RAGs & Tools – Introducing retrieval, tool-use, and external knowledge integration. Multi-Modal Workflows – Combining text, vision, and audio with emerging memory structures. Advanced Architectures – Incorporating decision-making, orchestration of tools, and multi-level memory (short-term, long-term, episodic). Future AI Agents – Moving beyond capability toward responsibility: safety, ethics, regulation, compliance, interoperability, and human collaboration. This progression highlights a clear trajectory: from narrow assistants to autonomous, enterprise-ready agents that operate within a framework of trust, governance, and accountability. The challenge now is not whether we can reach stage six, but how we ensure safety and control while advancing toward it.

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.

Invisible UX is coming 🔥 And it’s going to change how we design products, forever. For decades, UX design has been about guiding users through an experience. We’ve done that with visible interfaces: Menus. Buttons. Cards. Sliders. We’ve obsessed over layouts, states, and transitions. But with AI, a new kind of interface is emerging: One that’s invisible. One that’s driven by intent, not interaction. Think about it: You used to: → Open Spotify → Scroll through genres → Click into “Focus” → Pick a playlist Now you just say: “Play deep focus music.” No menus. No tapping. No UI. Just intent → output. You used to: → Search on Airbnb → Pick dates, guests, filters → Scroll through 50+ listings Now we’re entering a world where you guide with words: “Find me a cabin near Oslo with a sauna, available next weekend.” So the best UX becomes barely visible. Why does this matter? Because traditional UX gives users options. AI-native UX gives users outcomes. Old UX: “Here are 12 ways to get what you want.” New UX: “Just tell me what you want & we’ll handle the rest.” And this goes way beyond voice or chat. It’s about reducing friction. Designing systems that understand intent. Respond instantly. And get out of the way. The UI isn’t disappearing. It’s mainly dissolving into the background. So what should designers do? Rethink your role. Going forward you’ll not just lay out screens. You’ll design interactions without interfaces. That means: → Understanding how people express goals → Guiding model behavior through prompt architecture → Creating invisible guardrails for trust, speed, and clarity You are basically designing for understanding. The future of UX won’t be seen. It will be felt. Welcome to the age of invisible UX. Ready for it?

For years, the biggest players in CPG and FMCG—Unilever, Nestlé, Kraft Heinz—built their empires on food. But now? They’re making a massive pivot..if you had told me 5 years ago that these brands would be pulling back from food, I would’ve raised an eyebrow. -Unilever is cutting loose its $8 billion ice cream division, choosing to focus on higher-margin beauty and wellness. -Nestlé is doubling down on health-science-based nutrition as food brands struggle with pricing power. - #CPG giants are seeing stronger growth in self-care, supplements, and skincare than in traditional food categories. The global personal care market is expected to hit $758 billion by 2030, while processed food growth slows. Why This Shift? 1. Margins in food are shrinking. Consumers are trading down, private labels are winning, and inflation-wary shoppers aren’t absorbing cost hikes like they used to. 2. Health & wellness are driving premiumization. Customers will pay more for skincare, supplements, and functional beverages—but not for basic pantry staples. 3. Brand loyalty in food is eroding. Over 50% of consumers are comfortable switching food brands based on price, but loyalty remains strong in beauty, healthcare, and wellness. Winning Brands Are Already Moving: -L'Oréal’s skincare division posted 9.1% revenue growth last year, while traditional CPG food brands saw single-digit declines. -The Coca-Cola Company is investing in functional drinks and non-carbonated wellness categories to stay relevant. -PepsiCo’s biggest success? Gatorade’s expansion into hydration and performance-based drinks, not soda. CPG Leaders: ✅ Stop thinking of food as the core driver of growth. Instead, align with evolving consumer behavior. ✅ Invest in personalization, self-care, and functional health. That’s where demand (and pricing power) is strongest. ✅ Rethink your brand mix. Is your portfolio weighted toward categories that will still be relevant in 5-10 years? So, here’s my question to FMCG execs: Are you future-proofing your brand strategy—or just managing decline? Let’s talk. #FMCG #CPG #ConsumerTrends #GrowthStrategy #Beauty #Wellness #RevenueShift #BrandEvolution "

🧠 “How We Brainstorm And Choose UX Ideas” (+ Miro template) (https://lnkd.in/eN32hH2x), a practical guide by Booking.com on how to run a rapid UX ideation session with silent brainstorming and “How Might We” (HMW) statements — by clustering data points into themes, reframing each theme and then prioritizing impactful ideas. Shared by Evan Karageorgos, Tori Holmes, Alexandre Benitah. 👏🏼👏🏽👏🏾 Booking.com UX Ideation Template (Miro) https://lnkd.in/eipdgPuC (password: bookingcom) 🚫 Ideas shouldn’t come from assumptions but UX research. ✅ Study past research and conduct a new study if needed. ✅ Cluster data in user needs, business goals, competitive insights. ✅ Best ideas emerge at the intersections of these 3 pillars. ✅ Cluster all data points into themes, prioritize with colors. ✅ Reframe each theme as a “How Might We” (HMW) statement. ✅ Start with the problems (or insights) you’ve uncovered. ✅ Focus on the desired outcomes, rather than symptoms. ✅ Collect and group ideas by relevance for every theme. ✅ Prioritize and visualize ideas with visuals and storytelling. Many brainstorming sessions are an avalanche of unstructured ideas, based on hunches and assumptions. Just like in design work we need constraints to be intentional in our decisions, we need at least some structure to mold realistic and viable ideas. I absolutely love the idea of frame the perspective through the lens of ideation clusters: user needs, business problems and insights. Reframing emerging themes as “How-Might-We”-statements is a neat way to help teams focus on a specific problem at hand and a desired outcome. A simple but very helpful approach — without too much rigidity but just enough structure to generate, prioritize and eventually visualize effective ideas with the entire team. Invite non-designers in the sessions as well, and I wouldn’t be surprised how much value a 2h session might deliver. Useful resources: The Rules of Productive Brainstorming, by Slava Shestopalov https://lnkd.in/eyYZjAz3 On “How Might We” Questions, by Maria Rosala, NN/g https://lnkd.in/ejDnmsRr Ideation for Everyday Design Challenges, by Aurora Harley, NN/g https://lnkd.in/emGtnMyy Brainstorming Exercises for Introverts, by Allison Press https://lnkd.in/eta6YsFJ How To Run Successful Product Design Workshops, by Gustavs Cirulis, Cindy Chang https://lnkd.in/eMtX-xwD Useful Miro Templates For UX Designers, by yours truly https://lnkd.in/eQVxM_Nq #ux #design

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

Energy is once again dominating headlines all over the world. Gas and oil prices are volatile, key shipping routes face geopolitical pressure, and policymakers are concerned about supply risks. The renewed uncertainty is a reminder of an uncomfortable reality: the next energy crisis isn’t an if – it’s a when, and a question of how prepared we are. A defining challenge of this decade, and one that now feels more urgent than ever, is how to build a resilient energy system. One that minimises structural dependencies and is designed for rising electricity demand. The imperative of our time: The more we electrify, the less we import fossil fuels. The less we import, the more resilient we become. The course of action is clear: ▪️ Relentlessly scale renewables: Slowing the buildout will not reduce costs. Quite the opposite – delay compounds system costs for the entire economy. ▪️ Fix the grids: As fast as possible, as efficiently as possible, and at the lowest possible cost. Before they become even more of a bottleneck. ▪️ Secure 24/7 electricity supply: When the wind isn’t blowing and the sun isn’t shining, renewables need reliable backup in the form of battery storage and hydrogen-ready gas fired power plants. But gas should serve only as a backup, with renewables and batteries reducing its utilisation. ▪️ Reduce gas supply dependence with infrastructure and diversification: We must not replace old dependencies with new ones. Diversification of gas supplies is key. And the physical prerequisite is an import infrastructure with buffers. We need the planned LNG terminals, complemented by a nationally held gas reserve to help ensure secure supply in winter. ▪️ Electrify everything that makes sense: The more we can power with mostly homegrown electrons, the less dependent we become on fossil imports. Other energy import-dependent countries like Japan and China have electrification rates that are around 10 percentage points higher than Germany’s. This shows where the path forward lies. Electrification reduces reliance on imported fossil fuels, which in turn strengthens overall resilience. The time to act is now.

After spending three decades in the aerospace industry, I’ve seen firsthand how crucial it is for different sectors to learn from each other. We no longer can afford to stay stuck in our own bubbles. Take the aerospace industry, for example. They’ve been looking at how car manufacturers automate their factories to improve their own processes. And those racing teams? Their ability to prototype quickly and develop at a breakneck pace is something we can all learn from to speed up our product development. It’s all about breaking down those silos and embracing new ideas from wherever we can find them. When I was leading the Scorpion Jet program, our rapid development – less than two years to develop a new aircraft – caught the attention of a company known for razors and electric shavers. They reached out to us, intrigued by our ability to iterate so quickly, telling me "you developed a new jet faster than we can develop new razors..." They wanted to learn how we managed to streamline our processes. It was quite an unexpected and fascinating experience that underscored the value of looking beyond one’s own industry can lead to significant improvements and efficiencies, even in fields as seemingly unrelated as aerospace and consumer electronics. In today’s fast-paced world, it’s more important than ever for industries to break out of their silos and look to other sectors for fresh ideas and processes. This kind of cross-industry learning not only fosters innovation but also helps stay competitive in a rapidly changing market. For instance, the aerospace industry has been taking cues from car manufacturers to improve factory automation. And the automotive companies are adopting aerospace processes for systems engineering. Meanwhile, both sectors are picking up tips from tech giants like Apple and Google to boost their electronics and software development. And at Siemens, we partner with racing teams. Why? Because their knack for rapid prototyping and fast-paced development is something we can all learn from to speed up our product development cycles. This cross-pollination of ideas is crucial as industries evolve and integrate more advanced technologies. By exploring best practices from other industries, companies can find innovative new ways to improve their processes and products. After all, how can someone think outside the box, if they are only looking in the box? If you are interested in learning more, I suggest checking out this article by my colleagues Todd Tuthill and Nand Kochhar where they take a closer look at how cross-industry learning are key to developing advanced air mobility solutions. https://lnkd.in/dK3U6pJf