Social Impact Of AI

A teacher used AI to show her students as grown-ups in their dream jobs. 🤗 The result? A viral, heartwarming reel — and a powerful lesson about the human side of technology. AI didn’t just generate images. It generated belief. When those kids saw themselves as doctors, artists, astronauts, and engineers, they weren’t just imagining the future — they were feeling it. And that matters. Because psychology tells us that visualizing success makes it more achievable. Now, AI can make that visualization vivid, personal, and real. 📊 1 in 3 U.S. educators already use AI to personalize learning. 💡 Emotional storytelling with AI improves memory retention by 70%. ❤️ This teacher gave her students something few adults ever get — a visual future worth believing in. This is AI as empathy tech. A tool not for replacing humans, but for amplifying hope, motivation, and imagination. 👉 What if we used AI not just to optimize work — but to elevate people? #AI #Education #Innovation #Empathy #TechnologyForGood #FutureOfWork #Leadership

🚨 BREAKING: Singapore is launching a mandatory AI literacy course for all its public servants, who already have over 16,000 custom AI bots to automate tasks. 😱 This is their interesting approach to AI governance: Gan Kim Yong, Singapore's Deputy Prime Minister, said the following during their 'Annual Public Service Leadership Ceremony': "Today, about one-third of our 150,000 officers regularly use Pair, our in-house AI assistant, to support their work in research and writing. Many have gone even further – to date, our officers have created more than 16,000 custom AI bots to automate tasks and improve responsiveness. We also see a growing culture of ground-up innovation, with our people participating in hackathons and incubator programmes to tackle real operational problems. To raise the level of responsible AI use in our Public Service, we will launch a mandatory AI literacy course for all officers, to complement digital leadership training for leaders at different levels. As leaders, we must set the tone by being open to using AI in our own work, encouraging our teams to do the same, and guiding our people to use these tools effectively and thoughtfully. But adoption alone is not enough. We must apply AI meaningfully. We can already see the value of AI in various cases. We should challenge our teams to think of meaningful use cases in each of our respective domains, where we can apply AI-driven solutions to improve services and outcomes. We must also recognise that high-impact AI use cases depend on strong digital foundations, such as good governance and capabilities. And we must deepen our AI capabilities. Our mastery of these tools will also enhance our ability to regulate and protect. For example, with stronger capabilities in AI and data, we can detect scams earlier, respond faster to cyber threats, and reduce online harms. We must also build deeper expertise through partnership with the wider ecosystem." - It's great to see that even though they seem to be deploying AI at an extremely accelerated pace, their main concern remains to enhance their ability to regulate and protect (and serve the public). Additionally, AI literacy is a professional necessity for everyone, including those in the public service. Many countries could learn from Singapore's approach. - 👉 Understand how AI is reshaping society and stay ahead: join my newsletter's 78,300+ subscribers (link below)

The Alan Turing Institute 𝗮𝗻𝗱 the LEGO Group 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗰𝗵𝗶𝗹𝗱-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗔𝗜 𝘀𝘁𝘂𝗱𝘆! ⬇️ (𝘈 𝘮𝘶𝘴𝘵-𝘳𝘦𝘢𝘥 — 𝘦𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘭𝘺 𝘪𝘧 𝘺𝘰𝘶 𝘩𝘢𝘷𝘦 𝘤𝘩𝘪𝘭𝘥𝘳𝘦𝘯.) While most AI debates and studies focus on models, chips, and jobs — this one zooms in on something far more personal: 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝗰𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗴𝗿𝗼𝘄 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜? They surveyed 1,700+ kids, parents, and teachers across the UK — and what they found is both powerful and concerning. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 9 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝘀𝘁𝗼𝗼𝗱 𝗼𝘂𝘁 𝘁𝗼 𝗺𝗲 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁: ⬇️ 1. 1 𝗶𝗻 4 𝗸𝗶𝗱𝘀 (8–12 𝘆𝗿𝘀) 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 — 𝗺𝗼𝘀𝘁 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝗮𝗳𝗲𝗴𝘂𝗮𝗿𝗱𝘀 → ChatGPT, Gemini, and even MyAI on Snapchat are now part of daily digital play. 2. 𝗔𝗜 𝗶𝘀 𝗵𝗲𝗹𝗽𝗶𝗻𝗴 𝗸𝗶𝗱𝘀 𝗲𝘅𝗽𝗿𝗲𝘀𝘀 𝘁𝗵𝗲𝗺𝘀𝗲𝗹𝘃𝗲𝘀 — 𝗲𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗹𝘆 𝘁𝗵𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗻𝗲𝗲𝗱𝘀 → 78% of neurodiverse kids use ChatGPT to communicate ideas they struggle to express otherwise. 3. 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗶𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 — 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 → Kids still prefer offline tools (arts, crafts, games), even when they enjoy AI-assisted play. Digital is not (yet) the default. 4. 𝗔𝗜 𝗮𝗰𝗰𝗲𝘀𝘀 𝗶𝘀 𝗵𝗶𝗴𝗵𝗹𝘆 𝘂𝗻𝗲𝗾𝘂𝗮𝗹 → 52% of private school students use GenAI, compared to only 18% in public schools. The next digital divide is already here. 5. 𝗖𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗮𝗿𝗲 𝘄𝗼𝗿𝗿𝗶𝗲𝗱 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜’𝘀 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 → Some kids refused to use GenAI after learning about water and energy costs. Let that sink in. 6. 𝗣𝗮𝗿𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰 — 𝗯𝘂𝘁 𝗱𝗲𝗲𝗽𝗹𝘆 𝘄𝗼𝗿𝗿𝗶𝗲𝗱 → 76% support AI use, but 82% are scared of inappropriate content and misinformation. Only 41% fear cheating. 7. 𝗧𝗲𝗮𝗰𝗵𝗲𝗿𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗮𝗻𝗱 𝗹𝗼𝘃𝗶𝗻𝗴 𝗶𝘁 → 85% say GenAI boosts their productivity, 88% feel confident using it. They’re ahead of the curve. 8. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝘀 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗿𝗲𝗮𝘁 → 76% of parents and 72% of teachers fear kids are becoming too trusting of GenAI outputs. 9. 𝗕𝗶𝗮𝘀 𝗮𝗻𝗱 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗮 𝗯𝗹𝗶𝗻𝗱𝘀𝗽𝗼𝘁 → Children of color felt less seen and less motivated to use tools that didn’t reflect them. Representation matters. The next generation isn’t just using AI. They’re being shaped by it. That’s why we need a more focused, intentional approach: Teaching them not just how to use these tools — but how to question them. To navigate the benefits, the risks, and the blindspots. 𝗪𝗮𝗻𝘁 𝗺𝗼𝗿𝗲 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀? Subscribe to Human in the Loop — my new weekly deep dive on AI agents, real-world tools, and strategic insights: https://lnkd.in/dbf74Y9E

In our new paper we ran an experiment at Procter and Gamble with 776 experienced professionals solving real business problems. We found that individuals randomly assiged to use AI did as well as a team of two without AI. And AI-augmented teams produced more exceptional solutions. The teams using AI were happier as well. Even more interesting: AI broke down professional silos. R&D people with AI produced more commercial work and commercial people with AI had more technical solutions. The standard model of "AI as productivity tool" may be too limiting. Today’s AI can function as a kind of teammate, offering better performance, expertise sharing, and even positive emotional experiences. This was a massive team effort with work led by Fabrizio Dell'Acqua, Charles Ayoubi, and Karim Lakhani along with Hila Lifshitz, Raffaella Sadun, Lilach M., me and our partners at P&G: Yi Han, Jeff Goldman, Hari Nair and Stewart Taub Subatack about the work here: https://lnkd.in/ehJr8CxM Paper: https://lnkd.in/e-ZGZmW9

Net Positive AI Hello everyone! The past months have, along with a bit of a rest, given me the opportunity to figure out the next chapter of my career. It hasn’t been hard to land on the intersection of Energy, AI, Sustainability and Finance. Areas sometimes in harmony – often in tension - all exciting. Today, I’d like to share a bit about one opportunity: Prometheus Hyperscale. AI has the potential to transform our lives - from advances in drugs and disease control, increasing productivity, to accelerating the energy transition. There are also of course some downsides. One is that AI needs massive amounts of energy. Creating a basic AI-generated video is the energy equivalent of charging 119 phones! The power is needed to train and run complex models which use massive amounts of data. And this is what data centres do. So, the question becomes – how do we make AI Net Positive? How do we make the enormous benefits outweigh any downsides? In energy, the challenges are twofold. First – data centres often add to the local community’s power needs and can drive up electricity costs. The result is many won’t get built; and Second – more power, more emissions. People worry that instead of helping, AI may slow the transition and move us backwards. The opportunity to address these challenges is why I’ve joined Prometheus Hyperscale as Chairman. Trenton Thornock - Founder and CEO - saw this coming - several years ago he created a company to build sustainable data centres. Prometheus’s data centres aim to be largely self-sufficient, so they shouldn’t drive up local power prices. The flagship Evanston project in Wyoming is surrounded by abundant resources in wind, solar, and natural gas. Trenton’s family are 6th generation owners of a 12,000 acre ranch (a bit bigger than ours in Kenmare) which sits over a huge fibre network – carrying data to Silicon Valley and beyond. The cooling could come from underground reservoirs (I knew my drilling days would come in handy) and liquid (vs air) cooling reduces power usage up to 50%; waste heat is captured and reused or recycled. Groundbreaking stuff! We intend to scale to a massive 1GW, making it one of the world’s largest data centres. Speaking of groundbreaking – part of the 640 acre site (almost as big as Central Park) has been cleared and permits secured. We’re working with potential tenants – companies you know – who desperately need this help, as well as securing investors for a multi-billion-dollar program. Down the road, we aim to expand to Arizona, Colorado, and globally – with sustainability at the core. Low carbon power through wind, solar and natural gas. Nuclear through our strategic partnership with Oklo Inc and innovative systems technology. I can’t think of a more exciting challenge. How do we deliver Net Positive AI. How do we unleash AI to propel the world forward. Prometheus Hyperscale is one way. My thanks to Trenton Thornock and Trevor Neilson for including me. I can't wait to start.

What do you know about Physical AI—besides robots? (Part 1) '"Physical AI will revolutionize the $50T manufacturing and logistics industries" "The next wave of AI is Physical AI"... It is a much broader category, and you’ve probably already seen it without realizing it! So what is happening beyond these exciting predictions, l Let’s start with a few commercial products that are already on the market today: 1️⃣ AI-Powered Prosthetics Which is one of the biggest and most impactful uses of Physical AI. Companies like Ottobock and Össur are creating bionic limbs that learn and adapt to the user. These aren’t just mechanical replacements—they analyze movement in real time, adjusting automatically for a smoother, more natural gait. Some even interpret brain signals to predict motion. 2️⃣ AI-Powered Adaptive Glasses – More than just a cool gadget Traditional glasses have fixed prescriptions—but what if your lenses could adapt in real time? That’s exactly what Deep Optics’ 32°N glasses do. They use AI-powered liquid crystal lenses that shift focus dynamically—but instead of automatic adjustment, users swipe to fine-tune focus. No bifocals, no switching glasses—just one adaptable pair. 3️⃣ AI-Integrated Smart Fabrics – Yes, we can wear them now AI isn’t just in machines—it’s in clothing too. Take Hexoskin’s biometric shirts—they track heart rate, breathing, and even stress levels using built-in sensors. AI then analyzes long-term patterns, helping athletes optimize training and doctors monitor patient health. While real-time AI-driven coaching is still evolving, these fabrics mark a step toward AI-powered wearables that respond to your body’s needs. ... So, what exactly is Physical AI?💡 It’s about embedding AI into physical systems—allowing them to interact with the real world, learn from it, and adapt their behavior accordingly. Sounds like sci-fi, but it’s actually much closer to reality than AGI. Physical AI is still in early commercial adoption, but it’s already making an impact in industries like healthcare and smart infrastructure. The next breakthroughs will depend on better hardware, materials, and real-world adaptability. Unlike software AI—where challenges like scalability, reasoning, and efficiency are more universal, Physical AI faces industry-specific challenges depending on how AI interacts with the real world, the materials it controls, and the safety regulations involved. In Part 2, we’ll talk about some of the biggest breakthroughs we might see in the next 5-10 years. 🤖... 📍If you are a leader and looking for more in-depth Executive-level AI insights, check here (10 weeks program, expertise required) ➡️https://bit.ly/3C3GSgF So, which industry would you like to see the most progress in first? Img: Messari, Made Visual _______________ For more on AI and learning materials, please check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang

We've evaluated 50+ mental health apps for investments and I personally used 20+ products. None of them delivered meaningful results. The problem isn’t technology but the assumption that therapy is just pattern recognition & that AI can replace human presence. Here's a breakdown. #1 There is lack of emotional intelligence in the current AI models that are only designed to simulate conversation. But therapy is more than words. It’s about trust, micro-expressions, and intuitive emotional calibration—things AI still struggles with. Large Language Models (LLMs) like ChatGPT and others can generate empathetic responses, but they don’t “feel” anything. They simulate and patients know the difference. #2 There is a large cultural and linguistic barriers in Indian context. Most AI models are trained in western psychological frameworks. Mental health challenges in India are unlike other cultures -societal expectations, family structures, and stigma. An AI chatbot trained on CBT (Cognitive Behavioral Therapy) modules from Western research and university doesn't understand the pressure of an arranged marriage or parental expectations in an IIT-obsessed household. #3 Most mental health apps claim to be “personalized.” In reality, they offer generic suggestions based on symptom checklists. Real therapists go beyond checklists. They adapt in real time & modify their approach dynamically. AI, despite all its advancements, still struggles with this level of nuanced adaptability. Some thoughts how AI in Mental Health could actually work. #1 A good starting point is when AI supports therapists, not replace them. Imagine a model that helps analyze past therapy sessions and provide therapists with structured insights on patient progress. Instead of AI replacing human interaction, it should act as a co-pilot that enhances therapy effectiveness. #2 AI is fantastic at pattern recognition. We could use it to identify early warning signs of mental health deterioration by analyzing speech patterns, writing styles, and biometric data. Maybe hand over the interpretation and intervention to a real human therapist who can act on this data. #3 Mental health AI models need training data that reflect the Indian psyche. Solutions should be developed with inputs from Indian psychologists, using regional languages and culturally relevant therapy approaches. An AI that understands the shame associated with divorce in India or the anxiety induced by societal expectations will be far more effective. The future of AI in mental health isn’t about replacing therapists; it’s about amplifying human connection. Founders building in mental health should focus on integrating AI where it excels (data analytics, early diagnostics, and habit formation) while keeping humans where they matter most (trust, deep healing, and emotional intuition). AI in mental health isn’t about AI being human. It’s about AI making humans better. That’s where the real opportunity lies.

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