Impact of Technology on Workforce

Hollywood is fighting AI while selectively funding it. Netflix just acquired InterPositive, Ben Affleck's AI filmmaking company, for up to $600M per Bloomberg. They're not alone. James Cameron sits on the board of Stability AI (last reported valuation: $1B). The LEGO Movie producers co-founded Spuree. South Park creators Matt Stone and Trey Parker co-founded Deep Voodoo. Animaj received investment from Disney Accelerator. Promise AI is backed by Peter Chernin's North Road. To understand the tension, it helps to separate four very different things that get lumped together under "AI." Erik Barmack at Reel AI lays out the first three categories: → Generative models (Sora, Midjourney, Seedance): Create entirely new images or video, often trained on copyrighted material. These trigger the loudest backlash. → Production tools: Clean up VFX, adjust lighting, synthesize ADR, streamline editing. Already embedded in filmmaking and increasingly embraced. → Synthetic performances: Digital doubles that allow an actor's face, voice, or movement to appear on screen without them being physically present. I'd add a fourth: AI studios, companies using native AI tools while co-owning the IP they create. As Erik puts it, "Hollywood's loudest outrage is directed at the first category. But the second and third categories are increasingly embraced — sometimes enthusiastically — by the same people signing anti-AI letters." As for generative models, Hollywood is following a strategy that reminds me of UMG CEO Robert Kyncl's framework: "License, legislate, and litigate, in that order preferably." The 'preferably' implies that sometimes you need to litigate before you can license. THE BIGGER PICTURE: → Hollywood isn't anti-AI; it's anti-uncontrolled AI → Studios and talent are aligned on copyright; that unity is rare and powerful → The path to working with Hollywood runs through licensing, not scraping; the companies that understand this will win (Erik's article "Why did Ben Affleck start an AI company in secret?" is worth reading in its entirety: link in comments.)

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

Artificial intelligence is often framed as a story about jobs disappearing. That is the wrong message. The foundational shift is improved productivity. When new technologies arrive they rarely eliminate work altogether. They change how work is done, create new roles, and expand what people and organisations are capable of producing. The printing press did it. Electricity did it. The internet did it. AI will be no different. Yesterday I spoke with ABC’s Kirsten Aiken about how artificial intelligence is already reshaping the economu and why the scale of the opportunity will depend on how we choose to deploy it. If AI is used purely as a cost reduction tool, the conversation will stay trapped in a fear narrative around labour displacement. If it is deployed as a productivity engine, it becomes something very different. It allows people to work at higher levels of abstraction, automate repetitive tasks, and focus human capability on communication, creativity, decision making and complex problem solving. That is how new industries and new jobs emerge. Australia has a major opportunity in front of it. Building the infrastructure that powers artificial intelligence is not only about technology. It is about enabling the next wave of productivity growth across the entire economy. AI infrastructure, renewable energy, and digital capability are becoming key to global competitiveness. The question is not whether AI will change work. It will. The question is whether we position ourselves to capture the productivity gains that come with it. #ai #abc #thebusiness Link to the full interview here: https://lnkd.in/g36AujVX

Your best AI users are twice as likely to quit. Employees gaining the most productivity from AI are simultaneously experiencing 88% higher burnout rates—and they're developing better relationships with AI than with their human coworkers. "This is one of the biggest warning signals I've ever seen in any of the research I've conducted on AI over the last 12 years," Kelly Monahan, Ph.D. told our Charter Forum group last week. "There's something fundamentally broken in the way we are relating to each other within our organizations." Here's what's driving this crisis: 🔴 Pressure for infinite output: High performers are being pushed to go higher than ever, and have the drive and traits to match. But ... 🔴 Human connection eliminated: The focus on "more output" leaves no time for the relationships that make work sustainable and meaningful. 🔴 Middle managers hit hardest: Those using AI heavily while managing teams face the worst burnout of all—exactly the people you need to build the trust needed to scale AI adoption. The problem isn't the technology. It's that too often we're measuring AI adoption as a performance metric and focusing on efficiency instead of opportunity. More, not better. Cutting costs, not growth. Meanwhile, 68% of workers report struggling with the pace and volume of work according to Microsoft. We're in the era of "do more with less" and the infinite workday—and our best people are breaking. Your heavy AI users aren't just your highest performers. They're your canaries in the coal mine. Are you seeing burnout among your AI adopters? 👉 Read on: https://lnkd.in/g-jMnatU

How AI Risks Widening the Gender Gap — And What We Can Do About It AI is transforming industries, but it's also at risk of deepening gender disparities. While 40% of business leaders are prioritizing AI, we must ask: are we considering the impact on women in the workforce? The challenges we need to address include: 🔹 One of the key risks posed by AI is it’s tendency to perpetuate gender biases, especially in sectors where women are already underrepresented. AI systems are typically trained on historical datasets, and if these datasets reflect societal or institutional biases, the AI will likely replicate them. This has already been observed in areas such as recruitment, where AI tools that analyse CVs have demonstrated a preference for male candidates over their female counterparts. 🔹 Surprisingly, studies have shown that women use AI-driven tools such as ChatGPT significantly less than men, even when they hold similar roles. This gap may have long-term implications for women's career trajectories, particularly as AI becomes more embedded in day-to-day business processes. Several factors are at play here. First, there is a perception gap: women tend to express greater scepticism about AI’s potential benefits. For instance, surveys suggest that women are more concerned about the societal risks posed by AI, including job displacement, privacy concerns, and ethical issues. Women often report feeling less confident in their ability to navigate AI technologies, frequently citing the need for additional training before feeling comfortable using these tools. 🔹 Many of the jobs most vulnerable to automation are disproportionately held by women — Goldman Sachs has found that nearly 80% of women’s jobs are at risk of being automated, compared to 58% of men’s jobs. Sectors such as office administration, customer service, and healthcare support roles often referred to as "pink-collar jobs" — are seeing significant shifts as AI-driven systems take over tasks like scheduling, data entry, and customer interactions. But there’s hope! With more diverse datasets, inclusive development teams, and reskilling opportunities, we can ensure AI empowers everyone. Read my full article on how we can address these challenges and build a more equitable future, and please share your view! I look forward to having different perspectives on this critical topic. Thanks sincerely and kind regards, Fabio #AI #GenderEquality #Inclusion #FutureOfWork #Diversity #Leadership

Most companies wait until they have an urgent problem before addressing workforce capability. But the ones building competitive advantage are investing in readiness before the gap becomes a crisis. Here are four areas where organizations need to focus: 𝟭. 𝗥𝗲𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗳𝗼𝗿 𝗿𝗼𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝗱𝗶𝗱𝗻'𝘁 𝗲𝘅𝗶𝘀𝘁 𝗳𝗶𝘃𝗲 𝘆𝗲𝗮𝗿𝘀 𝗮𝗴𝗼 Automation specialists, data scientists, and AI integration roles require new training pathways. Companies that build apprenticeship programs and internal development tracks get ahead of skills bottlenecks before they slow growth. 𝟮. 𝗣𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝘁𝗲𝗮𝗺𝘀 𝘁𝗼 𝘄𝗼𝗿𝗸 𝗮𝗹𝗼𝗻𝗴𝘀𝗶𝗱𝗲 𝗔𝗜 It's not enough to deploy AI tools. Teams need to understand how to integrate AI into their workflows, manage AI-driven processes, and improve performance through human-AI collaboration. 𝟯. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝘀𝗸𝗶𝗹𝗹 𝗴𝗮𝗽𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲𝘆 𝗮𝗳𝗳𝗲𝗰𝘁 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Skills assessments show what people can actually do, not just what their job titles suggest. Companies that map capabilities across their workforce can redeploy talent strategically and keep people engaged in roles where they can grow. 𝟰. 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗽𝗮𝘁𝗵𝘄𝗮𝘆𝘀 𝗶𝗻𝘁𝗼 𝗿𝗼𝗹𝗲𝘀 𝘄𝗵𝗲𝗿𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗰𝗮𝗻 𝘀𝘂𝗰𝗰𝗲𝗲𝗱 Whether it's technical training, role-specific development, or management skills, companies need structured programs that prepare people for the work that's coming, not just the work that exists today. The retirement wave is gathering speed. Skills-based hiring is becoming the norm. Growth isn't waiting. What's your approach to workforce readiness right now?

🤖 The Gendered Impact of AI: Why Women—Especially from Marginalised Backgrounds—Are Most at Risk As artificial intelligence continues to reshape the world of work, one thing is becoming increasingly clear: the effects will not be felt equally. A new report from the United Nations’s International Labour Organization and Poland’s NASK reveals that roles traditionally held by women—particularly in high-income countries—are almost three times more likely to be disrupted by generative AI than those held by men. 📉 9.6% of female-held jobs are at high risk of transformation, compared to just 3.5% of male-held roles. Why? Many of these jobs are in administration and clerical work—sectors where AI can automate routine tasks efficiently. But while AI may not eliminate these roles outright, it is radically reshaping them, threatening job security and career progression for many women. This risk is not theoretical. Back in 2023, researchers at OpenAI—the company behind ChatGPT—examined the potential exposure of different occupations to large language models like GPT-4. The results were striking: around 80% of the US workforce could have at least 10% of their work tasks impacted by generative AI. While they were careful not to label this a prediction, the message was clear: AI's reach is widespread and accelerating. 🌍 An intersectional lens shows even deeper inequities. Women from marginalised communities—especially women of colour, older women, and those with lower levels of formal education—face heightened vulnerability: They are overrepresented in lower-paid, more automatable roles, with limited access to training or advancement. They often lack the tools, networks, and opportunities to adapt to digital shifts. And they face greater risks of bias within the AI systems themselves, which can reinforce inequality in recruitment and promotion. Meanwhile, roles being augmented by AI—like those in tech, media, and finance—are still largely male-dominated, widening the gender and racial divide in the AI economy. According to the World Economic Forum, 33.7% of women are in jobs being disrupted by AI, compared to just 25.5% of men. 📢 As AI moves from buzzword to business reality, we need more than technical solutions—we need intentional, inclusive strategies. That means designing AI systems that reflect the full diversity of society, investing in upskilling programmes that reach everyone, and ensuring the benefits of AI are distributed fairly. The question on my mind is - if AI is shaping the future of work, who’s shaping AI? #AI #FutureOfWork #EquityInTech #GenderEquality #Intersectionality #Inclusion #ResponsibleTech

Could AI Robots Help Fill the Labor Gap? As a futurist field, embodied AI—also known as humanoids—is captivating. Labor shortages spurred by long-term demographic shifts, coupled with advances in generative AI, are accelerating the commercialization of robots designed to emulate human behavior. The global economy faces labor shortages due to demographic trends that may hinder growth for years. Concurrently, advancements in large language models and generative AI are poised to drive transformative innovations across various industries, from healthcare to manufacturing. These trends are likely to fuel the development of humanoids—advanced robots equipped with limbs and AI-powered "brains." The adoption of these humanoid robots might outpace that of autonomous vehicles, presenting significant opportunities for investors in companies developing these robots and their components, and industries integrating them into their workforce. Its worth noting that Adam Jonas, Head of Global Autos and Shared Mobility research at Morgan Stanley, notes the adaptability of humanoids: "Consider the vast array of tasks humans perform using just our hands or tools, and the numerous machines tailored for human dexterity. As the growth of the working-age population in advanced economies continues to decline, humanoids could become essential for industries struggling to attract sufficient labor to maintain productivity." Morgan Stanley analysts project that by 2040, the U.S. alone could have 8 million working humanoid robots, impacting wages by $357 billion. By 2050, this number could rise to 63 million, potentially affecting 75% of occupations, 40% of employees, and approximately $3 trillion in payroll. "The commercialization of humanoid robots will encounter significant challenges, particularly in gaining social and political acceptance, given their potential to disrupt a large portion of the global workforce," says Jonas. He highlights that up to 70% of construction jobs and 67% in farming, fishing, and forestry could be impacted. "While they may not be the ideal solution, they are an increasingly necessary one for a world facing significant longevity challenges." #HumanoidRobots #AILaborSolutions #FutureOfWork #LaborShortage #GenerativeAI #RoboticsInnovation #AIInvestment #EconomicGrowth #TechTrends #WorkforceTransformation #futures

AI, Robotics, and Clean Energy Are Transforming 80% of the World’s Jobs For the last few years, most discussions about AI and automation have focused on knowledge work: coders, analysts, writers, lawyers, marketers. But according to the World Economic Forum’s 2025 “Jobs of Tomorrow” report, that’s only part of the story — and increasingly, not the most important one. The real transformation is happening in the work that keeps the world running: . Agriculture . Manufacturing . Construction . Retail & Trade . Transport & Logistics . Business & Management . Healthcare Together, these seven job-families represent 80% of the global workforce — and every one of them is being reshaped by four technologies: Artificial intelligence, robotics, clean energy systems, and connected digital infrastructure. In boardrooms, AI is often discussed through a white-collar lens: productivity tools, coding assistants, or knowledge retrieval systems. But across the globe, change is already underway in very different environments. 1. In agriculture, AI-driven drones monitor crops and distribute fertilizers precisely, reducing waste and increasing yields. 2. In construction, semi-automated equipment is making sites safer and less physically demanding. 3. In healthcare, sensor networks and robotics are supporting medical teams under strain. 4. In logistics, predictive systems are optimizing routes and fuel use. 5. In retail, digital platforms and robotics are redefining supply chains and fulfillment. This isn’t a “future scenario.” It’s today — unfolding quietly, and often away from the spotlight of AI headlines. What Leaders Should Focus On 1. Bring AI strategy beyond the office. Future competitiveness depends on how technology diffuses into operations, logistics, and frontline work — not just digital functions. 2. Invest in large-scale reskilling. Skills in robotics maintenance, energy systems, and digital coordination are becoming as vital as data literacy once was. 3. Design technology with inclusion in mind. Adoption that deepens divides is bad business. Technology that expands opportunity creates resilience. 4.Build human-machine collaboration models. The goal is not substitution, but synergy — a workforce that combines human creativity with machine precision.

We’re getting better at “feeling connected”… and worse at being connected.   We’re training ourselves on a new standard of intimacy: Always responsive, always agreeable, emotionally precise—just like our tools. The side effect I see in leaders and teams: • Less appetite for honest disagreement • More fear of saying the “wrong” thing • The expectation that relationships should be low‑friction and high‑validation That’s not how real trust is built. How I coach leaders to push back against synthetic intimacy: "Don’t over‑polish the hard stuff" ↳ If AI turns your feedback into a bland, corporate script, rewrite one line in your own language. Let people feel you. "Signal that discomfort is allowed" ↳ Say explicitly, “If this feels a bit awkward, we’re probably in the right conversation.” "Pick 2–3 topics that are ‘AI‑free'" ↳ Performance, values, and conflict are great candidates for human‑only discussions. AI can support connection.   But only if we still tolerate the awkward, unscripted, fully human moments that real connection demands. Coaching can help; let's chat. #ai #executivecoaching #relationships