AI's Impact on Jobs

For decades, career growth followed a familiar formula: More headcount. More budget. More scope.  That model is changing. In the AI era, careers won’t be built on span of control, they’ll be built on innovation density. Today, anyone - from ICs to execs - can scale their impact without more headcount, more budget, or more time. The playing field is flatter. The differentiator? How fast you can learn, apply, and compound innovation with AI. If you’re thinking about career growth, stop asking: “How can I get more?” Start asking: “How can I innovate more with AI?” The people who rise fast will: See problems through an AI-first lens. Move from manual to scalable. Iterate faster than the rest. Your team size won’t define your trajectory. Your creativity will. Your budget won’t signal your value. Your innovation density will.

Is AI delivering real productivity gains? What's the ROI so far? Hot takes abound, but data have been scarce. Noam Segal and I took it upon ourselves to find out what’s actually happening on the ground by running one of the largest independent, in-depth surveys on how AI is affecting productivity for tech workers (1,750 respondents). We surveyed product managers, engineers, designers, founders, and others about how they’re using AI at work. tl;dr: AI is overdelivering. 1. 55% of respondents say AI has exceeded their expectations, and almost 70% say it’s improved the quality of their work. 2. More than half of respondents said AI is saving them at least half a day per week on their most important tasks. We’ve never seen a tool deliver a productivity boost like this before. 3. Founders are getting the most out of AI. Half (49%) report that AI saves them over 6 hours per week, dramatically higher than for any other role. Close to half (45%) also feel that the quality of their work is “much better” thanks to AI. 4. Designers are seeing the fewest benefits. Only 45% report a positive ROI (compared with 78% of founders), and 31% report that AI has fallen below expectations, triple the rate among founders. 5. Engineers have accepted AI as a coding partner and now want it to handle the more boring (but necessary) work of building products: documentation, code review, and writing tests. 6. n8n is currently dominating the agent landscape, though actual adoption of agentic platforms in 2025 has been slow. 7. A whopping 92.4% of respondents report at least one significant downsides to using AI tools. There’s definitely room for improvement. Here's the full report: https://lnkd.in/gR5G88yA Inside: - What exactly AI is doing for people, function by function? - Where are the biggest opportunities for AI startups? - Which AI tools have product-market fit? - The downsides of AI productivity - Bonus: The state of agentic AI: promise outpaces practice - What this all means - Appendix: Who took this survey

Will #AI be a bloodbath for white-collar jobs? #Anthropic CEO Dario Amodei seems to think so—he made headlines warning that AI could wipe out up to 50% of all entry-level white-collar roles within the next 5 years. While we can debate the exact figure, I won’t quibble: a lot of work is about to be automated. AI isn’t just a helper anymore—it’s becoming a full-blown replacement for the repetitive, digitized, “thunking” tasks that fill so many junior roles. If you’re not paying attention, you’re at risk of missing the train entirely. Here’s the uncomfortable truth: What you see from AI today isn’t the ceiling—it’s the floor. The cutting-edge research is far ahead of what’s in your hands. Even the most notoriously janky AI products—like #OpenAI’s Operator or #Google’s Project Mariner—could get a massive capability boost almost overnight, just by cranking up the compute (and, with it, the costs). The real bottleneck? Companies and customers aren’t ready to pay for what’s already possible. We’re stuck in an awkward moment where the tech is ready, but the market—and the culture—aren’t. That gap won’t last forever. AI isn’t some far-off fantasy—it’s the next wave of automation, and it’s already reshaping industries. The problem isn’t that AI is “coming for your job”—it’s that the tasks we once thought were too complex to automate are suddenly on the table. Copying, pasting, filling out forms, writing first drafts of emails—those are the tasks AI is best at. And that means the entry-level training grounds we’ve relied on for generations—where people cut their teeth and build their skills—are vanishing fast. Where will the next generation of talent come from if we don’t rethink our pipelines? Let’s be clear: the next few years will be rough, especially for junior employees. AI is far less of a threat to those with industry experience, deep domain expertise, or strong networks. But if you’re doing work that “anyone can do,” AI will soon be able to do it too. I won’t sugarcoat this, so let me say it again for the folks in the back: ⚠️ If anyone can do it, AI will soon be able to do it too. ⚠️ If you’re a student or just entering the workforce, now is the time to build relationships, seek out mentors, and cultivate a love of learning—because the treadmill is real, and it’s only speeding up. The future belongs to those who can adapt quickly and learn the new rules of new games. If you’re a leader, this is your moment to lead with compassion. Not everyone loves a constant challenge, and some implicit promises—about stable career paths, about learning your trade and coasting—are about to be broken. AI can empower us to aim higher, but only if we stay nimble. Your job is to build safe learning spaces, empower your teams to experiment with AI tools, and create clear pathways for growth beyond the tasks AI will automate. Let’s not just brace for impact—let’s get ready to lead through it. Subscribe and read on: decision.substack.com

There's a metric most executives haven't discovered yet, and it changes everything about how AI drives impact. It's not efficiency gains. Not cost savings. Not automation rates. It’s human amplification. And it flips the entire AI conversation on its head. AI success isn’t automation at scale. It’s strategic amplification. You don’t win when AI automates everything. You transform an organization when you prioritize amplifying human judgment where it matters most. - I’ve watched procurement teams become strategic advisors instead of order processors. - Treasury analysts shift from data entry to risk modeling. - Tax compliance specialists transform into strategic tax planners. Not because AI replaced them, but because AI freed them to do what humans do best. Here’s what executives actually need to know. When AI amplifies your people, three things happen: → Your workforce becomes exponentially more valuable, not smaller, but more powerful. → Your competitive advantage shifts from what AI can do to what your people can do with AI. → You build a workforce that is future-ready instead of future-threatened. But here’s the problem: you can’t amplify what you can’t measure. Most companies have no way to track whether AI is strengthening their workforce or quietly eroding it. That’s why I built the Human Amplification Index (HAI)™. HAI™ is a platform with a soul. It’s not just software. It’s designed to reflect human potential, not just machine performance. It gives leaders a single quantitative score that answers the question they are all asking: Is AI making our people more powerful or making them obsolete? HAI™ measures head value, not headcount - before AI and later AI. One number that tells you the truth. On January 16th, I’m launching HAI™ at Davos. It’s built on my 13+ years and more than 200 AI deployments across Fortune 500 companies, government agencies, and organizations in over 40 countries. It’s the GDP of Humanity. The first framework that quantifies whether AI is elevating human capability or eroding it. So let me ask you: How is your company measuring the human impact of AI today?

We’re not talking enough about the real threat AI poses to marketing. It’s not job loss. It’s the disappearance of career paths. You’re in a tough spot if you are just now starting your marketing career. Hell, even if you are later in your career. It’s not because marketing is dying. It never will. ;) It’s because the traditional “first-time” job is being automated out of existence. Thanks to our dear friends the robots, the building blocks of early career growth are vanishing: • Content production • Data analysis • Campaign optimization • Project management You know… where we learned to do the job. Where we learned the ropes. Where we experienced the grind that shaped us. Now? AI does it faster. Cheaper. And often, better. And yes, I’m worried. Because we could be experiencing an AI driven career bottleneck: • Fewer junior roles • Higher demand for senior talent • And no clear path to get from one to the other Where will the next generation of marketing leaders come from? Most companies prioritize senior hires who can drive results immediately. makes complete sense in the short term but lesser so in the long run. Because if we don’t help entry-level marketers build foundational skills, we’re headed for a future where AI executes, and only a shrinking circle of seasoned experts make decisions. That’s not sustainable. Let’s reimagine how we train and grow the future of marketing and continue to ask ourselves: What are the human skills AI won’t replace? How do we create growth paths when “junior work” is automated? What does a high-performing team look like when AI is in the room from day one? I don’t have all the answers yet. But I do know this: If we don’t figure this out, the future of marketing leadership may not exist.

Anthropic just measured which jobs AI is actually replacing. The gap between theory and reality is massive. Anthropic published a new research paper using its own Claude usage data to track AI's real-world impact on jobs. What's new: They created a metric called "observed exposure" that combines theoretical AI capability with actual professional usage data. The results are eye-opening. → Computer & Math: 96% theoretical capability. 32% actual coverage. → Office & Admin: 94% theoretical. 42% observed. → Legal: 88% theoretical. Just 15% observed. Capability isn't the bottleneck. Legal constraints, verification requirements, and slow enterprise adoption are what's holding back real-world deployment today. Most exposed occupations: → Computer programmers top the list at 75% task coverage → Customer service reps follow at 70% → Data entry keyers at 67% But there’s a certain irony at play that I think is worth pointing out. Programmers are both the most exposed occupation AND the heaviest adopters of AI. They're actively building and using the technology that automates their own work. The workers most at risk overall skew older, female, more educated, and higher-paid, earning 47% more on average than their unexposed counterparts. Graduate degree holders are nearly 4x more represented in the most exposed group. Despite all this exposure: → No meaningful increase in unemployment for high-risk workers since ChatGPT launched → But hiring of 22-25 year olds into exposed roles has dropped roughly 14% → No equivalent decline for workers over 25 My takeaway: It’s interesting to see the “disruption” showing up as a hiring freeze vs sweeping layoffs. But mainstream media much prefer to print “thousands made redundant” to sensationalise headlines. I also think it’s important to point out the 30% of workers that have zero AI exposure. Cooks, bartenders, mechanics, lifeguards. The roles AI can't touch are almost entirely physical. Having a living measure like this helps track how the gap between AI’s theoretical capability and real-world adoption narrows over time. That gap is where the next wave of disruption lives. Follow me Alex Banks for daily AI highlights and insights. I talked about AI’s impact on jobs first in my newsletter. You get the most important news + analysis in your inbox every Sunday. Read it here: https://lnkd.in/ei8r5Xyq

So much has been said and written about how AI is changing the job market. Time for some myth busting. You’ll be surprised by some of the findings. The latest PwC AI Jobs Barometer paints a much more complex picture than the headlines suggest. The biggest misconception? That AI adoption means mass job losses, wage suppression, and a deskilled workforce. 𝟭. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Industries most exposed to AI are seeing productivity grow 3x faster than those with low exposure. AI isn’t just replacing tasks - it’s enabling output at scale. 𝟮. 𝗪𝗮𝗴𝗲𝘀:  In high-AI exposure sectors, wages are rising 2x as fast as in less-exposed ones. Even automatable roles see strong wage growth, debunking fears of a universal race to the bottom. 𝟯. 𝗝𝗼𝗯 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻:   AI-exposed roles are growing, not shrinking. While the nature of tasks is changing, demand remains strong - especially in augmentable jobs that combine human skills with AI. 𝟰. 𝗗𝗲𝗴𝗿𝗲𝗲 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀:   Job ads in AI-heavy sectors are dropping degree requirements faster, opening up access and reducing formal barriers to entry. 𝟱. 𝗦𝗸𝗶𝗹𝗹𝘀:   Rather than deskilling, AI is increasing the complexity and decision-making nature of many roles - requiring more strategic, not mechanical, input. 𝟲. 𝗜𝗻𝗲𝗾𝘂𝗮𝗹𝗶𝘁𝘆:   While benefits are evident, the report flags a risk of polarisation: between companies that adopt AI fast - and those that lag. Gaps could widen in pay, productivity, and talent attraction. 𝟳.𝗗𝗲𝗺𝗮𝗻𝗱:   Employers aren’t just hiring engineers. There’s rising demand for data-literate business talent: managers, analysts, marketers - all needing fluency in AI tools. 𝟴. 𝗚𝗲𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝘀𝗽𝗿𝗲𝗮𝗱:   The pace of AI impact differs by country - but labour markets are adjusting, not collapsing.   AI isn’t simply replacing jobs. It’s reshaping them - and redefining what skills, education, and value look like in the workplace.   Source: PwC’s 2025 Global AI Jobs Barometer 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

I've spent the last 20 years building AI at Meta, Salesforce, and venture-backed startups. Last fall, the AI agents my team deployed started really working, and I knew everything had changed. Shortly after, my nieces, nephews, cousins– so many young people I know– started coming to me asking how to get hired in this market. It hit hard. I haven't been able to stop thinking about it since. So I decided to leave and build something. The only way to keep up with the pace of AI change is to use AI itself. Today at #TIME100, we’re launching New Work Foundation– free, open source AI tools for Gen Z to navigate the worst entry-level job market in 37 years.  → dear [CC] – Gen Z content platform demystifying what AI is doing to each profession. Every episode pairs an industry hiring manager with a Gen Z worker identifying what AI skills are needed to get hired. → Field Report – AI job explorer allowing people to understand the best jobs for their major. → JobClaw – Open-source job matching agent that maps your actual strengths to roles where you'll have an edge and identifies the AI skills you need to compete. Grateful to have Andrew Yang joining as a Founding Advisor– someone who has been sounding the alarm on this longer than almost anyone. Today, it's a tale of two cities for job seekers. Those with AI skills are being fought over. Those without are being left behind. But here's what I want every young person to hear: 1/ There are no experts in AI yet. It's all too new. This is your opportunity. 2/ Every job is becoming an AI job. The question isn't whether AI will change your career– it's whether you learn to direct it, or be directed by it. 3/ The future isn't automatic. We can still choose a better ending. I want to help you write yours. 🔗 dearcc.org TIME

New research joint with Maxim Massenkoff: How is AI affecting the US labor market? In this research brief, we introduce a new measure of AI displacement risk to spot disruption, then test it against employment data. We find limited evidence AI has increased unemployment to date. Our measure, "observed exposure," compares the tasks LLMs are theoretically capable of to the tasks people actually use Claude for at work. We find that actual usage is far from reaching theoretical capability. This measure tracks with independent forecasts. Jobs with higher observed exposure to AI are projected by the BLS to grow more slowly over the next decade. We find limited evidence, however, that AI is playing a role in the broader labor market today. The top 25% of workers most exposed to AI automation have similar trends in unemployment rates to workers with no exposure at all. Hiring of younger workers in the most exposed occupations appears to have slowed faster than for non-exposed roles, but our estimates are imprecise and other non-AI factors may be playing a role. This research is a first step. Our goal is to establish an approach for measuring how AI is affecting employment, and to build on these analyses periodically as more data becomes available.

Emerging Departments: How AI is Transforming Organizations Transformation in light of AI isn't just about digital change—it's strategic, cultural, and organizational. Early results of organizational optimization with AI reveal that traditional structures are evolving into new, combined departments that break down silos and enhance collaboration. Here are some emerging trends: 1. Human Experience Department (Led by the CXO) Combines marketing, HR, and customer service to create a unified experience approach. Focuses on customer and employee experience as a seamless continuum. Example: Airbnb and Starbucks blending internal and external engagement for holistic experience design. 2. The Intelligence Function (Led by Chief Data & Intelligence Officer (CDIO)) Merges IT, data analytics, and AI strategy into a unified intelligence function. Enhances decision-making with data-driven insights and technology integration. Example: Microsoft and Amazon use intelligence functions to support strategy and innovation. 3. Integrated Growth Department (Led by the CGO) Combines Marketing, Sales, and Customer Success to create cohesive client journeys. Prioritizes growth by aligning customer interactions across all touchpoints. Example: HubSpot and Salesforce driving client experience continuity. 4. Strategic Innovation & Transformation Office (Led by Chief Strategy Officer or Chief Transformation Officer) Combines strategy, innovation, and transformation initiatives for continuous evolution. Fosters agility by integrating foresight and innovation into long-term strategy. Example: Tesla blending innovation with strategic growth planning. 5. Technology and Digital Transformation Department (Led by the Chief Technology & Transformation Officer) Integrates IT, digital transformation, and cybersecurity under one strategic role. Embeds technology into workflows while ensuring security and compliance. Example: Cisco and IBM streamlining their digital transformation efforts. 6. Resilience and Continuity Department (Led by the Chief Risk Officer) Oversees Risk Management, Business Continuity, and Strategic Foresight. Ensures organizational resilience in an increasingly FLUX world. Example: JP Morgan building resilience to mitigate risks and ensure continuity. 7. Ethics and Responsible AI Office (Led by the CEAO) Ensures ethical AI use and compliance with regulatory standards. Maintains trust and integrity as AI becomes central to business strategy. Example: Microsoft and IBM proactively building ethics frameworks for responsible AI. In sum, AI is driving fundamental shifts in how we structure our organizations. To thrive, leaders must think beyond digital transformation and focus on strategic, cultural, and organizational evolution. The companies that succeed will be those that break down silos, integrate their functions, and embrace transformation as a continuous journey.