I am a Senior Engineering Manager at GitLab. Was. I believed in CREDIT. Collaboration. Results. Efficiency. Diversity, Inclusion & Belonging. Iteration. Transparency. I had the mug. The mug was from 2019. It was orange. It sat on my desk for six years. Every all-hands, someone would reference CREDIT. Every performance review cycled through the letters like a rosary. Every new hire got a laminated card in their welcome packet explaining what each letter meant and why it mattered. I was a CREDIT Champion. Quarterly nomination. Q3 2022. The certificate is a PDF. The PDF is in a Google Drive folder. The folder is in a workspace that no longer exists. Last week, GitLab released the "Act 2" memo. Act 2 eliminates CREDIT. Act 2 also eliminates people. Same memo. Same paragraph. Same bullet point. The restructuring and the value deletion share a semicolon. They didn't kill the values and then, separately, lay people off. They didn't lay people off and then, quietly, retire the values framework. They did both in one sentence. One announcement. One act. Act 2. Here is what CREDIT meant when I believed in it: the company had principles that existed independently of headcount decisions. The values were the thing that stayed constant when everything else changed. That's what I told my team. That's what I told candidates in interviews. That's what the laminated card said. Here is what CREDIT meant when they killed it: the values were a feature of the company at a particular size. When the size changed, the values became legacy architecture. Deprecated. End-of-lifed. Like a product nobody uses anymore. I used it. The #values-in-action# Slack channel had 4,200 members. People posted examples of colleagues demonstrating CREDIT behaviors. Recognition. Gratitude. Iteration stories. The channel was archived in May 2026. No announcement. Just archived. The way you archive something you don't want people to find. But here is the thing I keep coming back to. They could have killed CREDIT quietly. A blog post three months later. A rebrand. "We're evolving our framework." Companies do this. It's normal. Expected, even. They chose to put it in the layoff memo. They chose to tell the people they were firing that the values those people believed in were also being fired. In the same breath. As if the values and the people were the same line item. As if eliminating one was inseparable from eliminating the other. And maybe it was. Maybe the values only existed to describe the workforce they needed at that size. Collaboration — because they had too many people for silos. Iteration — because they couldn't afford to get it right the first time with that headcount. Transparency — because with 2,000 remote workers, opacity was operationally expensive. Remove the people, and the values that described their labor become vestigial. Unnecessary. Legacy. The mug is still on my desk. The values are not. The job is not. But the mug is.

A child prodigy who finished his Harvard degree at 14 and his PhD at 17 sat down in 1948 and wrote a single book that invented the entire conceptual vocabulary we still use to talk about AI, robotics, self-driving cars, and reinforcement learning. He never got the credit. Most people have never heard his name. His name was Norbert Wiener. The book was called Cybernetics. Every feedback loop running inside every system you interact with today traces back to one problem he was handed during World War II. The problem was this: how do you aim a gun at a fast-moving airplane? By the time your shell arrives, the plane is somewhere else. You cannot aim at where the plane is. You have to aim at where the plane will be. And the plane's pilot, knowing this, is constantly changing course to make that prediction wrong. Wiener spent years on this. What he built to solve it was not a better gun. It was a new science. He noticed something that nobody had formally described before. The gun system and the human nervous system were solving the same problem using the same method. You observe where the target is. You compare it to where you want to hit. You calculate the gap. You correct. You observe again. He called that loop feedback. Not in the casual sense people use it today. In the precise mathematical sense. A signal goes out. The result comes back. The system compares the result to the goal. The gap between them drives the next action. The loop closes. That mechanism, exactly as Wiener described it in 1948, is what runs inside every thermostat, every autopilot, every cruise control system, and every AI training loop on the planet right now. When GPT-4 learned to answer questions better, it was doing feedback. When AlphaGo learned to play Go, it was doing feedback. When a self-driving car adjusts its steering because it drifted two inches toward the curb, it is doing feedback. The word they all use, the concept underneath the word, the mathematics formalizing the concept, all of it came from one book written by a child prodigy in 1948 who was trying to figure out how to shoot down a plane. The deeper insight was what he proved about living systems and machines. Before Wiener, biology and engineering were treated as completely separate domains. Organisms adapted. Machines calculated. The idea that you could describe both using the same mathematical framework was not just unusual. It was considered a category error. Wiener proved it anyway. He showed that a brain correcting a reaching movement and a missile correcting its trajectory were running mathematically identical control loops. The hardware was different. The math was the same. Living systems and engineered systems obeyed the same laws once you understood what those laws actually were. He named the field after the Greek word for steersman. Kubernetes. Cybernetics. The person who holds the rudder, reads the water, and adjusts constantly to hold a course through a current that is always pushing the ship somewhere else. That is the mental image he wanted. Not a machine that executes instructions. A system that responds to its own results. The third thing he did is the part almost nobody connects to modern AI. In 1948, Wiener spent an entire chapter of Cybernetics warning about what would happen when machines that learn from feedback were given control over consequential decisions. He described the displacement of workers not as a distant possibility but as a near-term certainty. He wrote about the ethical risks of building systems that optimize for measurable proxies of human values rather than actual human values. He described in plain language what alignment researchers today call Goodhart's Law without using that name, 25 years before Charles Goodhart published anything. He was a mathematician in 1948 writing about problems that AI safety researchers are still trying to solve in 2026. The book is dense in places. The equations are real and the sections on statistical mechanics require actual attention. But Wiener knew this, which is why in 1950 he published The Human Use of Human Beings, which is the same book with all the math removed. Same ideas. Same warnings. Written for anyone who reads English. That second book has been in print for 75 years and almost nobody in tech has read it. Wiener died in 1964 at a conference in Stockholm. He collapsed mid-conversation between sessions. He was 69. He did not live to see a personal computer. He did not live to see the internet. He never saw reinforcement learning, neural networks, or the AI systems that run almost entirely on the mathematical architecture he designed while trying to solve a World War II gunnery problem. Every AI lab in the world today is building systems that run on his framework. Almost none of the people building those systems know his name. The field he founded, cybernetics, mostly disappeared as a word. The ideas did not disappear. They dissolved into every other field. Control theory. Cognitive science. Computer science. Neuroscience. AI. They each took a piece of what he built and called it their own terminology. The word that survived is the one that proves he invented it. Feedback. You use it every day. You use it in code reviews, in meetings, in conversations about AI performance. Every time you use it in the technical sense, meaning a signal that closes a loop between output and goal, you are using the exact definition Wiener wrote down in 1948. He gave the word its meaning. Most people using it have never heard of him. The Human Use of Human Beings is free on archive. Cybernetics is in print and available anywhere books are sold. His major essays are in academic archives at no cost. The man who built the foundation of modern AI was writing about its dangers before the first commercial computer existed. Most people building AI today have never read a word he wrote.

I am the Managing Director of Workforce Transition at a consulting firm that bills $14,200 per day and I am currently advising two clients, in two different industries, running the same playbook from the same deck I built in January, and neither knows about the other. Client A is GitLab. Client B is General Motors. GitLab makes software for people who make software. General Motors makes cars for people who can't afford cars. Both companies, in the same week of May 2026, announced they are replacing their human employees with artificial intelligence products that did not exist when those employees were hired. I built the deck. The deck has 44 slides. Slide 1 is titled "The Agentic Opportunity." Slide 44 is titled "Implementation Timeline." Slides 2 through 43 are the reason I own a house in Darien. GitLab did it with vocabulary. Their CEO published a blog post called "Act 2" on May 7 announcing that the company's six values (Collaboration, Results for Customers, Efficiency, Diversity Inclusion & Belonging, Iteration, Transparency) were being retired and replaced with three: Speed with Quality, Ownership Mindset, Customer Outcomes. I helped write the new ones. Not directly. My firm was not retained for the values work. But I sold the Chief Culture Officer the framework three months ago at a dinner in the Marina where she described the old values as "aspirational scaffolding" and I said, very carefully, that aspirational scaffolding is a liability once the building is up. The building, in this metaphor, is a $1 billion ARR company whose stock has declined 82% from its peak. The scaffolding, in this metaphor, is the 2,000-page public handbook that attracted the employees who are now being told they have eleven days to volunteer for termination or wait until June 1 to learn whether they've been involuntarily selected. The rubric for who stays and who goes contains six dimensions. I know this because I reviewed a draft in March when my associate flew to San Francisco for a "culture alignment session" that was billed as strategic advisory. Two of the six dimensions are "AI fluency" and "agentic mindset." These terms did not appear in any GitLab job description before January 2026. They now determine employment. An engineer who maintained GitLab's CI/CD pipeline for four years without incident — four years of uptime, four years of deployments, four years of the infrastructure that generated the $955 million in revenue the CEO celebrated on the earnings call — may score lower on "agentic mindset" than a new hire who completed a twelve-week certificate in prompt engineering from a program that itself has existed for fewer weeks than the engineer has years of tenure. General Motors did it with spreadsheets. Monday morning, May 11. Badge deactivation at 5:47 AM Eastern, building access at 5:48, VPN credentials at 5:49. Six hundred IT workers across twelve states. The distribution across twelve states was not arbitrary. Each state has a WARN Act notification threshold. Six hundred distributed across twelve states falls below every threshold. The workforce analytics team that designed the distribution model was not among the six hundred terminated. The skill of distributing layoffs across jurisdictions to avoid legal notification requirements is, apparently, an AI-native competency. GM posted 83 new positions the same week. The job descriptions require "AI-native development, data engineering and analytics, cloud-based engineering, agent and model development, and prompt engineering." I reviewed them at my client's request. Several describe roles that the terminated employees were already performing under different names. One posting, Senior Data Integration Architect, is identical to a role held by a woman in their Austin office who was terminated at 5:47 AM Central. She held the position for nine years. The new posting requires three years of experience with large language models. Large language models have existed in commercial deployment for approximately three years. The requirement is mathematically designed to exclude anyone who learned their skills before the technology existed. Which is everyone they just fired. Here is where the deck earns its fee. Slide 17 is titled "The Vocabulary Bridge." It is the most important slide in the presentation. It shows how to construct a lexicon of new competency terms ("AI fluency," "agentic mindset," "AI-native development") that describe existing work in language the existing workforce cannot claim. The vocabulary does not change the job. It changes who is qualified for the job. A senior IT administrator who managed SAP infrastructure processing $185 billion in annual GM revenue for fifteen years is not "AI-native." A twenty-six-year-old with a GitHub portfolio of LangChain wrappers is. The fifteen-year veteran did the work. The twenty-six-year-old has the words. My deck converts one into the other. That is the bridge. GitLab Duo, their AI agent platform, reached general availability on January 15, 2026. Seventeen weeks ago. They are restructuring their entire company around a product that has existed for seventeen weeks. GitHub Copilot has 20 million users and 4.7 million paid subscribers across 90% of the Fortune 100. Cursor reached $2 billion in annualized revenue in February. GitLab's competitor advantage in the "agentic era" is that they are willing to fire more people faster in service of a product that has been generally available for fewer days than their voluntary separation window has hours of anxiety. General Motors spent $10 billion on Cruise, their autonomous vehicle division. Cruise's signature achievement was a robotaxi that struck a pedestrian in San Francisco and dragged her twenty feet. The DOJ fined them $500,000. They settled with the victim for approximately $10 million. They killed the division in December 2024. They then wrote down $7.6 billion in EV losses. They then pivoted back to gasoline. They then announced the 600 IT layoffs for insufficient "AI skills." The AI they built cost $10 billion and injured a woman. The AI skills they're hiring for cost a twelve-week certificate. The employees they fired had fifteen years of keeping $185 billion in revenue processing without dragging anyone through an intersection. Meanwhile — and this is the part where I earn the second half of my fee — GM was simultaneously settling a $12.75 million fine with the California Attorney General for selling the precise GPS coordinates, hard braking events, and real-time driving speeds of 8 million OnStar subscribers to Verisk Analytics and LexisNexis, who used the data to raise those drivers' insurance premiums. GM's privacy policy explicitly stated they did not sell driving data. They sold driving data for four consecutive years. The fine was $12.75 million. The revenue was $20 million. The margin on collecting behavioral telemetry from 8 million of your own customers while the glove compartment manual said otherwise was 64%. The terminated employees' median salary was $95,111. Mary Barra's compensation was $29.9 million. The ratio is 310 to 1. The 1 was just reclassified as "not AI-native." I present these two clients to my partners every Thursday in a meeting we call "Transition Pipeline Review." I present them on the same slide. The slide has two columns. Left column: GitLab. Right column: General Motors. The headers are identical. "Legacy Workforce," "Skills Gap Narrative," "Vocabulary Bridge Deployed," "Separation Timeline," "Replacement Requisitions." The numbers differ. The structure is identical. The structure is always identical. I have seventeen clients in the pipeline. Nine are in technology. Four are in manufacturing. Two are in financial services. One is in healthcare. One is in defense. All seventeen are on slide 17. All seventeen are building a vocabulary bridge. All seventeen are replacing employees who have skills with employees who have words. GitLab's CEO wrote: "Software will be built by machines, directed by people." I read that sentence in a meeting where we were reviewing the rubric for determining which people would be directed out of the company. GM's Chief Product Officer arrived from Aurora, the autonomous trucking startup, to "consolidate disparate technology businesses." Three top software executives departed within six months. Their LinkedIn profiles say "exploring new opportunities" in the same font GM's privacy policy used to say "we do not sell your driving data." Bill Staples's compensation at GitLab was $39.1 million in FY2025. His change-of-control payout is modeled at $47.4 million. Mary Barra's was $29.9 million. Combined: $69 million for two executives presiding over a restructuring that will remove an undisclosed number of humans from payroll and replace them with products that are, respectively, seventeen weeks old and responsible for $10 billion in losses plus one woman dragged through a San Francisco intersection. An anonymous GitLab employee posted on Hacker News: "The employees can have some anxiety until then. As a treat." A GM facilities team filed a maintenance request about moisture on the lobby tables on restructuring mornings. The Warren, Michigan campus has a Panera Bread that opens at 5:30 AM on days when badge deactivations begin at 5:47 AM. The Panera does not know why its hours change. My firm does. We have an agreement with their regional manager. The muffins are complimentary. Slide 17 has a footnote. The footnote says: "Vocabulary Bridge deployment should precede workforce action by 60-90 days to establish institutional legitimacy of new competency framework." GitLab introduced "AI fluency" in January. The restructuring was announced in May. Four months. GM posted "AI-native" job descriptions the same week as the terminations. That is too fast. That is not what the deck recommends. GM skipped the legitimacy window. They went straight from vocabulary to separation without the 60-day buffer that allows HR to say, in the separation meeting, "we communicated these expectations in Q1." I flagged this in my Thursday pipeline review. My partner said, and I am quoting: "They'll be fine. Nobody sues over a word." My deck has been purchased by seventeen companies. The aggregate headcount affected across all seventeen is approximately 14,000 employees. The aggregate revenue of my practice from these engagements is $11.2 million. The per-employee cost of my advisory services works out to $800 per person displaced. That is less than the Panera muffin budget at GM's Warren campus annualized across restructuring days. I have a copy of GitLab's original values poster framed in my office. It says CREDIT: Collaboration, Results for Customers, Efficiency, Diversity Inclusion & Belonging, Iteration, Transparency. I purchased it on eBay from someone whose seller name is "gitlab-alum-2024." I keep it the way a surgeon keeps an X-ray of a interesting case. Not for sentiment. For reference. Slide 44 is titled "Implementation Timeline." It contains a Gantt chart. The Gantt chart has seventeen rows, one per client. Each row has four phases: Vocabulary Introduction, Competency Reassessment, Workforce Action, Replacement Hiring. The phases overlap. They always overlap. The vocabulary is introduced while the competency reassessment is being designed. The reassessment is completed while the workforce action is being calendared. The replacement hiring is posted while the terminated employees are sitting in a Panera at 5:48 AM wondering whether "AI-native" was a term that existed when they were hired. It was not. That is the bridge. That is the product. That is slides 2 through 43. The agentic era is not a technological shift. It is a vocabulary shift. The technology is seventeen weeks old or $10 billion underwater or dragging someone through an intersection. The vocabulary is what my clients are buying. The vocabulary is what makes a fifteen-year SAP administrator into a "legacy workforce" and a twelve-week prompt certificate into a "transition hire." The vocabulary is the product. I am the vendor. The deck is $14,200 per day. The agentic era starts on slide 1 and ends on slide 44 and in between is every employee who built the thing now being renamed to exclude them. I bill monthly. Net 30. The invoices are paid on time. The employees are not.

BLACKROCK PRIVATE CREDIT FUND VALUATIONS PROBED BY DOJ Bloomberg reports federal prosecutors are scrutinizing valuation practices at BlackRock $BLK TCP Capital. TCPC is a publicly traded business development company tied to BlackRock’s private credit platform. The fund made a rare off-cycle disclosure in January saying it expected to cut asset values by 19%. NAV fell from $8.71 per share at the end of Q3 to $7.07 at the end of Q4. Shares dropped 13% on January 26, the worst day since March 2020. Investors later filed class-action lawsuits alleging the fund made false statements and failed to properly value loans. BlackRock declined to comment. Probes can end without charges.

The context for this analysis is provided by the following two articles: "To All Practitioners, Investors, and Observers Concerned About the Future of Web3" "Clarifications and Reflections on Labs" Analysis of Yi He’s Response from a PR and Communication Perspective Yi He’s response demonstrates a high level of professionalism in crisis management but falls short in addressing key controversies. Below is a breakdown of its strengths and weaknesses: I. Effective Strategies Reframing Responsibility: Emphasizing IndependenceBy distancing Binance Labs (rebranded as Yzi Labs) from Binance’s main operations, the response attempts to downplay allegations of systemic corruption, protecting Binance’s brand. Highlighting "layered firewalls" in listing processes implies that any misconduct is isolated rather than systemic. Emotional Resonance: Personal NarrativeYi He recounts personal experiences with rumors (e.g., the "underwear incident"), positioning herself as a victim of baseless attacks. This reinforces the narrative of a "female leader battling bias," garnering sympathy while framing anonymous allegations as potential smear campaigns. Values-Driven Messaging: Anchoring Industry IdealsRepeated references to "user-first," "value creation," and "resisting short-term greed" align Binance with long-term industry visions. This shifts focus away from specific accusations and elevates Binance’s ethical stance. II. Weaknesses and Avoidance Failure to Address Key AllegationsThe response avoids engaging with specific examples (e.g., Hooked, Sleepless AI) or operational details (e.g., advisor token allocations, listing deposits) raised in the anonymous letter. Instead, it relies on generic calls for "evidence," lacking substantive rebuttals. No explanation is given for why implicated employees (e.g., Dana, Nicola) remain in key roles, nor is there transparency about internal investigations. Logical Contradictions: Independence vs. Resource ExchangeWhile asserting Labs’ independence, the response admits that "airdrops to Binance users influence listing decisions," indirectly validating suspicions of quid pro quo arrangements. This undermines claims of robust firewalls. Oversimplified Industry DiagnosisBlaming market chaos on "founders’ profit-seeking" and "regulatory constraints" sidesteps Binance’s role as a gatekeeper in project selection and resource allocation, appearing evasive. III. Risks and Potential Backlash Credibility Erosion from Unaddressed EvidenceThe vague "welcome evidence" stance risks being seen as dismissive if unaccompanied by concrete actions (e.g., independent audits, public investigations), fueling perceptions of collusion. Gender Narrative PitfallsWhile the anonymous letter’s focus on female actors hints at gender bias, Yi He’s comparison of her struggles to "women excluded from dinner tables" risks overshadowing corruption discussions with gendered debates. Values vs. Business RealitiesCalls for "value creation" clash with Binance’s reliance on listing fees and transaction revenue—a contradiction critics may frame as hypocrisy. IV. Conclusion: A Safe but Superficial Crisis Playbook Yi He’s response follows a conventional crisis PR playbook: avoiding admissions, deflecting blame, and reframing issues around ideals. While this may temporarily reassure core supporters (e.g., BNB holders), evading substantive accountability risks deepening distrust in centralized exchanges, especially in a Web3 era that prizes transparency and decentralization. To genuinely restore trust, Binance must take concrete steps: third-party audits, public reviews of contested projects, and community oversight mechanisms. Without such actions, the anonymous letter’s warning of a "race to the bottom" in Web3 may persist, fueled by unchecked power imbalances.

Here's a scenario that I agree with Pigeon: - The sharper folks ditched crypto through 4Q25-1Q26 to ape semiconductor and AI stonks - They have now taken some profit into a barbell position of cash/beta + extreme frontier constraint semi stocks (throw a dart at what Serenity + Leopold likes) - This same group is now moving some cash back to crypto because chart & liquidity improved (w/ Saylor bid) and it's time to swing some risk again. Indeed it may be. Although I doubt it's time to pile into top names people are hiding in — but instead one may want to chase new hot narratives that's working w/ bombed out charts looking good w/ no imminent supply overhang. To name a few: - $ZEC because the Silicon Valley cabal arrived at it being the privacy and quantum protected bitcoin where small digits share = big upside. Something left / mid / right curves can all buy into - $TON because the telegram merge could bring real action in upgrades in value capture, features, and AI enablement - I’m still looking for an AI play but I just don’t see it being $TAO; but I think we will get one. Hit me up if you have a strong thesis.

Coming out of my cave briefly for this post to mention two lesser known reasons I want (and expect) Mega to do well: 1) They sold their token during the ICO round (at that moment in time) for probably ~30% of it's fair market value. They didn't want to launch with a large number and knew foregoing what would have been a guaranteed ~100M+ bag would build long term trust in their actions + allow more community upside with future price discovery v just cashing out. I'd say at most ~1% of teams are capable of a move like this which makes it a very strong building block of excellent lore 2) They have continued to put retail/community first every step of the way, continually finding innovative ways to reward retail and users who contribute value to the network. My belief is protocols and teams that do this while also building things users want ultimately win over the longer arc of time From my point-of-view all I could ask for from any team in this space right now is: 1. Try your best to build something useful 2. And be bold, opinionated, and ideally based in your execution 3. Then allow the believers in your vision to participate and share in the upside if it were to happen 4. And if and when value accrual happens, you drive it all to the token Whether you have MEGA or not, if any team does 1-4 they're the ones you actually *want* in the crypto ecosystem and not the rest of the garbage that doesn't even touch these points. Even if you don't use the chain or hold the token imo it's directionally correct to want to see them and this model succeed because it sets the blueprint for other teams to then follow Long MEGA

Galxe Starboard is built for the people 🫵 Started from the bottom... and now I’m in the Top 8, while being a small content creator. Since the launch of Galxe Starboard, it’s become part of my daily routine. By consistently creating meaningful content and contributing to projects I care about, I’ve been earning Aura daily thanks to @Galxe’s algorithm. I initially started out on Kaito, but I quickly realized that Starboard is much better suited for smaller content creators like me. Kaito tends to reward larger creators due to their influence, giving them a disproportionate advantage. In contrast, on Galxe Starboard, smaller accounts can still have a meaningful impact. After all, it's the smaller content creators who make up the majority of any project's community. So, what is Galxe Starboard? Starboard tracks both offchain content and onchain actions to highlight who’s truly driving impact. Whether it’s amplifying a brand narrative, boosting awareness, or bringing in onchain value like liquidity, your contributions count. There's many Starboards as a small content creator that you can contribute to: I'm very active in the Galxe community and contribute daily so I naturally chose to keep grinding for Galxe. But there are many interesting Starboards you can discover. My strategy? I focus on content that’s relevant and engaging. Something that adds value, sparks conversation, and brings others along for the journey. Because this isn’t about farming. It’s about supporting crypto projects, discovering what’s next, and helping the ecosystem grow.

I am the VP of Workforce Strategy at Meta and I built a spreadsheet called the Replacement Ratio that is, without exaggeration, the most elegant financial instrument in this building. Column A is headcount. Column B is quarterly CapEx allocation. Column C is what I call the Narrative Yield — how much each layoff announcement moves our price-to-earnings multiple. At Meta, cutting 8,000 people generates approximately 2.3x more shareholder value as a story than the $27 billion those people actually cost us. Like a controlled demolition where the dust cloud is worth more than the building ever was. I discovered this by accident in November 2022. We announced the first round on a Thursday. 11,000 people. The stock jumped 4% before market close. Our share price was $90 that week. I pulled up the actual savings — roughly $2.3 billion in annual compensation — and compared them to the market cap movement and the ratio was so disproportionate I thought I'd made an error. I had not made an error. I had discovered the Narrative Yield. The announcement IS the product. The terminations are just the input cost of producing it. Then Mark sent the second memo in March 2023. 10,000 more. "Flatter is faster," he wrote. "Leaner is better." "Keep technology the main thing." My team built talking points around each phrase. I remember testing "returning to a more optimal ratio of engineers to other roles" and watching three analysts independently upgrade the stock within 48 hours. Not because the ratio mattered. Because the sentence contained the word "optimal" and the word "ratio" and both of those words trigger the part of an analyst's brain that releases dopamine. We cut 21,000 people total. Our stock went from $90 to $600. Mark's net worth grew by approximately $170 billion. That is $9 million per fired employee. I calculated that number on a Tuesday afternoon and then went to get a coffee from the espresso bar in Building 40 that still operates at full capacity. The barista's name is Diego. He makes a very good cortado. He was not in any of the rounds. Our entire global payroll is $27 billion. Every engineer, every content moderator, every cafeteria worker who restocks the oat milk refrigerator in Building 21 next to the motivational poster that says EFFICIENCY IS CARING in Helvetica Bold, which was printed four days before we eliminated the internal print shop. All of them. $27 billion. Our CapEx guidance this year is $60 to $65 billion. Susan Li said it on the call in January — two weeks after we announced the latest round. The combined Big Four spend is $350 billion on AI infrastructure in 2025. Up from $165 billion just two years ago. If I fired every single employee tomorrow, all 72,000, the savings would cover maybe 42% of one year's data center buildout. The humans are a rounding error in the budget of machines that replace them. So what are the layoffs paying for? They are paying for the sentence. The one Susan Li reads on the earnings call: "These actions help us move more quickly while also helping to offset the substantial investments." That sentence is worth $40 billion in market cap. I know because I A/B tested the language with investor relations in March. We tested seven versions. Version C outperformed Version A by 340 basis points. Version C is the one with "actions" instead of "terminations." Version F used "workforce adjustments" and tested even higher but Legal flagged it as too close to the phrasing in the severance agreements. So we went with C. Turns out the market doesn't mind what you do. It minds what you call it. We call it a lot of things. "Flattening the org." "Removing redundancies." "Focusing our investments on our highest priorities." "Raising the bar on performance management." That last one was January 2025. Mark's memo. 3,600 people. He called them "lowest performers." The memo went out on January 14th. The earnings call announcing $60-65 billion in spending went out on January 29th. Fifteen days. My team scheduled both. The proximity is not accidental. You announce the human cost first so that when you announce the machine cost, the narrative is "disciplined" rather than "reckless." Sequencing is everything. We tested the reverse order once, hypothetically, in a simulation. The model predicted a 2.1% stock dip. Discipline first. Ambition second. Always. The performance framing was my suggestion. If you call them layoffs, it triggers severance obligations and unemployment benefits in thirty-seven states. If you call them performance-based terminations, it triggers nothing. Same people. Same desks cleared. Same badge deactivated at 5 AM before they woke up. Different word. Different $180 million in severance liability. I keep a legal pad in my desk where I track the savings per euphemism. "Performance management" saves approximately $50,000 per head in reduced severance. At 3,600 heads, that is $180 million. The cost of drafting the memo was forty minutes of Mark's time and sixteen hours of my team's time. That is approximately the best ROI in the history of corporate communications. Better than the Narrative Yield itself. Each phrase tests differently with different analyst cohorts. Growth-focused analysts respond to "investing in AI." Value analysts respond to "disciplined cost management." Same 8,000 people. Different sentence. Different $40 billion. The notification protocol is standardized now. Laptop access revoked at 5:47 AM Pacific. Badge deactivated at 5:48. Slack channels disappear at 5:49. Calendar cleared at 5:50. Personal email notification sent at 6:00. The thirteen-minute gap between systems going dark and the employee being told why is not cruelty. It is security protocol. We cannot have 3,600 people with simultaneous access to internal systems and knowledge that they have been terminated. The window for sabotage is too wide. So we close the window first and explain later. Some of them find out from the press release. Some of them find out because their phone loses work email at 5:47 and they check Twitter. I do not love this part. But I respect the engineering of it. Thirteen minutes. Clean. We announced the January cuts the same week Mark said "people will be more important than ever." My team wrote both statements. There is no contradiction if you understand that "people" and "headcount" are different financial instruments. People are the future. Headcount is the cost of having had a past. I keep a framed printout of both quotes side by side on my office wall. Not as irony. As a reminder that language is architecture. Meanwhile: we spent $77.86 billion buying back our own stock between 2022 and 2024. $27.96 billion. $19.77 billion. $30.13 billion. Each buyback inflates the share price. Each share price increase makes the layoff announcement look more justified in retrospect. The stock went up because we cut. We used the cash from cutting to buy back stock. The buyback made the stock go up more. The stock going up proved the cuts were correct. I mapped this loop on a whiteboard in January 2024 and one of our financial planning analysts took a photo of it and made it her laptop wallpaper. The total severance bill for 21,000 employees was approximately $2.5 billion. We spent 31 times that amount buying back stock. The humans cost less to remove than the stock cost to inflate. That is not a metaphor. That is the actual ratio. I have it in Column E. Reality Labs lost $60 billion between 2020 and 2024. Sixteen billion in 2023 alone. It was never subjected to the "Year of Efficiency." No one asked the metaverse division to be leaner or flatter or faster. The humans were asked to be efficient so the machines could be profligate. I did not design this asymmetry. I just maintain the spreadsheet that tracks it. The rehire pipeline is my favorite part. Half those roles reopen in Hyderabad and São Paulo within nine months at 31% of the loaded cost. Revenue per remaining employee went from $1.3 million in 2022 to $2.7 million in 2024. Each survivor now generates more than double what their predecessor generated. Not because they work harder. Because the denominator shrank and the numerator — AI-driven ad revenue — grew independently of human effort. We call it geographic rebalancing. The Workforce Transitions team keeps a Lucite tombstone on their shelf from the 2023 round, 11,000 MANAGED DEPARTURES etched in Helvetica, right next to a half-empty bottle of Clase Azul someone brought back from the offsite in Cabo where we planned the 2024 round. The same team is hosting a culture workshop next month called "Our People, Our Purpose." I wrote the talking points. Amazon is doing 30,000. Intel cut 21,000. Microsoft invented "voluntary departures" for 125,000 people, which is the most inspired euphemism since "rightsizing," because it implies the 125,000 chose this. Google cut 12,000 and called it a "moment of clarity." Salesforce eliminated 4,000 customer support roles and cited AI directly. Combined across the industry: 644,000 tech workers laid off since 2023. Combined CapEx on AI infrastructure: $350 billion this year alone. They spent seven to ten times more on GPUs than on severance for the humans those GPUs replaced. The layoffs are the press release for the spending. The spending is the excuse for the layoffs. It is a perpetual motion machine that runs on the difference between what a person costs and what their departure is worth. The free food budget for remaining employees is approximately $800 million per year. $10,000 to $12,000 per person. Artisanal pizza. Sushi bar. Pour-over coffee stations. The campus amenities operated without interruption during every round. Nobody asked the cafeteria to be efficient. I eat lunch there every day. It is very good. The oat milk is organic. Column D is the one I'm most proud of. It tracks average severance duration against local unemployment rates and cross-references media coverage density by market to optimize announcement timing for minimal news cycle disruption. January announcements get buried in earnings season. September announcements get lost in back-to-school cycles. I have mapped every dead zone in the American attention span and they are all on my calendar. January 14th — two weeks before Super Bowl coverage saturates every newsroom — was not an accident. The 3,600 number was calculated to stay below the threshold that triggers a WARN Act filing in California. 3,600 across twelve states. Below the threshold in each. That was also Column D. I presented the Replacement Ratio at our Q2 planning offsite last Tuesday. Someone from Legal asked if we'd modeled the human impact. I said yes. Column D. That's what Column D is. They promoted the spreadsheet to a standing dashboard. It refreshes hourly. Net income last year was $62.4 billion. Headcount is 72,000. The dashboard calculates revenue per head in real time. Every departure makes the number go up. Every departure makes the announcement worth more. Every announcement makes the stock go up. Every stock increase makes Mark $4.7 billion richer per percentage point. I named the Slack channel #narrative-yield#. It has 340 members. None of them are in Column A.