I'm a cardiologist. Senator Lindsey Graham is dead at 71, and I'll be honest with you — my gut had a reaction before my training did. A fierce fighter for this movement. A relentless hawk on Putin and Iran. A true friend of Israel. Dead within a day of flying home from a war zone. In an era when we've watched two attempts on President Trump's life, when we buried Charlie Kirk, when political violence stopped being hypothetical — you'd have to be asleep not to feel the question rise up. So I'm not going to insult you by pretending the thought doesn't cross a reasonable mind. It crossed mine. But I owe you the truth from both halves of who I am — the citizen and the cardiologist. Let me give you the medicine, straight. The preliminary finding is aortic dissection due to arteriosclerotic cardiovascular disease. Here's what that means. The aorta is the largest artery in the body — the grand highway out of the heart, five liters a minute under enormous pressure. After decades of high blood pressure and hardening arteries, the wall weakens. One day the inner layer tears, blood rips through at full force, and it splits like a pipe delaminating under pressure. A Type A dissection kills up to 1-2% of its victims every single hour. When the first symptom is collapse, even perfect CPR usually loses. For a 71-year-old man with a long cardiovascular history, this is not exotic. It is one of the most common catastrophic deaths in all of medicine. It killed John Ritter. It kills thousands quietly every year. And the cruelest part — a lethally enlarged aorta usually causes zero symptoms until the day it tears. No pain. Normal EKG. A time bomb with no ticking. So the cardiologist in me says: the likeliest story, by far, is that his own aorta took him. Occam's razor points hard at nature. And the citizen in me says something too: let it all come out. The final death certificate is still pending toxicology and microscopic testing — that's routine for every case like this. So let that process be complete, transparent, and public. Not because I have evidence of anything — I don't, and I won't pretend to. But because in a moment this raw, the antidote to suspicion isn't silence. It's sunlight. Full findings, out in the open, so no family and no citizen is left to wonder. That's not weakness. That's how you honor a man who spent his life demanding accountability from everyone else. Here's what I refuse to let get lost in the noise — because it's the part that can actually save your life. Whatever the final report says about Graham, the disease that's listed already is coming for people you love. Aortic dissection is driven by high blood pressure and hardened arteries — and it builds in silence for years. But it can be SEEN. An echo, a CT, or an MRI measures the aorta in minutes. A widening aorta can be watched and repaired electively, before it ever tears, with excellent outcomes. We do it all the time. The tragedy is almost nobody gets the picture taken until the autopsy. So do this, today, in his memory: Get your blood pressure under 130/80. It's the number one driver, and most people who have dangerous levels feel completely fine. Know your family history. Aneurysm, dissection, Marfan, bicuspid valve — they run in bloodlines. If a relative had any of them, or died suddenly and unexplained, demand a screening echocardiogram and don't let anyone wave you off. If a loved one dies suddenly, ask for an autopsy. Whatever took them may be written in your DNA too. Grieve the man. Demand the full, transparent findings — that's fair, and it's American. And then go check the one artery you've never once thought about, before it stops respecting you. Rest in peace, Senator. We'll take it from here.

Just some random thoughts, I do think AI is the most disruptive technology in human history. To the level of agricultural or industrial revolution. Since Anthropic, OpenAi, XAI, and others are racing to build superintelligence. The amount of economic impact can't be measured if AI helps find cures for cancer or accelerates discovery for Quantum Computing. Or if AI end up displacing the workforce, which increases profitability for companies. The US Gov has every incentive to keep the buildout going too, as the implications from Warfare, Cybersecurity, is also immeasurable if China takes the lead. So there's likely to be incentives and subsidies to win, even if there's not enough profit derived LLM training/inference. As for sustainability, when you look upstream, $GOOGL is able to fund it majorly with their own cashflow, same with $AMZN, $MSFT. More lukewarm on $META. Very iffy about $ORCL. But I do see some bubbles forming around debt interest like $CRWV. Maybe circular valuations that's happening with OpenAI backlog agreements or $NVDA / $AMD agreements with Neoclouds to buy their GPUs. But as seen with $MSFT and having OpenAI be a major part of the backlog, it did correct off the information, so "bubbles" like that do pop despite the overall markets increasing. Definitely don't see a bubble in upstream semiconductors from $LITE to Sk Hynix though since the amount of profit they get from the buildout would likely be insane to make up for capex decreasing. OpenAI was actually my biggest fear from contagion, eg. $CRWV, $CBRS and others, but they just raised a lot. So think it will be fine for another 1 1/2 years of capex, especially if they IPO this year. I also don't think we'll get massive Fed tightening despite "predictions" since this will trigger a contagion since many of these players rely heavily on debt. And although the Fed is independent, don't think Trump would have supported someone who is against his administration goals. As for semiconductor valuations going up every day like $AMD or $MU, there's probably going to be some corrections here and there. Everything going up together is kinda unhealthy. Can't time the capex peak but just from $AVGO and other projections, it just keeps accelerating exponentially into 2028. Especially as everyone is starting to sign multi year agreements as well. OpenAI contagion / hyperscaler capex decreasing / fed tightening was what I'm looking out for, and no blaring signs of any of those yet. So I think the music will keep playing for this year at the bare minimum.

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.

Litecoin is like a traditional wine cork (fig 1) where cryptos that try to imitate real value but fall short are like the contemporary attempt to reinvent what already works (fig 2). Why is that important you ask? Wine corks are critical because they enable micro-oxygenation, a controlled exchange of oxygen that allows fine wines to age gracefully. This process softens harsh tannins and transforms fresh fruit notes into complex tertiary aromas like leather and tobacco, which cannot be achieved in completely anaerobic environments like those sealed by screw caps. Beyond chemical aging, corks provide a hermetic seal that protects wine from spoilage while being a renewable, biodegradable resource. The cork oak tree is harvested without being cut down, making cork an eco-friendly choice that supports Mediterranean ecosystems. Additionally, the ritual of opening a corked bottle remains a significant sensory experience for consumers, distinguishing premium wines from those with alternative closures. To best understand the nuances of why one thing is used more than another you must understand what hole they really fill in terms of usage. Like a fine wine, money needs the right type of cork and Litecoin fills that hole perfectly. As for the other cork, we'll leave that up to your imagination.

SpaceX/xAI has just announced that it has signed an agreement with Anthropic to provide access to its Colossus 1 supercomputer. "Anthropic plans to use this additional compute to directly improve capacity for Claude Pro and Claude Max subscribers. As part of this agreement, Anthropic also expressed interest in partnering to develop multiple gigawatts of orbital AI compute capacity. The compute required to train and operate the next generation of these systems is outpacing what terrestrial power, land, and cooling can deliver on the timelines that matter. Built from the ground up in record time, Colossus delivers unprecedented scale for AI training, fine-tuning, inference, and high-performance computing workloads. Colossus 1 features over 220,000 NVIDIA GPUs, including dense deployments of H100, H200, and next-generation GB200 accelerators. The cluster delivers extreme parallel performance for large language models, multimodal systems, scientific simulations, and generative AI at frontier scale. SpaceX is the only organization with the launch cadence, mass-to-orbit economics, and constellation operations experience to make orbital compute a near-term engineering program rather than a research concept. If engineering challenges can be overcome, space-based compute offers near-limitless sustainable power with less impact on Earth." I think this is smart for SpaceX/xAI, as it seems to have some excess capacity as Colossus, so why not make a some money on the side by renting that compute capacity out. I think we'll see some more similar partnerships soon.

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THESE ARE THE TWO QUESTIONS YOU SHOULD ASK CLAUDE AT THE END OF EVERY AI SESSION TO CATCH THE STUFF THE MODEL SILENTLY SKIPPED the whole point is forcing the ai to admit what it rushed past instead of pretending everything is fine 1\ "what are you least confident about right now" it'll list 6 or 7 things it didnt properly check about one in four times, one of them is a real problem it took action on without ever understanding. then you just have it go investigate each one. 2\ "whats the biggest thing im missing here, what dont i realize" this ones from sam altman. it pulls back and catches the blind spot in how youre thinking about the whole thing, not just the code. some other ones that people added on that are just as good: > "if this breaks in 3 months, whats the most likely reason" catches assumptions that wont survive scale > "if you could add one unrequested thing that would make this industry leading, what would it be" for actual creative ideas > "what could i have done differently to make this session more efficient" to fix your own prompting the funny part is we now have to literally ask ai what it skipped past but it works