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.

Leaders, if you're going ahead with mass layoffs, you can't seriously be thinking that your #diversity, #equity, or #inclusion work will have any credibility left after the fact. Fundamentally, DEI work is about showing people that they matter by building a workplace where they can thrive. And fundamentally, mass layoffs communicate the exact opposite: that no matter a person's skill, experience, productivity, contribution, passion, or loyalty, they ultimately are just another cost to be cut. That people mean nothing in the face of short-term profit. The consequences of mass layoffs on your people, your biggest assets, are immediate and catastrophic. 📉 One study found a 41% decline in job satisfaction among survivors of a layoff, leading to a 36% decline in their desire to stay with the workplace. 📉 Another study found that a 1% workforce layoff resulted in a 31% increase in voluntary turnover. 📉 One study found a 20% decline in job performance, with another finding that 77% of layoff survivors see more errors and mistakes made. 📉 Another study found that layoffs tanked the quality of products, the safety of the workplace, and the quality of layoff survivor mental health and wellbeing. 📉 A bevy of other studies find a cascading set of issues triggered by layoffs that create a vicious cycle: worse morale and wellbeing leads to poorer job performance, overwork and forced productivity drives mass exoduses of skilled workers; reputational damage and loss of trust dampens the ability to hire fresh talent. Trying to achieve any sort of DEI impact amid this kind of avoidable chaos is like trying to renovate your house after setting it on fire. It's downright offensive to employees, especially those with marginalized identities, to be asked to continue their unpaid, voluntary efforts to benefit the business after you've destroyed any reason for them to undertake this extra work. It's a moot point—they're far too busy applying to your competitors, anyways. This is the point in time when those workplaces and leaders with empty promises and performative actions will be weeded out from those that get ahead by doing right by their people, their customers, and the world. There are many ways for a workplace to earn a spot in the latter group, but in case it wasn't clear? Mass layoffs aren't one of them.

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

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.

Sam Altman, the co-founder and CEO of OpenAI, made a provocative statement at a JP Morgan conference earlier this year. He believes a solo founder will soon reach a billion-dollar valuation without hiring a single employee. This one-person company would instead be powered by AI and “employ” dozens of AI agents to do the work. Not only do I believe this is entirely possible, but I think when it does happen, the company will be one of the fastest-growing unicorns ever. As I invest in AI-powered startups and teach my students how to use AI in their businesses, I have identified 5 general AI use cases that align with critical phases of the startup journey: 1. Research-Driven Ideation: The genesis of any successful startup is a deep understanding of market needs, pain points, and the competitive landscape. My colleague Scott Brady of Stanford calls this process Research-Driven Ideation (RDI). There are now AI-based tools for competitive analysts, automating competitive monitoring for senior managers—effectively Google Alerts on steroids, tracking personnel changes, marketing launches, traffic, and other publicly available data. 2. Customer Persona Development and Market Research: Understanding your target customer is crucial. Gen AI helps founders create multiple hyper-specific customer personas by analyzing customer data and building hyper-realistic, "living" customer personas to test key hypotheses quickly. 3. Experimentation and Validation: Gen AI facilitates rapid experimentation to validate key hypotheses such as CVP, GTM, and PF by enabling deeper business data insights and rapid prototyping. I have a founder friend who lost his technical cofounder and has been using ChatGPT to build his MVP. By learning to be more effective at writing prompts to generate the desired code output, he has been able to continue building as a solo founder. He told me, “The result is that my burn rate is incredibly low, and velocity has shot through the roof.” 4. Marketing and Customer Engagement: Founders will see major productivity boosts in marketing, community building, and sales prospecting. Flybridge has a portfolio company that builds super smart AI agents that can be used for just about anything. One of their customers trained their agent to automatically generate customized sales collateral and follow-up materials based on customer needs that a sales representative inputs into the system after a prospect call—and then the AI agent sends that tailored material to the customer. 5. Continuous Learning and Iteration: The path to PMF is iterative. Gen AI supports continuous learning by analyzing customer feedback and product usage data to improve their product, GTM, and onboarding processes quickly. How are you using AI to build your startup?

This week at Fortune Brainstorm Tech, I sat down with leaders actually responsible for implementing AI at scale - Deloitte, Blackstone, Amex, Nike, Salesforce, and more. The headlines on AI adoption are usually surveys or arm-wavy anecdotes. The reality is far messier, far more technical, and - if you dig into details - full of patterns worth stealing. A few that stood out: (1) Problem > Platform AI adoption stalls when it’s framed as “we need more AI.” It works when scoped to a bounded business problem with measurable P&L impact. Deloitte's CTO admitted their first wave fizzled until they reframed around ROI-tied use cases. ➡️ Anchor every AI proposal in the metric you’ll move - not the model you’ll use. (2) Fix the Plumbing Every failed rollout traced back to weak foundations. American Express launched a knowledge assistant that collapsed under messy data - forcing a rebuild of their data layer. Painful, but it created cover to invest in infrastructure that lacked a flashy ROI. Today, thousands of travel counselors across 19 markets use AI daily - possible only because of that reset. ➡️ Treat data foundations as first-class citizens. If you’re still deferring middleware spend, AI will expose that gap brutally. (3) Centralize Governance, Decentralize Application Nike’s journey is a case study: Phase 1: centralized team → clean infra, no traction. Phase 2: federated into business-line teams → every project tied to outcomes → traction unlocked. The pattern is consistent: centralize standards, infra, and security; decentralize use-case development. If you only push from the top, you have a fast start but shallow impact. Only bottom-up ownership gives depth. ➡️ You can’t scale AI from a lab. It has to live where the business pain lives. (4) Humans are harder than the Tech Leaders agreed: the “AI story” is really a people story. Fear of job loss slows adoption. ➡️ Frame AI as augmentation, not replacement. Culture change is the real rollout plan. (5) Board Buy-In: Blessing and Burden Boards are terrified of being left behind. Upside: funding and prioritization. Downside: unrealistic timelines and a “go faster” drumbeat. Leaders who navigated best used board energy to unlock investment in cross-functional data/security initiatives. ➡️ Harness board FOMO as cover to fund the unsexy essentials. Don’t let it push you into AI theater. (6) Success ≠ Moonshot, Failure ≠ Fatal. - Blackstone's biggest win: micro-apps that save investors 1–2 hours/day. Not glamorous, but high ROI. - Nike's biggest miss: an immersive AI Olympic shoe designer - fun demo, no scale. Incremental productivity gains compound. Moonshots inspire headlines, but rarely deliver durable value. ➡️ Bank small wins. They build credibility and capacity for bigger bets. In enterprise AI, the model is the easy part. The hard part - and the difference between demo and value - is framing the right problem, building the data plumbing, designing the org, and bringing people along.

Klarna bet big on AI. Now they're rehiring humans. After their valuation plunged from $45B to $7B in 2022, the company faced enormous pressure. One cost-saving measure was replacing 700 customer-service roles with AI. Then they learned a critical lesson: Some AI savings carry steep human costs. "It’s critical that customers know there will always be a human if you want." – Sebastian Siemiatkowski (CEO) The insight is strategic, not operational: → AI is transactional → Humans are relational → Automation optimizes predictable interactions → Humans manage unpredictable trust moments → AI builds efficiency → Humans build loyalty Firms that find balance will outperform those blindly bolting on technology. The new service blueprint: → Clearly map trust vs. transactional moments → Position humans strategically, not universally → Use AI to complement rather than to replace → Measure success beyond cost savings → Prioritize trust metrics (retention, advocacy, loyalty) Beyond fintech: → Consulting faces the same trust dilemma → Legal automation risks client trust → Finance must automate tasks, not judgment The winners won't automate fastest. They'll automate everything except trust itself. Because trust, judgment, and empathy never scale. And that's exactly why they're valuable.

AI handled 75% of customer chats at Klarna… and they still brought humans back. Why? Because speed isn’t the same as quality! Because customers noticed the difference. And it wasn’t good. Speed? Great. Empathy? Missing. Trust? Slipping. After a year of leaning heavily on AI, they’re rehiring human support agents. Real people. Not because AI failed—but because it wasn’t enough. AI can answer your question. But only a human can make you feel heard. Klarna is now hiring in rural areas and among student communities—betting on empathy, not just efficiency. This should be a wake-up call. You can automate tasks. But relationships? They still need people! This is why the future isn’t human vs AI. It’s human with AI. And the companies who get that balance right? They’ll win customer loyalty, and talent, faster than any chatbot ever could.

Every customer and government leader I meet is asking, “How can we make AI a force for good for our people, and not a threat?” 92% of jobs are expected to undergo some level of transformation due to advancements in AI. The work begins with identifying and enabling the new skills and training needed for AI preparedness. That’s why I’m honored to share the insights from the AI-Enabled ICT Workforce Consortium's inaugural report, “The Transformational Opportunity of AI on ICT Jobs.” This report examines the impact of AI on 47 ICT job roles and offers tailored training recommendations. It's a unique guide to the skills needed for the AI future, with recommendations that couldn't be clearer, timelier, or more urgent. Here are some of the top takeaways: - 92% of ICT jobs will undergo high or moderate transformation due to AI. - 40% of mid-level and 37% of entry-level ICT positions will see high levels of transformation. - Skills like AI ethics, responsible AI, prompt engineering, and AI literacy will become crucial. - Foundational skills such as AI literacy and data analytics are essential across all ICT roles. Read the full report here: https://lnkd.in/gWfPc8WT The risks associated with an under-skilled, unprepared workforce are global in scale, ranging from economic wage gaps to trade imbalances, technological stagnation, social and ethical issues, and national security threats. This creates a pressing need for a coordinated effort to reskill and upskill employees around the world. By investing in a long-term roadmap for an inclusive and skilled workforce, we can help all populations participate and thrive in the era of AI. Led by Cisco and joined by industry giants like Accenture, Eightfold, Google, IBM, Indeed, Intel Corporation, Microsoft, and SAP the Consortium will train and upskill 95 million people over the next 10 years through their individual organizations' commitments.

Most job seekers are using AI wrong. They ask vague questions like: 👉 “Can you write me a cover letter?” 👉 “What’s the best way to find a job?” And then they wonder why the answers sound generic. Here’s the truth: AI works best when you know how to work with it. Think of it less as a “magic machine” and more as a sparring partner. Here’s a simple 3-step framework I use with clients: 🔹 Step 1 – Give it a persona Need a recruiter’s POV? Tell AI to act like one. Want industry insights? Make it your market researcher. Looking for career strategy? Ask it to be a career coach. 🔹 Step 2 – Share specific info about you The clearer you are, the better the output. Don’t just say “I am in finance.” Say: “I am a mid-level finance professional with 8 years in risk management, looking to pivot into fintech in Singapore.” 🔹 Step 3 – Break it down into milestones Instead of asking, “Find me a job,” ask for smaller, actionable steps. Use AI to brainstorm, refine, and pressure-test your strategy. ⸻ 💡 Example: Building a Target Company List Instead of Googling endlessly, you could ask AI: “You are a market researcher. Based on my 8 years in risk management and my goal to move into fintech in Singapore, identify 5 fintech sub-sectors I should explore. For each sub-sector, give me 10 fast-growing companies with at least 200 employees and a track record of hiring foreigners.” From there, you refine the list, research deeper, and start networking with intention. ⸻ AI won’t do the job search for you. But when used right, it will make you sharper, faster, and more strategic. And in this market, that’s the edge you want. 👉 Have you tried using AI in your job search yet? What worked (or didn’t) for you?