Innovation

AI just helped a couple get pregnant - after 19 years and 15 failed IVF cycles. The breakthrough came with an AI tool built by a team at Columbia University. It’s called STAR - the world’s first AI system trained to find sperm that embryologists can’t. The husband had azoospermia - a condition where no sperm is visible under the microscope. Dozens of attempts, surgeries, and even overseas experts had failed. But the team at Columbia didn’t give up. They spent 5 years building STAR (Sperm Track and Recovery). The system scans 8 million images per hour using a chip and computer vision, then gently isolates viable sperm missed by even the most experienced lab techs. And it worked. ▶︎ STAR found 44 sperm in a sample that had been manually searched for two full days. ▶︎ That one breakthrough led to a pregnancy that had felt impossible for nearly two decades. ▶︎ And it did so without chemicals, donor samples, or invasive extraction methods. For millions of couples dealing with infertility, this is a glimpse of what AI-assisted reproductive medicine could unlock. But more importantly - this shows us what AI in healthtech should be aiming for: Not just more data. Not just smarter models. But real clinical results that change lives. And as a healthtech investor, this is what I look for in AI-driven care: → A clear pain point → A targeted intervention → And a story no one can ignore What’s your take - could AI reshape fertility care the way it’s starting to reshape diagnostics and mental health? #entrepreneurship #healthtech #innovation

𝗧𝗵𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗦𝘁𝗮𝗶𝗿𝗰𝗮𝘀𝗲 represents the 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 from passive AI models to fully autonomous systems. Each level builds upon the previous, creating a comprehensive framework for understanding how AI capabilities progress from basic to advanced: BASIC FOUNDATIONS: • 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: The foundation of modern AI systems, providing text generation capabilities • 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Critical for semantic understanding and knowledge organization • 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Optimization techniques to enhance model responses • 𝗔𝗣𝗜𝘀 & 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝗲𝘀𝘀: Connecting AI to external knowledge sources and services INTERMEDIATE CAPABILITIES: • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Handling complex conversations and maintaining user interaction history • 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀: Short and long-term memory systems enabling persistent knowledge • 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 & 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: Enabling AI to interface with external tools and perform actions • 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗲𝗽 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: Breaking down complex tasks into manageable components • 𝗔𝗴𝗲𝗻𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: Specialized tools for orchestrating multiple AI components ADVANCED AUTONOMY: • 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: AI systems working together with specialized roles to solve complex problems • 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Structured processes allowing autonomous decision-making and action • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴: Independent goal-setting and strategy formulation • 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴: Optimization of behavior through feedback mechanisms • 𝗦𝗲𝗹𝗳-𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗜: Systems that improve based on experience and adapt to new situations • 𝗙𝘂𝗹𝗹𝘆 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜: End-to-end execution of real-world tasks with minimal human intervention The Strategic Implications: • 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻: Organizations operating at higher levels gain exponential productivity advantages • 𝗦𝗸𝗶𝗹𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Engineers need to master each level before effectively implementing more advanced capabilities • 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹: Higher levels enable entirely new use cases from autonomous research to complex workflow automation • 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀: Advanced autonomy typically demands greater computational resources and engineering expertise The gap between organizations implementing advanced agent architectures versus those using basic LLM capabilities will define market leadership in the coming years. This progression isn't merely technical—it represents a fundamental shift in how AI delivers business value. Where does your approach to AI sit on this staircase?

If students don’t learn how to think with AI, they’ll let AI think for them. Last Thursday at Shanghai American School, I got to "beam in" to give a keynote presentation on one of the most urgent conversations in education today: How do we integrate AI without losing what makes learning human? Here are the key takeaways from our time together: • Generative AI can amplify learning—or weaken it. Studies show that when students engage critically with AI, they learn more. But when they rely on it to do the work for them, learning declines. The key? Teach students to think with AI, not just use it. • Confidence in AI can lower critical thinking. Research suggests that when people trust AI too much, they question it less. The best educators will teach students how to balance trust and skepticism when using AI tools. • Ethical AI use starts with values. We discussed how every school needs guiding principles for AI integration—beyond just policies. What should we protect? What should we enhance? These questions shape AI’s role in education. We concluded with "Three Ts" for responsible AI use: 1. Talk – Normalize generative AI discussions with students and teachers. I shared my "Generative AI Guidelines Canvas" to support conversations. https://lnkd.in/gyjTkK7d 2. Teach – Build generative AI literacy into the curriculum. I shared Cora Yang and Dalton Flanagan's C.R.E.A.T.E. framework for teaching students to prompt. https://lnkd.in/g-KYt4Uy 3. Try – Teachers should experiment with generative AI tools in meaningful, ethical ways. I shared Darren Coxon's Hattie Bot to let teachers experiment with building lessons that have high effect size. https://lnkd.in/g44gZzA3 This conversation isn’t over—it’s just beginning. Critical thinking isn't optional if machines do the easy thinking for us. Much gratitude to Alan Preis & Scott Williams for crafting such a great experience. Photo Credit Alex McMillan 🙏 P.S. I asked everyone at Shanghai American School: What values should guide our approach to AI in education? What's your answer? #generativeAI #guidelines #teachers #ethics

A couple reflections on the quantum computing breakthrough we just announced... Most of us grew up learning there are three main types of matter that matter: solid, liquid, and gas. Today, that changed. After a nearly 20 year pursuit, we’ve created an entirely new state of matter, unlocked by a new class of materials, topoconductors, that enable a fundamental leap in computing. It powers Majorana 1, the first quantum processing unit built on a topological core. We believe this breakthrough will allow us to create a truly meaningful quantum computer not in decades, as some have predicted, but in years. The qubits created with topoconductors are faster, more reliable, and smaller. They are 1/100th of a millimeter, meaning we now have a clear path to a million-qubit processor. Imagine a chip that can fit in the palm of your hand yet is capable of solving problems that even all the computers on Earth today combined could not! Sometimes researchers have to work on things for decades to make progress possible. It takes patience and persistence to have big impact in the world. And I am glad we get the opportunity to do just that at Microsoft. This is our focus: When productivity rises, economies grow faster, benefiting every sector and every corner of the globe. It’s not about hyping tech; it’s about building technology that truly serves the world. Read more about our discovery, and why it matters, here: https://aka.ms/AAu76rr

AI is not hype. At IBM we've completed 1,000+ Generative AI projects in the last 12 months, prioritizing business applications over consumer ones. Top use cases are: ▪️ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿-𝗳𝗮𝗰𝗶𝗻𝗴 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 - Customer service: Empower customers to find solutions with easy, compelling experiences. Automate answers with 95% accuracy - Marketing: Increase personalization, and improve efficiency across the content supply chain. Reduce content creation costs by up to 40% - Content creation: ex. enhance digital sports viewing with auto-generative spoken AI commentary. Scale live viewing experiences cost-effectively - Knowledge worker: Enable higher value work, improve decision making, increase productivity. Reduce 90% of text reading and analysis work ▪️ 𝗛𝗥, 𝗙𝗶𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗦𝘂𝗽𝗽𝗹𝘆-𝗖𝗵𝗮𝗶𝗻 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 - HR automation: Reduce Manual work and automate recruiting sourcing and nurturing job candidates. Reduce employee mobility processing time by 50% - Supply chain: Automate source-to-pay processes, reduce resource needs, and improve cycle times. Reduce cost per invoice by up to 50% - Planning and analysis: Make smarter decisions, and focus on higher-value tasks with automated workflows and AI. Process planning data up to 80% faster - Regulatory compliance: Support compliance based on requirements/risks, and proactively respond to regulatory changes. Reduce time spent responding to issues ▪️ 𝗜𝗧 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 - App modernization, migration: Generate code, and tune code generation response in real time. Deliver faster development output - IT automation: Identify deployment issues, avoid incidents, and optimize application demand to supply. Reduce mean time to repair (MTTR) by 50% - AIOps: Assure continuous, cost-effective performance and connectivity across applications. Reduce application support tickets by 70% - Data platform engineering: Redesign the approach for data integration using generative AI. Reduce data integration time by 30% ▪️ 𝗖𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 - Threat management: Reduce incident response times from hours to minutes or seconds. Contain potential threats 8x faster - Asset management: Optimize critical asset performance and operations while delivering sustainable outcomes. Reduce unplanned downtime by 43% - Product development: ex. expedite drug discovery by inferring structure with AI from simple molecular representations. Faster and less expensive drug discovery - Environmental intelligence: Provide intelligence to proactively manage the impact of severe weather and climate. Increase manufacturing output by 25% ______ Please repost it ♻️ and follow me, Armand Ruiz , for more similar posts.

Every founder has a slide that says, “We’ll acquire customers through content marketing, paid social or partnerships.” And in 2025, nearly every investor has learned to ignore it 😿 The old go-to-market playbooks are not working anymore. The channels are saturated, the costs are high and the returns are diminishing. From where I sit as an early stage investor, a generic GTM plan is no longer a sign of preparation - it’s a red flag. It shows a founder is ready to execute someone else’s old strategy, not discover a new unique one tailored to their own business. Founders who are successful finding their first customers and raising capital are demonstrating something else entirely - not a polished plan, but a series of insightful discoveries. Here’s what I see actually working to prove you can access a market: ✔️ A Portfolio of Scrappy Experiments. Before you can find a scalable channel, you need to prove you can find ANY channel. The most impressive founders show up with stories of things that don't scale. They acquired their first 50 users by building a free tool that solved a tiny problem for their target user or by personally engaging in a specific Subreddit or Slack community. This proves you have the creativity and grit to find customers where others aren’t even looking. ✔️ A Founder Who Is the Distribution Channel. Early on, your most powerful GTM advantage can’t be bought because it’s actually YOU. Investors are looking for a founder with a unique ability to reach the market. Are you an expert with a following in your industry? Have you built a deep, trusted network that represents your initial customer base? Show how your personal brand and unique insights give you an unfair advantage that no amount of ad spend or marketing can replicate. ✔️ Mastery of a "Micro-Funnel." Instead of a broad, leaky funnel, demonstrate that you can dominate a tiny, efficient one. Prove that you can convert a very specific persona from a very specific source with incredible efficiency. For example: "We can turn a clinical research coordinator from a specific LinkedIn group into a qualified lead for $15." This level of precision is far more impressive than a vague, top-down plan to capture a massive market. It shows you have a data-driven foundation from which to grow. The goal of an early-stage GTM isn't to prove you can scale, it's to prove you can learn. Your first GTM strategy shouldn't be a playbook - it should be a lab notebook full of weird and (hopefully) winning experiments. 🙌🏼 Shout out to Alex Iskold from 2048 Ventures for teaching me a lot about funnels over the years and what he calls 'magic moments' 🙏🏼

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 couldn’t be more excited to share our latest AI research breakthrough in video generation at Meta. We call it Movie Gen and it’s a collection of state-of-the-art models that combine to deliver the most advanced video generation capability ever created. Movie Gen brings some incredible new innovation to this field including: • Up to 16 seconds of continuous video generation – the longest we’ve seen demonstrated to date. • Precise editing – unlike others that are just style transfer. • State-of-the-art video conditioned audio which is better than all the text to audio models • Video personalization in a way never done before – not image personalization with animation. We’ve published a blog and a very detailed research paper along with a wide selection of video examples that you can check out: https://lnkd.in/gTfwRsHm

The Future Walks on a SoftFoot Nature has spent millions of years perfecting the human foot—an intricate masterpiece of bones, tendons, and muscles that absorb impact, adapt to terrain, and propel us forward with unmatched efficiency. Now, technology is catching up. Meet SoftFoot Pro, a game-changing prosthetic foot that mimics the biomechanics of a real human foot—without motors, just pure engineering brilliance. Developed by the Istituto Italiano di Tecnologia (IIT) and the University of Pisa, this flexible, waterproof prosthetic is not just for people with limb loss. It’s also designed for the humanoid robots of the future. What makes it special? ✅ A built-in windlass mechanism – just like the natural plantar fascia, storing and releasing energy with every step. ✅ Adapts to uneven terrain – rigid prosthetics struggle with slopes, but this one flexes and conforms. ✅ Lightweight yet strong – supports up to 100kg, with cutting-edge materials from aerospace and automotive tech. ✅ Artificial intelligence in its purest form – not software, but design. It doesn’t just simulate a foot; it behaves like one. This is biomimicry at its best: taking cues from nature to build technology that moves, balances, and interacts with the world like we do. A foot designed for humans—but also for the future of robotics. Innovation keeps bringing us closer to nature. What other human abilities do you think technology should replicate next? 🚀 #ai #tech #robotics

Just out in Harvard Business Review, summary of the Hybrid Experiment results and lessons on how to make hybrid succeed. Experiment: randomize 1600 graduate employees in marketing, finance, accounting and engineering at Trip.com into 5-days a week in office, or 3-days a week in office and 2-days a week WFH. Analyzed 2 years of data. Two key results A) Hybrid and fully-in-office showed no differences in productivity, performance review grade, promotion, learning or innovation. B) Hybrid had a higher satisfaction rate, and 35% lower attrition. Quit-rate reductions were largest for female employees. Four managerial lessons 1) Hybrid needs a strong performance management system so managers don’t need to hover over employees at their desks to check their progress. Trip.com had an extensive performance review process every six months. 2) Coordinate in-office days at the team or company level. Schedule clarity prevents the frustration of coming to an empty office only to participate in Zoom calls. Trip.com coordinated WFH on Wednesday and Friday. 3) Having leadership buy-in is critical (as with most management practices). Trip.com’s CEO and C-suite all support the hybrid policy. 4) A/B test new policies (as well as products) if possible. Often new policies turn out to be unexpectedly profitable. Trip.com made millions of dollars more profits from hybrid by cutting expensive turnover.