Impact of Technology on Workforce

The tech workforce is splitting in two A year ago, we ran the first large-scale survey of how tech workers feel about their jobs and careers. What emerged we summarized in four words: burned out, but optimistic. Today, we're back with the results from our 2026 survey, and it's a tale of two workforces. Half of tech right now feels amplified by AI—more capable, more confident, more excited than they've been in their entire career. The other half feels shaken by it—less sure of their value and whether there’s still a place for them. Which side of that line you fall on predicts how you feel about your career more than anything else, including your role, seniority, company size, or any other measure we collected. The workforce is bifurcating into two realities. A few other takeaways that surprised us: + Significant burnout jumped from 44.7% to 55.7% in one year, while career optimism fell from 54.8% to 48.7%. A worrisome trend. + 53% of tech workers would steer a newcomer away from a career in their own role, even when they're optimistic about their own future. + The biggest AI fear is of being squeezed to do more work. Only 22% worry about “losing my job to AI.” Far more worry about being expected to do more for the same pay, getting trapped in an unsustainable pace, and the quality of their work declining. The question that best predicts how a tech worker feels about their work, in 2026, is no longer "What do you do?" or "Where do you work?" It’s "What has AI done to your sense of who you are?" Read the full report here: https://lnkd.in/gKq4E2uC (Massive shoutout to my partner Noam Segal for designing the survey, synthesizing the results, and writing this report, two years running. We're dropping a special podcast episode this Sunday, diving even deeper into the results.)

The most important skills today and in the next years will be human capabilities: critical and analytic thinking, resilience, leadership and influence, overlaid with technological literacy and AI skills to amplify these human capacities. World Economic Forum's new Future of Jobs Report provides a deep and broad analysis of the drivers of labour market transformation, the outlook for jobs and skills, and workforce strategies across industries and nations. It's a really worthwhile deep dive if you're interested in the topic (link in comments). Here are some of the highlights from the Skills section, which to my mind is at the heart of it. 🧠 Analytical Thinking Leads Core Skills. Skills like analytical thinking (70%), resilience (66%), and creative thinking (64%) top the list of core abilities for 2025. By 2030, the emphasis shifts even more towards AI and big data proficiency (85%), technological literacy (76%), and curiosity-driven lifelong learning (79%). This shift underscores the critical role of technology and adaptability in future workplaces. 📉 Skill Stability Declines but at a Slower Rate. Employers predict that 39% of workers' core skills will change by 2030, slightly lower than 44% in 2023. This reflects a stabilization in the pace of skill disruption due to increased emphasis on upskilling and reskilling programs. Half of the workforce now engages in training as part of long-term learning strategies compared to 41% in 2023, showcasing the growing adaptation to technological changes . 🌍 Economic Disparities in Skill Disruption. Middle-income economies anticipate higher skill disruption compared to high-income ones. This disparity highlights the uneven challenges of transitioning labor forces across global regions, particularly in economies still grappling with structural changes. 🚀 Tech-Savvy Skills in High Demand. The adoption of frontier technologies, including generative AI and machine learning, is increasing the demand for skills like big data analysis, cybersecurity, and technological literacy. These trends indicate that businesses are aligning workforce strategies to integrate these advancements effectively. 📚 Upskilling Is the Norm, Not the Exception. By 2030, 73% of organizations aim to prioritize workforce upskilling as a response to ongoing disruptions. This reflects a shift in corporate investment priorities towards human capital enhancement to maintain competitiveness.

In 2007, Circuit City fired 3,400 of its best employees at 8:15 a.m. on the same day. Wall Street cheered. The stock rose. Eighteen months later, the company filed for bankruptcy. In 2026, the same playbook is back, just dressed in AI language. In Q1 alone, tech companies announced over 45,000 layoffs; a fifty-one percent jump from last year. Every CEO points to the same justification: AI. Block cited it while cutting nearly half its workforce. Salesforce cited it while slashing support from 9,000 to 5,000. Each announcement becomes the next company's permission slip. One CEO cites AI, the stock pops, and suddenly every board wants the same memo. Circuit City should haunt every one of them. For years, leadership didn't know their stores were underperforming. A hidden credit card bank masked the losses, making retail look profitable when it wasn't. Best Buy was winning on every metric, and nobody inside could see it. So when they fired their best people, nobody at the top understood what would break. Customers walked in and found no one who could explain how a home theater system worked. Same-store sales fell twelve percent that holiday season. When Hubert Joly took over a dying Best Buy in 2012, he didn't slash headcount. Day one, he drove to a store in Minnesota, put on a blue shirt tagged "CEO in Training," and spent three days listening to frontline workers. What he learned in seventy-two hours on the floor, he could never have learned from a spreadsheet. That is the test for 2026. When a company announces AI has made thousands of roles redundant, ask one question: did anyone put on the blue shirt first? P.S. Full breakdown in the first comment 👇

467 people turned their iPhones into dumb phones for 2 weeks. Calls and texts only. The results were closer to "new medication" than "digital detox." Here's what the researchers actually did. They blocked all mobile internet on participants' phones for 14 days. The only remaining things were calls, texts, and desktop internet. Then they measured well-being, mental health, and sustained attention three times across the month. Average screen time dropped from ~314 minutes per day to ~161. Roughly two and a half hours of life returned to people, every day. After the block lifted, screen time rebounded. But it stayed below baseline. Two weeks of enforced reduction recalibrated what "normal" felt like. The outcomes were striking: → Well-being up (Cohen’s d ≈ 0.45) → Mental health up (d ≈ 0.56) → Sustained attention up (d ≈ 0.23) In psychology, these are big effect sizes. The mental health improvement was larger than the average effect of antidepressants in meta-analyses, and similar to the effect of cognitive behavioral therapy. Attention gains were roughly equivalent to reversing 10 years of age-related cognitive decline. From two weeks without Instagram. Let that one sit for a second. 91% of participants improved on at least one outcome. → 73% improved well-being → 70% improved mental health → 59% improved sustained attention This wasn't a lucky subset. It was almost everyone. Why did it work? More time in the offline world: walking, exercising, being outside, talking to humans in person. Less media. More sleep. Better self-control. By the way, more sleep increases feelings of aliveness across the board. One important nuance. Only about 25% of participants kept the block for 10+ of the 14 days. The average effects held anyway. Meaning: even partial reduction moves the needle. You don't have to be perfect to benefit. Who gained the most? → People with high FOMO → biggest well-being and mental health gains. → People with more ADHD symptoms → biggest gains in self-reported attention. If the phone feels like it's running your nervous system, you're likely to benefit most from unplugging from it. A 14-day protocol for anyone who wants to try it: → Block mobile internet (keep calls and texts) → Batch desktop use into 1-3 windows a day → Pre-plan what fills the space: walk, gym, friend, book → Keep a one-line mood and energy log If a full block feels unrealistic, time windows, app blockers, and batched notifications still help. Two weeks. The world doesn't end. What comes back is focus, sleep, calm, and time. That's a better ROI than most things you'll try this year. SOURCES: Castelo, N., Kushlev, K., Ward, A. F., Esterman, M., & Reiner, P. B. (2025). Blocking mobile internet on smartphones improves sustained attention, mental health, and subjective well-being. PNAS Nexus, 4(2), pgaf017.

These workers are opting to retire instead of taking on artificial intelligence. They had planned on working for a few more years. But AI was a last straw. After rising for decades and then hovering around 40% in the 2010s, the share of Americans over 55 years old in the workforce has slipped to 37.2%, the lowest level in more than 20 years. The financial cushion of rising home equity and stock-market returns is driving some of the decline, economists and retirement advisers say. But for some older professionals, money is only part of the equation. They say they don’t want to spend the last years of their career going through the tumult of AI adoption, which has brought new tools, new expectations and a lot of uncertainty. In general, older Americans are less likely than younger counterparts to use AI, research shows. About 30% of people from ages 30 to 49 said they used ChatGPT on the job, nearly double the share of those 50 and older, according to a 2025 Pew Research Center survey of more than 5,000 adults. Baby boomers and members of Generation X also experienced the sharpest declines in confidence using AI technology, according to a ManpowerGroup survey of more than 13,900 workers in 19 countries. Luke Michel has already lived through two technology overhauls in his career, first desktop publishing in the 1980s and online publishing later on. But AI? He’s had enough. So when his employer, the Dana-Farber Cancer Institute, made an early-retirement offer to some staff last year, the 68-year-old content strategist decided to speed up his exit. Before, he had expected to work a couple more years. “The time and energy you have to devote to learning a whole new vocabulary and a whole new skill set, it wasn’t worth it,” he said. It isn’t that he’s shunning artificial intelligence—he is learning Spanish with the help of Anthropic’s Claude. But, at this point, he’s less than eager to endure all the ways the technology promises to upend work. “I just want to use it for my own purposes and not someone else’s,” he said. Lauren Weber and I report.

Are leaders preparing people for change, or simply announcing it? Imagine you've spent ten years becoming excellent at your job. You know which problems matter, which shortcuts are dangerous, which questions to ask, and how to recognize good work before anyone else does. Then your company introduces AI. Within months, the technology can perform a large part of the work that took you years to master. Your leader tells you this is good news because you'll now have more time for "higher-value work." But nobody tells you what that work is. I think this is where many AI transformations misunderstand change. We prepare people to use the technology, but we don't always prepare them for what happens to their professional identity when the technology becomes good at something they were proud to be good at. In The Human-Agent Orchestrator, I describe three risks that can follow. → Scope Collapse: A rich role shrinks into reviewing machine output. → Mastery Vacuum: People no longer know what expertise they should develop. → Purpose Drift: The work that once gave people satisfaction moves elsewhere. Training alone cannot solve those problems. In my opinion, leaders need to design the next human role before automating the current one. People need new areas of mastery, greater responsibility for judgment and strategy, and a clear answer to a question that matters far more than "How do I use this AI?" "What am I becoming better at now?" Perhaps preparing people for AI isn't primarily about helping them accept change. It's about making sure there is somewhere meaningful for them to grow after it. What do you think? Are leaders genuinely preparing people for an AI-powered workplace, or simply announcing the transformation and expecting people to adapt? #HumanAgentOrchestrator #MasteryVacuum #AIReadiness #HybridManagement #LeadershipInAIEra

Some thoughts about AI and the event industry. At this rate, many event tech companies will be replaced by Anthropic / OpenAI / Gemini. We are already very close to: Building complex event registration flows Creating event websites, constantly optimized Tracking conversions for events and their impact on pipeline On-site check-in apps Matchmaking at scale with no user input This essentially replaces most event tech products available today. Event tech companies will increasingly become service companies. Who can survive? - Tech geared toward extremely large events (this buys a few more years) - Tech for extremely private events or with very high privacy requirements (Germany and some other EU countries). I don’t see many other clear paths for most event tech players. Virtually 80% of event tech CEOs ignored AI for 3 years, assuming that being an in-person industry or relying on sales relationships would keep them afloat. The truth: event planners themselves are getting hands-on, building workflows and systems and bypassing vendors. There will be no moat soon. Planners should be concerned too. I’ve worked with 25+ clients over the past three years (Fortune 500 companies, associations, trade shows, tech companies). My strategic consulting will soon be replaced by AI. I’ll be needed (probably) for high-stakes decisions that mix experience, creativity, and real-time trend interpretation — but those cases are rare. You might need one or two people to strategically plan extremely complex events. Most of the industry is built on small meetings. The lack of education and proper career paths has made many event operators extremely vulnerable. Operations, on the other hand, will require more people. Making things happen on site will remain human-driven. We will increasingly execute what AI tells us to do. In-person execution is safe for a while, but the job shifts toward execution — essentially pushing roles back 30 years. If you’re entering the events industry: -> Get your hands dirty with on-field, operational, practical experience -> Understand how trends shift and what people want now. The transient nature of events will shield you from best-practice automation. -> Focus on ROI. Literally nothing else will matter. Explore new ways to get to ROI. We are at a dramatic and exciting turning point. Events are the alternative to AI. They are the natural environment where business happens and humans connect. But industry operators are not protected. Lack of education, weak career paths, and limited product development in event tech will lead to many being replaced, marginalized, or demoted. It’s time to build higher barriers to entry and protect the jobs of event planners.

I actually find this chart encouraging. Yes, manufacturers are investing heavily according to Deloitte. And yes, the headlines will focus on AI. But what caught my attention was where leaders are actually putting their money: automation, analytics, scheduling, quality, execution. The unglamorous stuff. The systems that decide whether a day goes smoothly or turns into a fire drill. That resonates with me, because every factory I’ve ever been in tells the same human story. Good people. Smart people. Working hard. But stuck reconciling numbers, debating whose system is right, escalating decisions that shouldn’t require a meeting. So when I see investment flowing into the operational backbone, I’m encouraged. It tells me leaders are tired of asking people to compensate for broken systems. They want fewer heroics and fewer surprises. What stood out just as much was what hasn’t caught up yet... Human capital is still the least mature part of the stack. Fewer than half of companies have a real training and adoption standard. Yet it’s the number one area leaders say they need to improve. That gap isn’t about intent. It’s about follow-through. We say we want the factory of the future. But we’re hesitant to slow down long enough to prepare the people who have to run it. So my takeaway isn’t “we need more AI” or “we need bigger budgets.” It’s simpler than that. If we’re serious about smarter manufacturing, we have to stop asking people to work around systems that don’t support them. And we have to be honest about how much change we’re actually willing to lead. Technology is moving fast. People need space, clarity, and support to move with it. 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐫𝐞𝐚𝐝 𝐦𝐨𝐫𝐞?  https://lnkd.in/e25rs7eU ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Block just laid off roughly half its workforce, citing AI. At the same time, Gartner’s AI Impacts Forecast projects that by 2028 to 2029, AI will create more jobs than it eliminates. More than 32 million jobs a year will be significantly transformed. Two headlines. One reality. AI is an elimination engine in the short term. AI is a transformation engine in the long term. Right now, we are seeing elimination activities. They are visible and immediate. The transformation curve is harder to see. It requires new roles, new skills, new operating models. It requires people who can adapt faster than their job descriptions change. Whether companies make it to the transformation curve may very well depend on one capability: change fitness. Change fitness is the capacity to anticipate disruption, reskill ahead of it, and redesign your value before the market forces you to. Transformation requires change skills. We don't know the full scope of transformation efforts at Block. We can clearly see the elimination activity. The Gartner data suggest that transformation is coming at scale. But it will not be evenly distributed. It will favor the change fit. The future of work is not about AI fluency alone. It is about change fitness.