Understanding AGI's Impact on Future Employment

Former Google CEO says AGI is 3-6 years away.  Are you rethinking your org structure yet? Let that timeline sink in. Now maybe that timeline is off. But if that timeline is even half right, then your company, your hiring plan, team structure, and internal priorities should already be changing. Because AGI won't just change how we work. It will change what work is. In the next wave: - Engineers won’t just write code - they’ll orchestrate autonomous systems. - Designers won’t just design - they’ll prompt, direct, and curate from AI workflows. - Marketers will co-create with agents, analyze real-time behavioral data, and launch at a speed most orgs can’t even fathom today. Does your team know what’s coming? Are your hiring plans built for yesterday’s roles or tomorrow’s capabilities? Is leadership aligned on how AI will shift their departments? You don’t need all the answers yet. But you do need to start asking the right questions. We’re entering a decade where: - Roles become fluid, not fixed. - AI-first teams outperform traditional hierarchies. - Speed, adaptability, and creative judgment become your new unfair advantage. So what does preparation actually look like? Audit your org: Which roles will shrink, shift, or scale with AI? Redesign hiring: Stop recruiting based on past roles. Start hiring for adaptability, learning velocity, and AI fluency. Create AI-enabled teams: Not AI “tools” - teams built to collaborate with intelligent systems. Build cross-functional pods: Fluid groups that ship fast, adapt constantly, and retrain frequently. Train leaders to lead in chaos: Most execs are optimized for stable systems. AGI-era leadership requires decisiveness in uncertainty. You don’t need to predict the future. You need to build a company that thrives in any version of it. Because whether AGI takes 3 years or 20 the shift has already begun. If you need help navigating this shift feel free to connect: Vas Grygorovych

AGI isn’t going to “disrupt” your job. It’s going to erase the layer you sit in. Most people think in terms of professions. AGI thinks in terms of compression. If your value is translating intent into output, you are standing on thin ice. A few uncomfortable examples: - Translators are almost gone. Not because language stopped mattering but because translation did. - Writers survive better. Maybe only 30% get wiped out because voice still matters. - Comedians are mostly fine. Not because AI can’t write jokes, but because it can’t read the room. That’s the pattern most people miss. AGI doesn’t kill creativity. It kills middle layers. It erases: - execution without ownership - output without judgment - speed without taste - work that exists to “help” someone else decide If your job can be described as: “Take this idea and turn it into something” you should be nervous. Because in a few (months) years, the person with the idea won’t need you. They’ll just… type (dictate more likely). Here’s the part that’s hard to accept: Most roles aren’t being replaced. They’re being collapsed upward. The builder disappears. The idea-holder absorbs the work. And the people in between vanish quietly. But here’s the twist, and this is the optimistic part: The moment you can clearly see how you’ll be replaced is the moment you become replaceable only if you do nothing. Because now you know where not to stand. AGI can generate output. It can’t carry accountability. It can’t choose what matters. It can’t feel when something is wrong. It can’t decide what to kill. Those things still belong to humans. The future doesn’t belong to the fastest builders. It belongs to: - people with judgment - people with taste - people who decide what’s worth doing - people who can make other humans feel something AGI isn’t the end of work. It’s the end of hiding in the middle. And once you see that clearly, you can move. Upstream.

Last year, Silicon Valley was gripped by predictions of an imminent “AGI takeoff.” Today, the narrative has shifted. Instead of sudden superintelligence, the focus is on a more gradual, practical transformation — what I call reasonable person’s AGI. The key idea is the 5% Rule: In any domain where AI can outperform the bottom 5% of practitioners, billions of dollars in value will be created. This threshold sounds modest, but its impact is enormous. We already see it in writing, coding, customer service, therapy, and companionship These domains are experiencing economic disruption not because AI replaces the best, but because it reliably outperforms the worst. Over time, this raises the baseline of competence — humans adapt, industries transform, and new opportunities emerge. For entrepreneurs, one implication is that rather than trying to beat the metaphorical Garry Kasparov, it may be more straightforward to simply outperform Dwight Schrute. https://lnkd.in/gYfYTjsF

Incisive piece by the The New York Times Steve Lohr on first of its kind research by The Burning Glass Institute and SHRM on the likely impact of Generative AI on employment. Initial analyses, including our hear at Harvard Business School Project on Managing the Future of Work have identified important a number of likely outcomes. This report drills down deep, confirming many of those hypotheses. The core of the report is The Burning Glass Institute identifying the 200 occupations that are most likely to be affected by Generative AI (GAI). It isn't going to wipe out jobs wholesale. GAI will displace some tasks altogether and speedup others. It will make people more productive-- a huge boon to the U.S. economy, given lackluster productivity growth in recent years. That productivity growth will lead to companies reducing their staff or hiring needs. The biggest impact will be on classic, white collar jobs-- marketers, business and financial analysts, supply chain managers and purchasing agents, auditors, attorneys, etc. Industries will be affected asymmetrically with professional services, banking and tech. In some industries that will be less affected, specific competitors may be more vulnerable. A retailer like Tiffany's might only restructure marginally; a retailer like Williams-Sonoma with a significant web presence much more so. So, what should executives do? One, develop a strategy. Huge value is on the table and, if your competitors get out in front of you, the consequences will be significant. Companies that slide down the learning curve faster have the prospect of gaining a significant, even insurmountable data-drive advantage. Two, start demystifying GAI for your workforce. Too many companies are holding their cards close to their vests. Left to their own imaginations, workers are increasingly likely anxious and skeptical. That will undermine future reskilling initiatives. Three, start thinking about future job design. If GAI is going to unburden many white collar workers of 40%, 50%, even 60% of their current tasks, what should they be directed to do. What upskilling or reskilling should we be undertaking? How should job descriptions change? What about incentives and metrics? Start probing these questions now, don't wait and find yourself trying to change the engines on the plane while you're flying at 30,000 ft. Four, use tools like this to evaluate your organization's current design. How much disruption is coming your way? How can you start preparing for it now, such as reining in hiring for positions that are likely to be substantially transformed in the next year or two. Five, revisit your talent pipeline strategies. Where will the talent you need in the GAI world come from? Seems implausible that your talent suppliers from the pre-GAI world will all be perfect fits for the what's coming. #artificialintelligence #workforcetransformation #generativeai

Have you seen it? The paper "Scenarios for the Transition to AGI" by Anton Korinek and Donghyun Suh is a provocative dive into a future many of us are barely ready to imagine. It doesn’t just ask what happens when Artificial General Intelligence (AGI) arrives—it demands we grapple with the economic and social upheaval that may follow. Key Takeaways: 1️⃣ Wages Could Collapse: If automation outpaces capital accumulation, labor could lose its scarcity value, leading to plummeting wages. This isn’t a dystopian prediction—it’s a mathematical outcome of economic models. 2️⃣ The Scarcity Tipping Point: Once AGI surpasses human capabilities in bounded task distributions, all bets are off. Labor and capital become interchangeable at the margin, leveling wages to the productivity of capital. 3️⃣ Automation Winners and Losers: If AGI automates most cognitive and physical tasks, the economy may shift towards "superstar workers" earning exponentially while the rest are sidelined. 4️⃣ Fixed Factors Create Bottlenecks: Scarcity of resources like land, minerals, or energy might reintroduce constraints, impacting economic growth despite technological advances. 5️⃣ Societal Choices Matter: Retaining "nostalgic jobs" like judges or priests as human-exclusive could slow the pace of labor devaluation but at a cost to productivity. 6️⃣ Innovation Beyond AGI: Automating technological progress itself could create a growth singularity, driving output to unprecedented levels. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: ➡️ This isn’t just an academic exercise. ➡️ Leaders in AI, including those at OpenAI and DeepMind, warn we’re closer to AGI than many think. ➡️The implications go beyond economics: societal cohesion, equity, and governance will be tested like never before. Reading this paper, one thing becomes clear: how we transition to AGI is as important as when. Without intentional policies—on redistribution, education, and innovation—we risk deepening inequality and destabilizing economies. Yet, with the right guardrails, AGI could usher in a new era of abundance. What Do You Think? Should governments mandate slower automation to protect wages? Or should we embrace AGI at full throttle, trusting innovation will create new opportunities? We need to have answers —because the future is closer than you think.

Just released: the new LEAP expert report on AI. I pulled a late night, and have read the full report. Rather than recap, here are the signals that matter for leaders. 1. The hype quotes are real. The expert consensus is not. The report highlights bullish claims from frontier lab leaders. Dario Anthropic: “By 2026 or 2027, AI will be broadly better than almost all humans at almost all things.” Sam OpenAI: “AGI will probably be developed during [Trump’s second] term.” Elon X: “AI will exceed any single human by end of 2025 and all humans by 2027/2028… ~100% chance it exceeds all humans combined by 2030.” Demis Google DeepMind: “We’ll have something we could reasonably call AGI in the next 5 to 10 years.” Note that these are founders whose business model depends on investor-friendly optimism. The LEAP panel offers a different view. By 2030, experts assign 23% confidence to rapid progress and 28% to slow progress. AGI by 2026–2029 is not a given. 2. Work is transformed long before “all jobs are gone” By 2030, experts expect 18% of US work hours to be AI-assisted, up from 4.1% in 2024. Even cautious forecasts land at 9%. This is not clean replacement. It is a decade where 1/5 of work becomes machine-mediated while organisations remain built on human processes. That is the real disruption! Leaders will need to: Redesign roles Build long-term scalable governance for assisted work Prepare for productivity shocks 3. Electricity is the new bottleneck By 2030, AI is expected to consume 7% of US electricity. By 2040, 12%. This is huge! Seven percent already exceeds today’s total data-centre load! Experts warn that limits on data centres and power generation could “make it impossible to build,” slowing adoption more than any capability gap. My take-away? If your AI strategy ignores electricity, it is not a strategy. 4. Companionship, mental health, and where people turn for help Baseline: 6% of US adults already use AI for companionship daily. LEAP forecasts: 10% by 2027, 15% by 2030, 30% by 2040. This maps directly onto the loneliness and mental-health crisis: 1/2 of US adults report loneliness and about 1/4 of all US adults received counselling or medication last year. With services stretched, people turn to AI, even when the guidance can stray outside clinical standards and, in documented cases, has contributed to severe outcomes. The line between AI companionship and AI mental health is already thin and will get thinner. Leaders must: Set boundaries in HR support, health, and education Build safety and escalation into tools Prepare staff for people who arrive with pre-existing emotional ties to AI Three leadership implications in this report: Treat aggressive AGI timelines as one point in a wide distribution. Act now on the “boring” numbers: 18% work hours assisted, 7% electricity, 30% daily companionship. Assume mental-health AI use will grow. Plan accordingly. If you plan in years rather than quarters, dig in. This report is worth your time.

When discussing AI and jobs, the debate often jumps too quickly to headline numbers. That is not what our latest study is about. With the Observatory of Threatened and Emerging Jobs, we chose a different starting point: tasks. By breaking down 923 professions into the concrete activities that make them up, we assess the technical potential for automation under different scenarios, as the technology matures. What emerges is a clear shift. Agentic AI is not primarily targeting repetitive or manual work. It is increasingly capable of handling cognitive and complex tasks. As a result, some of the most qualified – and often best‑paid – jobs appear among the most exposed. This exposure is highly uneven. Between occupations, job families and sectors, the potential for automation varies widely. Long before this transformation becomes visible in national employment statistics, it will first materialise in the most exposed areas. Identifying them early is precisely the purpose of this work. It is important to stress that this study does not predict a net number of jobs lost or created! The real impact on employment will depend on choices: how fast companies adopt AI, how roles are redesigned, how productivity gains are shared, how demand evolves, and whether new tasks and new jobs emerge. For businesses, the question is therefore less about inevitability than about preparation. Understanding where change is technically possible is a first step toward managing it. Find the full study here: https://lnkd.in/ezxDaYBz

From Tooling to Talent: Navigating the Era of Functional AGI 🌐 NVIDIA’s Jensen Huang recently made a declaration that should be on every executive's radar: #AGI (Artificial General Intelligence) is no longer a "future state", it is a functional reality. When the leader of the world’s most valuable AI infrastructure company defines AGI as an agent capable of "launching and running a billion-dollar company," the conversation shifts from technical feasibility to strategic execution. The Key Shift: Functional Autonomy We are moving past "Generative AI" (which creates) into "Agentic AI" (which executes). With the rollout of NVIDIA’s Rubin architecture and Blackwell-2, the physical bottleneck for reasoning is disappearing. This isn't just "smarter software"; it's a new layer of industrial-scale intelligence. What’s Beyond: The Leap to ASI If AGI matches human proficiency, ASI (Artificial Superintelligence) represents a scale of problem-solving—from climate logistics to molecular biology—that surpasses collective human capability. For leaders, the transition to ASI won't be a product launch; it will be a paradigm shift in how we define competitive advantage. My Strategic Takeaways : 1. AI as Infrastructure, Not Add-on: Leadership can stop viewing AI as a productivity tool and start viewing it as a core utility. In an era of functional AGI, the "Intelligence Factory" is as vital as the power grid. 2.#Workforcetransformation : As AGI takes over functional execution, human leadership must pivot toward high-order Agent Orchestration and ethical governance. Our role is no longer to manage tasks, but to steer autonomous systems. 3. The Agility Mandate: The gap between AGI and ASI may be shorter than we think. Organizations that aren't "AI-native" in their decision-making processes risk becoming legacy entities overnight. The question for #ExecutiveLeadership is no longer "When will AI be ready?" but "Are we ready to lead an autonomous workforce?" Source : Lex Fridman Follow #PaulineA to understand how workforce transformation evolves with AI and how to lead your organization through the next wave of #upskilling. #Leadership #AIForBusiness #FutureOfWork #CorporateEvolution #AIStrategy