Technology Investment Benefits

Separate reports by the publicity firm Edelman and Pew Research (links in orig text, below) show that Americans, and more broadly large parts of Europe and the western world, do not trust AI and are not excited about it. Despite the AI community’s optimism about the tremendous benefits AI will bring, we should take this seriously and not dismiss it. The public’s concerns about AI can be a significant drag on progress, and we can do a lot to address them. According to Edelman’s survey, in the U.S., 49% of people reject the growing use of AI, and 17% embrace it. In China, 10% reject it and 54% embrace it. Pew’s data also shows many other nations much more enthusiastic than the U.S. about AI adoption. Positive sentiment toward AI is a huge national advantage. On the other hand, widespread distrust of AI means: - Individuals will be slow to adopt it. For example, Edelman’s data shows that, in the U.S., those who rarely use AI cite Trust (70%) more than lack of Motivation and Access (55%) or Intimidation by the technology (12%) as an issue. - Valuable projects that need societal support will be stymied. For example, local protests in Indiana brought down Google’s plan to build a data center there. Hampering construction of data centers will hurt AI’s growth. Communities do have concerns about data centers beyond the general dislike of AI; I will address this in a later letter. - Populist anger against AI raises the risk that laws will be passed that hamper AI development. To be clear, all of us working in AI should look carefully at both the benefits and harmful effects of AI (such as deepfakes polluting social media and biased or inaccurate AI outputs misleading users), speak truthfully about both benefits and harms, and work to ameliorate problems even as we work to grow the benefits. But hype about AI’s danger has done real damage to trust in our field. Much of this hype has come from leading AI companies that aim to make their technology seem extraordinarily powerful by, say, comparing it to nuclear weapons. Unfortunately, a significant fraction of the public has taken this seriously and thinks AI could bring about the end of the world. The AI community has to stop self-inflicting these wounds and work to win back society’s trust. Where do we go from here? First, to win people’s trust, we have a lot of work ahead to make sure AI broadly benefits everyone. “Higher productivity” is often viewed by general audiences as a codeword for “my boss will make more money,” or worse, layoffs. As amazing as ChatGPT is, we still have a lot of work to do to build applications that make an even bigger positive impact on people’s lives. I believe providing training to people will be a key piece of the puzzle. DeepLearning.AI will continue to lead the charge on AI training, but we will need more than this. [Truncated for length. Full text, with links: https://lnkd.in/gUgMDMGS ]

As GenAI becomes more ubiquitous, research alarmingly shows that women are using these tools at lower rates than men across nearly all regions, sectors, and occupations.   A recent paper from researchers at Harvard Business School, Berkeley, and Stanford synthesizes data from 18 studies covering more than 140k individuals worldwide.   Their findings:   • Women are approximately 22% less likely than men to use GenAI tools • Even when controlling for occupation, age, field of study, and location, the gender gap remains • Web traffic analysis shows women represent only 42% of ChatGPT users and 31% of Claude users   Factors Contributing the to Gap:   - Lack of AI Literacy: Multiple studies showed women reporting significantly lower familiarity with and knowledge about generative AI tools as the largest gender gap driver. - Lack of Training & Confidence: Women have lower confidence in their ability to effectively use AI tools and more likely to report needing training before they can benefit from generative AI.   - Ethical Concerns & Fears of Judgement: Women are more likely to perceive AI usage as unethical or equivalent to cheating, particularly in educational or assignment contexts. They’re also more concerned about being judged unfairly for using these tools.   The Potential Impacts: - Widening Pay & Opportunity Gap: Considerably lower AI adoption by women creates further risk of them falling behind their male counterparts, ultimately widening the gender gap in pay and job opportunities. - Self-Reinforcing Bias: AI systems trained primarily on male-generated data may evolve to serve women's needs poorly, creating a feedback loop that widens existing gender disparities in technology development and adoption.   As educators and AI literacy advocates, we face an urgent responsibility to close this gap and simply improving access is not enough. We need targeted AI literacy training programs, organizations committed to developing more ethical GenAI, and safe and supportive communities like our Women in AI + Education to help bridge this expanding digital divide.   Link to the full study in the comments. And a link also to learn more or join our Women in AI + Education Community. AI for Education #Equity #GenAI #Ailiteracy #womeninAI

Groundbreaking Insurance Payouts for Migrant Laborers Digit Insurance has made history by settling claims for migrant laborers in Noida due to intense heat exceeding 42°C. This innovative approach uses weather thresholds to trigger payouts, providing quick financial relief without lengthy claim assessments. Key Highlights: - *Heat-Index Based Cover*: Digit Insurance, in partnership with K.M. Dastur Reinsurance Brokers and Jan Sahas Foundation, offers this pioneering policy to workers across Delhi-NCR, including Noida, Ghaziabad, Gurgaon, and Faridabad. - *Payout Structure*: The policy pays up to ₹3,000 when temperatures cross thresholds for five consecutive days, with additional payouts if the breach lasts 10 days. - *Temperature Thresholds*: The heatwave parametric insurance has threshold temperatures ranging from 42°C to 43.7°C, varying by city. - *Industry Precedent*: ICICI Lombard introduced a similar policy for 50,000 women laborers in May 2023, marking the first heat-related livelihood loss cover in India. Impact on Migrant Laborers: This initiative provides much-needed financial support to migrant laborers who depend on daily wages and are exposed to heat-related risks. By leveraging parametric insurance, Digit Insurance is setting a new standard for innovative risk management solutions. Expert Insights: "Digit's heatwave parametric insurance is a crucial step in providing migrant laborers with a much-needed safety net," said Adarsh Agarwal, Chief Actuary and Product Officer at Digit Insurance. TOI #InsuranceInnovation #MigrantLaborers #HeatwaveProtection #ParametricInsurance #DIGITINSURANCE #ICICILOMBARD #IRDA

NEW ANALYSIS: Clean energy investment isn’t slowing for a simple reason: the economics are here. 55% of low-carbon technologies are already cost competitive in most situations (or will be soon), and another 10% are only marginally more expensive. From 2016 to 2024, companies pursuing green growth achieved higher revenue valuations, according to new Boston Consulting Group (BCG) analysis. Climate tech is moving from “nice to have” to “smart capital allocation.” The winners will be those who build or back scalable solutions now across energy, materials, industrials, and the infrastructure that underpins them. Full analysis here: https://lnkd.in/eu9_BGmk

🌟 The ground just shifted beneath the world of risk! And most leaders missed it. Here is why...💫 Did you see this? Last week, Munich Re began insuring AI model errors for mortgage lenders. While this certainly demonstrates that AI is becoming a more prominent emerging risk in our lives, it also signals a seismic shift: the #AgenticFrontier is no longer a theoretical future—it has arrived. For years, we've talked about transformation. Yet Boston Consulting Group (BCG)'s data shows a stark reality: while 78% of P&C insurers are “dabbling” with AI in the claims process, only 4% have successfully scaled it. Imagine what this means across the insurance operations and the overall enterprise. The rest are caught in the “pilot trap,” a sinkhole for laggards. The gap between the talkers and the doers has become a chasm. The 4% are fundamentally redesigning their businesses around AI. This is no longer about whether you'll embrace #agenticAI. It's about how you'll lead the transformation. For corporate leaders, the mandate is clear. For founders, the 18-month enterprise sales cycle is now optional for those who can provide de-risked, insured solutions. Here is the playbook for those ready to move from ambition to action: 1️⃣ Stop the science projects. Pick one end-to-end process—claims, underwriting, finance, customer support—and commit to a complete, AI-driven redesign. The real ROI is in redesigning the unglamorous, high-impact back-end operations, not bolting AI onto broken workflows. 2️⃣ De-risk your transformation. AI error insurance is now a board-ready mandate. Use it to turn AI from a high-risk experiment into a scalable, enterprise-grade asset. 3️⃣ Reframe the protection gap as an innovation mandate. The same creativity used to insure algorithms must be turned toward insuring humanity against Nat Cat/ extreme weather risks and other systemic risks. This is the largest market opportunity of the next decade. The uninsurable world is a choice, not a necessity. The leaders of 2026 will be those who use the tools of the agentic frontier to rewrite the rules of risk. What is the most fundamental “gap” you see in your organization’s AI strategy right now? Please share... Is it the tech, the talent, or the trust? And enjoy this week's newsletter. 👏🏽 #CapacityGap #TrustbyDesign

𝗧𝗵𝗲 𝗚𝗹𝗼𝗯𝗮𝗹 𝗚𝗲𝗻𝗱𝗲𝗿 𝗔𝗜 𝗚𝗮𝗽: 𝗪𝗵𝘆 𝗱𝗼 𝘄𝗼𝗺𝗲𝗻 𝘂𝘀𝗲 𝗔𝗜 𝗺𝘂𝗰𝗵 𝗹𝗲𝘀𝘀? 🚨 Despite the massive potential of Generative AI to boost productivity and bridge inequality gaps, an „𝗮𝗹𝗮𝗿𝗺𝗶𝗻𝗴 𝗴𝗲𝗻𝗱𝗲𝗿 𝗴𝗮𝗽 𝗲𝘅𝗶𝘀𝘁𝘀 𝗶𝗻 𝗶𝘁𝘀 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘂𝘀𝗮𝗴𝗲“! That’s the outcome of a recent UC Berkeley and Harvard Business School study. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄: ➜ The paper compared the results of 16 studies involving over 100,000 people from 26 countries, finding that "women are 25% less likely to use AI tools than men." Even when access is equalized, usage remains lower among women and is "nearly universal from mothers in Mumbai to managers in Madrid". ➜ The study finds an explanation in "structural factors" such as fewer mentors, higher household responsibilities, and lack of STEM representation that contribute to this gap. The problem is not only about access; it's about social and behavioral barriers that must be addressed. ➜ So, what needs to change? Policy efforts and workplace initiatives must go beyond providing access. According to the study to overcome these ingrained barriers, there needs to be more tailored mentorship, diverse AI design, and policies that address them head-on. 𝗧𝗵𝗲 𝘀𝘁𝘂𝗱𝘆 𝗰𝗼𝗻𝗰𝗹𝘂𝗱𝗲𝘀: "𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝘂𝗰𝗵 𝗲𝗳𝗳𝗼𝗿𝘁𝘀, 𝗔𝗜’𝘀 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗼 𝗱𝗿𝗶𝘃𝗲 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗴𝗿𝗼𝘄𝘁𝗵 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝘄𝗶𝗹𝗹 𝗱𝗶𝘀𝗽𝗿𝗼𝗽𝗼𝗿𝘁𝗶𝗼𝗻𝗮𝘁𝗲𝗹𝘆 𝗯𝗲𝗻𝗲𝗳𝗶𝘁 𝗺𝗮𝗹𝗲 𝘂𝘀𝗲𝗿𝘀, 𝗻𝗼𝘁 𝗼𝗻𝗹𝘆 𝗲𝗻𝘁𝗿𝗲𝗻𝗰𝗵𝗶𝗻𝗴 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗴𝗲𝗻𝗱𝗲𝗿 𝗴𝗮𝗽𝘀 𝗯𝘂𝘁 𝗮𝗹𝘀𝗼 𝗿𝗶𝘀𝗸𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲 𝘄𝗵𝗲𝗿𝗲 𝘀𝗼𝗰𝗶𝗲𝘁𝘆 𝗺𝗶𝘀𝘀𝗲𝘀 𝗼𝘂𝘁 𝗼𝗻 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 𝘄𝗼𝗺𝗲𝗻 𝗰𝗼𝘂𝗹𝗱 𝗯𝗿𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝘁𝗵𝗶𝘀 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆." This is a really interesting finding! What are your thoughts? Link to the full study: https://lnkd.in/d8eU26HD #AI #GenderEquality #Innovation #DiversityInTech

📘 Goodbye Globalization — a few takeaways that stuck with me: I used the Easter break to read Elisabeth Braw’s Goodbye Globalization — a sharp, sober reflection on how the rules of global business are being rewritten, quietly but fundamentally, as everything we knew over the past 30 years since the post–Cold War era is being re-evaluated. In short, globalisation meant trade and national security operated in separate lanes; even when geopolitical tensions rose, governments were careful not to disrupt the flow of commerce. Companies spent decades building ultra-efficient global supply chains, and for a while, it worked — manufacturing boomed, goods flowed freely, and consumers enjoyed low prices. That era is now over. From COVID-19 to Russia’s war in Ukraine to the US–China tariff wars, recent shocks revealed just how fragile our interconnected systems are. After decades of economic integration, a new world order is taking shape. So what does it mean for #climatetech companies? 1️⃣ 𝐃𝐞𝐜𝐚𝐫𝐛𝐨𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧 𝐁𝐲 𝐃𝐞𝐬𝐢𝐠𝐧 – Energy security is national security. The push for energy independence — often more than climate concern — is now the primary driver of the renewables transition. Cost-competitive, decentralised, and less geopolitically risky, clean energy is becoming a strategic asset. Just look at China: its electrification push isn’t driven by decarbonisation, but by the race for global competitiveness. 2️⃣ 𝐑𝐞-𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥𝐢𝐬𝐚𝐭𝐢𝐨𝐧 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 – As global supply chains fragment, local production is becoming the answer. “Friendshoring” and “regionalisation” are the new buzzwords. Bringing manufacturing “home” — while staying economically viable — now relies on technology like AI, robotics, and localised supply chains (think raw materials and minerals). Climate tech companies have a real opportunity to build “resilient by default” industrial models that don’t depend on low-cost overseas labour, but on smart, scalable systems closer to their markets. 3️⃣ 𝐅𝐫𝐨𝐦 𝐆𝐫𝐞𝐞𝐧 𝐏𝐫𝐞𝐦𝐢𝐮𝐦 𝐭𝐨 𝐆𝐫𝐞𝐞𝐧 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 – Being green isn’t just about values anymore — it’s becoming a competitive edge. Today, clean technologies unlock government incentives, help avoid trade barriers, and align with new industrial policies. The climate tech winners won’t just be the cleanest — they’ll be the most strategically aligned with Globalisation 2.0. At Extantia, we’re backing the climate tech founders who see this shift not as a headwind — but as the biggest tailwind of the decade!! #venturecapital #GoodbyeGlobalization

Humanoids may dominate the headlines—pouring coffee, doing backflips, and walking across factory floors—but they’re not where the real money is. The quiet winners? Specialized, task-focused robots. From Unbox Robotics boosting warehouse efficiency by 25% to Zipline delivering life-saving medical supplies, these single-task machines are quietly transforming industries. Okibo and Canvas are tackling drywall finishing, while Moxi roams hospital halls delivering supplies so nurses can spend more time with patients. Investors love them for one simple reason: ROI is clear and immediate. They’re cheaper to build, faster to deploy, and easier to justify on a balance sheet than flashy humanoids still stuck in pilot programs. The comparison is simple: forklifts changed the world, not Iron Man suits. The next wave of robotics won’t be about building machines that look like us—it will be about building machines that do the job better than us.

AI doesn’t stumble on technology. It stumbles on trust. Most companies still deploy AI like old IT systems: top-down, pre-baked, “here’s your new workflow.” And then they wonder why adoption stalls. The numbers say it all: Trust in company-provided gen-AI fell 31% in two months. Trust in autonomous tools fell 89%. That’s not resistance — that’s feedback. You can’t mandate trust. You have to earn it — and track it. If you can measure sentiment, friction, and confidence, then Trust Health becomes a KPI. Treat it like latency or uptime: if the trust baseline drops, you stop the rollout. Simple. And once trust is a KPI, the approach shifts: - Co-create workflows with the people who actually do the work. - Ship in small loops to reveal friction early. - Make “No trust → No scale” a rule, not a slogan. The companies winning with AI aren’t the ones with the flashiest models. They’re the ones that understand one thing: Technology is cheap. Trust is the moat. What’s the one trust metric you’d track before scaling any AI tool in your organisation? https://lnkd.in/eRShuVSs #AI #Transformation #Business #Strategy