Jacob Morgan

We tried to make work happier—and ended up making it hollow. Cultures softened. Leaders hesitated. Performance drifted. After 100+ CHRO interviews, I wrote The 8 Laws of Employee Experience—a blueprint to bring strength and humanity back to work. Learn more about it here: linkedin.com/pulse/my-new-b…

We are seeing a concerning trend where "AI strategy" is becoming a synonym for subtraction. Companies like @Meta and @TheBlockCo are implementing 20% to 40% cuts, betting on an efficiency that @Forrester Research suggests 55% of leaders already regret. When your only response to increased capability is to have fewer people, you’ve run out of imagination. The infographic highlights a critical choice: Strategy A (Subtraction), which trades long-term talent for short-term margins. Strategy B (Expansion). @nvidia is choosing expansion, scaling to 75,000 humans managing 7.5 million agents to solve once-impossible problems. But to navigate this dilemma requires "Context Engineering." That means designing the environments where AI operates. As Lao Tzu said, "If you do not change direction, you may end up where you are heading." I’ve broken down the full diagnostic on bridging this imagination gap in the latest episode. Check out the top stories shaping the future of work today and futurist insights: podcasts.apple.com/us/podcast/nvi…

The vendors like @AnthropicAI @OpenAI, etc are doing an absolutely atrocious job of communicating the value and opportunity of AI. They are so focused on the technology that they are forgetting about the narrative. There is no dream, no vision, no value for the average user.

Half of every venture dollar on the planet is now flowing into a single sector: AI. We often talk about "AI hype" as if it’s a bubble waiting to burst. Most people assume this is just Silicon Valley being Silicon Valley, a localized trend that won't change the fundamental nature of their industry or their job. That is a dangerous miscalculation. Capital is the ultimate leading indicator of reality. As I reveal in my latest Substack article, the Stanford AI Index calculated that total corporate AI investment from 2013 to 2024 was $1.6 trillion. We are now on track to match that entire eleven-year history in a single year by 2027. This isn't just "investment"; it's a massive capital concentration that is reshaping the global economy in real-time. When costs to train a single model jump from $900 to $1 billion in a decade, only the most well-funded players survive. We are witnessing the creation of a new kind of oligopoly, and if you're waiting for things to "settle down," you're ignoring the fact that the world’s financial engines have already pivoted. The "normal" you’re waiting for no longer exists. This is your sign to stop treating AI as a "tech initiative" and start treating it as a fundamental transformation. When half of the world's startup money goes into one bucket, it means the smartest financial minds believe that every future company will be an AI company. Essentially, the "wait and see" period is over. The people who fund the future have already decided where it’s going. To help you map out this shift, I’ve analyzed this capital explosion alongside 16 other critical charts that show exactly where we are headed. Follow the money. It’s telling a story most people are still too afraid to read. Check out the 17 charts that reveal the true scale of the transformation here: greatleadership.substack.com/p/ai-technolog…

You can’t build a high-performing team on logic alone. Rationale provides the "what," but sharing your feelings provides the "why." If you want people to open up, you have to go first. Stop hiding behind the data and start being a human being.

We’ve all seen the headlines about the latest AI models and the "magic" of agentic workflows. It’s easy to get caught up in the 10% (the math) and the 20% (the tech). But here is the reality most companies are learning the hard way: Buying the tools is the easy part. Getting people to actually use them is where the value lives. This is known as the 10-20-70 Rule. If you spend all your budget and energy on the algorithms and data, you’ve only addressed 30% of the equation. The remaining 70% of the value comes from the messy, human work of redesigning processes and building trust. Why does this matter for the C-Suite? It means the CHRO actually holds more latent power than almost any other position in the C-suite right now. While the CTO builds the engine, the CHRO is the one who decides if the organization is actually ready to drive it. Success doesn't come from deploying AI faster; it comes from maintaining the trust and psychological safety required for your best people to innovate with tools they might otherwise fear. Stop treating AI as a tech rollout. Start treating it as a human transformation. In your organization, are you spending more time on the 30% or the 70%? Let’s talk about it.

Your balance sheet might look perfect while your customer base is vanishing. If every organization uses AI to slash payroll simultaneously, we trigger the "Human Intelligence Displacement Spiral." It is a mathematical loop. Firms cut labor to preserve margins, displaced workers stop spending, and global demand craters. This moved the Dow 800 points this week after @Citrini7 released their "2028 Global Intelligence Shock" memo. We are flirting with "Ghost GDP." This is economic output that looks great on paper but never circulates through a household. Machines do not earn wages, and they certainly do not buy what you are selling. Resilience is not about replacement. It is about architecting a "Human-Agent Mesh." If you automate your workforce out of existence, you eventually automate your own revenue away. The narrative is moving faster than the data right now. I have deconstructed the full Citrini essay and the Citadel rebuttal in today’s episode. Check it out here: podcasts.apple.com/us/podcast/lib…

Something is happening at Meta that most people are missing. Yes, the headline is 15,000-16,000 layoffs. 20% of the company. But the real story is how the market reacted. Nobody freaked out. In fact, Wall Street cheered. And once you see the math, you understand why. Bank of America estimates this move saves Meta around $8 billion a year. Average total comp at Meta )salary, bonus, equity) runs about $375k per employee. Cut 16,000 people and you get there fast. But here's what makes this different from a typical cost-cutting story. Meta made $200 billion in revenue last year with 79,000 employees. $2.5 million per employee. They generated $83 billion in operating income. This is not a company that's struggling. The P/E angle is where it gets really interesting. Meta trades at roughly 26x earnings. $1.6 trillion market cap. When you free up $8 billion in labor costs and that flows to the bottom line at a 26x multiple you're not just saving $8 billion. You're potentially adding over $200 billion in market cap. That's the number that has Wall Street excited. And then there's what they're doing with the savings. $115-135 billion in AI infrastructure spend this year. The trade is explicit: fewer people, more compute. One more thing worth noting, this isn't even an official announcement yet. Meta leaked the number. Watched the reaction. The market gave them a green light. So now it's not really a question of if. It's when. Every company watching this just got a very clear signal about what the market rewards right now and Block helped pave the way a few weeks ago.

In 2020, if you asked an AI a question about law, medicine, or philosophy, it was essentially flipping a coin. It scored 35% on the MMLU benchmark, barely better than random guessing. By 2025, it crossed 91%. We are witnessing the death of the "knowledge monopoly." For centuries, "expertise" was defined by the human ability to absorb, recall, and synthesize vast amounts of specialized information. We spent decades in school to reach the "Human Expert" line. But as this chart shows, AI didn’t just reach that line, it blew right past it in less than five years. This isn't just a "tech update"; it’s a fundamental shift in what it means to be a professional. The MMLU covers 57 subjects across abstract algebra, clinical medicine, and international law. In 2020, AI was a toy. By 2023, it was a high-school graduate. Today, it outperforms a panel of PhD-holding subject matter experts. If your value proposition is simply "knowing things" or "retrieving information," you are competing against a curve that is already superhuman. The gatekeepers of expertise have changed, yet most organizations are still hiring and training for a world that disappeared in 2022. We have to stop competing with AI on "knowledge retrieval" and start leading AI through "creative judgment." The goal isn't to be a walking encyclopedia anymore; it’s to be the conductor of an orchestra of expert-level models. To help you understand this shift, I’ve broken down this performance leap and 16 other staggering trends in my new article on Substack. We have never seen a technology go from "random guessing" to "superhuman" in half a decade. You can’t afford to ignore this data. Read the full article and see the 17 charts that define the future of work: greatleadership.substack.com/p/ai-technolog…

We’ve all been taught that as leaders, our job is to provide a roadmap with every turn mapped out. But in the current climate, that old-school approach is actually backfiring. CHROs are facing a workforce that feels a massive "intensity" and "fear" regarding the pace of AI. The natural executive reflex is to launch a rigid corporate "program" or promise certainty to calm the room. Here’s the hard truth: promising certainty in an unpredictable platform shift is a trap. When you pretend to have all the answers, but the landscape shifts again next week, you erode trust and increase the "proximity" or distance between leadership and employees. You can't re-skill a team that is too paralyzed by fear to become "beginners" again. I sat down with Amy Coleman, Executive Vice President and Chief People Officer at @Microsoft, at Future Ready Leadership, and she shared a powerful pivot: Trade certainty for clarity. Instead of another polished HR program, Amy is using "listening systems" to name the fear directly and create a shared language for the discomfort. This concept of Adaptive Leadership is just the tip of the iceberg of what we discussed regarding the future of work and culture at Microsoft. Stay tuned for the full episode with Amy Coleman, dropping this March 23!

The "Great AI Disconnect" is currently unfolding as Wall Street rewards record valuations and a 250% surge in AI capability while enterprises grapple with a 40% headcount deficit and massive skill gaps. While @Meta recently utilized a potential 20% workforce cut as a "trial balloon" to add $160 billion to its market cap, many companies are simply bolting new technology onto legacy steam-era workflows. We are seeing a dangerous trend where enterprise AI spending rises by 44% while employee learning time drops by 15%, leaving 60% of knowledge workers with zero formal training. To bridge this gap, it requires moving beyond "using AI" to "managing AI" through judgment and ethical guardrails. Check out the full episode at the link below to learn how to navigate the 1895 electrification lag and avoid the organizational mistakes that are currently sabotaging the AI productivity boom. podcasts.apple.com/us/podcast/met…

This chart is the ultimate "reality check" for anyone who thinks they still have a few years to "wait and see" how AI pans out. We are wired to think linearly. When the PC came out in 1981, it took years to change how we worked. When the internet went commercial in 1995, it was a slow burn for nearly a decade. Naturally, we assume Generative AI will follow that same "gradual" path. We think we have time to form committees, run two-year pilots, and wait for the dust to settle. The dust isn't going to settle because it's moving too fast to land. Look at the steepness of that gold line. In just three years, Generative AI has reached 55% adoption among U.S. adults. To put that in perspective: at the same three-year mark, the PC was only at 15% and the Internet was at 20%. AI is diffusing through our society and economy three times faster than the technologies that defined the last two generations. Why? Because the barrier to entry is zero. You didn’t have to buy a $3,000 beige box or install a phone line; you just had to type a question into a browser. This isn't just a new tool; it’s a total collapse of the adoption curve. If you are still "evaluating" your AI strategy while over half the workforce is already using it, you’re becoming obsolete in real-time. We have to stop treating AI as a "future trend" and start treating it as a current utility. The gap between the technology’s speed and your organization’s adaptation is the single biggest risk to your career and your business. To help you close that gap, I’ve analyzed this adoption curve alongside 16 other critical charts in my latest article on Substack. The window for being a "fast follower" is closing. See the full data-backed picture of where we are headed here: greatleadership.substack.com/p/ai-technolog…

Are you leading the AI era with a playbook from 1995? 86% of CHROs admit their role is changing dramatically, yet only 5% feel "very prepared" for an AI-led future. We are asking leaders to navigate an existential shift in the human-organization relationship using a toolkit designed for a fundamentally different era. The problem isn't the people but the structural design of the role itself. When a CHRO is brought in to "manage the communication" rather than "shape the decision," the organization is structurally positioned to fail. To unlock the 70% of AI value that comes from people and process transformation, we must close these five critical gaps. Check out the 5 Structural Gaps below and tell me: Which one is the biggest bottleneck in your company?

In 2000, one "GFLOP," the unit we use to measure a billion mathematical operations per second, cost $47,000. Today? It costs two cents. Most of us are suffering from what I call "the normalization of the extraordinary." Because technology changes gradually (a new phone here, a faster app there) we’ve lost the ability to see the cumulative picture. We see AI today and think it’s a sudden miracle when it’s not. It’s actually the result of an economic wall finally being torn down. Why didn't AI happen in the 90s? The math was already there. The neural networks that power ChatGPT were invented decades ago. But as I share in my latest Substack article, the "key" was missing: Economics. In the year 2000, training a large neural network would have cost tens of millions of dollars in compute alone. It was theoretically possible but practically impossible. Fast forward to today: that cost has dropped by a factor of more than two million. When the cost of "raw horsepower" collapses from the price of a luxury SUV to the price of a stick of gum, the "theoretically possible" becomes "practically inevitable." Most organizations are still making linear plans for an exponential reality. If your strategy assumes "gradual change," you aren't looking at the data. I’ve pulled together data from @EpochAIResearch, Stanford, and the Federal Reserve to build 17 charts that act as a "photo" of how much the world has actually changed. This Cost Per GFLOP chart is just one of them. My full Substack article covers everything from the infrastructure revolution to the five exponentials driving AI today. We’ve never seen technology go from "useless" to "superhuman" in five years, until now. Stop guessing and start looking at the numbers. Read the full deep dive with all 17 charts here: greatleadership.substack.com/p/ai-technolog… Your move.

@chamath There's a lot of talk in my CHRO group about the ROI of AI. If you're ever up for joining us in one of our monthly CHRO sessions or coming on my podcast to chat about this let me know. It would be great to have you.

There’s no single version of the future. The next 18 months could unfold very differently depending on one key variable: how quickly companies move from individual AI use to real organizational restructuring. That’s why I frame the near-term outlook around 3 scenarios. Scenario A: Gradual Squeeze The most likely path. Companies quietly hire fewer entry-level workers while change moves slowly due to inertia, failed pilots, and messy implementation. The labor market weakens gradually, especially across customer support, back-office work, junior analytics, and routine content production. Scenario B: Agent-Driven Acceleration The scenario many leaders underestimate. A few high-profile AI deployments show credible public results. Boards notice. CEOs want their own version of the same playbook. What starts as experimentation quickly becomes a rush around agents, workflow automation, and headcount reduction across support, finance ops, HR ops, legal ops, and content-heavy roles. Scenario C: Reality Check / Plateau The most optimistic path. AI reliability issues persist, enterprise projects stall, and companies realize the tools aren’t yet strong enough to justify large-scale restructuring. Some AI-framed layoffs quietly reverse, slowing the disruption. The real value of these scenarios is that it teaches us to identify the trigger points that show which direction the market is moving. Here are a few I’m watching: ▪️Companies reporting Salesforce-style AI results with metrics ▪️Entry-level hiring falling sharply in knowledge work ▪️AI agents moving from pilots into core workflows ▪️No quiet rehiring after AI-attributed layoffs ▪️Continued pressure on freelance and contract work Scenario thinking prepares leaders before the data catches up. By the time the narrative is obvious, the advantage is gone. But one conclusion already holds across all three scenarios: entry-level knowledge work won’t return to its old form. AI can now handle much of the boilerplate work junior employees once used to learn the business. That means the career ladder itself is changing. The real question is: will leaders redesign the career ladder or allow it to quietly disappear? Full breakdown in the paid edition of my Substack: greatleadership.substack.com/p/the-ai-trans…

If you’re cutting headcount to fund an AI subscription, you’re likely just trading a predictable payroll for a volatile utility bill you haven't even seen yet. This "Substitution Playbook" is a financial myth because agentic AI doesn't wait for prompts; it runs autonomous loops that can burn through $270,000 in annual compute costs, matching the salary of the senior developer it supposedly replaced. Beyond the API, hidden enterprise costs for data engineering and security typically inflate the total price by 200% to 400%, while human oversight requirements mean enterprises are often assigning multiple full-time employees just to supervise a single autonomous node. Right now, most organizations are flying blind by hollowing out their workforce to fund massive IT infrastructure expenses that never even appear on the HR ledger. By the time the true cost of these "digital workers" hits the bottom line, you may have already lost the human judgment layer required to govern them. Instead of chasing a substitution mirage, shift your strategy toward an augmentation model where AI compresses routine pattern matching to amplify rather than replace your most experienced talent. I dive much deeper into these hidden economics and the "Radiologist Precedent" in the full episode here: podcasts.apple.com/us/podcast/the…

Is toxic leadership something people choose… or something they slowly become? The recent news around René Redzepi and the culture inside @nomacph has triggered a necessary conversation about leadership under extreme pressure. In this clip, Redzepi reflects on something deeply uncomfortable: becoming the kind of leader he once criticized. As a young cook traveling the world, he saw angry chefs leading through fear and told himself he would never become like them. Then he opened his own restaurant at 25. Suddenly, everything was on the line. Every guest mattered. Every mistake felt catastrophic. That’s when leadership pressure reveals something most people never prepare for: you’re not just managing others, you’re managing yourself. Without the tools to handle that pressure, even the best intentions can turn into control, anger, and fear. This is how toxic cultures are often born. Not from evil intentions, but from unprepared leaders under extreme stakes. In my work on Leading with Vulnerability, I often say this: Self-awareness without leadership training is a dangerous gap. If leaders don’t learn how to regulate themselves, pressure will eventually do the managing for them. The real question for leaders today isn’t whether pressure exists. It’s this: When the pressure hits, who do you become?