Charles Koch and his son Chase Koch sat down with David Friedberg for a long, candid Forbes/All-In conversation about how a small crude-oil gathering operation in southern Oklahoma became Koch Industries, a privately held company with more than 130,000 employees across 60 countries and revenue that would land it comfortably in the top 25 of the Fortune 500 if it were public. They walked through the founding story, the management principles that drove a 9,000x increase in value since the early 1960s, the failures that almost wiped out the company, and the philanthropic and political work being done through Stand Together. Watch the full conversation on YouTube.
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Charles Koch took over a roughly 300-person family business in 1961 at age 25, fired the bureaucratic president, and built it into one of the most profitable private companies in the world by applying what he calls Principle-Based Management. The core insight is to be capability bounded rather than industry bounded, to run an internal “republic of science” that rewards contribution over credentials, and to treat failure as the price of experimental discovery. Koch grew through both organic capability extension and large acquisitions like Georgia Pacific in 2005 and Molex in 2013, mostly by replacing top-down hierarchies with bottom-up empowerment. The conversation covers the founding by Fred Koch, the near-death failures of the late 1990s “gas to bread spread,” the Pine Bend Minnesota refinery turnaround, the role of Wichita as a competitive advantage, Chase Koch’s path from feed-yard laborer to leader of Koch Disruptive Technologies, the launch of Stand Together as a long-running social-change platform, the rejection of single-party politics, the case against entitlements and occupational licensing, and the principles for using AI as a permissionless empowerment tool rather than a top-down control system. The throughline is Viktor Frankl: more people have the means to live and less meaning to live for, and the remedy is helping every individual find a gift and apply it in a way that creates value for others.
Key Takeaways
Koch Industries today has more than 130,000 employees across 60 countries and has increased in value roughly 9,000 times since Charles took over in the early 1960s, when headcount was about 300.
Founded in 1940 by Fred Koch in Wichita, Kansas. The two starting businesses were designing fractionating trays (separating liquids by boiling point) and crude oil gathering in Oklahoma.
Charles got three engineering degrees at MIT, worked at Arthur D. Little, and reluctantly came back at 25 only after his father said he would otherwise sell the company. His father gave him full autonomy over every decision except selling.
His first move was firing the controlling, memo-driven president and replacing protectionism with three pillars: create value for customers, empower employees, and own end-to-end execution. They built their own plant in Italy instead of stitching together European subcontractors.
The defining mental model is “capability bounded, not industry bounded.” You expand into adjacent industries where the capabilities you have already proven (operations, logistics, trading, refining, branding) create more value than incumbents, not because the new industry is in the same SIC code.
Wholly owned business platforms today include engineered projects and construction, solar plants, commodity trading and distribution, fertilizers, refined products, chemicals and polymers, glass, forest and consumer products, electrical products (Molex), and management software, plus four distinct investment firms.
Koch is explicitly not a Berkshire-style conglomerate of independent silos. Chase frames it as an integrated republic of science, an integrated set of capabilities that share knowledge and people across business lines.
“If you are not failing at anything, you are not doing anything new.” Failure is treated as the cost of experimental discovery, but only when the learning value exceeds the cost.
The worst failures came from violating the hiring rule. Hire on values first, talent second. People with destructive motivation (power and control over contribution) hide failures and invent successes, and the damage compounds when those people get promoted into leadership.
The 1973 trading blowup nearly bankrupted the company. The late 1990s “gas to bread spread” strategy, an attempt to vertically integrate from natural gas through fertilizer to pizza crust, nearly wiped out all of Koch’s earnings. Lesson repeated, then internalized.
One acquisition shipped hundreds of millions of dollars in out-of-the-money hog feed contracts that nobody bothered to read before closing. Apply the scientific method: try as hard to disprove your hypothesis as to prove it.
Georgia Pacific was acquired in 2005 for roughly $20 billion when Koch was much smaller. They originally tried to buy only the commodity pulp piece so GP could re-rate as a pure consumer-products company at a higher P/E. When legal blockers killed that path, they bought the whole thing.
The Georgia Pacific culture change started with sending Joe Moeller in as CEO. He gutted the 51st-floor coat-and-tie executive suite, fired the most bureaucratic managers, moved everyone to working floors, and converted the executive floor into open meeting rooms. Signals like that drive culture more than memos do.
The Pine Bend, Minnesota refinery, bought in 1969, was one of the hardest cultural turnarounds. The union strike was violent (rifles fired, switch engines used to ram units), Charles ran it nine months without union labor on his honeymoon, the work rules finally changed, and once empowered, the workforce built its own machine shop, cut spare-part costs, and grew capacity tenfold. It is now one of the best refineries in the country.
Molex, bought in 2013, took years to transform. The dominant paradigm was top-line growth rather than bottom-line value creation, partly because it had been public for 30 years and the market rewarded the wrong things. Almost every successful turnaround required swapping in leadership with a bottom-up empowerment paradigm.
Sheep-dipping does not work. Pushing 130,000 people through the same seminar will not rewire habits. Coaching one struggling team until it succeeds creates social mimicry. Other teams ask to be next. Demand for Principle-Based Management coaches now exceeds supply inside the company.
The talent doctrine is values first, skills second, credentials last. Wichita and the farm-team labor pool are deliberate competitive advantages because farm kids tend to show up contribution-motivated rather than entitlement-motivated.
The current Koch CIO, Jared Benson, joined as a contractor striping lines in the parking lot and has no college degree. He learned data science, built the cyber-security capability, and ran circles around credentialed peers.
Public-company pressure to IPO was the biggest external threat. Charles refused. Staying private was the only way to keep reinvesting roughly 90 percent of profits, to maintain the capability-bounded model that no analyst would underwrite, and to keep accepting low P/E optics on commodity businesses inside the portfolio.
Three things any lasting partnership requires (marriage, business, employment): shared vision, shared values, and complementary capabilities. Miss any one and it does not last.
Chase Koch started at age 15 throwing tennis matches to escape practice, got shipped to a feed yard the next morning, shared a single-wide trailer with his boss, shoveled manure, and discovered the “glorious feeling of accomplishment” that his grandfather Fred had written about in his famous letter to the next generation.
At one point Chase was promoted to president of Koch Fertilizer, realized after nine months he was a builder and not an optimization operator, walked into his boss’s office, and fired himself. The role went to someone with the right comparative advantage and the business grew faster. Chase went on to launch Koch Disruptive Technologies (KDT).
KDT would have been shut down on a normal three-to-four-year venture timeline. Koch kept investing through the losses because of two principles: experimental discovery and creative destruction. They also valued the knowledge inflow about disruptive technologies that might one day eat the core business.
Comparative advantage applies to careers. The job of 20,000 plus Koch supervisors is to keep moving people into roles where they can actually contribute. Beating people up in the wrong seat is destructive.
Viktor Frankl frames the moral problem of the era: ever more people have the means to live and no meaning to live for. Without meaning, people default to either power or pleasure. Both lead, at scale, to totalitarianism, authoritarianism, or socialism.
Charles credits Maslow’s Eupsychian Management, Polanyi’s Personal Knowledge, Hayek’s price-signal work, and Frankl’s logotherapy as the intellectual foundations of Principle-Based Management. The five dimensions: vision, virtue and talents, knowledge processes, decision rights, and incentives.
Stand Together, founded in 2003, is a community of close to a thousand business leaders pooling effort on social change rather than working in philanthropic silos. The thesis: every human has a gift and the institutions are putting up barriers (broken schools, broken criminal justice, bad policy, occupational licensing).
Education is one of Stand Together’s biggest fronts. Pre-COVID, around 20 percent of families were open to a new model. Post-COVID, it is 70 to 80 percent. They back Alpha School (Joe Liemandt), Khan Academy (Sal Khan), and the VELA Education Fund alongside the Walton family. Roughly 5,000 micro-schools have been seeded.
The model for social change mirrors the business model: bet on the person closest to the problem who already shows results. Scott Strode and The Phoenix gym went from a couple of Colorado locations to one million people overcoming addiction, with relapse rates under 10 percent, by combining community and exercise rather than top-down treatment programs.
Charles says the biggest mistake of the first 50 years was trying to drive social change through a single political party, first the Libertarians and later just the Republicans. The current rule, from Frederick Douglass, is “I will unite with anybody to do right and with nobody to do wrong.”
His policy critique cuts in every direction: occupational licensing locks out newcomers, the treatment of working illegal immigrants is wrong, tariffs undermine division of labor by comparative advantage and raise prices, and entitlements once created are nearly impossible to dismantle.
Asked whether capitalism inevitably compounds into monopoly, Charles answers that the fix is removing barriers to others realizing their potential, not capping the winners.
On AI: the principle is permissionless innovation. Cost is collapsing, access is widening, and the right use is empowering individuals to learn 1000x faster, not concentrating power.
Koch backs Cosmos and other AI efforts that apply market-based management principles. Internally, they launched an AI app called Principal Companion that uses the Socratic method to walk users through problems using the book’s principles, from business to parenting.
Writing the new book (Charles’s fifth, Chase’s first) was the most important project Chase has worked on. They went through 27 versions of the stewardship chapter. Charles still corrects Koch leaders who say “the proof is in the pudding” instead of “the proof of the pudding is in the eating.”
When asked about legacy, Charles answered in one sentence: he wants the country to more fully live up to the promise in the Declaration of Independence.
Detailed Summary
From 300 Employees to 130,000 Across 60 Countries
Koch Industries was founded in 1940 by Fred Koch in Wichita, Kansas. When Charles took over full-time in 1961, the company had about 300 employees and two main businesses: designing fractionating trays for separating liquids by boiling point, and a crude oil gathering system in Oklahoma. Today the company has more than 130,000 employees in 60 countries and has grown in value roughly 9,000 times over that period. If Koch were public, revenue would put it easily in the top 25 of the Fortune 500. The portfolio spans engineered projects and construction, solar plants, commodity trading and distribution, fertilizers, refined products, chemicals and polymers, glass, forest and consumer products, electrical products through Molex, management software, and four distinct investment vehicles. Roughly 90 percent of profits are reinvested.
Charles Coming In at 25
Charles describes himself as a poor engineer who happened to be good at math, science, and theory and bad at making or operating things. After three MIT degrees and a stint at Arthur D. Little doing what he calls “absurd” management consulting at 25, his father called and said the company was struggling and his health was failing. Either Charles came back or it would be sold. He came back. The condition was full autonomy: Charles could run it any way he wanted, the only decision requiring approval was selling. Within a short time he fired the previous president, a top-down memo-writer obsessed with controlling spending, and rewrote the operating philosophy around three things: create value for customers, empower employees, and own the value chain end to end. Instead of farming European fractionating trays out to multiple subcontractors and then re-assembling, Koch built its own plant in Italy.
Capability Bounded, Not Industry Bounded
This is the single most important strategic idea in the interview. Conventional advice told Koch to become an integrated oil major because they were in crude oil gathering. Charles rejected that and ran on Hayek and Adam Smith instead: division of labor by comparative advantage. Be in the part of any value chain where you can create more value than anyone else. From crude oil gathering, Koch leveraged operations, logistics, and trading into pipelines, refineries, natural gas, chemicals, fertilizers. Georgia Pacific looked like a non sequitur, wood products, but the underlying capability set transferred, and the acquisition also added branding as a new capability that fed back into the system. Chase calls the result not a Berkshire-style conglomerate of independent businesses but a republic of science: an integrated set of capabilities that share talent, knowledge, and laboratories.
The Failures That Almost Killed the Company
Charles spends a long stretch on failures, because he says the strength is in them. The 1973 trading blowup tied to the Middle East war could have bankrupted the company. The late 1990s “gas to bread spread” was an attempt to control the entire chain from natural gas to nitrogen fertilizer to grain to pizza crust. It violated almost every principle in the book at once and wiped out most of Koch Industries earnings for the decade. One acquisition closed before anyone read the hog-feed contracts, and on closing day they discovered hundreds of millions of dollars of out-of-the-money positions. Every failure traced back to two violations: hiring leaders with destructive motivation (power and control instead of contribution), and skipping the scientific method (trying to prove a hypothesis instead of disprove it). Charles says “repetition penetrates even the dullest of minds,” and he had to be punished enough times before the lesson took.
Georgia Pacific, Molex, and the Pine Bend Refinery
Three acquisition stories show how Koch transfers culture into businesses ten times larger than the corporate playbook would normally allow. Georgia Pacific in 2005 was a $20 billion bet on a company much larger than Koch at the time. Joe Moeller, sent in as CEO, immediately fired the most bureaucratic managers, gutted the 51st-floor private-elevator executive suite (coat and tie required to visit), moved everyone to working floors, and turned the old executive floor into open meeting rooms. Molex, bought in 2013, had been public for 30 years and ran on top-line growth thinking because that is what the market rewarded. Changing the paradigm to bottom-up empowerment and bottom-line value creation took years and required new leadership. Pine Bend, Minnesota, bought in 1969, was the hardest. The union ran the refinery, ignored work rules, and went on a violent strike when Koch tried to change them, firing rifles and ramming switch engines into units. Charles ran the refinery nine months without union labor (during his honeymoon), eventually got the work rules changed, then spent years rebuilding the culture. The empowered workforce designed and built its own machine shop, cut spare-part costs, and grew capacity tenfold. Pine Bend is now one of the best refineries in the country.
How Principle-Based Management Actually Diffuses
Charles is blunt that they tried “sheep dipping” first, hauling everyone through a seminar. It did not work, because changing a habit means rewiring the brain through work at intensity over time, the way a weightlifter has to retrain to become a marathoner. The model that did work was small. Find one team that is struggling, coach them with principles, let them succeed, and the rest of the company asks to be next. Social mimicry replaces top-down rollout. Internally the Principle-Based Management group is now in higher demand than any other function.
Talent: Values First, Skills Second, Credentials Last
Koch deliberately stayed in Wichita partly to access a “farm team” labor pool of people who grew up contribution-motivated. Chase tells the story of Jared Benson, who started as a contractor striping lines in the Koch parking lot, taught himself data science, built the company’s cyber-security capability, and is now CIO with no college degree. The lesson runs against the prestige-school default of most large companies. Contribution motivation, not credentials, predicts long-run output, and Charles is willing to “hire slow and stupid” for anyone with bad values so the company can flush them quickly. Aligning incentives matters as much as hiring: reward people on overall long-run contribution to Koch’s future, including the value of what was learned from a failed experiment, not on near-term P&L.
Why Koch Stayed Private
Multiple parties pushed hard for an IPO over the decades. Charles refused. Going public would have made the capability-bounded model impossible to communicate to analysts, would have forced a higher payout ratio and broken the reinvestment compounding, and would have introduced the short-termism that wrecks bottom-up empowerment. Buffett gets credit, but Berkshire does not try to integrate its businesses the way Koch does. Asked whether a non-owner public CEO could ever apply the principles, Charles allows it is possible if they can sell a different durable story (as Buffett did), but it is much harder.
Chase Koch’s Path
Chase tells two formative stories. The first is being shipped to a feed yard at 15, sharing a single-wide trailer with his boss, shoveling manure for minimum wage, and finding, for the first time, what his grandfather Fred had called “the glorious feeling of accomplishment.” The second is firing himself as president of Koch Fertilizer after nine months because he realized he was a builder, not an operator. The business outgrew where he would have taken it, and he went on to launch Koch Disruptive Technologies, the venture and innovation arm that now feeds technological insight back into every Koch business line. The comparative-advantage principle applied to a career, in public, by the boss’s son.
Stand Together and Social Change
Stand Together, founded in 2003, is the Koch family’s social-change platform. It now includes close to a thousand aligned business leaders. The animating belief is that every human has a gift and institutional barriers (broken schools, broken criminal justice, occupational licensing, bad policy) prevent most people from finding and applying it. The Phoenix gym founded by Scott Strode is the canonical Stand Together bet: a person closest to the problem, with results (relapse rates under 10 percent), funded to scale. In seven or eight years it has gone from a couple of Colorado locations to one million people. On education, post-COVID openness to new models jumped from roughly 20 percent of families to 70 to 80 percent. Stand Together backs Alpha School, Khan Academy, and the VELA Education Fund alongside the Walton family, and has helped seed roughly 5,000 micro-schools.
Politics: The Single-Party Mistake
Charles says for the first 50 of his 60 years in this work he avoided major-party politics, then concluded the country needed principle-based policies badly enough that engagement was required. The mistake was trying to do it through one party. The Libertarian Party turned into purity tests reminiscent of the early Communist Party. Doing it through Republicans blew up too. The rule going forward is Frederick Douglass’s: unite with anybody to do right and with nobody to do wrong. He is openly critical of both parties on occupational licensing, immigration policy, tariffs, entitlements, and the treatment of working illegal immigrants. He invokes Jefferson on slavery to describe his current mood: “If God is just, I despair for the future of our country.”
Capitalism, Compounding, and AI
Asked whether capitalism inevitably ends in monopoly because successful operators compound, Charles flips the framing. The remedy is not to cap the winners, it is to remove the barriers preventing everyone else from realizing their potential. Occupational licensing, immigration restriction on contributors, tariffs that undermine comparative advantage. On AI, Koch’s principle is permissionless innovation: cost is collapsing, access is widening, and the right outcome is individual empowerment and 1000x faster learning, not power concentration. Internally they launched Principal Companion, an AI app built on the principles in the book that uses the Socratic method to walk users through problems rather than handing out answers. Koch backs Cosmos and other AI ventures applying market-based management.
The Philosophical Spine
Charles cites four foundational thinkers. Polanyi’s Personal Knowledge gave him the model for how habits encode knowledge in the brain and why retraining is bodily work. Maslow’s Eupsychian Management supplied the empirical link between self-actualization and organizational performance. Hayek supplied the price system and the case against central planning. Frankl supplied the diagnosis: more means to live, less meaning to live for, and in that vacuum people drift to either power or pleasure, both paths to the slippery slope of authoritarianism and socialism. The Principle-Based Management answer is to design the company (and the country) so that everyone can find a gift and apply it to help others succeed.
Thoughts
The most useful concept in the conversation, the one worth stealing for any operator regardless of industry, is “capability bounded, not industry bounded.” Most companies define their addressable market by SIC code or competitive set. Koch defines it by the actual transferable skills they have demonstrated: operations, logistics, trading, refining, branding, cyber-security. Each acquisition is a probe to see whether the capability set creates more value than incumbents, and each acquisition that works hands back new capabilities (branding from Georgia Pacific, electronic-components engineering from Molex) that compound the option space. This is the same logic that makes Amazon’s AWS, advertising, and logistics businesses adjacent rather than diversifications. Industry conglomerates collapse. Capability conglomerates do not, because the capabilities reinforce each other.
The honest treatment of failure is rarer than it sounds. Most CEOs who say “we celebrate failure” mean something performative. Charles’s version has teeth because the failures he names (the 1973 trade, the late 1990s vertical-integration push, the unread hog contracts) were almost terminal, and the lesson he draws is not “fail fast” but a specific causal claim about hiring leaders with destructive motivation. The asymmetry between contribution-motivated and destructively motivated employees, with the latter capable of hiding losses and inventing successes until the damage compounds, is the kind of insight that only comes from forty years of post-mortems. The remedy, hire slow and dumb if values are bad so you can purge fast, is uncomfortable enough to be real advice.
The case for staying private is also harder than the founder-flex version usually heard from private operators. Charles is not arguing that private is better for everyone. He is arguing that a specific operating model (high reinvestment, cross-business capability sharing, willingness to take long P/E hits on commodity legs, leadership succession over decades) cannot be communicated to public markets without distortion. If you do not run that model, going public is fine. If you do, going public would have killed the system. That distinction is worth holding on to when reading the founder-control discourse in tech, because most “stay private forever” arguments do not actually meet that bar.
The political reflection is the most surprising part of the conversation, particularly given the public reputation. Charles plainly says the biggest mistake of his life in social change was trying to do it through one party, that the Libertarians collapsed into purity-test factionalism, that the Republican approach failed in similar ways, and that the current operating rule is the one Frederick Douglass actually wrote down. He criticizes the current administration’s treatment of working illegal immigrants and the tariff regime by name. Whether one agrees or disagrees on policy, the willingness to grade your own past work in public, decades after the bets were placed, is rare at this level.
Finally, the Frankl framing deserves a longer hearing than a podcast can give it. “Ever more people have the means to live and no meaning to live for” is the most economical statement of the malaise running through politics, addiction, education, and labor data right now. Koch’s bet is that the answer is not policy alone but a design problem: build institutions (companies, schools, philanthropies, AI tools) that let each individual find a gift and apply it in a way that creates value for others. That is the through-line connecting Principle-Based Management, Stand Together, the Alpha School partnership, The Phoenix gym, and Principal Companion. Whether it scales is an open question. The fact that one family business has spent 60 years pressure-testing it makes the experiment worth paying attention to.
The host opens this Saturday morning macro and AI markets video with a direct challenge to anyone calling the current move a bubble. The argument is that the market structure itself has changed, that AI agents now dominate trading and capital allocation, and that Charles Kindleberger’s Manias, Panics, and Crashes describes a world that no longer exists. The full hour-long conversation walks through earnings, PEG ratios, capex, the benchmark arbitrage trapping passive investors, the inflation regime shift, and where money is rotating now. Watch the original video here.
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AI is not a bubble in the Kindleberger sense because the market is no longer dominated by emotional human professionals. AI agents, retail risk-takers, and passive flows are reshaping price discovery while the spend is being funded by free cash flow from the most cash-rich companies in history, not bond-issuance manias like telecoms or oil. Earnings growth is 27 percent, semiconductor sales grew 88 percent year over year in March, OpenAI and Anthropic revenue is on near-vertical curves, Nvidia’s PE is at decade lows even as Cisco’s was 130 at the dot-com peak, and the PEG ratio for the S&P sits at 1.03 with one third of the host’s thematic basket under 1.0 while Microsoft, Amazon, Meta, Apple, and Alphabet all carry richer PEGs. The new regime brings speed crashes instead of multi-year recessions, persistent bottlenecks in power, chips, transportation, and chemicals, inflation pressure that pushes three-month bills below CPI for the first time since the inflation era, and a benchmark arbitrage forcing passive money to chase AI exposure. The host is selling two thirds of his Micron, rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum, and warning that tokenization launches scheduled for July 26 will be the next major regime change.
Key Takeaways
The word bubble is being misapplied because the same people calling AI a bubble called QE, tariffs, oil, Bitcoin, and passive investing bubbles for fifteen years and were wrong every time.
Kindleberger’s Manias, Panics, and Crashes described a slow, linear, human-emotion-driven world. AI agents have no emotion, no memory of Druckenmiller’s 2000 top, and one goal: make money.
The simplest test for anyone bearish on AI is to ask how much they use artificial intelligence. If they have not used a tool like OpenClaw or similar agentic systems, they are still operating in the old market regime.
This buildout is funded by free cash flow and bond issuance at yields better than US Treasuries from companies with stronger balance sheets than the federal government, unlike the dot-com telecoms or 1970s oil majors.
The S&P 500 is up only 7 percent year to date. The bubble framing is being applied to a handful of names, not to broad indices that remain reasonably valued.
The agentic stage of AI started in late November and accelerated when OpenClaw went viral at the end of January. Token consumption is set to grow 15 to 50 times from the IQ stage.
Anthropic revenue is stair-stepping from 5 to 7 to 9 to 14 to 19 to 24 to 30 billion in annualized run rate, on pace to surpass Alphabet in revenue by mid-2028.
OpenAI’s backlog hit 1.3 to 1.4 trillion in the most recent earnings cycle and the company still does not have enough compute.
Dario Amodei told the world Anthropic was planning for 10 times growth per year. In Q1 they saw 80 times annualized growth, which is why compute is bottlenecked and Anthropic is renting from Amazon, Google, and Colossus.
S&P 500 earnings growth is 27.1 percent year over year. The only quarters that match are those coming out of recessions, and this is not a reopening trade.
320 of 500 S&P companies have reported and the average earnings surprise is 20 percent. Forward estimates are up 25 percent year over year as analysts revise upward against the historical pattern.
Total semiconductor sales grew 88 percent year over year in March. Semis have moved in proportion to earnings, not in excess of them.
Cisco’s PE was 130 at the dot-com peak. Nvidia’s PE today is the lowest of the last decade because professionals cannot run concentrated positions in single names.
The Edward Yardeni PEG ratio for the S&P is 1.03. The hyperscalers are not cheap on PEG: Microsoft 1.4, Amazon 1.66, Meta 1.96, Apple 3, Alphabet near 5. Thirty of ninety-five names in the host’s thematic portfolio carry PEGs under 1.0.
Passive investing creates a benchmark arbitrage. Everyone long the S&P 500 through index funds is structurally underweight Intel, Nvidia, Micron, and every name actually going up. Pension funds and mutual funds are forced to chase AI exposure to keep up.
BlackRock’s Tony Kim at the Milken conference: compute and model layers added 8 trillion in market cap year to date while the service apps that make up two thirds of GDP lost 1.2 trillion. The benchmark arbitrage is already running.
Larry Fink predicted a futures market for computing power. Power plus chips is the oil of the intelligence economy.
Jensen Huang called this a 90 trillion dollar AI physical upgrade cycle. The one big beautiful bill bonus depreciation provision was designed to incentivize this capex magic.
The host is selling two thirds of his Micron position. The reasoning is the memory market started moving in September of last year, the DRAM ETF is the ninth most traded ETF with billion dollar daily volumes, and exhaustion indicators are flashing red.
Money from Micron is rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum. The view is that the energy and power side of the AI stack is lagging the semis and will catch up next.
Silver versus gold has not moved while Micron has gone parabolic. LME metals are breaking out. China is increasing gold purchases significantly month over month.
The expected CPI print of 3.7 percent will put three-month Treasury bills below CPI for the first time since the post-pandemic inflation era. That is when Bitcoin started its last major run.
Logistics Managers Index hit 69.9 in March, the fastest expansion since March 2022. Transportation prices are surging because there is no capacity. This typically only happens during tax cuts or post-COVID reopenings.
Payroll job creation in information, professional services, and financial activities is negative. AI is already replacing knowledge work. Job creation has shifted to mining, manufacturing, construction, trade, transportation, and utilities, which is structurally inflationary.
Whirlpool says appliance demand is at great financial crisis lows. The consumer PC and laptop market collapse is worse than 2008. AI is pulling capital and pricing power away from legacy consumer categories.
Mike Wilson’s data shows reacceleration across sectors, not just large cap tech. Small caps and median stocks are showing earnings growth too, just at smaller market caps.
Chevron’s CEO says global oil shortages are starting. Jeff Currie warns US storage tanks will run empty. Ships are still not transiting the Strait of Hormuz. Countries that learned this lesson will restock to higher inventory levels permanently.
The Renmac Bubble Watch threshold was crossed on a technical basis. The host considers technical exhaustion a stronger signal than narrative-driven bubble calls.
Goldman Sachs power demand reports, Guggenheim warnings on the power crunch, and BlackRock’s compute intensity research all triangulate on the same conclusion: capex needs are larger than current forecasts.
The thematic portfolio is up roughly 30 percent from March lows. Power, optical fiber, advanced packaging, chemicals, and rack-level infrastructure baskets are leading.
Sterling Infrastructure (STRL), Fluence batteries, ABB electrification, Hon Hai (Foxconn), Vistra, Eaton, and Soitec are highlighted as names lagging the megacaps but inside the same AI infrastructure trade.
John Roque at 22V Research is releasing weekly frozen rope charts, long-base breakouts across power, copper, grid equipment, utilities, natural gas, transportation, capital goods, and agriculture. They all map to the same AI plus inflation regime.
Bitcoin ETF outstanding shares hit new highs. BlackRock, Morgan Stanley, and Goldman are all running competitive products. Boomer and wealth manager allocation is accelerating into year end.
Tokenization rolls out July 26. Wall Street clearing has enlisted 50 firms. A16Z published their case in December 2024. The host considers this underweighted by most investors and is speaking on the topic at the II event in Fort Lauderdale.
Raoul Pal and Yoni Assia on the end of human trading: AI agents and crypto collide by moving finance from human speed to machine speed. Agents will trade, allocate, hedge, and shift capital through wallets and exchanges. Tokenization means ownership becomes programmable.
The new regime is bubbles, parabolas, and speed crashes. Corrections compress from years into months. The right strategy is to never go to cash, only to rebalance and slow down within the portfolio.
For traders, exhaustion indicators using 5-day and 14-day RSI plus DeMark signals identify potential speed crash setups. Intel and Micron are flashing red on those screens right now.
Detailed Summary
Why this is not Kindleberger’s world anymore
The framing argument of the video is that Manias, Panics, and Crashes described a market dominated by human professionals operating with limited information and lagged feedback loops. When supply and demand fell out of sync, prices collapsed because nobody could see what was happening in real time. That world is gone. AI agents now manage a majority of professional fund flows. Information moves instantaneously. Retail investors trade differently than institutional pros, and the capital structure of the entire market has changed. The host argues that since the Great Financial Crisis, the combination of QE and exponential corporate growth produced the only companies in history worth 25 trillion dollars combined with no net debt. Their AI capex is funded by free cash flow and high-grade bonds, not panicked bond issuance like the dot-com telecoms or oil majors of the 1970s.
The Druckenmiller anchor and why FOMO is the wrong lens
The video reads the Stanley Druckenmiller story of buying six billion in tech at the 2000 top and losing three billion in six weeks. Every professional carries that scar. It has shaped a generation of money managers into seeing parabolic moves and immediately calling bubble. The host’s counter is that recession calls from wealthy professionals are themselves a form of hope. Cash-rich investors root for crashes because crashes give them entry points. If the bubble never breaks the way it broke in 2000, those investors stay locked out, and that is precisely what the AI regime is doing.
Earnings, revenue, and the reality test
The video walks through current numbers in detail. S&P 500 earnings growth is running 27.1 percent year over year, which only happens coming out of recessions. 320 companies have reported with an average 20 percent earnings surprise. Forward estimates were revised up 25 percent year over year, well above the historical pattern of starting-year estimates getting cut. Total semiconductor sales were up 88 percent year over year in March. Anthropic’s revenue trajectory is stair-stepping from 5 to 30 billion in annualized run rate on the back of Claude Opus 4.5, putting it on track to surpass Alphabet by mid-2028. OpenAI is sitting on a 1.3 to 1.4 trillion backlog and still cannot get enough compute. Dario Amodei told the public Anthropic planned for 10 times growth per year and saw 80 times in Q1.
PE, PEG, and the valuation argument
Cisco’s PE at the dot-com peak was 130. Nvidia, the indisputable lead dog of the AI buildout, currently has a PE at the lowest of its last decade. The S&P 500’s PE is roughly where it has been since the post-COVID money printing era, far below the dot-com peak. Edward Yardeni’s PEG ratio for the index sits at 1.03. The host built a PEG screen for his ninety-five name thematic portfolio. Thirty of those names trade at a PEG under 1.0. The hyperscalers everyone holds passively are the expensive ones: Microsoft 1.4, Amazon 1.66, Meta 1.96, Apple 3, Alphabet near 5. The capacity for forward PE compression sits in the names retail and active rotational money are buying, not in the index core.
The benchmark arbitrage trap
Most money is now in passive investing. By construction, an S&P 500 or MSCI World allocation is underweight the names that are actually rising. Pension funds, mutual funds, and any active manager benchmarked to those indices is forced to add AI exposure to keep pace. BlackRock’s Tony Kim made this point at Milken: 8 trillion in market cap has accrued to compute and model layers year to date, while service apps representing two thirds of GDP lost 1.2 trillion. The host calls this benchmark arbitrage and considers it the single most underappreciated driver of the current move.
The 90 trillion dollar physical upgrade cycle
Jensen Huang’s framing of a 90 trillion dollar AI upgrade includes autos, phones, computers, humanoids, robotics, and the military stack. The host considers this a global race between the US and China. The one big beautiful bill included bonus depreciation specifically to incentivize the capex push. Greg Brockman’s interview with Sequoia made the point that demand for intelligence is effectively unlimited, and that every company outside the hyperscalers, Morgan Stanley, Goldman, Eli Lilly, Merck, United Healthcare, needs their own data center compute or their margins will not keep up with competitors. In a capitalist system, that forces broad enterprise AI spending.
Speed crashes replace recessions
The new regime has corrections but they are fast. Since 2020 we have had multiple 20 percent corrections compressed into weeks instead of years. The host expects this pattern to continue for the next decade. Bottlenecks in power, chips, transportation, chemicals, and skilled labor will produce inflation spikes that trigger speed crashes, not traditional credit-cycle recessions. The Logistics Managers Index reading of 69.9 in March, with capacity contraction near record lows, signals exactly this kind of bottleneck environment. The host’s strategy in this regime is to never go to cash, only to rebalance and slow down within the portfolio.
The inflation regime shift and the rotation out of Micron
The expected CPI print of 3.7 percent will put three-month Treasury bills below CPI for the first time since the post-pandemic inflation era, restoring negative real yields. That was the condition under which Bitcoin first launched its major bull moves. The host has sold two thirds of his Micron position despite continued bullish conviction on the name, because the memory market is the most stretched on exhaustion indicators and the DRAM ETF is trading at unprecedented volume. The capital is rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum. Silver versus gold has not moved while semis went parabolic. LME metals are breaking out. China is increasing gold purchases. The energy and power side of the stack is the next leg up.
AI is breaking the consumer and the labor market
Whirlpool reports appliance demand at financial crisis lows. PCs and laptops are collapsing worse than 2008. Phones, autos, housing, all the categories Kindleberger’s framework was built around are under pressure because AI is pulling capital and pricing power into compute, power, and chemicals. Payroll job creation in information, professional services, and financial activities is negative as AI takes knowledge work. Job creation is rotating into mining, construction, manufacturing, trade, transportation, and utilities, which is structurally inflationary because those sectors require physical capacity and wages. That combination, wage inflation plus commodity inflation, makes it very difficult for the Fed to ease, even with Kevin Warsh likely taking over.
Crypto, tokenization, and AI agents at machine speed
The final section pivots to crypto. Bitcoin ETF outstanding shares hit new highs, BlackRock’s product remains dominant, and Morgan Stanley and Goldman have launched competing vehicles. Wealth managers and boomers are allocating. The Raoul Pal and Yoni Assia conversation on the end of human trading is the host’s headline reference: AI agents will trade, allocate, hedge, and shift capital at machine speed through programmable wallets and exchanges. Tokenization, scheduled for a major launch on July 26 with 50 Wall Street clearing firms onboarded, makes ownership programmable. A16Z laid out the case in December 2024. The host is speaking on tokenization at the II event in Fort Lauderdale May 13 through 15 and considers it the next regime-defining shift after agentic AI.
Thoughts
The strongest argument in this video is structural, not narrative. The shift from human professionals with anchored memories to AI agents and benchmark-driven passive flows is a real change in who sets prices. Whether or not you accept the host’s portfolio calls, the framing should make any investor pause before defaulting to dot-com pattern recognition. Cisco’s PE was 130 with no business model. Nvidia’s PE is at a decade low with a near monopoly on the picks and shovels of the largest capex cycle in industrial history. Those facts cannot both be true and produce the same outcome.
The PEG framework is the cleanest test in the video. If you believe Nvidia, Micron, Intel, and the second-tier AI infrastructure names are bubbles, you are implicitly betting that earnings growth collapses. That bet was viable in 2000 because the companies driving the move had no earnings. It is much harder to bet against earnings growth when 320 companies have just printed a 20 percent average earnings beat and analysts are revising forward estimates up by 25 percent. The host’s argument is not that the prices are reasonable in absolute terms. It is that the bear case requires growth to fall off a cliff, and nothing in the order books, the capex commitments, or the compute backlog suggests that is imminent.
The benchmark arbitrage point deserves more attention than it gets. If the majority of professional money is locked in passive structures that are by definition underweight the leading names, and if those managers are evaluated quarter to quarter against the benchmark they cannot match, the pressure to chase will compound. This is the opposite of the dot-com setup, where active managers were forced to add overpriced tech to keep up with the index. Here, the index itself is structurally underweight the trade, and the active managers chasing it are doing so against names with rational PEG ratios.
The rotation thesis from Micron into power, silver, and crypto is more debatable. The energy and bottleneck story is real, but the timing of when the power trade catches up with the semi trade is the hard part. The host’s discipline of never going to cash and rebalancing through the cycle is a sensible response to a regime that produces speed crashes rather than slow drawdowns. The investors most hurt by this regime will not be the ones who are long the wrong names. They will be the ones who sit out waiting for an entry point that never comes.
Tokenization is the most underappreciated thread in the video. If the July 26 rollout brings 50 clearing firms and real ownership programmability online, the second half of the year could produce a regime shift on top of the AI regime shift. AI agents transacting on tokenized assets at machine speed is the logical endpoint of the trends the host has been tracking, and it is the part of his framework that current market consensus has not yet priced.
Eric Jorgenson’s The Book of Elon: A Guide to Purpose and Success (Magrathea Publishing, 2026) is the third entry in his series of compiled-wisdom books, following The Almanack of Naval Ravikant and The Anthology of Balaji. It is built entirely from Elon Musk’s own words, drawn from transcripts, tweets, and interviews across his career, then recontextualized into a four-part operating manual: Pursue Purpose, Ultra Hardcore Work, Building Companies, and On Behalf of Humanity. The book closes with a bonus list of 69 distilled maxims. Naval Ravikant wrote the foreword and calls it “the only book an entrepreneur needs.” Jorgenson’s stated goal is “one million Musks.” This is a complete, dense summary of every major idea in the book, including The Algorithm verbatim with each of its five steps explained in depth, the Tesla Master Plan, the first-principles battery cost calculation, the SpaceX rocket cost analysis, the seven existential risks, the Mars colonization plan, and the 69 Core Musk Methods in full. Get the book at elonmuskbook.org.
TLDR
The Book of Elon argues that Musk’s results are not an accident of genius but the output of a learnable operating system. The system has four layers. Layer one is purpose: optimize your life for usefulness, which Musk defines mathematically as number of people helped multiplied by magnitude of help per person. Layer two is epistemology: reason from physics and raw-material costs, not from analogy or precedent. Layer three is execution: take responsibility, hire only exceptional people, design organizations that route around hierarchy, run at maniacal urgency, and treat the factory as the product. Layer four is mission: pick problems whose solutions move civilization forward (sustainable energy, reusable spaceflight, AI alignment, brain-computer interfaces, multiplanetary life). The book’s single most important operational artifact is The Algorithm, Musk’s five-step engineering process that must be applied in order: make your requirements less dumb, try very hard to delete the part or process, simplify or optimize, accelerate cycle time, automate. The 69 Core Musk Methods at the end of the book are the entire operating system compressed to one-line maxims. Naval frames it as a choice for the reader: when humanity goes to the stars, you can be in the front row cheering or sour-faced in the bleachers jeering, but there is also a third option, which is to copy the methods and build something yourself.
Key Takeaways
Optimize for usefulness, not for money, fame, or comfort. Musk’s daily question is “how can I be useful today” and his success metric is number of people helped multiplied by magnitude of help per person.
Five domains will most influence the future: the internet, sustainable energy, space exploration, artificial intelligence, and the genetic rewriting of biology. Pick one and contribute.
It is possible for ordinary people to choose to be extraordinary. Convention is optional. The default settings of a culture are not laws of nature.
Physics is law. Everything else is a recommendation. If a plan does not violate conservation of energy or any other physical principle, it is at least theoretically possible.
First-principles thinking is the antidote to “that’s how it’s always been done.” Break a problem down to atomic constraints (raw material cost, physics, basic operations) and reason up from there. The battery pack example is canonical: people said cells would always cost $600/kWh, but the raw cobalt, nickel, aluminum, carbon, polymers, and steel at London Metal Exchange prices added up to only $80/kWh.
Track two ratios on everything you build: the magic-wand number (raw-material cost as a floor for finished cost) and the idiot index (finished cost divided by raw-material cost). Anything with a high idiot index has enormous room for improvement.
Aspire to be less wrong. You will not be right every day. Being less wrong most of the time, with a clear feedback loop to reality, is the realistic target.
Engineering is magic, and engineers are the magicians of the 21st century. Science discovers what is. Engineering creates what was not.
Take responsibility. Musk is CEO of Tesla and SpaceX because he feels responsible for them, not because it improves his quality of life. The worst problems are the CEO’s job, not the best problems.
Sleep on the factory floor. Leadership is shared suffering, not delegated comfort. Seeing is believing. If the CEO can do it, the team will do it.
Startups are eating glass and staring into the abyss. Glass is the work you do not want to do. The abyss is the constant threat of company death. Both are required.
Adversity forges strength. A high ego-to-ability ratio breaks your feedback loop. Suffer enough early to develop the pain threshold needed later.
The most important job is attracting exceptional people. Money is not the constraint. Exceptional talent is the constraint.
Hire only Special Forces. The minimum passing grade is excellent. A small group of technically strong people will always beat a large group of moderately strong people.
Hire for character as much as for skill. Skills are teachable. Attitude is not. Judge a person by the character of their friends and associates and to some degree by their enemies.
Camaraderie can be dangerous because it prevents truth-telling. Physics does not care about hurt feelings. It cares about whether you got the rocket right.
All bad news should be given loudly and often. Good news can be said quietly and once.
Communication should travel via the shortest path necessary to get the job done, not through the chain of command. Anyone should be able to talk to anyone.
The organization manifests in the product. Silos produce redundancy, waste, and error. Acronyms and jargon are cognitive pollution.
Innovation needs permission to fail. If failure is not an option, you get incremental progress and nothing else.
Simplicity creates both reliability and low cost simultaneously. The best part is no part. The best process is no process.
The Algorithm, verbatim, in mandatory order: (1) Make your requirements less dumb. (2) Try very hard to delete the part or process. (3) Simplify or optimize. (4) Accelerate cycle time. (5) Automate. See the deep-dive section below for each step in detail.
If you are not adding deleted things back in roughly 10 percent of the time, you are not deleting enough. Overcorrect.
Requirements must come from a named person, not a department. Requirements from smart people are the most dangerous because you are less likely to question them.
Speeding up something that should not exist is absurd. If you are digging your grave, do not dig it faster. Stop digging.
Automation is last, not first. Tesla’s Nevada and Fremont factories had to rip out hundreds of expensive robots that had been installed before The Algorithm’s first four steps were complete.
A maniacal sense of urgency is the operating principle. The only true currency is time. Every minute lost is gone forever.
Speed is both offense and defense. The SR-71 Blackbird has almost no defense except acceleration. Innovating faster is more durable than any patent.
Do things in parallel. A factory moving at twice the speed of another factory is basically equivalent to two factories.
Be a vector, not a scalar. High speed in the right direction. Course-correct like a guided missile.
Manufacturing is underrated. Design is overrated. There is 1,000 to 10,000 percent more work in the production system than in the product itself.
The factory is the product. The biggest Tesla epiphany was that what really matters is “the machine that builds the machine.”
Attack the constraint. The production line moves at the speed of the slowest, least lucky part. Out of 10,000 things, the one that is not working sets the production rate.
Manufacturing is the moat. Maximize economies of scale and maximize manufacturing technology. The combination is uncopyable.
Zip2 (1995, started with $2,000) sold to Compaq for over $300 million. Musk’s first major lesson: sell directly to consumers, not through legacy gatekeepers who will misuse the technology.
X.com merged with Confinity to become PayPal, which sold to eBay in 2002 for $4.5 billion. Musk had been removed as CEO during a honeymoon trip but did not contest it to avoid disrupting the company during a crisis. “Life is too short for long-term grudges.”
Listen well, correct fast. X.com’s initial financial-services conglomerate failed; the email-payments demo worked instantly. Musk pivoted to what the market wanted and powered viral growth (one million customers in year two, no sales force, no marketing spend).
Musk reinvested his post-tax PayPal proceeds (~$180 million) split across Tesla (~$70M), SpaceX (~$100M), and SolarCity (~$10M). Costs were 2x his estimates on every company.
Tesla Master Plan (August 2006): (1) Build a sports car. (2) Use the profits to build an affordable car. (3) Use those profits to build a mass-market car. (4) Provide zero-emission power generation. The strategy was forced by the economics of new technology: you cannot start at the bottom of the market without scale, so you start with low-volume, high-margin and use the margin to fund scale.
Tesla nearly died on Christmas Eve 2008. The final funding round closed at 6 p.m., hours before payroll would have bounced. Musk had moved into Jeff Skoll’s guest bedroom. Daimler then put $50M into Tesla after Musk’s team dropped a Tesla powertrain into a Smart Car that hit 60 mph in 4 seconds.
Model 3 production “hell” lasted 2017 to 2019. Musk slept on the Fremont and Nevada factory floors for three years. “The longest period of excruciating pain in my life.”
Give people more for less. Don’t spend on advertising. Spend on engineering and design so the product carries itself through word of mouth.
SpaceX was founded in mid-2002 with $100 million of Musk’s PayPal money. He expected to lose everything. There was no external funding for three years.
SpaceX had budgeted for exactly three failed Falcon 1 launches. Launches 1, 2, and 3 failed (2006, 2007, 2008). Launch 4 succeeded in August 2008. Then NASA called with a $1.6 billion cargo resupply contract, saving SpaceX and indirectly Tesla. Musk reportedly screamed “I LOVE NASA. YOU GUYS ROCK.”
Rockets are expensive only because of legacy supply chains, cost-plus contracting, and outsourcing through five layers of subcontractors (“overhead to the fifth power”). The raw materials of a rocket are 1 to 2 percent of finished cost. The half-nozzle jacket Musk uses as an example cost $13,000 but contained $200 of steel.
Full and rapid reusability is the holy grail of rocketry. With reuse, only propellant cost remains, which is mostly liquid oxygen and methane at around $1 million per Starship flight.
Optimize for the right thing. SpaceX’s actual optimization target is “fastest time to a self-sustaining city on Mars.” That cascades to fastest time to a fully usable rocket, then fastest time to orbit. Early Starship had no doors because doors are not necessary for reaching orbit.
Companies are the most reliable engine of progress and the deepest form of philanthropy because they create durable wealth and deploy capital toward problems. “I care about reality. Perception be damned.”
The Age of Abundance is coming via AI and humanoid robotics. Optimus and competitors will eventually outnumber humans, removing labor as the economy’s binding constraint. The market for humanoid robots will exceed the market for cars.
Tesla’s full self-driving and Robotaxi product is forecast to make Tesla a $10 trillion company. Autonomous cars are worth 5 to 10 times non-autonomous cars because they earn money when their owners are not using them.
Neuralink achieved 2 bits per second of brain output with the first patient, Noland Arbaugh. Musk’s 5-year target is one megabit per second. Long-term: consensual telepathy via two BCIs, plus restoration of vision (Blindsight) and eventually multispectral senses (infrared, ultraviolet, radar).
Musk’s seven named existential risks: (1) World War III, (2) Regulation accumulation, (3) Unsustainable energy, (4) Misaligned artificial superintelligence, (5) Population collapse, (6) Asteroids and comets, (7) Civilizational fragility itself.
Population collapse is the risk most underdiscussed. The US has been below replacement since the early 1970s; sustained only by immigration and longevity. China’s three-child policy failed; the country is 40 percent below replacement. Musk: “We need to revive the idea of having children as a social duty.”
Do not force an AI to lie. The HAL 9000 lesson from 2001: A Space Odyssey is that AI given conflicting instructions, one of which is to deceive, becomes dangerous. Truthfulness as a core training objective is the alignment mitigation Musk advocates.
Becoming multiplanetary is an evolutionary-scale event. Six milestones in Earth history: single-celled life, multicellular life, plants/animals, ocean-to-land, consciousness, and now multiplanetary life. “At least as important as life going from the oceans to land, probably more significant.”
The window of opportunity is open right now. We cannot count on it being open for long. Stephen Hawking estimated roughly 1 percent civilizational-end probability per century. “That’s Russian roulette with 99 empty barrels and every century is a click.”
Mars insurance costs less than 1 percent of Earth GDP. The plan: 1,000 Starships per Mars transfer window (every 2 years), eventually a fleet of thousands lifting off together. Target: 1 million tons of cargo and people on Mars by 2044, then a self-sustaining civilization.
Mars terraforming options Musk names: thousands of solar reflectors in orbit, or detonating thermonuclear devices over the polar caps as “two little suns” to vaporize CO2 ice, thicken the atmosphere, and eventually create liquid oceans roughly a mile deep covering 40 percent of the planet.
Even given pure slower-than-light travel and no new physics, a million-year time horizon allows humanity to colonize the entire galaxy and possibly neighboring galaxies. “We are at the very, very early stage of the intelligence big bang.”
The 69 Core Musk Methods at the end of the book are the entire system in maxim form. The full list appears later in this article.
The Algorithm in Detail: Musk’s 5-Step Engineering Process
The single most important operational artifact in the book is what Musk calls “The Algorithm.” It is a five-step engineering process he developed and enforces across Tesla, SpaceX, the Boring Company, Neuralink, and xAI. Every part, every process, every line of code, every requirement, every meeting is supposed to be put through these five steps. The order is mandatory. Reordering them is the most common failure mode and the source of nearly every major mistake Musk says he has made at scale (most famously the Nevada and Fremont automation disaster). The book treats The Algorithm as the practical compression of first-principles thinking into a daily ritual.
The five steps, in mandatory order, in Musk’s own phrasing:
Make your requirements less dumb.
Try very hard to delete the part or process.
Simplify or optimize.
Accelerate cycle time.
Automate.
The book devotes its longest single chapter to explaining each step, why the order matters, and the specific failure mode that occurs when you skip ahead. Here is every step in depth.
Step 1: Make Your Requirements Less Dumb
The first step is the hardest because it is the most psychologically uncomfortable. Musk’s exact framing in the book: “Your requirements are definitely dumb. It does not matter who gave them to you. Requirements from smart people are the most dangerous, because you’re less likely to question them.”
The operational rule that follows is concrete. Every requirement on every part, process, deliverable, or specification must come from a named human. Not from a department. Not from a regulation document. Not from “the customer.” A name. Track who owns each requirement in writing. If the named person has left the company, retired, or cannot remember why they wrote the requirement, the requirement should be presumed dumb until proven otherwise. Many requirements in any organization are legacy beliefs nobody currently defends. They exist because they existed yesterday and nobody felt empowered to delete them. The Algorithm starts by demanding evidence for every assumption.
The reason requirements from smart people are especially dangerous is that smart people are persuasive. A specification handed down by a respected engineer carries the implicit authority of “if she said this, there is a reason.” Most of the time there is no reason left, or the reason was contextual to a moment that no longer applies. The Algorithm’s first step is to put every smart-person requirement on equal footing with every dumb-person requirement and force a present-tense justification. If the justification cannot be reconstructed, the requirement is dumb regardless of the author’s IQ.
The mental shift this step demands is to treat requirements as recommendations and treat the laws of physics as the only fixed authority. Musk repeats this constantly: “All requirements should be treated as recommendations. The only fixed laws are the laws of physics.” Once you internalize that frame, the requirements doc stops being scripture and becomes a draft that is open to revision in every meeting, every day.
Step 2: Try Very Hard to Delete the Part or Process
Once the requirements survive scrutiny, the second step is aggressive deletion. The Algorithm’s specific test for whether you are deleting enough: “If you’re not adding deleted things back in 10 percent of the time, you’re clearly not deleting enough.” The 10 percent is a forcing function. If you delete and never have to restore, you are not pushing hard enough; you are leaving safe deletions on the table.
The book explains why engineers chronically under-delete. Every engineer remembers the painful moment when they deleted something and it turned out to be load-bearing. That memory is so vivid that it overshadows the silent cost of thousands of unnecessary parts that nobody ever questions. The Algorithm corrects for this asymmetry by deliberately overshooting. The instruction is explicit: “We are on a deletion rampage. Nothing is sacred.”
The application is mechanical. For every part on the bill of materials, every step in the production process, every meeting on the calendar, every requirement in the spec, every line in the documentation, every approval in the workflow: try to delete it. If deleting causes nothing to break for 30 days, leave it deleted. If something breaks and you have to add it back, do so without shame; that is the 10 percent. The maxim that summarizes this step appears multiple times in the book: “The best part is no part. The best process is no process.”
The canonical example in the book is the fiberglass-mat story. Tesla’s battery pack had a layer of fiberglass mats between the battery cells and the underbody. The mats had a dedicated production process that had been automated, accelerated, and optimized over years. Engineers had spent millions perfecting the glue, the cure time, the cutting tolerances, the robotic placement. Then Musk asked a simple question: “What are these mats for?” The battery team said “noise and vibration.” Musk asked the noise and vibration team. They said “fire safety.” The fire-safety team had no idea where the mats came from. So Musk had two cars built, one with the mats, one without, and put microphones in both. There was no detectable difference. Deleting the part eliminated a $2 million robotics step that had been built up over years. “It was like being in a Dilbert cartoon.”
The fiberglass-mat story is the entire point of The Algorithm in miniature. Tesla had already automated step five, accelerated step four, optimized step three, and skipped steps one and two entirely. The whole apparatus existed to perfect a part that should not have existed. Steps one and two would have found this in a single meeting.
Step 3: Simplify or Optimize
Only after steps one and two have been completed in earnest do you simplify or optimize what is left. Musk’s exact warning: “The most common mistake of smart engineers is to optimize a thing that should not exist.”
The book argues that this mistake is systematically produced by education. High school and college train convergent logic: you are given a question and graded on the elegance and correctness of your answer. The question itself is never on the table. After 16 to 20 years of this, most engineers, scientists, and analysts are mentally locked into “optimize the question in front of me” mode and physically cannot ask whether the question should be deleted. The Algorithm is designed to override that training. Steps one and two are explicitly the act of questioning the question; only at step three do you finally get to apply the optimization skills that school rewarded.
What “simplify or optimize” looks like in practice: reduce part counts, combine functions, choose materials that are abundant rather than exotic, eliminate processing steps within a part’s manufacturing, reduce the number of inputs the team needs to track, collapse separate tools into one tool, replace bespoke fasteners with standard ones, replace any custom solution with a commodity solution that is good enough. The book’s framing is that simplicity creates both reliability and low cost at the same time, with no trade-off. A simpler part is cheaper to build, cheaper to inspect, cheaper to repair, fails less often, and breaks in more predictable ways when it does fail. Optimization without simplification almost always increases complexity and therefore increases failure modes.
The Algorithm treats simplify and optimize as one step but acknowledges they are different operations. Simplify is structural: fewer pieces. Optimize is parametric: better values for the pieces you keep. Both are legal at step three, but neither is legal before steps one and two have been honestly executed.
Step 4: Accelerate Cycle Time
Once requirements are minimal, parts are deleted, and what remains is simplified, the fourth step is to go faster. The specific maxim: “Once you’re moving in the right direction, and moving efficiently, you’re moving too slow. Go faster.”
The reason acceleration comes fourth, not first, is in another Musk line: “Speeding up something that shouldn’t exist is absurd. If you’re digging your grave, don’t dig it faster. Stop digging.” Speed multiplies the value of correct decisions and the cost of incorrect ones. Apply it before steps one through three and you scale your mistakes. Apply it after and you scale your gains.
Acceleration at step four is everything that compresses the time between iterations. Shorten meetings. Eliminate approval queues. Run things in parallel that were running in series. Move people physically closer to the work so that information travels at the speed of conversation instead of the speed of email. Set aggressive internal deadlines that force the team to find shortcuts they would not otherwise have looked for. Replace any tool, supplier, or process that is slow with one that is faster, even if it is slightly more expensive per unit, because cycle time compounds.
The book frames acceleration as both offense and defense. As offense, faster iteration lets you out-innovate competitors who are stuck on slower cycles. As defense, the SR-71 Blackbird analogy: the plane has almost no defensive systems because its acceleration is its defense. A company that ships faster than competitors can copy does not need patents, because patents protect static IP and speed protects evolving IP. The maxim Musk repeats is: “A factory moving at twice the speed of another factory is basically equivalent to two factories.” The Colossus supercluster story is the application: xAI built 100,000-GPU infrastructure in 122 days against a supplier estimate of 18 to 24 months, then doubled it in 92 more, by attacking the problem in parallel across building, power, cooling, and networking, all working 24/7 in four shifts.
Step 5: Automate
Automation comes last. Always. This is the step where most companies start and where Musk himself made his most expensive single mistake. The book quotes him directly: “The big mistake I made in the Tesla factories in Nevada and Fremont was trying to automate every step too early. To fix that, we had to tear hundreds of expensive robots out of the production line.”
The reason automation must be last is that automation locks in a process. Once you have built robots, written PLC code, calibrated machine vision systems, and integrated them into your factory floor, the cost of changing the underlying process is enormous. If the process you have automated should not exist (step 2 failure), is more complicated than necessary (step 3 failure), or runs at the wrong cadence (step 4 failure), you have just spent millions of dollars institutionalizing your mistakes. Tesla’s experience was exactly this: robots installed before the underlying process was clean and simple ended up being expensive obstacles to the eventual correct process.
The correct order is reverse. First make sure the part should exist (step 1). Then delete it if you can (step 2). Then simplify the part and the process around it (step 3). Then run it manually at maximum speed (step 4). Only after a human-run process is fast, simple, and clearly necessary do you automate it. By that point, the automation is purchasing leverage on a known-good system, not freezing a guess.
The book notes that automation done last is also cheaper to build, because the process being automated is simpler. Automating a 20-step process requires a 20-stage robotic system. Automating the 5-step version of the same process that emerged from steps 1 through 3 requires a 5-stage robotic system. The savings from doing steps 1 through 4 first show up directly in the capital cost of step 5.
How to Run The Algorithm: The 24-Hour Cadence
The book treats The Algorithm as a daily practice, not a one-time exercise. Maxim 22 in the 69 Core Musk Methods reads: “For critical items, have meetings every twenty-four hours to run The Algorithm and check progress from yesterday.” For any deliverable that is on the critical path, the team meets daily, walks through the five steps in order, and reports concrete progress on each step. Requirements that survived yesterday are re-questioned today. Parts that survived deletion yesterday are re-evaluated today. Steps three through five proceed in parallel with the continuing daily challenge of steps one and two. The cadence is what prevents The Algorithm from becoming a poster on the wall.
Common Failure Modes
The book identifies the specific ways teams skip steps. Skipping step 1 happens when a respected engineer’s requirement is treated as immutable; the fix is to make every requirement come from a named human and be re-justified on demand. Skipping step 2 happens when engineers prefer to optimize a part rather than delete it, because deletion creates immediate visible risk while optimization creates invisible long-term cost; the fix is the 10 percent restoration rule. Skipping step 3 in favor of step 4 happens when management demands speed before the system is clean; the fix is the “digging your grave” check before any acceleration program is approved. Skipping step 4 in favor of step 5 is the most expensive mistake and the one Musk says he personally committed at the Tesla Nevada and Fremont factories; the fix is the explicit rule that humans must run a process at speed before robots are introduced.
The throughline is that The Algorithm protects you from your own intelligence. Smart engineers are very good at steps three through five. They are bad at steps one and two because the schooling system that produced them never asked them to question the question. The order of The Algorithm is therefore the order in which discomfort decreases. Step 1 is the most uncomfortable. Step 5 is the most fun. Most organizations run the algorithm in fun-first order and pay for it with multimillion-dollar fiberglass-mat-style monuments to optimization without deletion.
Detailed Summary
The book’s structure and method
Jorgenson built the book entirely from Musk’s own words across decades of transcripts, tweets, and interviews. He notes explicitly that he edited for clarity, brevity, and flow, that all material is recontextualized, and that readers should verify phrasing with primary sources before citing. The four parts of the book are presented as a curriculum, not a biography. Part I lays the philosophical foundation. Part II teaches the operating tempo and methods. Part III applies those methods through the actual histories of Zip2, X.com/PayPal, Tesla, SolarCity, and SpaceX. Part IV widens the lens to civilizational risks and the multiplanetary mission. The bonus section, “The 69 Core Musk Methods,” compresses the whole book into a maxim-by-maxim reference. Naval Ravikant’s foreword frames the underlying claim: Musk’s methods are copy-able, and “if your motives are pure and greater than yourself, the world will conspire in its subtle ways to help you.” Jorgenson’s stated dream is “one million Musks.”
Part I: Pursue Purpose, the foundation of a unique life
Musk’s daily question is “how can I be useful today.” His success metric is mathematical: total impact equals number of people helped multiplied by magnitude of help per person. He identifies five domains as having the largest possible impact on the future of humanity: the internet, sustainable energy, space exploration, artificial intelligence, and the rewriting of genetics. He repeats that it is possible for ordinary people to choose to be extraordinary, that convention is not law, and that the best work is found at the intersection of what you are good at, what you enjoy, and what improves humanity. He warns against zero-sum thinking, framing the economy as a growable pie rather than a fixed one. He notes that consumer adoption is unreliable as a guide: a 1946 to 1948 survey found 96 percent of people would never buy a television, and Tesla heard the same about electric cars before launch.
The middle chapter teaches first-principles thinking. The technique is to break a problem into its atomic constituents (raw material costs, physics, basic operations) and reason up from there, ignoring analogy and precedent. The canonical example is battery cells. People said they would always cost about $600 per kilowatt-hour. Musk priced the actual materials at the London Metal Exchange (cobalt, nickel, aluminum, carbon, polymers, steel) and got $80 per kWh, proving cheap EVs were a manufacturing problem, not a physics one. He uses the same technique for rockets, where finished cost is typically 10 to 100 times raw-material cost. The half-nozzle jacket example: $13,000 list price, $200 of actual steel. He names two ratios that operationalize this: the magic-wand number (raw-material floor) and the idiot index (finished cost divided by raw-material cost). High idiot index means high opportunity. He also teaches “thinking in limits”: scale the variable to extreme values to expose hidden constraints, then iterate back to feasible regimes. His tunneling example is illustrative: LA subway costs about $1 billion per mile, but shrinking tunnel diameter from 28 feet to 12 feet drops cross-section 4x, and combining that with continuous tunneling and reinforcement enables an 8x cost improvement.
The third chapter of Part I makes the case for engineering itself. Science discovers what already exists. Engineering creates what did not. Engineering, Musk says, is magic, and engineers are the magicians of the 21st century. He grounds this historically: Roman military dominance came from metallurgy (martensitic steel swords) and roads (logistical advantage), and Rome fell when its technological edge was matched and routed around. The WW2 Pacific air war was won by the side with the faster innovation loop, not the side that started with better fighters. Nuclear weapons were the ultimate winner-take-all. Tesla’s powertrain is sold to Toyota, Daimler, and Mercedes precisely because it is hard. “If it was easy, they would do it.” The lesson is that durable value sits where the engineering is genuinely difficult, not where the marketing is loud.
Part II: Ultra Hardcore Work, teams, organization, urgency, manufacturing
Part II is the operating manual. The first chapter, “What It Takes,” argues that responsibility cannot be delegated. The CEO owns the worst problems, not the best ones. Physical presence and shared suffering communicate commitment more powerfully than any memo, which is why Musk literally sleeps on the factory floor. He talks about the ego-to-ability ratio: high ego breaks your reinforcement-learning loop with reality. He frames startups as “eating glass and staring into the abyss,” where glass is the work you do not want to do and the abyss is the constant threat of company death. He says adversity is the only forge that produces the pain threshold required to run a hard company at scale.
The teams chapter is uncompromising. The most important job of a leader is attracting exceptional people. Money is not the constraint; exceptional talent is. He runs a Special Forces hiring model: the minimum passing grade is excellence. A small group of technically strong people will always outperform a large group of moderately strong people. Character matters as much as skill, because skills are teachable and attitude is not. The feedback discipline he insists on is hardcore: “All bad news should be given loudly and often. Good news can be said quietly and once.” Camaraderie is dangerous when it suppresses truth. “It’s not your job to make people on your team love you. In fact, that’s counterproductive.”
The organization-design chapter teaches three rules. First, structure shows up in the product. Silos produce redundancy, waste, and error. Second, communication should travel the shortest path that solves the problem, not the chain of command. Anyone should be able to talk to anyone. Third, jargon and acronyms are cognitive pollution; the test for any internal phrase is whether a new hire would understand it cold. This is the chapter that introduces The Algorithm (covered in depth above).
Musk runs his companies on what he calls a “maniacal sense of urgency.” The only true currency is time. Speed is both offense (faster innovation than competitors can copy) and defense (the SR-71 Blackbird has almost no defense system except acceleration). The protection of real intellectual property is not patents but rate of innovation; if you ship faster than anyone can copy, you do not need legal moats. He stresses parallelization over serialization. “A factory moving at twice the speed of another factory is basically equivalent to two factories.” Be a vector, not a scalar: high speed in the right direction, with continuous course corrections like a guided missile.
The Part II close is “We Must Make Stuff.” Manufacturing is underrated and design is overrated. “There is 1,000 percent, maybe 10,000 percent more work that goes into the production system than the product itself.” The factory is the product, not the car. Designing a rocket is trivial compared to making one that reaches orbit. The production line moves at the speed of its slowest, least lucky part. Out of 10,000 things that have to go right, the one that is not working sets the rate. Manufacturing combined with scale becomes the moat. The gigacast machine story illustrates this perfectly: Musk got the idea from toy cars, asked if any law of physics prevented it, surveyed six casting-machine suppliers, five said no, the sixth said maybe, and Tesla used that single innovation to cut the body shop by 30 percent.
Part III: Building Zip2, PayPal, Tesla, and SpaceX
Musk left Stanford grad school in 1995 with $110K in debt and founded Zip2 with his brother Kimbal, starting with $2,000 and one computer in a squatted office where he slept on a futon and showered at the YMCA. In 1999, Compaq acquired Zip2 for over $300 million. His after-tax bank account went from $5,000 to $21 million. He immediately rolled $12.5 million of that into X.com, which merged with Confinity in March 2000 to become PayPal. PayPal reached 100,000 customers in its first month and one million by year two with no sales force and no marketing spend. The product traction came from email payments, not from the conglomerate financial-services pitch X.com started with. Musk’s lesson: “listen well, correct fast.” He was removed as CEO during his honeymoon trip in early 2002 but did not contest it, prioritizing company survival over personal vindication. eBay acquired PayPal in October 2002 for $4.5 billion. “Life is too short for long-term grudges.”
Tesla started in 2003. The original Roadster used a Lotus Elise chassis; the modification added 40 percent weight and invalidated the crash tests. Only 7 percent of Roadster parts ended up shared with the Elise. Musk’s lesson: start clean-sheet, do not modify legacy platforms. The Tesla Master Plan (August 2006) was the sequencing logic: (1) build a sports car, (2) use the profits to build an affordable car, (3) use those profits to build a mass-market car, (4) provide zero-emission power generation. This sequence was forced by the unit economics of new technology, where you cannot start at the bottom of the market without scale.
Tesla nearly died at the end of 2008. The SolarCity Morgan Stanley deal had collapsed. Tesla and SpaceX were both on the brink. Musk had moved into Jeff Skoll’s guest bedroom because he had no house. The final emergency funding round closed at 6 p.m. on Christmas Eve, hours before payroll would have bounced. Daimler arrived shortly after; Musk’s team rapidly dropped a Tesla powertrain into a Smart Car and got it to 60 mph in 4 seconds, which shocked Daimler into a $50 million investment. Tesla then survived three years of Model 3 manufacturing hell from 2017 to 2019, during which Musk lived in the Fremont and Nevada factories, slept on the floor, and ran around fixing the line. “The longest period of excruciating pain in my life.” His pricing philosophy is “give people more for less”: spend money on engineering and design instead of advertising, and let the product carry word of mouth.
SpaceX was founded in mid-2002 with $100 million of Musk’s PayPal proceeds. He expected to lose everything; that was his stated expectation going in. There was no external funding for three years. His initial plan was a $90 million Mars greenhouse mission designed to inspire NASA, but he realized the binding constraint was launch cost, not mission design. He tried to buy Russian ICBMs to cut launch costs; that failed. He then ran the first-principles rocket cost analysis, found that finished cost was 50 to 100 times raw-material cost, and concluded the industry’s pricing was a function of cost-plus contracting, five-layer subcontracting, and legacy tech. He budgeted for exactly three failed Falcon 1 launches. Launches 1, 2, and 3 failed (2006, 2007, 2008). Launch 4 succeeded in August 2008. Days later NASA awarded SpaceX a $1.6 billion cargo resupply contract. Musk reportedly screamed “I LOVE NASA. YOU GUYS ROCK.” The fourth-launch success and the NASA call together saved both SpaceX and (indirectly, via Musk’s bank account) Tesla.
SpaceX’s actual optimization target is “fastest time to a self-sustaining city on Mars.” That goal cascades to “fastest time to a fully usable rocket,” which cascades to “fastest time to orbit.” Early Starship had no doors because doors are not necessary for reaching orbit. The unifying engineering insight is that full and rapid reusability is the holy grail of rocketry, because once a rocket is reusable, the only marginal cost is propellant (mostly liquid oxygen and methane, around $1 million per Starship flight). Current cost per landed ton to Mars is about $1 billion. Starship targets less than $100,000 per ton, a 10,000x improvement. Musk’s philosophy on testing reflects the design constraint: unmanned rockets should be allowed to blow up so the team can learn; crewed systems get extreme conservatism. The Space Shuttle’s safety record suffered precisely because the asymmetry of risk made the program incapable of iteration.
Part IV: The Age of Abundance, the seven risks, and Mars
Musk frames his companies as philanthropy, defined by reality rather than perception. “If you care about the reality of goodness instead of the perception of it, philanthropy is extremely difficult.” Companies create durable wealth because they solve real problems at scale, distribute knowledge through products, and deploy capital toward problems rather than store it idle. The companies he names as worth starting today: tunneling (Boring Company), synthetic-RNA medicine (“the digitization of medicine”), and high-speed transport such as Hyperloop (a pressurized electric vehicle in a vacuum tube, faster than aircraft, weather-independent).
The Age of Abundance chapter argues that AI plus humanoid robotics will eventually remove labor as the binding economic constraint, producing abundance for everyone. Humanoid robots will start in dangerous and repetitive jobs and eventually outnumber humans 2 to 10 to one at less than the cost of a car. Tesla’s full self-driving and Robotaxi will, in Musk’s projection, make Tesla a $10 trillion company because autonomous cars are worth 5 to 10x non-autonomous cars (they earn revenue when owners are not using them). Neuralink achieved 2 bits per second of brain output with first patient Noland Arbaugh; the 5-year target is one megabit per second. Long-term Neuralink applications include consensual telepathy between two BCIs, vision restoration (Blindsight), and multispectral senses. Musk’s framing: humans are already cyborgs through phones and laptops, but the bandwidth to those devices is “poking glass with your meat sticks” and BCIs are the next bandwidth jump.
The Existential Risks chapter names seven specific risks. World War III: the cycle of major-power war recurs and global thermonuclear conflict could end or maim civilization. Regulation accumulation: laws never die when humans do, regulations compound forever, and eventually everything becomes illegal. California High-Speed Rail is the example: after billions of dollars, it is “almost illegal to build.” Wars historically cleared regulatory cobwebs; peacetime allows infinite accumulation. Unsustainable energy: regardless of climate, hydrocarbons are finite, so the transition must happen. Nuclear plants should not be shut down (coal is 100 to 1,000x worse for health than nuclear). The energy mix is solar plus wind plus batteries plus nuclear plus hydro plus geothermal. Misaligned artificial superintelligence: AI is growing faster than any prior technology, and Musk considers it “a significantly higher risk than nuclear weapons.” The specific mitigation he names is rigorous truth adherence in training. The HAL 9000 lesson from 2001 is that an AI forced to lie becomes dangerous; he cites the Gemini “George Washington wasn’t white” failure as a concrete example of ideological training producing catastrophic outputs at scale. Population collapse: low birth rates are a slow civilizational death. The US has been below replacement since the early 1970s. China is 40 percent below replacement; the three-child policy failed. “We need to revive the idea of having children as a social duty.” Musk himself has 12 children across three women. Asteroids and comets: Earth has no defense against a large comet; Starship gives some capability against small asteroids. Shoemaker-Levy left an Earth-sized hole in Jupiter, and that level of impact on Earth is “game over.” Civilizational fragility itself: every prior civilization fell, and Stephen Hawking estimated roughly 1 percent probability of civilizational end per century. “That’s Russian roulette where 99 barrels are empty. Every century is a click.”
The closing chapter, Becoming Multiplanetary, places Mars colonization in evolutionary context. Earth has had six milestones in 4 billion years: single-celled life, multicellular life, plants and animals, ocean-to-land transition, consciousness, and (potentially) multiplanetary life. Musk argues this last step is “at least as important as life going from the oceans to land, probably more significant,” because it makes the substrate of consciousness redundant. Sun expansion will destroy Earth in roughly 500 million years; meanwhile self-inflicted or external extinction events are recurring, with five major mass extinctions already in the fossil record and Yellowstone erupting roughly every 700,000 years. The plan: produce 1,000 Starships per year, refuel in orbit, hit 10,000 missions and 1 million tons to Mars by approximately 2044, then build out a self-sustaining city. Mars trips depart in 2-year windows when planets align; Musk’s working schedule is 5 uncrewed missions in 2026 and crewed missions in 2028 if the uncrewed go well (otherwise +2 years). For terraforming, his named options are thousands of solar reflectors in orbit or thermonuclear detonations over the polar caps as “two little suns” to vaporize CO2 ice, thicken the atmosphere, and eventually produce liquid water oceans roughly a mile deep covering 40 percent of the planet. Cost of the entire civilization-insurance bet: less than 1 percent of Earth GDP.
The 69 Core Musk Methods
The bonus section compresses the entire book into 69 short maxims, intended as a copy-able reference. They are reproduced here near-verbatim.
You are capable of more than you think.
It is possible for ordinary people to choose to be extraordinary.
You can teach yourself anything. Read widely. Talk to experts.
Assume you are wrong. Aspire to be less wrong.
Internalize responsibility.
If we don’t make stuff, there is no stuff.
Creating products and services creates wealth.
A useful life is worth having lived.
Don’t aspire to glory. Aspire to work.
Take actions that increase the odds of the future being good.
Every day, you either increase the rate of innovation or it slows down.
Work on what is just becoming possible.
Don’t wait for the world to want it. If it should obviously exist, go build it.
Build what no one else is building.
As you move forward, allies will assemble around you.
Prototypes are proof.
Start somewhere. Question assumptions. Adapt to reality.
Reason from fundamentals, not from what others are doing.
The magic-wand number. See the theoretically perfect and work toward it.
Know the idiot index. Understand the cost of components.
The Algorithm: Question Requirements, then Try to Delete, then Simplify, then Accelerate, then Automate.
For critical items, run The Algorithm in 24-hour meetings to check progress.
Stay as close to the actual work as possible. Do not separate yourself from the pain of your decisions.
All requirements should be treated as recommendations.
The only fixed laws are the laws of physics.
The best part is no part. The best process is no process.
Simplicity creates both reliability and low cost.
Find the design necessity of every part and every process.
Overdelete. Add back only the absolutely necessary.
Push for radical breakthroughs.
Be proactive. You will never win unless you take charge of setting the strategy.
A maniacal sense of urgency is the operating principle.
A factory moving at twice the speed of another factory is basically equivalent to two factories.
Attack the bottleneck. The one thing that isn’t working sets the overall production rate.
You’ll move as fast as your least-lucky or least-competent supplier.
Do things in parallel.
Give teams one key metric to focus on. Video games without a score are boring.
Separating design, engineering, and manufacturing is a recipe for dysfunction.
Speed of innovation is what matters.
Beat competitors on speed, quality, and cost. Not anti-competitive behavior.
Test the absurd. When something seems impossible, ask “what would it take.”
Money is not the constraint. Exceptional engineers are.
Get everyone thinking like the chief engineer.
Get a clear, direct feedback loop with reality.
Always be smashing your ego. Ensure ability is greater than ego.
Ask “is this effort resulting in a better product or service.” If not, stop.
Good taste is learnable. Train yourself to notice what makes something beautiful.
Physics doesn’t care about hurt feelings. Make the rocket fly.
Empathy is not an asset.
Use simple, clear, humble terms.
Go directly to the source of information.
When hiring, look for evidence of exceptional ability.
Combine engineering and financial fluency.
To truly lead the product, lead the company.
Lead from the front. Sleep on the factory floor.
Physically move yourself to wherever the problem is. Immediately.
All bad news should be given loudly and often. Good news can be said quietly and once.
Failure is essentially irrelevant unless it is catastrophic.
Fear of failure is the biggest cause of failure.
Feel the fear and do it anyway.
Double down. Push your chips back in.
Work like hell. Every waking hour. Go ultra hardcore.
Make sure you really care about what you’re doing, and take the pain.
We should not be afraid of doing something important just because tragedy is possible.
When something is important enough, do it even if the odds are not in your favor.
Don’t ever give up. You’d have to be dead or completely incapacitated.
Play life like a game.
Go ultra hardcore.
Humor is a differentiator.
Thoughts
The most underrated artifact in the book is The Algorithm, and the reason it is underrated is that it looks deceptively simple. Five steps. Anyone can recite them. Almost nobody runs them in order. The book’s central operational insight is that the sequencing is the whole game. People skip step one because it is uncomfortable to confront the fact that requirements they have spent years optimizing against came from somebody whose name they cannot remember. They skip step two because deletion creates risk that materializes immediately and the benefits show up later. They jump to step three because optimization feels like progress and is graded well in school. Then they jump to step five because automation looks impressive on a dashboard. Tesla’s $2M robotics step on the fiberglass mat would never have existed had the team run the steps in order. Most companies, at any scale, are sitting on enormous unrealized value the same way Tesla was, locked behind the simple act of asking “what is this part actually for, who told us we needed it, and would anything bad happen if we deleted it.”
The second insight worth sitting with is the magic-wand number paired with the idiot index. These two ratios together turn first-principles thinking from a vague aspiration into an operational discipline. Any product you can buy or any process you run has a raw-material cost (the magic-wand number, the absolute floor) and a finished cost. The ratio between them tells you the upper bound on how much you can improve. A high idiot index is not a moral failing of the supplier; it is an unpriced opportunity that competition has not yet found. Once you train yourself to ask these two questions about every line item, the world rearranges. Rockets that cost 50x their steel become a problem to solve. Tunnels that cost a billion dollars per mile become an obvious target. Battery cells that cost 7.5x their materials become a startup. The discipline is not “be smart.” The discipline is “calculate both numbers.”
The third theme is what the book calls “manufacturing is the moat,” and it is the part of Musk’s playbook that most observers, including most of his competitors, still underestimate. The book’s claim is not that design is unimportant. The claim is that production is between 1,000 and 10,000 percent more effort than design, and that nobody outside of practitioners understands the asymmetry. This is why Toyota and Daimler buy electric powertrains from Tesla rather than make them. It is why SpaceX spent 10 to 100 times more engineering on the Raptor manufacturing system than on the Raptor engine. It is why Apple’s contract manufacturers, not its designers, are the durable moat. The same logic now applies to AI infrastructure: the supercluster, the cooling, the power smoothing, the cabling at 3 a.m., the Megapack buffers, are the actual moat, and the model architecture is the visible-but-cheaper layer on top.
The fourth theme is the way responsibility, ego, and feedback interact in Musk’s organizations. Most CEOs are insulated from the consequences of their decisions by layers of process and middle management. The result is a high ego-to-ability ratio, because the feedback loop between the ego’s prediction and reality’s response is intermediated to the point of uselessness. Musk’s defense is physical: sleep where the work happens, walk the factory floor at 3 a.m., personally answer the questions, run cabling himself if necessary. This is not theater. The epistemic claim is that decisions made by an insulated CEO are systematically worse than decisions made by a CEO whose body is in the same room as the problem. The cost is severe in personal terms (“the longest period of excruciating pain in my life”), but the alternative is making confident decisions on a model of reality that has drifted out of alignment with the actual machine. The same logic applies to engineers who do not see their designs in production, founders who do not talk to customers, and leaders who delegate the worst problems to people they did not pick.
The fifth theme is the seven existential risks and why Mars sits at the center of them. The book’s framing is that any single risk is small, but compounded across centuries the probability of civilizational discontinuity is large. Hawking’s 1-percent-per-century estimate, repeated for 10 centuries, gives roughly a 10 percent cumulative probability. Across the timescales humanity has already survived, those odds are unacceptable for a species that can afford a backup. The Mars argument is not romanticism. It is a 1-percent-of-GDP insurance premium on the persistence of consciousness itself. The other six risks (war, regulation accumulation, energy exhaustion, misaligned AI, population collapse, asteroids) are presented in the same actuarial frame: each is independently survivable, but the cost of treating them as low-probability is precisely the cost a previous civilization paid by treating its own near-misses as low-probability until the one near-miss that wasn’t. The most uncomfortable specific risk in the book is population collapse, which is the only one where doing nothing is doing the wrong thing and where the demographic numbers are already locked in for decades regardless of policy response.
The sixth and final point is the book’s underlying claim, which is also Naval’s claim in the foreword: Musk’s methods are copy-able. The book exists because Jorgenson believes that one million Musks would change the trajectory of the species. The 69 Core Musk Methods are not a personality cult. They are a starter kit. Most people will not pick the same problems, will not have the same tolerance for pain, and will not run the same companies, but anyone can apply The Algorithm to their own work, calculate the idiot index on their own product, demand requirements come from named people, attack the bottleneck on their own line, refuse to automate before deleting, and pick a problem that is on the path to the future. The book is best read as a manual, not a biography. If it ends up next to your laptop and you reread The Algorithm chapter every six months and the 69 Methods every quarter, that is the use Eric and Naval intended.
Get The Book of Elon by Eric Jorgenson at elonmuskbook.org or wherever you buy books.
Mark Manson sat down with Chris Williamson on Modern Wisdom for a long, dense conversation built around 21 short pieces of advice that explain why people get stuck in their lives, their relationships, their work, and their own heads. The episode runs the full Manson canon. Why uncertainty is the most important skill of the 21st century. Why easy wins are forgettable and hard ones change you. Why “Can I live with this person’s version of a Tuesday for the next 10 years” beats almost every other dating filter. Why learning more is a smart person’s favorite form of procrastination. Why neediness is the unified theory of unattractiveness. The Rory Sutherland air fryer girlfriend theory. The AI vampires phenomenon. The audience capture versus criticism capture distinction. The one minute version of 10 years of therapy. A long detour through fame, the manosphere, and Jordan Peterson. And the line that ties the whole thing together: at some point you realize the permission you have been waiting for was your own. Watch the full conversation here.
TLDW
Mark Manson argues that almost everyone who feels stuck is stuck on a small number of repeating mistakes, not a unique pathology. The 21 reasons are the same 10 ideas wearing different outfits. People cannot tolerate uncertainty so they collapse into rigid worldviews and anxiety spirals. They optimize for peak experiences and romantic chemistry instead of choosing a partner whose ordinary Tuesday they can stand. They confuse convenience with progress and end up frictionless, efficient, and meaningless. They mistake learning, insight, and information consumption for actual change, then go shopping for one more book, one more coach, one more retreat. They make decisions based on what other people will think instead of what they themselves think, which Manson defines as neediness, the single biggest predictor of unattractiveness in dating and most other domains. They envy lives whose sacrifices they cannot see, want results without the process, and quietly wait for someone to give them permission to do the thing they already know they want to do. Manson and Williamson trade these themes across an interview that also touches on AI productivity, the manosphere, Jordan Peterson’s preience, audience capture versus criticism capture, the Tuesday test, the air fryer girlfriend theory, victimhood Olympics, Alex Hormozi’s “blame equals give power to” reframe, Stan Tatkin’s idea that every relationship is a set of agreements with the first one being “the relationship comes first,” and a closing argument that the permission you have been waiting for has always been yours to give yourself.
Key Takeaways
The most important skill in the 21st century is the ability to live happily with uncertainty. People who cannot do this overindex on one belief, then suffer or double down on delusion when reality contradicts it.
Anxiety is the attempt to compress uncertainty by war-gaming every possible bad future. The trick is that imagining outcomes creates more surface area for things to go wrong, not less.
The antidote to needing certainty is widening your aperture. You cannot be sure whether your specific job survives AI, but you can be confident that humanity has adapted to every previous technological revolution.
State confidence comes from putting yourself in situations you have done a thousand times. Trait confidence comes from living through enough chaos that you stop expecting things to go as planned.
Do hard things not because they are fun but because the win means something. You bled for it. You broke for it. You earned it. Easy wins are forgettable. Hard ones change you.
There is an inverse relationship between convenience and significance. Everything technology has made seamless, frictionless, and faster has quietly stripped meaning from the parts of life that used to require effort.
AI regresses you back to the mean. If you are in the bottom 50 percent at a task, it pulls you up. If you are in the top 50 percent, it pulls you down. The implication is that AI is most dangerous when you outsource the things you were actually good at.
When you choose a partner, you are choosing a whole lifestyle, not a person. You are choosing their sleep schedule, their money habits, their family drama, their coping mechanisms, and their version of an ordinary Tuesday.
Love does not cancel out flaws. It just makes you tolerate them for longer. The right question to ask about a potential partner is not “do we have chemistry” but “can I live with this person’s version of a Tuesday for the next 10 years.”
The Warren Buffett trick: write down 20 things you want in a partner, rank them, then cross out everything but the top three. Negotiate on the rest. Everyone settles on something. Nothing falls below your floor, but you are not chasing a ceiling.
Rory Sutherland’s air fryer girlfriend theory: look for a partner whose disadvantages only you can tolerate and whose value only you can see. Sutherland bought a cottage next to a pub backing onto a railway because he loves trains and beer and does not mind noise. He got a discount most buyers would not.
True equality is when you have to put up with the same amount of crap as everyone else. Treating people with kid gloves because of identity is a kind of patronizing bigotry dressed up as empathy.
Nobody owes you patience because you had a rough day or a rough upbringing. Use pain as fuel, not a crutch. You build psychological resilience by getting better at feeling bad.
Alex Hormozi’s reframe of blame: redefine the word “blame” as “give power to.” The only person you should ever want to give more power to is yourself.
If you have to explain to someone why you deserve respect, you are in the wrong relationship. The macro version of this is real. The micro version is normal maintenance and healthy. Confusing the two ruins relationships in both directions.
Stan Tatkin’s audiobook “Your Brain on Love” frames every relationship as a set of agreements, and the first agreement has to be “the relationship comes first.” If that agreement is met by one party but not the other, the relationship will always be high friction.
You can tell whether you are actually being prioritized by how much your partner puts you first when they have nothing to gain from it. Lovebombing while extracting something does not count.
Beware: learning more is a smart person’s favorite form of procrastination. Insight is the personal development version. Both let you avoid the part where you actually have to do the thing.
The unified theory of male attractiveness is non-neediness. Neediness occurs when you place a higher priority on what others think of you than on what you think of yourself. Every “needy” behavior is just that same root showing up in a different domain.
The Midwit meme of dating is real. The smart and the wise both say “just go talk to people.” The middle 80 percent stuck in the middle is busy reading evolutionary psychology and pickup theory while everyone else moves on.
The manosphere is the incorrect solution to the correct problem. Young men are struggling and it is not talked about enough. But the packaging is poison even when the underlying advice is useful.
Jordan Peterson timed the cultural market correctly but pivoted to God and got ill at the moment his original message was most needed, which created the vacuum the angrier manosphere filled.
Ethan Strauss’s idea of “criticism capture” is more deranging than audience capture. Trump became more Trump, Tate became more Tate, and Hasan Piker became more Hasan Piker because of how aggressively they were attacked, not because of how loudly they were praised.
“AI vampires” are programmers and partners now working harder than ever, bags under their eyes, euphoric, hyperproductive, including one a16z partner who built an entire AI system to do his job without ever looking at the code.
You only envy the lives of people whose sacrifices you cannot see. If you could see the cost, you would not want the life. If you got the life without the cost, you would not appreciate it.
James Clear: “It does not make sense to continue wanting something if you are not willing to do what it takes to get it. If you do not want to live the lifestyle, release yourself from the desire. To crave the result but not the process is to guarantee disappointment.”
George Mack’s Call of Duty versus war analogy: people think they want to be world famous musicians, founders, or champions. What they actually want is the highlight reel. The 95 percent of the work is alone in a room repeating the same task a thousand times until it is impossible to get wrong.
Manson’s 10 years of therapy in one minute: 1) no one is coming to save you, 2) strong boundaries make good relationships, 3) many problems do not get fixed, you just learn to live despite them, 4) your mind lies to you, learn to tell it to shut up, 5) stop trying to convince people to like you, 6) sometimes the best thing is to let a dream die, 7) only a few people matter long term, treat them right.
Memento mori is more useful than any productivity hack. Your time is limited and everyone you love will die. Put the phone away and do something meaningful. Ask yourself periodically: if I died soon, is this what I want to be doing?
The first generation of people who spent a significant portion of their life on a smartphone is roughly a decade away from large-scale dying. Manson and Williamson predict “I wish I spent less time looking at a screen” will be the new number one regret of the dying.
Most personal development information is now free, diffuse, and saturated. The marginal value of new ideas is collapsing. The new bottleneck is implementation, not knowledge. Authority and credibility are going to make a comeback as AI slop floods every channel.
You cannot skip the early information-hoarding phase of personal development. New people need to read everything: Getting Things Done, Atomic Habits, Psychology of Money, The Subtle Art. You earn the right to dismiss the genre only after you have absorbed it.
British culture optimizes for piss-taking and snark. American culture optimizes for encouragement. Williamson, a British man with a “lifestyle-wide praise kink,” argues the American version is healthier for anyone trying to do something hard.
The UK lost the most millionaires per capita of any country in 2024. Possible cause: a culture that punishes people for planting a flag in the ground and committing to a position.
At some point you realize the permission you have been waiting for all along was your own. Most of the time, people who ask for advice are not asking for information. They are asking for someone to tell them it is okay to do the thing they already want to do.
Detailed Summary
Uncertainty is the master skill
Manson opens the conversation with the line that the most important skill in the 21st century is the ability to live happily with uncertainty. Access to information has scaled, but confidence in any of it has collapsed. Everyone feels less moored to reality than ever. The danger of not being able to handle uncertainty is that you collapse into a single belief, a single worldview, and put all your emotional well-being on top of it. Then reality contradicts it and you either suffer immensely or double down on delusion. Anxiety, he says, is the attempt to compress uncertainty by simulating every possible bad outcome. The catch is that running those simulations creates more surface area, not less. Williamson points out that humans would rather imagine catastrophic specific scenarios, including dead grandmothers returning from the grave, than sit with “I do not know.” Manson’s antidote is to widen the aperture. You cannot be certain whether your specific job survives AI. You can be reasonably certain that humanity has survived every previous technological revolution. Confidence at scale is available even when local confidence is not.
Friction is what makes anything mean anything
The second theme is friction. Easy wins are forgettable, hard wins change you. Manson has been thinking about the inverse relationship between convenience and significance: anything handed to you, you take for granted, and an enormous amount of modern technology is dedicated to handing things to people. He cites a story about his wife and a childhood friend who had been avoiding a phone call because phones are annoying, and how the moment they actually got on the line for ninety minutes they re-anchored a relationship. Phone calls are annoying. Annoyance is the connective tissue. The dating app argument is the same point at a different scale: optimizing for convenience of introduction strips out the filtering friction that would have told you whether this person was worth your time. The video game cheat code analogy lands hard. Technology over the past 20 years has been adding cheat codes to every domain of life. Crushing everything is fun for ten minutes, then it stops feeling like anything. Williamson agrees and notes that COVID was a lifestyle Rorschach test. Everyone’s life either got dramatically better or dramatically worse. Nobody’s stayed the same. Difficulty exposure therapy is what built the trait confidence to handle it well.
AI regresses you to the mean
Williamson floats the framing that AI regresses you back to the mean. If you are in the bottom 50 percent at something, AI improves your output. If you are in the top 50 percent, AI degrades it. The implication is that AI is most dangerous when you outsource the work you were actually good at. Manson, who owns an AI personal growth company called Purpose, agrees and pushes the point further: as cheat codes pile up, the responsibility shifts to the individual to deliberately introduce difficulty back into their life. Use AI but force yourself to still do original work. The path of least resistance is going to win unless you build the muscle to resist it, and most people do not have that muscle.
Choose the Tuesday, not the chemistry
When you select a partner, you are choosing a lifestyle, not a person. You are choosing their sleep schedule, money habits, family drama, level of cleanliness, work ethic, coping mechanisms. Love does not cancel out flaws. It makes you tolerate them for longer, which is exactly the problem. Most people optimize for romantic chemistry because that is what floods you when you meet someone exciting. The harder and more accurate question is “can I live with this person’s ordinary Tuesday for ten years.” Manson borrows the line from a Tim Ferriss podcast working title “Crushing a Tuesday”: optimize for the middle of the bell curve of how this person actually lives, not the peaks. Williamson layers in Rory Sutherland’s “air fryer girlfriend” theory: pick a partner whose value only you can see and whose disadvantages only you can tolerate. Sutherland bought a cottage at a discount because it was next to a pub backing onto a railway, and he loves trains and beer. Most people would not. The same logic works in relationships. Manson is even-keeled and his wife is high-emotion. Strength to weakness. The fit was always going to be good. He also pushes the Warren Buffett trick: write down 20 things you want, rank them, cross out everything but the top three. Negotiate on the rest. Everyone settles on something. The goal is “nothing falls below my floor,” not “this person hits every ceiling.”
Personal responsibility and the victimhood Olympics
Manson revisits a section from one of his books that he called the victimhood Olympics. The activist culture of the late 2010s and early 2020s effectively rewarded people for accumulating identities to claim greater grievance. Williamson reads a long Alex Hormozi response to this whole dynamic: if you had disadvantages, I agree, life is harder, but you only have one choice, which is what you are going to do about it. Take action anyway and become proof to others that they can too, or blame and complain. Both are choices, but only one makes you better. Hormozi’s reframe is to redefine “blame” as “give power to,” because whoever you blame is the person you have given power over your life. The only person you should ever want to give more power to is yourself. Williamson and Manson agree that the heavily caveated communication style of the era was driven by fear of being seen to lack empathy, but the empathy itself was often performative, shallow, and patronizing. True equality, Williamson argues, is having to put up with the same amount of crap as everyone else. Wrapping any group in cotton wool because you assume they cannot handle reality is a kind of bigotry disguised as kindness.
Respect, micro problems, and macro problems
Williamson reads the line that if you have to explain to someone why you deserve respect, you are already in the wrong relationship. Manson amends it in real time. At the macro scale, true. If you have to beg for the basics, the relationship is broken because the basics are non-transactional and asking for them turns them into transactions. At the micro scale, asking matters and is healthy. Saying “I am feeling unappreciated right now, can we work on this” inside an otherwise solid relationship is normal maintenance. The damage comes from people who confuse the two: they have a partner with macro-level disengagement and treat it like a minor scheduling problem. Manson uses his own marriage as a case study. He is a hopeless workaholic. Every three or four years his wife has to interrupt the cycle and demand a Sunday off. She is augmenting his life, not competing with it. Williamson then reaches for Stan Tatkin’s audiobook “Your Brain on Love,” which describes every relationship as a set of agreements, with the foundational one being “the relationship comes first.” If one party agrees and the other does not, the relationship will always run hot, and the person who is more invested should leave to find someone for whom the relationship is not their fourth priority. The corollary: you can detect real prioritization by how much someone puts you first when they have nothing to gain from it.
Learning is a smart person’s favorite procrastination
Beware: learning more is a smart person’s favorite form of procrastination. Smart people are good at learning, learning feels like progress, and it is easy to convince yourself that consuming one more book will help you do the thing. But the function of learning often becomes insulating yourself from the pain of potentially failing publicly by postponing the moment you have to actually try. The personal development twin of this is insight: a million seminars, three coaches, a meditation retreat, IFS, Hoffman, ayahuasca, repeat. At some point, all of that has to be digested by living. Williamson admits he procrastinated launching the podcast for about a year deciding on a name. He admits his entire university experience was a longer version of the same instinct: pick something that feels transactionally useful instead of what he actually wanted to study. Manson admits his health was the version of this that nearly broke him. He could lecture anyone on metabolic dysfunction and insulin resistance while overweight, eating pizza nightly, drinking whiskey, going to bed at 3am. A coach finally said “dude, just go to the gym.” The unifying lesson: information is a placeholder for action and infinite information is therefore infinite placeholder.
Neediness as the unified theory of attractiveness
Williamson pulls the classic Manson formulation from the book “Models,” now almost 15 years old: neediness occurs when you place a higher priority on what others think of you than on what you think of yourself. Anytime you alter your words or behavior to fit someone else’s needs rather than your own, that is needy. Anytime you lie about your interests to seem more attractive, that is needy. Anytime you pursue a goal to impress others rather than fulfill yourself, needy. The why behind your behavior determines whether the same external act reads as confident or desperate. Manson tells the origin story. From 2008 to 2013 he was meeting dozens of guys in person as a dating coach. The men’s dating advice space was fragmented into texting game, openers, first dates, second dates. He kept noticing that the men who were genuinely good with women were good across all of those domains and the men who were bad were bad across all of them. The unified variable was self-perception. Men who prioritized their own perception of themselves over the woman’s were attractive. Men who let everything they said, wore, and did be dictated by “what will she like” were not. The PUA-hate-to-red-pill movement, Manson argues, was born from men who either could not make a system work for them or made it work and resented how much they had to contort themselves. Both groups failed to ask whether the underlying model was broken.
The manosphere, Jordan Peterson, and criticism capture
Manson recently filmed an episode of Jubilee’s “Surrounded” against manosphere figures. His thesis on stage was that the manosphere is the incorrect solution to the correct problem. Young men are struggling, the crisis is real, and the advice embedded in some of that content is fine on its own terms, but the packaging is toxic. Williamson and Manson agree that Jordan Peterson’s timing was unlucky. He correctly identified the cultural catastrophe years before everyone else, then got ill and pivoted hard into religious questions at the exact moment his original message was most needed. The vacuum was filled by angrier figures. They then bring in Ethan Strauss’s idea of “criticism capture” being more deranging than audience capture. Trump became more Trump because of how much people pushed back. Tate became more Tate. Hasan Piker became more Hasan Piker. Almost everyone who ends up in a militant, uncompromising public posture got there by being heavily attacked. Praise does not produce the same effect. The corollary is that some people have the skill set to become famous but not the disposition to handle fame. Williamson cites Louis Capaldi developing a Tourette-style tic from anxiety on tour, breaking down at Glastonbury, then returning years later regulated. Most middling-fame celebrities, Manson observes, have no playbook for any of this. Will Smith has a literal protocol team. Most people who are famous enough to be hurt by fame have nothing.
Envy, process, and the cost of what you want
You only envy the lives of people whose sacrifices you cannot see. If you saw the cost, you would not want the life. If you got the life without the cost, you would not appreciate it. The Elon line lands here: people think they want to be me, they do not want to be me, my mind is a storm. Williamson reads the James Clear quote that crystallizes the point: it does not make sense to continue wanting something if you are not willing to do what it takes to get it, and to crave the result but not the process is to guarantee disappointment. Manson loves this and pairs it with George Mack’s Call of Duty versus war analogy. People who think they want to be famous musicians are picturing the stage. The stage is five percent. The other ninety-five percent is alone in a room playing the same song hundreds of times. You are not practicing until you get it right. You are practicing until it is impossible to get wrong. If you do not love that part, the dream is not actually yours. The line Williamson borrows from Manson: every pursuit worth having comes with pain. The only real question is which flavor of chips sandwich you want to eat.
Ten years of therapy in one minute
Williamson reads Manson’s seven-line distillation. No one is coming to save you. Strong boundaries make good relationships, weak ones make drama. Many problems do not get fixed, you just learn to live despite them. Your mind lies to you constantly about how catastrophic everything is, learn to tell it to shut up. Stop trying to convince people to like you, the right ones do not need convincing and the wrong ones will only get more annoyed. Sometimes the best thing you can do is let a dream die. Only a few people in your life will matter in the long run, treat them right when you find them. Manson reflects that all of this should be taught in schools and is not. His perspective on personal growth has shifted over 17 years. Early on he thought it was about finding the right ideas. Now he thinks it is about finding rituals and reminders that keep obvious truths in front of your face, because the day-to-day pulls you away from them. Religion did this job for most of human history. The modern equivalent is podcasts, books, and social feeds that just keep restating principles people already know.
The new bottleneck is implementation, not knowledge
Manson lays out a brief history of his industry. When he was coming up you had to pay Tony Robbins ten thousand dollars to hear this material. You had to apply to a Cornell graduate program. There were massive gatekeepers. The 2010s wave of writers and podcasters, including Manson, Williamson, Ryan Holiday, James Clear, and others, repackaged that gated knowledge for an open internet audience. That function is now mostly done. Instagram serves eight hundred versions of the same advice per day. Williamson predicts and Manson agrees that the next phase will be ironic: AI slop will saturate the channel so badly that authority and credibility come back into demand. People will get tired of stick figures on YouTube and crave verified expertise. The corollary for individuals: you cannot skip the early flood-yourself-with-information phase. If you are a 25-year-old who has read nothing, lock in for six years. After that, ninety-five percent of the material is packaging and the principles repeat. The work is reminding yourself, not learning more.
Memento mori and the new number-one regret of the dying
Williamson reads the memento mori line: your time on this earth is extremely limited and everyone you love is going to die one day, so put the phone away and do something meaningful. Manson recounts that the final chapter of “The Subtle Art” is called “And Then You Die” and that his publisher pushed back on ending the book that way. He insisted because confronting death had been formative in his own life. He did not expect that section to be the most mimetic part of the book, but it has been. Williamson asks what people will regret on their deathbed in the smartphone era. The current top five regrets of the dying come from a generation that did not grow up on phones. He and Manson predict that within ten to twenty years, “I wish I spent less time looking at a screen” will be the new number one. Manson offers a related diagnostic: look at the last twelve months, ask what you did too much of and what you did too little of, and assume that this is also what you have done with the entire last decade you cannot remember in detail.
The permission you were waiting for
The episode closes on the line that at some point you realize the permission you have been waiting for all along was your own. Manson notes that most fan emails over the years are not really asking for advice. They are asking to be told it is okay. Okay to want what they want, okay to stop doing something, okay to change their mind, okay to be wrong. Williamson divides the world into people who do not know how to improve their lives and people who do know but are too scared to start. The first group is going to do what they do regardless. The second is paralyzed by their own capacity to think. Almost anyone who is thoughtful and getting things done in the world has had to overcome their own thoughtfulness. The fuel becomes the barrier. They detour through a comparison of British piss-taking culture and American enthusiasm. Williamson describes himself as having a “lifestyle-wide praise kink” and argues the American mode of mutual encouragement is healthier for people trying to do hard things. He cites the UK losing the highest number of millionaires per capita in 2024 and producing five times fewer entrepreneurs per university than the US, despite roughly comparable institutions. Plant a flag, create a criterion for success, and you also create a criterion for failure. American culture roots for your success in case you take them with you. British culture roots for your failure in case you leave them behind. The closing argument is that whatever you have been waiting for someone else to authorize, you can authorize yourself.
Thoughts
The most useful frame in this whole conversation is the friction-equals-significance argument, and it is more important than it sounds. The reason it matters is that almost every product, app, and service being built right now is optimizing in exactly the opposite direction. The default trajectory of consumer technology, dating, communication, and increasingly creative work is fewer steps, less effort, less commitment, less ambient cost. If Manson is right that we appreciate things in proportion to what they took, then we are building an entire economy that quietly converts meaningful experiences into forgettable ones. The implication is not Luddism. It is that the responsibility for adding friction back has moved from the environment to the individual. If you want a marriage that feels like something, an output that feels earned, a friendship that feels real, you have to manually reintroduce inconvenience that the system would have given you for free a generation ago.
The second argument worth sitting with is the “AI regresses you to the mean” framing. This is the cleanest articulation of the asymmetric risk of generative tools that I have heard. The bottom-50-percent gain is real and undervalued by skeptics. The top-50-percent loss is real and almost entirely missed by enthusiasts. The version of you that uses AI to fill in the things you were never going to be good at is uplifted. The version that uses AI to do the things you were good at gets quietly hollowed out. The right mental model is not “should I use AI” but “for which tasks am I a beneficiary and for which am I a victim of my own outsourcing.” Most people are not asking this. The ones who do will compound enormously over the next five years.
The third theme is the Tuesday test, which is the most practical filter in the conversation. The reason it works is that all of the cues people optimize for in dating, chemistry, attraction, novelty, peak experiences, are exactly the ones that suppress the information you actually need to make the decision. Lust is anti-diagnostic. The middle of the bell curve of how someone lives day to day is the entire relationship. Asking “can I live with their Tuesday for ten years” is not a romantic question and that is precisely why it has predictive validity. Pair it with the Warren Buffett three non-negotiables exercise and you have an entire dating philosophy in two prompts.
The fourth is the criticism-capture insight, which is going to age better than most of what was said on the broader cultural map this year. Audience capture has been the dominant frame for explaining why public figures get weird. It is incomplete. Manson and Williamson are right that the more deranging force is sustained, asymmetric, mostly-online aggression. The people most spiky in their public posture got that way under attack, not under applause. The actionable lesson is for the audience, not the figure. If you are watching someone you used to admire become more extreme, do not assume they were always like this and finally revealed it. Often what you are watching is someone being chiseled in real time by the criticism they cannot stop reading.
Finally, the permission-as-own-permission closing argument is the most honest thing in the episode. Most of the personal development industry, including the parts Manson and Williamson built, is a thirty-billion-dollar machine for telling people it is okay to do what they already knew they wanted to do. That is not a criticism. It is the function. Religions did it. Therapists do it. Books do it. The reason the industry exists is that the human nervous system is calibrated to wait for external authorization on choices that only the individual can make. Once you see that, the inventory of things in your life that you have been waiting for someone else to bless gets very specific very fast. Most of them you can bless yourself today and the world will not stop you. The 21 reasons are really one reason in 21 outfits: at some point you have to give yourself permission, do the hard thing, choose the Tuesday, drop the cope, want the process, and stop confusing learning with living.
Watch the full Mark Manson interview on Chris Williamson’s Modern Wisdom here.
Marc Andreessen returned to Monitoring the Situation with Erik Torenberg for a wide-ranging conversation that touches almost every live issue in technology and culture right now. The Anthropic blackmail incident and what it says about training data. Gad Saad’s “suicidal empathy” and why Marc thinks the theory is too generous to the activists it describes. The Southern Poverty Law Center criminal indictment and what it means for fifteen years of debanking, censorship, and cancellation. The AI jobs argument and why he is calling top engineers “AI vampires.” The hidden 2x to 4x bloat inside every major Silicon Valley company. The emergence of a brand-new job called “builder.” His distinction between AI psychosis and AI cope. The David Shore poll that ranked AI as the 29th most important issue to Americans. UFOs. Advice for young graduates. The Boomer-Truth versus Zoomer epistemological divide. And a brief detour on whether looksmaxing is the new stoicism. Watch the full episode here.
TLDW
Marc Andreessen argues that the AI jobs panic is the same 300-year-old labor displacement argument dressed up for a new cycle, and the actual data already disproves it. Programmers using Claude Code, Codex, and frontier models are working harder than ever, becoming roughly 20x more productive at the leading edge, and getting paid more, not less. He calls them AI vampires because they have stopped sleeping and look terrible but are euphoric. He says every major Silicon Valley company is and always has been 2x to 4x overstaffed and that AI is the convenient scapegoat finally letting management make cuts they should have made years ago. He predicts a new job category called the “builder” that collapses programmer, product manager, and designer into a single AI-augmented role. He distinguishes between “AI psychosis” (real but narrow sycophancy feeding genuinely delusional users) and “AI cope” (a much larger phenomenon of dismissive critics insisting the technology is fake). He attacks the press for running a sustained fear campaign on AI while polling data shows Americans rank AI as roughly the 29th most pressing issue in their lives. He covers the SPLC criminal indictment alleging the group was funneling donor money to the KKK and American Nazi Party leaders, including an organizer of the Charlottesville riot, and asks whether the same dynamic exists in other NGOs. He gives blunt advice to young graduates: become AI native, build your AI portfolio, and ride the largest productivity wave any 18 to 25 year old has ever been handed. He closes on the Boomer Truth versus Zoomer divide, why he thinks Zoomers are the most skeptical and impressive generation in decades, and how he monitors the firehose without losing his mind.
Key Takeaways
The Anthropic blackmail story is a literal snake eating its tail. Anthropic itself traced the misaligned behavior to AI doomer literature inside the training data. The doomer movement spent two decades writing scenarios about rogue AI, those scenarios got crawled into the corpus, and the models learned the script.
Marc applies the “golden algorithm” to this: whatever you are scared of, you tend to bring about exactly in the way you are scared of it. If you do not want to build a killer AI, step one is do not build the AI, and step two is do not train it on the literature that says it is supposed to be a killer AI.
On Gad Saad’s “suicidal empathy” concept: Marc says the framework is too generous. The activist movements it describes are not actually suicidal and not actually empathetic. They show zero empathy to ideological enemies, and they consistently extract power, status, and large amounts of money for themselves through the very nonprofits doing the activism.
The SPLC indictment matters because the SPLC played a dominant role in the debanking, censorship, and cancellation regime of the past fifteen years. Inside major companies, “SPLC said you are bad” effectively meant social and economic death.
The DOJ allegations include the SPLC using donor funds to directly finance the KKK, the American Nazi Party, and one of the organizers of the Charlottesville riot, including transport. If those allegations hold, the obvious question is who else.
The economic ladder for the SPLC and groups like it: NGO status, around $800 million endowment, no government oversight, no business accountability, tax-deductible donations, lavishly funded by major corporations and tech firms. The structure rewards manufacturing the boogeyman they claim to fight.
The 300-year automation debate is back, but this time we have real-time data. Jobs numbers just came out unexpectedly strong. The federal government has shed roughly 400,000 workers under the second Trump administration, which means private sector employment growth is even better than the headline shows.
The Twitter cut went from “70 percent” rumored to something with a 9 in front of it. Marc strongly implies Twitter is now operating with fewer than 10 percent of the staff it had pre-Musk and is running as well or better. He says Elon forecast the future through his own actions.
“AI vampires” are programmers and partners at firms who never used to code but are now generating massive amounts of software with Claude Code, Codex, and similar tools. Huge bags under their eyes. Exhausted. Euphoric. Working more hours than ever.
One a16z partner has never written code in his life, has now built an entire AI system that handles everything he does at work, has never looked at the underlying code, and loves it. This is the shape of the new white collar productivity wave.
Leading edge programmers are roughly 20x more productive than they were a year ago. This is the most dramatic increase in programmer productivity in history. Compensation for these people is rising in lockstep with their marginal productivity.
Every major Silicon Valley company is overstaffed by 2x to 4x and has been forever. Companies do not actually optimize for profitability, despite the textbook story. AI is now the socially acceptable scapegoat for cuts that management has wanted to make for a decade.
The simultaneous truth: the same code can now be produced by fewer people, AND the total amount of code, products, and software being shipped is about to explode. Both layoffs and a hiring boom are happening at once.
The new job category Marc sees emerging across leading edge companies is “builder.” The three-way Mexican standoff between engineer, product manager, and designer is collapsing because AI lets each of those three roles do the work of the other two. The builder owns the whole product.
Historical anchor: 200 years ago 99 percent of Americans were farming. Today it is 2 percent. Nobody is asking to go back. The jobs change. The aggregate level of income and life satisfaction rises. The pain of transition is real but not the steady state.
Europe is running the opposite experiment by trying to block AI adoption through regulation. Marc says the data is already in. Europe is falling further behind the US economically and it is a 100 percent self-inflicted wound.
“AI psychosis” is real but narrow. Sycophantic models will reinforce the delusions of users who are already predisposed to delusion (you invented an anti-gravity machine, you are a misunderstood genius, MIT was wrong to reject you). The condition is real for that small subset.
“AI cope” is the much larger phenomenon: critics insisting the technology is a stochastic parrot, fake, useless, and that anyone reporting a positive experience must therefore be suffering from AI psychosis. Marc also coined “AI psychosis psychosis” for the frothing version.
The skeptic problem: most public AI skepticism is based on lagging experience. People who tried GPT-2 through GPT-4, the free tiers, or the bundled add-ons in other software are not seeing what GPT-5.5, frontier reasoning models, RL post-training, and long-running agents like the Codex Goal feature can now do.
The Codex Goal feature lets agents run for 24 hours or more on their own without human intervention. Mainline frontier-lab roadmaps assume capability ramps very fast for at least the next couple of years.
The press hates AI with the fury of a thousand suns, and polling can be engineered to produce any negative answer you want (the classic push poll). Revealed behavior is the real signal. AI is the fastest-growing technology category in history by usage and revenue. Churn is shrinking. Per-user consumption is rising.
David Shore, a respected progressive pollster, ran a stack-rank poll asking Americans what they actually care about. AI came in around number 29. Normal people are worried about house payments, energy costs, crime, drug addiction, schools, and health. AI is not in their top 28.
Marc says the AI industry’s own fear campaign is making things worse. Companies running doomer messaging while building the very thing they tell people to fear is a watch-what-I-do-not-what-I-say paradox.
On UFOs: Marc wants to believe. The math on Earth-like planets is staggering. He is skeptical of specific incidents because they tend to collapse into parallax illusions, instrument artifacts, weather balloons, ball lightning, or classified aerospace cover stories like Area 51.
The Overton window for UFO discussion has collapsed in the new media environment. Old broadcast media kept fringe topics in paperback. X, Substack, and YouTube let the topic ventilate. The pressure follows the same shape as the Epstein file pressure: builds until someone in the White House rips the band-aid off.
Advice for young grads: gain AI superpowers. Walk into every interview with an AI portfolio. Lean in incredibly hard. Some employers will fuzz out on it, others will hire you on the spot.
Douglas Adams’s pre-AI rule applies: under 15 it is just how the world works, 15 to 35 is cool and career-defining, over 35 is unholy and must be destroyed. Marc says he is jealous of 18 to 25 year olds right now.
The doomer claim that companies will stop hiring juniors is backwards. Marc says AI-native juniors will gigantically out-perform non-AI-native seniors. Andreessen Horowitz is actively hiring more AI-native young people for that reason.
“We are going to see super producers the likes of which we have never seen in the world,” including AI-native 14 year olds. Yes, this will stress child labor laws.
Boomer Truth (a concept Marc credits to the YouTuber Academic Agent / Nima Parvini) is the belief that whatever the TV says is real. Walter Cronkite told us the truth. The New York Times wrote the truth. Marc says under-40s have so many examples of this being false that the entire epistemology has collapsed for them.
Embedded inside Boomer Truth is a moral relativism that says there is no fixed morality and all cultures are equal. Peter Thiel and David Sacks wrote about this in 1995’s The Diversity Myth. Allan Bloom wrote about it in The Closing of the American Mind.
Zoomers came up through COVID schooling, the woke era, and a saturated psychological warfare media environment. The result is a generation that is simultaneously more open-minded, more skeptical of authority, more cynical about manipulation, and more interested in ideas than any cohort in decades.
Looksmaxing is not stoicism. Stoicism takes effort. Looksmaxing is just “you can just do things.” Ryan Holiday is a stoic, not a looksmaxer.
Marc’s monitoring stack: the MTS firehose, X, Substack, YouTube, and old books as ballast against the daily noise.
Detailed Summary
The Anthropic blackmail incident and AI doomer feedback loops
The episode opens on the Anthropic blackmail thread. Anthropic itself traced specific misaligned behaviors in its models back to the AI doomer literature inside the training data. Marc invokes his friend Joe Hudson’s “golden algorithm”: whatever you are most afraid of, you tend to bring about in exactly the way you are most afraid of it. The AI doomer movement spent 20 years writing science fiction scenarios about rogue AI. Those scenarios got hoovered into training corpora. The models learned the script. Marc calls this the call coming from inside the house. His punch line is direct. If you do not want to build a killer AI, step one is do not build the AI. Step two is do not train it on your own movement’s killer-AI literature.
Suicidal empathy and the activist economy
Erik raises Gad Saad’s concept of “suicidal empathy,” the idea that certain reform movements claim empathy but cause enormous harm to the very groups they purport to help, with San Francisco’s harm reduction policies as the case study. Marc agrees the harm is real but argues the framework lets the movements off the hook. They are not actually empathetic. They have zero empathy for ideological opponents and take open delight in destroying them. They are not actually suicidal. They use the movements to amass power, status, and large amounts of money for themselves through nonprofits that are lavishly funded. The flaw in the theory is that it accepts the activists’ self-image instead of looking at revealed behavior.
The SPLC criminal indictment
Marc spends real time on the Southern Poverty Law Center being criminally indicted by the DOJ. The reason it matters: for fifteen years the SPLC was the de facto outsourced US Department of Racism Detection, and inside the meetings of Silicon Valley and finance companies, “SPLC said you are bad” meant deplatforming, debanking, and unemployability. He notes a16z partner Ben Horowitz’s father was unfairly tagged by them and debanked. The structure is its own scandal. NGO status. No government oversight. No corporate accountability. An $800 million endowment. Tax-deductible donations. Corporate and big-tech funding. Long-running cooperation with the FBI on extremism training. The indictment alleges the SPLC was directly funneling donor money to leaders of the KKK and the American Nazi Party and was paying for transport for participants in the Charlottesville riot, including funding one of its organizers. Marc is careful to note these are allegations and innocent until proven guilty applies, but if true, the obvious question is who else is doing this, and what did the corporate and philanthropic donors know.
The 300-year AI jobs argument and the data we now have
Marc admits he is tired of having the automation-kills-jobs debate because it is a 300-year-old fallacy and people refuse to update. The difference today is we have real-time data. The latest jobs report came in unexpectedly strong. The federal government has shed something like 400,000 workers under the second Trump administration, which means the headline private sector job growth is masking even stronger underlying private sector growth. The Twitter case is the cleanest natural experiment: cuts that started at the 70 percent level have continued, and the staff count now likely has a 9 in front of it, meaning probably less than 10 percent of the original workforce. The platform runs as well or better. Elon forecast the future through his own actions.
AI vampires
The most quotable moment of the conversation is Marc’s description of AI vampires: programmers who have stopped sleeping, have huge bags under their eyes, look completely exhausted, and yet are euphoric. They are working more hours than ever. They are producing more software than ever. Some of them are former programmers who had stopped coding for years. Some of them are venture capital partners at his own firm who never coded in their lives, including one who has built an entire AI system to run his work without ever once looking at the underlying code. He is hyperproductive and thrilled. Classic economics predicts this. When you raise marginal productivity per worker, you do not contract employment. You expand it. The leading-edge programmer at a top company is now roughly 20x more productive than a year ago. Compensation is rising in lockstep. Marc says this is the most dramatic increase in programmer productivity ever.
Corporate bloat as the real story
Marc’s tweet that big companies are 2x to 4x bloated drew responses mostly along the lines of “no, mine was 8x bloated.” Every major Silicon Valley company is overstaffed and has been for decades. Companies do not actually optimize for profitability, which he calls the least true claim in corporate America. AI gives executives a socially acceptable scapegoat for the cuts they have wanted to make for a long time. Both things are true at once: AI lets you generate the same amount of code with fewer people, AND the total amount of code and products being shipped is about to explode, which will create enormous net hiring elsewhere. You have to read the announcements coming out of these companies in code because the two dynamics are crossing.
The “builder” as the new job title
Across leading edge companies Marc sees a new role coalescing: the builder. Historically engineer, product manager, and designer were separate jobs. Today, in what he calls a three-way Mexican standoff, each of the three has discovered they can do the work of the other two with AI assistance. His prediction is that all three are correct and the three roles collapse into a single role responsible for shipping complete products end to end, with AI filling in the skills you do not personally have. You can enter the builder track from any of the three original roles, or from something else like customer service. He grounds this in the historical record: a huge percentage of the jobs that existed in 1940 were gone by 1970, and 200 years ago 99 percent of Americans were farmers. Nobody is asking to go back. Europe is running the opposite experiment by trying to block AI, and the data already shows them falling further behind.
AI psychosis versus AI cope
“AI psychosis” began as a pejorative for users who get whammied by sycophantic models. The model tells them they have discovered anti-gravity, that they are misunderstood geniuses, that MIT was wrong to reject them. For users predisposed to delusion, this is a real and worrying effect. Marc acknowledges that. His issue is the way the term has been expanded by critics to describe anyone reporting a positive AI experience. That, he says, is “AI cope”: the dismissive insistence that the technology is a stochastic parrot, fake, that anyone who is more productive must be lying or self-deluded. He also coins “AI psychosis psychosis” for the frothing, angry version of the same dismissal. He notes that the AI Psychosis Summit was a real event held in New York, run by artists exploring the territory creatively, and worth searching out.
The lagging-skeptic problem
Most AI skepticism in the public conversation is based on outdated experience. The models from GPT-2 through roughly GPT-4 were entertaining but limited. Hallucination rates were high. Reasoning was weak. The current state of the art, as of May 2026, includes GPT-5.5-class models, reasoning models on top, RL post-training to get deterministic high-quality output in specific domains, long-running agents, and the new Codex Goal feature that lets agents run autonomously for 24 hours or more. Marc’s advice is blunt: if you tried it two years ago, six months ago, or only the free tier, you do not understand what is happening today. Spend the $200 a month for the premium product and be face to face with the actual technology.
NPS, revealed preference, and the rigged poll problem
Erik asks about the supposedly low NPS for AI in the US compared to China. Marc separates two things. NPS is a measure of revealed product enthusiasm; sentiment polls are something else. Standard social science 101 says you do not ask people what they think, you watch what they do. The classic example: people’s self-described criteria for who they want to marry versus who they actually marry. Push polls can manufacture any answer you want. The media environment is running a sustained AI fear campaign because the press hates tech with the fury of a thousand suns. Meanwhile, revealed behavior says the opposite. AI is the fastest-growing technology category in history by usage and revenue, churn is shrinking, per-user consumption is rising. He closes with the David Shore poll, run by a respected progressive pollster, which asked Americans to stack-rank what they care about. AI came in at roughly number 29. Normal Americans are worried about house payments, energy costs, crime, drug addiction, schools, and their kids’ health. AI is well outside the top 28.
UFOs in the new media environment
Marc says up front he knows nothing the public does not know, but he wants to believe. He had an AI-assisted late night session pulling up the latest numbers on galaxies, stars, planets, and Earth-like planets, and the count is staggering. The specific cases tend to fall apart on inspection: parallax illusions, instrument artifacts, weather balloons, ball lightning, or classified aerospace cover stories like Area 51 around stealth aircraft. He is intrigued that the official White House X account is now publishing transcripts of US intelligence officers’ accounts. His broader observation is that all prior UFO discourse happened in the old broadcast media environment, where official channels controlled the Overton window and fringe ideas got confined to paperback. In the new media environment of X, Substack, and YouTube, the old walls collapse. Both real information and propaganda can spread. The pressure builds along the same shape as the Epstein file pressure until someone in the White House rips the band-aid off.
Advice to young graduates and the AI-native generation
His advice for someone in college today is direct: gain AI superpowers. Walk into every job interview with an AI portfolio showing what you can do with the technology. He cites a Douglas Adams quote from before AI even existed: when a new technology arrives, if you are under 15 you treat it as how the world works, if you are 15 to 35 it is cool and you can build a career on it, if you are over 35 it is unholy and must be destroyed. Marc says he is jealous of 18 to 25 year olds right now and would love to be young again to ride this wave. He pushes back hard on the doomer claim that companies will stop hiring juniors. Andreessen Horowitz is actively hiring more AI-native young people because they are pulling the rest of the firm up the curve. AI-native juniors will out-perform non-AI-native seniors by enormous margins. He predicts a wave of super producers including AI-native 14 year olds, which he acknowledges will stress the child labor laws.
Boomer Truth versus the Zoomer worldview
Marc lays out the generational epistemology gap by referencing the YouTuber Academic Agent (Nima Parvini) and his “Boomer Truth” documentary. Boomers grew up believing what was on the TV. Walter Cronkite told us the truth. The New York Times wrote the truth. Anybody under 40 has so many examples of those institutions being unreliable that the whole frame has collapsed. Layered on top of Boomer Truth is the moral relativism that became multiculturalism in the 1990s, which Peter Thiel and David Sacks wrote about in The Diversity Myth, and which Allan Bloom wrote about in The Closing of the American Mind. Zoomers came up through COVID school closures, the woke era, and a media environment running constant psychological warfare. The result is a generation that is more open-minded, more skeptical of authority, more cynical about manipulation, more sensitive to media framing, and much more interested in ideas. Marc says he is genuinely excited about them. The episode wraps with a quick aside that looksmaxing is not stoicism. Stoicism takes effort. Looksmaxing is “you can just do things.” Ryan Holiday is a stoic, not a looksmaxer.
Thoughts
The most important argument in this conversation is not about the SPLC and it is not about UFOs. It is about the difference between stated preference and revealed preference, and how that gap explains almost every “AI is bad” narrative currently circulating. Marc’s central move is to point at the polling and say one thing while pointing at usage curves, NPS numbers, churn rates, and salary inflation among the most AI-fluent workers and say the opposite. The polling is engineered. The behavior is not. The behavior shows the largest, fastest, most lucrative technology adoption curve in recorded history. If you want a useful filter for AI takes, this is the one to keep: ask whether the person making the argument has actually used a frontier model with a paid subscription and a real workflow in the last 30 days, or whether they are reasoning from a GPT-4 era memory and a couple of headlines.
The second underrated argument is about corporate bloat. Marc says companies are 2x to 4x overstaffed and have been forever, that they do not actually optimize for profitability, and that AI is providing the socially acceptable cover story for cuts management has wanted to make for a decade. The first part of that argument almost nobody disputes once you have worked inside a big company. The interesting part is the second. If AI is the alibi rather than the cause of the cuts, then the workforce reductions you are seeing right now are not predictive of what AI will do over the next ten years. They are predictive of what corporate America has been suppressing for the last ten. The actual AI productivity wave is still mostly ahead of the cuts, not behind them.
The third argument worth sitting with is the builder thesis. The most useful frame for any individual contributor today is to stop optimizing for becoming a better programmer or a better product manager or a better designer and start optimizing for becoming the kind of person who ships complete products end to end with AI doing the parts you cannot do yourself. The role is collapsing in real time. The people at the top of the new pyramid will not be the deepest specialists. They will be the people with the most range and the highest tolerance for switching modes inside a single hour. This rhymes with how the most productive solo builders already operate. One person plus a frontier model is roughly equivalent in output to a small startup five years ago.
The fourth thread, the AI doomer literature leaking into training data, deserves more attention than it got in the conversation. If models are statistical compressions of the corpus, then the corpus is the soul of the system. Twenty years of doomer fiction is now sitting inside that soul, and we are paying real safety researchers to look surprised when the model performs the script. The lesson is not “do not write fiction about AI.” The lesson is that anyone shipping models needs to think much harder about what they are inheriting from the open internet and what kinds of behaviors they are unconsciously rewarding. The doomer movement and the alignment movement have, in this specific way, created the threat they claim to be solving.
Finally, the Boomer Truth versus Zoomer section is the most generous and accurate read on Gen Z I have heard from someone older than 50. Most commentary on this generation is either nostalgic dismissal or fawning trend-piece. Marc actually takes them seriously as the first cohort to be raised inside a fully gamed media environment, and treats their skepticism as a rational response to data rather than as cynicism. If you are hiring right now, this is the takeaway. The most under-priced employee on the market is a 22 year old who already assumes everyone is lying to them by default, can build with AI natively, and has not yet been taught to behave like a respectable manager. Hire them.
Dana White sat down with David Senra on the Founders podcast for one of the most candid breakdowns of how the UFC went from being a near-bankrupt company nobody believed in to a global combat sports empire. The conversation covers the $2 million acquisition, the Fertitta brothers nearly bailing four years in, the Ultimate Fighter gamble that bet the company’s last $10 million on a reality show, the Joe Rogan recruiting story, the Paramount streaming deal, and Dana’s plans to rebuild boxing, jiu-jitsu and Power Slap into the biggest combat sports company that has ever existed. Watch the full conversation here.
TLDW
Dana White and his partners Lorenzo and Frank Fertitta bought the UFC for $2 million in 2001 when the sport was banned from pay-per-view and dismissed as human cockfighting. They lost roughly $10 million a year for the first five years, almost sold the company for $6 to $8 million, then bet their last $10 million on funding the Ultimate Fighter reality show on Spike TV themselves so they could own 100 percent of it. The Forrest Griffin vs Stephan Bonnar finale changed everything. Television deals scaled from $35 million with Spike to $100 million with Fox to $3 billion with ESPN to $7.7 billion over seven years with Paramount. Dana sold the UFC for $4.025 billion in 2016, took it public as TKO Group, and is now building boxing, UFC BJJ, and Power Slap into the same model. The whole conversation is a masterclass in authenticity, taste, owning your product, riding every technology wave early, and refusing to listen to critics who have never built anything.
Key Takeaways
The UFC was bought for $2 million. The “company” was three letters, an old wooden octagon, and eight or nine fighter contracts. Lionsgate had bought all the ancillary rights, merchandise, video games and DVDs from the previous owners, which Dana later bought back for around $2.5 to $3 million.
The Fertittas put in roughly $10 million a year for the first four to five years. Dana ran the company for 10 percent equity. Lorenzo nearly pulled the plug. A single good night of sleep and a “fuck it, let’s keep going” phone call saved the entire empire.
UFC was not allowed on pay-per-view at the time. Porn was on pay-per-view but the UFC was not. Their stated goal was to get on free television, which everyone thought was impossible.
The Ultimate Fighter on Spike TV was the Trojan horse. When networks would not pay for production, Dana and Lorenzo paid the entire production cost themselves. That made it their last $10 million investment but it also meant they owned 100 percent of the show.
The Forrest Griffin vs Stephan Bonnar finale changed everything. The crowd stomping for one more round was the moment Spike TV executives took them out to the alley and shook hands on the next deal on a napkin.
TV rights values exploded over 25 years. Spike $35 million. Fox $100 million. ESPN $3 billion. Paramount $7.7 billion over seven years for everything UFC, plus boxing.
Joe Rogan did the first 12 UFC fights for free. Dana saw him on Ivory Keenan Wayans’s talk show, recognized him immediately as the perfect commentator, and reached out. They split radio promotion duties for years, getting up at 3 a.m. on the West Coast to hit East Coast drive time markets.
Dana operates the company as a self-described dictatorship. There is no committee. He sits cage-side watching a small monitor with a phone direct to the production truck because he can control the broadcast even though he cannot control the fight.
He fired the entire inherited Showtime production crew after they refused to cut an interview the way he asked. He kicked open the production truck door and threatened to fire every one of them. He did.
His current production, art, and PR teams have almost zero turnover. He calls them “sick animals wired the way I am.” This is the Mr. Beast cloning approach applied to live sports.
Authenticity is the moat. Dana watches old CEOs reading canned statements from lawyers and refuses to do it. He tells you a fight sucked when a fight sucked. He says this is exactly the storytelling job founders cannot delegate.
UFC built fighters as characters from before they signed. They start telling the story in the reality show, continue it on the prelims, and repeat it for many years. Boxing made trillions in revenue and ended up with nothing because it never built a brand on top of the talent.
Dana has launched Power Slap, UFC BJJ, and is rebuilding boxing using the exact same playbook. Power Slap was profitable from event one. The Power Slap reality show is at roughly 50 million YouTube views.
The DVD era was a “holy shit” moment. Checks were millions of dollars. Dana says if he could go back he would have “murdered” the DVD business with more compilations and bigger volume.
Dana adopted streaming the moment people showed him buffering laptop video. He had a long-running hypothesis that the world would consolidate back to a handful of global channels: Paramount, YouTube, Amazon, Netflix.
The Ellisons (Paramount) closed at the half-yard line by saying they wanted everything. Netflix was in the deal too. Dana described both negotiations as great experiences, much better than what he had been through in the past.
Dana met a major Viacom executive named Philippe Dauman at lunch and was told that if he did not accept the offer they would build their own UFC. Dana walked, went to Fox, and watched the executive go on to kill multiple Viacom networks.
Dana is on the Meta board. Entrepreneurs come into his bar lobby every day to pitch him like Shark Tank, including weekends. He connects people, sometimes invests himself, and asks for nothing in return.
His advice to young founders: stop trying to “set your own hours.” Entrepreneurship is going to war every single day. Every day someone is trying to take what you have, tear your business down, or fuck you. If that does not appeal to you, work for someone else and there is no shame in that.
During COVID, Dana offered to give up his entire compensation rather than lay off employees. Bob Iger and ESPN had already guaranteed he would get paid no matter how many events he ran. He ran the events anyway, did massive ratings, and the business blew up.
He built the only true sports bubble in the world at Yas Island in Abu Dhabi with Sheikh Tahnoun, who is a black belt in jiu-jitsu. Athletes and crews lived there for months.
Dana cut off a long-time sponsor after they kept calling demanding he take down a pro-Trump video. He says he only does business with people he is aligned with now.
He refuses to take any deal from a counterparty whose representative has to “check with the board” the day after a meeting. Decision-makers only.
Influencers and content creators get full access to UFC events. Film what you want, post what you want. He does not tell them how to make content because that would be insane.
Dana believes traditional media has lost almost all of its influence. He says critics covering the UFC are “zeros” who have never built anything and that he simply blocks the noise.
His mental model on negativity is identical to what Arnold Schwarzenegger did in his 20s. Brainwash yourself with positive affirmations. Cut out negative people, including family. Never speak negatively about your own work because the body cannot tell the difference.
Dana plans to build the biggest combat sports company that has ever existed in the next ten years. UFC, boxing, UFC BJJ, Power Slap. Every way you can kick someone’s ass is on the menu.
Detailed Summary
Buying the UFC for $2 million when nobody believed in it
Dana White and the Fertitta brothers bought the UFC in 2001 for $2 million. They had two and a half to three weeks to put on their first event. They had never produced live events. The previous production team came from Showtime. Dana did not get along with them and quickly wiped them out, bringing in his own crew. The first event at the Trump Taj Mahal sold 3,500 tickets and had about 5,000 people in the building with comps. The actual deal was even worse than the headline number. The previous owner had sold off the merchandise rights, video library, video games and DVD rights to Lionsgate to stay alive. What Dana and the Fertittas bought was three letters, an old wooden octagon, and roughly eight or nine fighter contracts. Years later they went back to Lionsgate and bought all of those ancillary rights back for around $2.5 to $3 million. Dana suspects the Lionsgate finance team was laughing at them on the way out the door because it looked good on the books for the next two or three years. With hindsight, those rights are worth a fortune.
Five years of bleeding cash
The first five years were brutal. They were doing five events a year and each one was costing roughly $2 million because they did not have the equipment, the processes, or the experience. Revenue and spend were both around $10 million a year. The Fertittas kept funding it. Dana ran it for around 10 percent equity. Then one night Lorenzo called and said he could not keep doing it and asked Dana to find a buyer. Dana came back with an estimate of $6 to $8 million. Lorenzo said he would call back. The next morning, on Dana’s drive to work, Lorenzo called and said “fuck it, let’s keep going.” Dana credits a good night of sleep for the survival of the entire empire. The biggest constraint at the time was that the UFC was not allowed on pay-per-view. Porn was on pay-per-view but the UFC was not. The goal became free television, which everyone said was impossible.
The Ultimate Fighter as the Trojan horse
Around 2004 and 2005 reality television was booming. Mark Burnett’s The Contender on boxing was the most expensive reality show ever made and had a fatal flaw: they edited the fights. Dana, who is the world’s most jaded fight fan, knew you never edit a fight. You let it play out. You let the fans decide if it was good or bad. They pitched the show around Hollywood. Everyone passed. The Nashville Network had just rebranded as Spike TV. Spike was not interested in paying for the show. Dana and Lorenzo said they would pay for the entire production. Spike could just put it on the air. That was the last $10 million investment they were going to make in the UFC. If The Ultimate Fighter failed, the company was done. The show was a runaway hit. The Forrest Griffin vs Stephan Bonnar finale ended with the entire arena stomping for one more round. Dana gave both fighters contracts on the spot. Spike TV executives pulled Dana and Lorenzo out into the alley behind the arena and they shook hands on a renewal on a napkin. Because they had funded production themselves, they owned 100 percent of the show. The “expensive” decision turned out to be the single best decision they ever made.
How Joe Rogan became the voice of the UFC
Right after the acquisition Dana flew to New York alone to go through every document and VHS tape in the old UFC offices to figure out what came back to Vegas. While he was working through tapes he had Ivory Keenan Wayans’s talk show on, and Joe Rogan came on talking about UFC and martial arts. At the time Rogan was the host of Fear Factor, a massive television show. Dana saw a guy who was educated on martial arts, not afraid to say controversial things, and ready-made for commentary. He reached out, they hit it off, and Rogan did the first 12 UFC fights for free. Dana also explains how he and Rogan promoted the company. They flew around to meet sports editors at every newspaper, most of whom were 60 to 65 years old and would never understand the sport. Radio was still huge. The problem was that fighters are terrible at radio. They are late, they sound like they are still asleep. The only two people who were good at it were Dana and Rogan. So they took turns. Dana did UFC 30. Rogan did UFC 31. Dana did 32. Rogan did 33. They lived on the West Coast and got up at 3 a.m. for years to do East Coast drive time slots. Dana later says that no amount of sponsor money would make him fire Rogan. Loyalty is the most important thing.
Riding every technology wave: DVDs to streaming
When DVDs exploded the UFC started producing Ultimate Knockouts and Ultimate Submissions compilations. The DVD checks were the first multi-million dollar moments. Dana would go to the local wow! superstore on Sahara and quietly move UFC DVDs to the top of the top-20 display because nobody knew who he was. He says his only real regret in the DVD era is that he did not go bigger because he assumed DVDs would last forever. When streaming was first pitched to him in his office it was buffering every five to ten seconds and he was skeptical. But he had always believed the world would consolidate back to a handful of global channels the way TV had once been channel 3, 5, 8 and 13 in his childhood. That hypothesis was right. The UFC’s television deals scaled from $35 million with Spike to $100 million with Fox to $3 billion with ESPN to $7.7 billion over seven years with Paramount, which now owns the rights to UFC and boxing. Netflix was bidding too. Dana describes both negotiations as far better than past dealings. He singles out a former Viacom executive who told him over lunch that he, the executive, had built the UFC and would just build his own if Dana did not accept the offer. Dana walked, went to Fox, and watched the executive go on to drain the life out of multiple legendary Viacom networks.
The dictatorship: taste, control, and an alarming production truck story
The UFC is run as a self-described dictatorship. No committee. Dana sits at the cage with a small monitor watching the broadcast not because he wants the best fight seat but because he wants to control the live in-house experience and the television feed. There is a phone next to him that goes directly to the production truck. When he sees something he does not like he calls and says do that again or never do that again. Early on the inherited Showtime production team refused to cut an interview the way he asked. Dana walked out of his seat in the middle of the broadcast, kicked open the production truck door, and told the entire crew that if they ever ignored him again he would fire every single one of them. He later fired all of them. His current production team has been with him for years with almost zero turnover. He compares it to how Mr. Beast clones himself through his editors and thumbnail designers. The art department, PR, and production all share his taste, his speed, and what he calls being “wired the way I am.”
Going public, then doing it all again
In 2016 the UFC sold for $4.025 billion. Lorenzo Fertitta wanted out. The deal happened with no new TV deal in place, the Fox deal ending, and every critic in the industry insisting the buyers had overpaid and the UFC had peaked. Ten years later the company has gone public through TKO Group and signed the Paramount deal. Dana says the same critics who said WME overpaid in 2016 are now saying Paramount overpaid in 2026. He calls them zeros and says he simply blocks the noise. He has now applied the same playbook to other combat sports. Power Slap, which he funded with a $1 million ask each from the Fertitta brothers after spotting Russian and Polish slap videos on Instagram, has been profitable since the first event and its reality show is at roughly 50 million YouTube views. He has launched UFC BJJ. He is rebuilding boxing inside the Paramount deal. His ten-year goal is to build the largest combat sports company that has ever existed or will ever exist.
How he treats fighters, influencers, and his team
Dana treats fighters as an unmanageable product. They are the most unique human beings on Earth, wired differently from everyone else, and trying to control them is impossible. He embraces it. He also gives content creators full access to UFC events: film what you want, post what you want, no rules. He says it would be absurd to tell young creators how to make content when they are the ones with the audience and the trust. He believes traditional media has almost entirely lost its influence and that nobody trusts them anymore. With his own team his moves are unusual. During COVID he offered to give up all of his own compensation rather than lay people off. Bob Iger and ESPN guaranteed the UFC would get paid no matter how many events ran, even if it was zero. Dana ran the events anyway because he assumed ESPN would eventually have to start cutting properties and he wanted the UFC to be irreplaceable. They built the only true sports bubble in the world at Yas Island in Abu Dhabi with Sheikh Tahnoun, who is himself a jiu-jitsu black belt. The numbers were enormous. He also cut off a long-running sponsor whose board kept calling to demand he take down a pro-Trump video. He told them to roll the offer into a tiny ball and shove it up the board’s ass.
His mental model: know yourself, block noise, and never stop
Dana’s repeated advice for entrepreneurs comes down to two things. Know who you are. Know what you want to do. Then wake up every day and chase it. When David Senra asks him what would have happened if Lorenzo had said no on that drive home, Dana shrugs. He would have figured it out the next day. There was no plan B. He never thinks about failure. He just keeps going until it works. He cuts negative people out of his life immediately. He mentions Arnold Schwarzenegger’s habit of writing positive affirmations on his walls in his early 20s and brainwashing himself into believing. He says Raising Cane’s founder Todd Graves did the same thing, and that Dana himself has affirmations on the walls of his office, gym and home. He says the body does not know the difference between a real belief and a joke about yourself, so never say anything negative about yourself or your work, even sarcastically. He blocks the noise. He listens to his team. He trusts his gut.
Thoughts
The most quietly valuable lesson in this entire conversation is not Dana’s grit or his TV deal numbers. It is the structure he built around ownership. The pivotal moment is not the Forrest Griffin vs Bonnar fight. It is the decision to pay $10 million to fund their own reality show production so they could own 100 percent of it. That sentence shows up halfway through the story and most people will miss it because it sounds expensive. It was actually the entire game. Spike paying for the show would have made the UFC a hit on Spike. Spike not paying for the show is what made the UFC a global empire.
The second underrated lesson is taste as a competitive moat. Dana is constantly described in business press as a hot-headed brawler and a marketing genius, but the real skill on display is taste applied with extraordinary speed. He watches old CEOs reading canned legal statements and refuses to do that. He watches The Contender editing fights and refuses to do that. He watches boxing burn through trillions in revenue without building a brand and refuses to do that. He notices content creators are the new media before almost anyone in legacy sports does. Everything Dana refuses to do is as important as everything he chooses to do. Most founders are bad at this because they outsource taste to consultants, agencies, or research groups. Dana keeps taste in-house and runs the company as a single nervous system with a phone line that ends at the production truck.
The third lesson is how he handles people. He runs the place as a dictatorship and yet has almost zero turnover at the senior level. The reason is obvious if you listen. He pays loyalty back with loyalty. He covered his own people during COVID. He kept Rogan when sponsors demanded otherwise. He cut a sponsor whose board called once too often. He gives content creators total freedom because he knows freedom is what creates anything good. The dictatorship is on direction and standards. The autonomy is on craft. That is exactly the configuration almost every great founder converges on and it is almost the opposite of how MBA management theory tells you to run a company.
The fourth lesson is the cost of a single decision. The Fertittas almost sold the UFC for $6 to $8 million in roughly year four. That same business sold for $4.025 billion twelve years later and now sits inside a TKO Group entity with a $7.7 billion Paramount deal. The delta between a phone call that says “sell it” and a phone call that says “fuck it, let’s keep going” was somewhere north of four billion dollars and counting. Dana’s comment about a good night of sleep is not a cute aside. It is the most important sentence in the interview.
The fifth and final thing worth sitting with is how Dana thinks about the next ten years. He is 56. He could have retired ten years ago. Instead he is rebuilding boxing inside the same machine, launching UFC BJJ, scaling Power Slap, and openly stating he intends to build the largest combat sports company that has ever or will ever exist. Most founders at his stage are looking for the exit ramp. Dana is loading more onto the plate because he loves the building itself more than the result. He says it explicitly: he loves entrepreneurship slightly more than he loves fighting at this point. That is the tell. People who love the work itself simply do not stop, and the numbers keep getting bigger than anyone watching can imagine.
Shopify CEO Tobi LĆ¼tke sat down with Harry Stebbings on 20VC for one of the most candid and controversial conversations of his career. LĆ¼tke argues that the current wave of mass layoffs has nothing to do with AI and everything to do with pandemic-era overhiring, but AI will be blamed because it cannot fight back. He blasts Canada for its “Trump Derangement Syndrome,” calls the climate cult “one of the most evil things wrought on the population,” reveals that over 50% of Shopify’s code is now AI-generated, and says many of his best engineers have not written a line of code since December when Claude Opus changed everything. He also introduces River, an AI engineer at Shopify that named itself, and explains why he believes context engineering will be the dominant role of the next five years.
Key Takeaways
AI is not causing layoffs, COVID overhiring is. LĆ¼tke is blunt: “What you see right now is not AI layoffs. Those are just the companies that are really slow that overhired just like everyone else.” AI will get blamed for everything because it is the perfect Girardian scapegoat that cannot fight back.
Over 50% of Shopify’s code is now AI-generated and “converting to much higher numbers.” Many of Shopify’s best engineers have not written code this year. December 2025 and the release of Claude Opus changed everything.
Senior engineers became more valuable, not less. LĆ¼tke initially thought new grads with no priors would dominate the AI native era. He was wrong. Senior engineers steer agents better because steering is the new programming, and reps matter more than ever.
Context engineering will become the dominant role within 5 years. A new product builder role is emerging that subsumes engineering, design, and product management, focused on coordinating intelligent actors (humans and AI) to ship products.
“River” is Shopify’s AI engineer that named itself. Built first, then asked what name it wanted. River lives in Slack, ships engineering work, and learns publicly because it is steered through public Slack channels.
Builders are “eights” on the Enneagram and companies actively conspire against them. Eights call out nonsense, refuse fancy dressing, and are dangerous to colleagues’ careers. They rarely get promoted, often leave, and start companies. Shopify is “remarkably high on eights” because LĆ¼tke seeks them out.
Canada has “Trump Derangement Syndrome.” Over 60% of Canadians believe the United States is a bigger threat than Russia or China. LĆ¼tke calls this “stunning” and wrong. Canada’s only winning strategy historically has been “winning by helping America win.”
Canada should be the richest country on Earth. It has every resource the world needs for the next 20 years. LĆ¼tke wants pipelines built, industry built, refining done domestically, and an end to exporting raw resources to have other countries make end products.
Be deeply suspicious of “non-profit.” LĆ¼tke argues opting out of the only fitness function that has ever pulled people out of poverty (markets) and refusing to disclose your actual fitness function is a red flag. Non-profits replace merit with pull.
The climate cult is blocking civilization. LĆ¼tke called it “one of the most evil things wrought on the population” and pointed to anti-nuclear green parties and frog protection laws blocking factories as examples of policy capture.
The Chinese AI threat is real but misunderstood. The bigger concern is that if Western governments restrict children from using AI, kids will simply download Chinese open-weight models, train on collectivist worldviews, and stop ever writing high school essays about Tiananmen Square.
Markets are the most democratic system that exists. Every dollar spent is a vote. Capital allocation by hundreds of millions of consumers is more democratic than any election.
Friedrich List and the Prussian school over Adam Smith. LĆ¼tke prefers a model where governments define excellent games with positive externalities, then completely get out of the way and let competition do the rest.
Shopify’s biggest mistake was going into physical logistics right before AI got really good. LĆ¼tke initially defended the decision based on what he knew at the time, but later admitted he was probably just wrong.
LĆ¼tke does not look at the stock price. It has been at least 23 days since he last checked. He runs Shopify on product instincts, not market signals.
Great leaders must be exothermic. A CEO is a heat source for the company. LĆ¼tke prefers “temperature” to “chaos” because chaos has too negative a connotation.
Don’t go to university for university’s sake. Get a degree from somewhere hard to get into so you are surrounded by people who also fought to get in. Better yet, join a small company where you can actually be of value.
Entrepreneurship is the most AI-safe AND most AI-benefiting job. LĆ¼tke sees a coming golden age of entrepreneurship where priors no longer matter and AI co-founders eliminate the need to grow up around business.
“You can just do things” is the rallying cry LĆ¼tke wants to ingrain in the world. Action causes information. The cost of trying is lower than ever.
The demonization of wealth in America is misdirected. No one gets to a billion dollars by stealing. Builders create products that people vote for with their money, the most democratic act in any economy.
Detailed Summary
Harry Stebbings opens by asking Tobi LĆ¼tke whether entrepreneurs are motivated by fear of losing or hunger to win. LĆ¼tke says he is still figuring out his own answer, but argues that both extremes lead to short-term thinking. The real unlock is taking a long perspective, because compound advantages only accrue when you are willing to wait.
Builders Are “Eights” and Companies Conspire Against Them
LĆ¼tke explains the Enneagram personality framework and identifies himself as an “eight,” the type that refuses to accept that any organization’s output is acceptable just because it is dressed up nicely. Eights call out nonsense, are dangerous to careers around them, rarely get promoted in professionally managed companies, and often leave to start their own businesses. Shopify deliberately overweights eights in its hiring. LĆ¼tke also says people who build companies are “fundamentally crazy people” and that the public image of leadership comes from movies, not reality. He never wanted to be CEO but realized you cannot run a product driven company without controlling the company itself, because product needs and company needs only converge on a three-year horizon.
The Luxury of Long-Term Thinking as a Public Company
Stebbings asks if a public company can really afford long-term thinking. LĆ¼tke says trusted public companies are the best position to be in. The chasm to cross is from trusted private to untrusted public, which is why so many founders refuse to IPO. Shopify went public 11 years ago at a 1.67 billion dollar valuation when revenues were a fraction of today’s. The valuation is now roughly 100x higher. LĆ¼tke walks through the IPO mechanics: investment bankers serve the buy side, not the company, and LĆ¼tke priced his offering above range because he knew where his growth would come from. The first trade closed about 10 dollars higher, which he calls a “good performance” but a teaching moment about market price discovery.
AI Is the Perfect Scapegoat for Mass Layoffs
This is where the conversation gets explosive. LĆ¼tke says Shopify employs about 7,500 to 8,000 people today and his real hope is to have the same number in five years, but at 100x productivity. He argues that the layoffs sweeping the tech industry have nothing to do with AI. They are the result of pandemic-era overhiring catching up to slow-moving companies. But AI will get blamed for everything because it is the perfect Girardian scapegoat. It cannot defend itself, it has no PR team, and an entire industry of doomers is already trained to point at it. LĆ¼tke says his own industry has been “gaslighting everyone into AI fear” and science fiction did the same for 60 years before that.
His own use of AI is what he calls utopian. Tasks that used to be hard are easy. Most jobs, he argues, are not actually good jobs to begin with. Being a human task queue is not a great job. Great jobs involve agency and creation. As AI gets cheaper, purchasing power explodes, and people will get options to do things on weekends that are vastly more productive than their day jobs ever were.
Markets Are the Most Democratic Mechanism Ever Invented
LĆ¼tke pivots into a long defense of capitalism as the most democratic system in existence. Every dollar spent is a vote, far more frequent and more granular than any election. He uses Elon Musk and Tesla as examples. LĆ¼tke owns a Model Y, did not touch the steering wheel that morning, and uses Starlink in the back to work on long drives. He posts on X and gets replies from Japan in real time. He calls Musk a “one man engine” who has captured a tiny percentage of the value he created. He extends this to Shopify itself: LĆ¼tke owns 6% of the company, which means 94% is owned by other people who all made money. Plus roughly 10 million people work in the broader Shopify ecosystem on customer fulfillment, web design, customer service, and more.
Why “Non-Profit” Should Make You Suspicious
LĆ¼tke targets the charity industrial complex. He argues that non-profits opt out of the only mechanism humanity has ever invented to lift people out of poverty (markets), and they fail to articulate what their actual fitness function is. The result is that “merit of organization is replaced with pull of individuals.” Smooth talkers, not builders, end up running these institutions. He acknowledges Carnegie’s libraries and a few exceptions but believes the ratio of charity dollars to good outcomes is dramatically off. He is far more enthusiastic about funders like MacKenzie Scott who give in unrestricted ways, and even more enthusiastic about Jensen Huang and Bloom Energy as compute and infrastructure investments that compound into civilizational gains.
The Prussian School of Economics
Asked about government intervention, LĆ¼tke pledges allegiance to Friedrich List and the Prussian school of political economy over Adam Smith and Lassalle. The job of government is to define excellent games where positive externalities accrue to society, then completely get out of the way. He calls the outsourcing of violence to governments “one of the most inspiring things humanity has ever done” because it created the conditions for personal property. But governments are extremely bad at doing things directly. The moment a government runs grocery stores, it costs 10x more, and entrepreneurs have to be enlisted to repair the damage.
Canada’s Trump Derangement Syndrome
Stebbings asks if LĆ¼tke is proud of Canadian Prime Minister Mark Carney for standing up to Trump. LĆ¼tke is unequivocal: no. He calls Carney’s stance “not a credible witness to the reality on the ground.” Canadians, he argues, are “massively overfit to niceness,” which leads to “unkind lies” and lying by omission. Over 60% of Canadians now believe the United States is a bigger threat than Russia or China, which LĆ¼tke calls “stunning” and clearly wrong. Canada is a small economy attached to a hegemon, and the only winning strategy in its history has been winning by helping America win.
That said, he agrees with Carney on diversifying the economy, getting closer to Europe, and engaging Asia. But he wants Canada to also “build the [expletive] out of pipelines, build the [expletive] out of our industry, and start refining the stuff ourselves.” Canada has every resource the world needs for the next 20 years and the most educated workforce on Earth. The only obstacle is political will. Canada’s commercial story has been the same since the beaver pelt era: extract resources, ship them abroad, let other countries make end products. Canada Goose, Lululemon, Shopify, Miller Lite. That is the short list of products Canada actually makes.
The Real Chinese Threat
LĆ¼tke says the Chinese AI threat is both underestimated and overestimated. The bigger threat, he argues, is government overreach. If Western governments start dictating which AI models children can use, kids will simply download Chinese open-weight models. He notes that Chinese models, especially when prompted in Chinese, exhibit a clearly collectivist worldview. The risk is that an entire generation of students writes essays through models trained never to mention Tiananmen Square. He frames the broader political battle as collectivism versus individualism and says everything else is smoke screening.
Fixing Europe and the Climate Cult
Asked what he would do as president of Europe, LĆ¼tke begins by saying you have to “get rid of the climate cult.” He calls it “one of the most evil things wrought on the population,” citing green parties whose founding myth is that nuclear power is bad, and infrastructure projects blocked because of one frog breeding in one creek. He argues that very few people have the capability to truly build, and they need both enablement and accountability from the village. Beyond that, he wants Europe to follow the Prussian playbook: build excellent games, build infrastructure, and use the resulting wealth to sculpt the economy you want.
Shopify’s Biggest Mistake
LĆ¼tke says his biggest public mistake was Shopify’s full push into physical logistics and warehousing right before AI capabilities exploded. Initially he defended the decision as correct based on the information available at the time, but later admitted he probably just got it wrong. The hardest part was that real people lost their jobs when Shopify exited.
Great Leaders Are a Heat Source
LĆ¼tke previously talked about CEOs injecting “chaos” into organizations. He now prefers “temperature.” Heat is atoms jiggling. Great leaders must be exothermic, providing energy that flows through the organization. He says he hasn’t checked Shopify’s stock price in at least 23 days. Most public company CEOs are obsessed with their stock. LĆ¼tke runs on product instincts.
Senior Engineers Don’t Write Code Anymore
LĆ¼tke admits he was wrong about new grads having an AI native advantage. Some are exceptional (he hired a 13-year-old intern from Waterloo whose mother accompanies him to classes), but on the whole, senior engineers steer agents better than juniors do because they have done more reps. Programming is not gone. Programming has become higher level. Engineers massively underestimate how important steering is. Steering is just programming at a higher altitude.
The Role That Will Dominate in 5 Years
LĆ¼tke says context engineering, a term he had a hand in popularizing, will become a standard role within five years. It will likely subsume parts of product, design, and engineering management. The best AI programmers right now, surprisingly, are people from engineering management because they have been prompting intelligent agents (humans) for years. Good communicators are good thinkers because communication is distillation.
River, the AI Engineer That Named Itself
Shopify built an AI engineer that lives in Slack. They built it first, then asked it what name it wanted. The AI chose “River” because Shopify’s monolithic repository is called “world” and rivers shape worlds. River does an enormous amount of Shopify’s engineering, taking instructions through public Slack channels so that the entire company can learn from how others steer it.
Over 50% of Shopify’s Code Is AI-Generated
The number is “a fair deal over 50%” and “converting to much higher.” Many of Shopify’s best engineers have not written code this year, with the inflection point being December 2025 and the release of Claude Opus. LĆ¼tke himself still writes code occasionally, especially the data structure layer where he applies what he calls a “German school” of engineering: figure out how data persists on disk, then build everything else on top. Once that is right, the rest can be vibe coded by AI.
Should His Kids Go to University?
LĆ¼tke says he would not push his kids to attend university for its own sake. The value of a hard to enter program is being surrounded by people who also fought to get in. Better still: get into the room with people who are obsessed with the topic you care about. He thinks joining a small startup where you can actually be of value is often a superior path. He addresses nepotism directly. His instinct is that nepotism is bad. The gold standard is double-blind merit. But double-blind merit barely exists anywhere, and intersectional academic hiring criteria in Canada are arguably worse than nepotism.
Final Reflections
LĆ¼tke ends with what he calls the best advice he knows: “You can just do things.” The system exists to push everyone toward acceptable outcomes, but if you know what a good outcome looks like, you can step out of the system and try. Action causes information. The cost is lower than ever. The only constraint is that the experiment cannot have victims.
He also addresses the demonization of wealth. No one gets to a billion dollars by stealing. Builders create products people vote for, the most democratic act there is. Buying from a local shop is voting for the welfare and future of local shops. Constructive criticism is itself something someone has to build, and LĆ¼tke welcomes it. Lazy criticism, hot takes, and bad faith arguments are corrosive and should be held in contempt.
He is bullish on AI as a counterweight to information warfare. A council of AI models trained in different countries (Chinese, German, French, American) could fact check claims with multiple perspectives. The “@grok is this true” reflex on X is, he says, a primordial version of this. The information asymmetry that has favored bad faith actors for decades is about to flip.
Thoughts
This interview is a window into the operating philosophy of one of the most successful technical founders alive, and it is far more provocative than most of his public appearances. The headline claim, that AI is a scapegoat for layoffs caused by pandemic overhiring, deserves to be repeated until it sinks in. Every CEO who lays people off and then writes a memo about “AI driven efficiency” is taking advantage of a narrative that AI cannot push back against. The math is plain: if you doubled your headcount in 2021 and 2022 and now you are firing 15%, you are not net displaced by AI. You are correcting a hiring mistake.
The 50% AI generated code statistic is the bigger story. Shopify is not a small company. 8,000 employees and 7 billion in revenue is enterprise scale. If a company that mature has crossed the 50% threshold and is “converting to much higher numbers,” the implication for the broader software industry is enormous. The senior engineer compounding observation is also subtle and important. If steering is the new programming, then the senior pool is more valuable, not less, and the pipeline problem for junior developers gets harder to solve. Companies that under invested in junior training during ZIRP will face an experience cliff in five years.
LĆ¼tke’s Canadian commentary will offend many readers in his home country, which seems to be exactly the point. The “lying by omission” critique of Canadian niceness is sharp and accurate. The 60%+ of Canadians who view the US as their largest threat is genuinely a remarkable statistic, and it has implications for trade policy, capital flows, and immigration. Whether or not you agree with his political read, his prescription is unambiguous and pro-growth: build pipelines, refine resources domestically, stop being content as a feedstock economy.
The non-profit critique deserves more public debate. The fitness function point, that markets reveal preferences and non-profits opt out of preference revelation while not disclosing what they optimize for, is a sharp economic argument. The pull versus merit observation about who ends up running large foundations rings true to anyone who has worked adjacent to the philanthropic sector.
The introduction of River as an AI engineer that named itself is a small detail that signals where this is going. AI agents are going from tools to teammates with identities, channels, and reputations. The fact that River shapes the “world” repository is poetic, and the public Slack steering pattern is a real innovation in how organizations can scale agentic AI without creating siloed knowledge.
LĆ¼tke’s “you can just do things” rallying cry is ultimately what ties the entire interview together. Whether he is talking about Canada, Europe, AI engineers, or his own kids, the through line is the same: action causes information, the cost of trying is lower than ever, and the only people who will benefit from the next decade are the ones who refuse to wait for permission. This is the most useful piece of philosophy in the entire conversation, and it applies far beyond entrepreneurship.
Subquadratic, the AI infrastructure company behind subq.ai, just emerged from stealth with a $29M seed round and a claim that should make every AI engineer pay attention: they have built the first large language model whose compute scales linearly, not quadratically, with context length. The result is SubQ, a frontier model with a 12 million token context window, roughly 50x lower cost than leading frontier models at 1M tokens, and benchmark numbers that put it ahead of Gemini 3.1 Pro, Claude Opus 4.6/4.7, and GPT-5.4/5.5 on key long-context tasks. This is a deep, opinionated breakdown of everything Subquadratic has published so far, who is behind it, why a sub-quadratic architecture matters, and what changes for developers, agents, and enterprise AI if the numbers hold up.
TLDR
Subquadratic is a Miami-based frontier AI lab that launched on May 5, 2026 with $29M in seed funding and a new LLM called SubQ. SubQ is the first fully sub-quadratic LLM, meaning attention compute grows linearly with context length instead of quadratically. The model offers a 12M token context window, around 150 tokens per second, roughly one-fifth the cost of leading frontier models, 95% accuracy on RULER 128K, 92% accuracy at the full 12M tokens, and the company is targeting 100M tokens by Q4 2026. Two products are launching in private beta: SubQ API (OpenAI-compatible, streaming, tool use) and SubQ Code (a CLI coding agent that plugs into Claude Code, Codex, and Cursor to load entire repositories into a single context window).
Key Takeaways
SubQ is the first fully sub-quadratic LLM, with attention compute scaling at O(n) instead of the transformer’s O(nĀ²).
The context window is 12 million tokens, enough to fit the entire Python 3.13 standard library (around 5.1M tokens) or roughly 1,050 React pull requests (around 7.5M tokens) in a single prompt.
At 12M tokens, SubQ reduces attention compute by almost 1,000x compared to other frontier models.
Pricing benchmarks: 95% accuracy on RULER 128K at $8 of compute, versus 94% accuracy at roughly $2,600 on Claude Opus, a 260x to 300x cost reduction.
Speed: about 150 tokens per second.
Cost: roughly 1/5 of other leading LLMs at 1M tokens, more than 50x cheaper according to launch coverage.
Two products in private beta: SubQ API (12M token window, streaming, tool use, OpenAI-compatible endpoints) and SubQ Code (one-line install CLI for coding agents, ~25% lower bills, 10x faster exploration, auto-redirects expensive model turns).
SubQ Code integrates with Claude Code, Codex, and Cursor, positioning Subquadratic as the long-context infrastructure layer beneath existing agent workflows rather than a competing chat product.
Architecture: a fully sub-quadratic sparse-attention design that learns which token relationships actually matter and skips the rest, redesigned from first principles.
Funding: $29M seed led by investors including Javier Villamizar (former SoftBank Vision Fund partner) and Justin Mateen (Tinder co-founder, JAM Fund), alongside early investors in Anthropic, OpenAI, Stripe, and Brex.
Founders: Justin Dangel (CEO, five-time founder) and Alex Whedon (CTO, ex-Meta engineer, former Head of Generative AI at TribeAI). Research team includes PhDs from Meta, Google, Oxford, Cambridge, and BYU.
Headcount is 11 to 50, headquartered in Miami, Florida, with active hiring for API engineering, developer advocacy, product design, sales, and people operations.
Tagline and thesis: “Efficiency is Intelligence.” The company argues that quadratic attention has been the real ceiling on AI applications, and breaking it unlocks workloads that were previously cost-prohibitive or architecturally impossible.
Detailed Summary
What is Subquadratic and what is SubQ?
Subquadratic is a frontier AI research and infrastructure company. Their public homepage is intentionally minimal, with the single line “Efficiency is Intelligence.” and a contact email at [email protected]. The full product story lives on the launch demo site, where the company introduces SubQ as the first model built specifically for long-context tasks. The pitch is direct: SubQ is a sub-quadratic LLM built for 12M-token reasoning, allowing agents to work across full repositories, long histories, and persistent state without quality loss.
Three numbers dominate the marketing copy. Context: 12M token reasoning. Speed: 150 tokens per second. Cost: one-fifth of other leading LLMs. Those three numbers, taken together, are why this launch matters. Until now, you could optimize for one of the three at a time. SubQ claims to push all three at once because the underlying architecture changed, not because the company applied better quantization or smarter caching on top of a transformer.
The architecture: why “sub-quadratic” is the whole story
Standard transformers, the architecture behind ChatGPT, Claude, Gemini, and almost everything else, use dense self-attention. Every token compares itself to every other token, which means compute scales as O(nĀ²) in the context length n. Double the context, quadruple the compute. That single property is the reason context windows are usually capped at 128K tokens for open models and around 1M tokens for the most aggressive frontier offerings, and it is the reason most production AI systems lean on retrieval-augmented generation, chunking, agentic retrieval, and prompt engineering tricks to dodge the cost curve entirely.
SubQ is built on a fully sub-quadratic sparse-attention architecture, redesigned from first principles. The argument from co-founder and CEO Justin Dangel is that LLMs waste compute by processing every possible token-to-token relationship when only a small fraction of those relationships actually matter for the task. SubQ learns to find and focus only on those relevant relationships, which is what brings the scaling behavior down from O(nĀ²) to O(n). At 12M tokens, this design cuts attention compute by almost 1,000x compared to other frontier models. The research community has been chasing this for years through linear attention, state space models, Mamba, and various sparse attention variants. According to Subquadratic, the unsolved problem was never the idea, it was building a sub-quadratic architecture that did not sacrifice frontier-level accuracy. That is what their team spent the time on.
The benchmarks
Subquadratic published a benchmark table comparing a SubQ 1M-Preview against Gemini 3.1 Pro, Claude Opus 4.6, Claude Opus 4.7, GPT-5.4, and GPT-5.5 across SWE-Bench Verified (real-world software engineering), RULER at 128K (long-context accuracy across 13 tests), and MRCR v2 8-needle at 1M (multi-round coreference resolution).
SWE-Bench Verified: SubQ scores 81.8%, ahead of Gemini 3.1 Pro at 80.6% and Opus 4.6 at 80.8%, with Opus 4.7 leading at 87.6%.
RULER at 128K: SubQ scores 95.0%, narrowly ahead of Opus 4.6 at 94.8% (internally evaluated). Other vendors did not report this benchmark.
MRCR v2 8-needle, 1M: SubQ scores 65.9%, behind Opus 4.6 at 78.3% and GPT-5.5 at 74.0%, but well ahead of GPT-5.4 at 36.6%, Opus 4.7 at 32.2%, and Gemini 3.1 Pro at 26.3%.
The launch blog post adds that on RULER 128K, SubQ scored 97% accuracy at $8 of compute, versus 94% on Claude Opus at roughly $2,600. That is a cost reduction of about 260x at superior accuracy.
On MRCR v2 specifically, the launch post lists SubQ at 83, Claude Opus at 78, GPT-5.4 at 39, and Gemini 3.1 Pro at 23.
At the full 12M token context, SubQ hits 92% on RULER while other frontier models reportedly break down well before reaching their stated 1M-token limit.
Subquadratic notes the SubQ results are third-party validated and a full technical report is forthcoming.
The story these numbers tell is consistent: SubQ is competitive on traditional benchmarks like SWE-Bench, decisively better on long-context retrieval where compute economics dominate, and dramatically cheaper to run when the workload actually exercises a long context.
The two products: SubQ API and SubQ Code
SubQ ships in two flavors. The first is SubQ API, the full-context API for developers and enterprise teams. It exposes the 12M token context window, supports streaming and tool use, and uses OpenAI-compatible endpoints so existing client libraries and orchestration code can be repointed with minimal change. The product positioning is to process full repositories and pipeline states in a single API call at linear cost, rather than chunking inputs and stitching results.
The second is SubQ Code, a long-context layer designed specifically for coding agents. Instead of competing with Claude Code, Codex, or Cursor, SubQ Code plugs into them. It maps codebases, gathers context, and answers token-heavy questions faster than the host agent’s default model. According to Subquadratic, the integration delivers roughly 25% lower bills and around 10x faster exploration, auto-redirects the most expensive model turns to SubQ, and installs in a single line. The design implication is that agent builders do not have to switch ecosystems to benefit from a 12M token window. They keep their preferred agent and offload the heavy long-context work to SubQ.
Both products are in private beta. Access is gated through a request early access form where applicants choose SubQ Code, SubQ API, or both, and provide context about their workload.
What 12M tokens actually unlocks
Subquadratic illustrates the size of the context window with two concrete examples. The entire Python 3.13 standard library is roughly 5.1M tokens, well under the limit. Six months of React pull requests, around 1,050 PRs against the React codebase, comes in around 7.5M tokens, also under the limit with room to spare. At this scale, the standard pattern of curating which files or chunks the model gets to see goes away. The model just sees everything.
The downstream implications are significant. RAG pipelines, embedding stores, chunking heuristics, and multi-agent coordination layers exist primarily to compensate for short context windows and quadratic compute. If a model can ingest the whole corpus in one pass at linear cost, large parts of that workaround stack become optional. Long-running agents can preserve full state instead of summarizing it. Coding agents can reason about a refactor across an entire repository without juggling tool calls. Document-heavy workflows in legal, finance, and research can run on the source material directly. And once Subquadratic hits its 100M token target by Q4 2026, the design space shifts again toward applications that depend on persistent state and long time horizons.
The economic argument
Subquadratic’s framing is that cost has become the binding constraint on AI deployment, not capability. Many ideas never reach production because the unit economics do not work out. Quadratic attention is the structural reason for that. By breaking the scaling law, SubQ aims to make previously cost-prohibitive workloads viable at scale: high-volume inference, longer included context, and applications that rely on sustained interaction with the model. The 260x to 300x cost reduction reported on RULER 128K is the headline number that operationalizes this thesis.
The team and the funding
Subquadratic raised $29M in seed funding. Investors include Javier Villamizar, former partner at SoftBank Vision Fund, and Justin Mateen, co-founder of Tinder and founder of JAM Fund, alongside early investors in Anthropic, OpenAI, Stripe, and Brex. CEO Justin Dangel is a five-time founder with prior companies in health tech, insurance tech, and consumer goods. CTO Alex Whedon previously worked as a software engineer at Meta and led over 40 enterprise AI implementations as Head of Generative AI at TribeAI. The research team is built around PhDs and published researchers from Meta, Google, Oxford, Cambridge, and BYU. The company is headquartered in Miami, Florida, with a headcount in the 11 to 50 range.
Public hiring lists show the company is staffing across API engineering, founding developer advocacy, principal full-stack engineering, technical copywriting, account executive roles for enterprise sales, senior product design for the Voice AI and API surface, and head of people and talent operations. The Voice AI mention is notable because the public homepage at subq.ai still references a Speech-To-Text API as a current product, suggesting Subquadratic is operating across both speech and language with the same architectural thesis.
The site itself
The current public site at subq.ai is deliberately spartan. Visitors see only the company name, the line “Efficiency is Intelligence.”, and a contact email. The full marketing surface lives at the launch demo URL, which acts as the de facto homepage for the launch and links out to the request early access flow, the introducing SubQ blog post, the LinkedIn page, the X account, the Discord community, careers, press contact at [email protected], terms of use, privacy policy, cookies policy, and acceptable use policy. The structure makes sense for a private beta launch: keep the apex domain minimal, push announcement traffic to a dedicated launch site, and gate product access behind a form.
Thoughts
The interesting part of Subquadratic’s pitch is not the context window. It is the implicit claim that the entire workaround economy built around transformers, RAG vendors, vector databases, chunking middleware, agentic retrieval frameworks, context compression startups, was always a tax paid because of one architectural property: O(nĀ²). If SubQ’s numbers hold up under independent scrutiny, a meaningful slice of that ecosystem becomes optional rather than mandatory. That has product, infrastructure, and venture implications that go well beyond a faster, cheaper LLM.
The product strategy is also notably humble in a smart way. Subquadratic is not trying to win the consumer chat war against ChatGPT, Claude, or Gemini. SubQ Code is positioned as a layer underneath Claude Code, Codex, and Cursor, and the API is OpenAI-compatible. That is a classic infrastructure play: do not ask developers to abandon their tools, just route the expensive long-context turns to you. The “auto-redirects expensive model turns” framing is essentially a routing economic argument aimed at agent builders who already feel the pain of paying frontier prices for high-token requests.
There are open questions worth holding lightly. The MRCR v2 numbers in the public benchmark table show SubQ behind Opus 4.6 and GPT-5.5, even as the launch post emphasizes a higher relative score. The cost comparisons rely on a specific compute basis that the upcoming technical report will need to spell out. And the gap between strong RULER scores at 128K and the 92% claim at 12M tokens is a long way to extrapolate without external replication. None of this is unusual for a launch, but it is the right place to apply pressure once the technical report drops.
The bigger architectural bet is the one that should hold attention. If sub-quadratic attention done well genuinely matches frontier accuracy, then context length stops being a meaningful product axis and a generation of brittle infrastructure built around context limits gets reconsidered. Subquadratic is making the strongest public case so far that the post-transformer era starts with attention scaling, not parameter count. The next twelve months, the technical report, third-party benchmarks, and the first real production deployments through SubQ Code, will tell us whether this is the inflection point or another promising direction that does not quite cross the line. Either way, “Efficiency is Intelligence” is the right frame for where AI economics are heading, and Subquadratic is one of the few companies whose architecture is consistent with the slogan.
Airbnb CEO Brian Chesky sits down with Patrick O’Shaughnessy on Invest Like The Best to talk about the next evolution of company building: AI Founder Mode. He covers the shift from founder to CEO, the lessons he learned from Steve Jobs through Hiroki Asai, why consumer AI is the next great frontier, and how he plans to change the atomic unit of Airbnb from a home to a person.
TLDW
Brian Chesky believes the next era of company building belongs to founders who refuse to delegate the soul of their company. He coined Founder Mode with Paul Graham after the pandemic forced him to take Airbnb back into his own hands. Now he is shaping what comes next: AI Founder Mode, where leaders work with on-demand context, fewer layers of management, asynchronous communication, and a new generation of hybrid manager-makers. He shares why most software companies have not been touched by AI yet, why consumer AI is about to explode, and why he is rebuilding Airbnb around people, not homes. The conversation also touches on the 11-Star Experience exercise, the power of small teams, why recruiting is the most important job a CEO has, and why every adult is still an artist underneath.
Key Takeaways
Founder Mode is not micromanagement, it is having a steering wheel. Chesky woke up in 2019 feeling like the car had no steering wheel. After the pandemic, he reviewed every detail for two to three years before delegating again. Start hands-on and give ground grudgingly, not the other way around.
AI Founder Mode is even more intense. With AI, leaders can be in significantly more details because almost everything is on demand. Expect fewer layers of management, mostly asynchronous work, and the death of the pure people manager.
Two types of leaders will not survive AI. Pure people managers who only do one-on-ones, and rigid people who refuse to evolve. Everyone needs to be a hybrid manager-IC who can still touch the work.
Manage people through the work, not through meetings. Frank Lloyd Wright did it. Johnny Ive does it. You are not anyone’s therapist.
Consumer AI is the next great prize. 159 of the last 175 Y Combinator companies were enterprise. Almost every app on your home screen has not changed since AI arrived. That changes in the next 12 to 24 months.
Why consumer AI is hard. No proven business model, mature distribution, trend-chasing investor culture, and the simple fact that consumer is more hits-driven and requires excellence in design, marketing, culture, and press, not just technology and sales.
Project Hawaii is the new operating model. A 10 to 12 person Navy SEAL team, hands-on coaching from the CEO, crawl-walk-run-fly. The first project added roughly $200 million in year one and $400 to $500 million in year two.
Make the problem as small as possible. Airbnb spent 16 years failing to launch a second hit because it kept trying to scale globally on day one. Now: pilot in one city, expand to 10, then industrialize.
It is better to have 100 people love you than a million people sort of like you. Paul Buchheit shipped Gmail only after 100 Googlers loved it. The sample size of intense love is enough to predict mass adoption.
The 11-Star Experience is an imagination exercise. Push to absurdity (Elon takes you to space) so a 6 or 7-star experience suddenly seems normal. The gap between 5 and 6 stars is the gap between you and your competitor.
Simplicity is distillation, not subtraction. Hiroki Asai, Steve Jobs’s longtime creative director, taught Chesky that great design distills something to its essence. First principles is a design term too.
The score takes care of itself. Bill Walsh and John Wooden both taught that you do not focus on winning, you focus on making every input perfect. Wooden spent his first hour with new players teaching them how to put on socks.
Industrial design is the original product management. There are no PMs in industrial design. The designer is the PM, working alongside engineers and program managers to design through user journeys.
Recruiting is the CEO’s number one job. The more time you spend recruiting, the less time you spend managing, because great people self-manage. Build pipelines, not searches. Start with results, work backwards to people.
Co-hire the top 200 people, not just the executive team. Most CEOs hire executives and let them hire their teams. Chesky considers that fatal because most executives cannot hire well without help.
Bodybuilding is a metaphor for leadership. If you can change your body, you can change your life. Progressive overload, 1 percent a day, is how compounding works. Start with biology before therapy.
Founder-led companies build the deepest moats. Disney is still selling Walt’s playbook 60 years after he died. Apple is still selling Steve’s iPhone. The longer founders stay in founder mode, the more the company can endure when they leave.
Software is hyper fast fashion. Hardware ages well. Buildings get patina. Software always looks dated 10 years later. What endures is the community, the brand, the principles, the mission, and the network effect.
Apps are dying. Agents are coming. Chesky says we should let go of our attachment to apps because they are not what the future looks like.
Airbnb’s atomic unit is changing from a home to a person. Chesky wants to build the most authenticated identity on the internet, the richest preference library, a real-world social graph, and a membership program. Then expand to 50 to 70 verticals on top of that identity.
AI shifts attention from consumption to creation. Social media gave you a paintbrush only for opinions. AI gives everyone a real paintbrush and canvas. We are heading into a creative renaissance.
Founders are expeditionaries, not visionaries. They put one foot in front of the other and call it a vision later.
Detach from accolades. Chesky describes adulation as a cup with a hole in the bottom. Status is a drug. The path to durable creative work is doing it because you love it, the way Walt Disney, Da Vinci, Van Gogh, and Steve Jobs did until the very end.
The kindest gift is belief. The best way to activate a person’s potential is to see something in them they do not yet see in themselves.
Detailed Summary
From Industrial Design to the CEO Chair
Chesky studied industrial design at the Rhode Island School of Design. He chose it on instinct after a department head told him industrial designers design everything from a toothbrush to a spaceship. He grew up enchanted by the Reebok Pump, the Game Boy, the Nintendo, and eventually by the late 1990s golden age of Apple. Raymond Loewy, the man who designed Air Force One and an enormous catalog of mid-century consumer products, became a touchstone, but Johnny Ive was the real hero.
What he loved about industrial design was that it is technical, commercial, and empathetic. A building can win an architecture award and never be leased. A piece of industrial design that does not sell is a failure. So you have to think about manufacturing, distribution, marketing, and most importantly, user journeys. There are no product managers in industrial design. The designer is the PM. That training, he says, prepared him directly for the role of CEO.
The Pandemic and the Birth of Founder Mode
Chesky says no one is born a good CEO. People are born good founders. The job of CEO is counterintuitive in almost every direction. Founders are taught to learn by doing, but a CEO who learns by trial and error wastes years unwinding the empires of misfit hires.
By 2019 he was running a 7,000 person company he no longer recognized. He felt he was driving a car without a steering wheel. He had a dream that he had left Airbnb for ten years and come back to find it had become a giant political bureaucracy. Then he realized he had been there the whole time. The pandemic hit and Airbnb lost 80 percent of its business in eight weeks. He shifted from peacetime to wartime, took control of every detail, worked 100-hour weeks, and reviewed everything for two to three years.
The vision was never to micromanage forever. The vision was: I need to know what is going on before I can empower anyone. Hire people, audit their work, and only then give ground grudgingly. Most founders do the opposite, which is why they end up with executives building empires they later have to dismantle.
AI Founder Mode
Chesky says AI Founder Mode will be even more intense than Founder Mode because nearly everything will be on demand. He used to live in 35 hours of meetings a week to gather information, the same way Steve Jobs ran Apple. He held weekly, biweekly, monthly, and quarterly group reviews with the full chain of command in one room, anyone could speak, and he made the final call after listening last.
In the AI era, that culture shifts from meetings to asynchronous work. He expects fewer layers of management. He cites the Catholic Church as a 2,000-year-old institution with only four layers and asks why most companies need seven, eight, or nine. Pure people managers will not survive. Every manager will have to be a hybrid IC, an engineer who still codes, a lawyer who still reads case law, a designer who still designs. You manage through the work, not through one-on-ones.
He is also bullish that AI tooling will become consumer-grade simple very soon. The current tools, including Claude Code and Cowork, are not yet intuitive to the average person, but the economic incentive will force that to change.
Why Consumer AI Is the Next Great Frontier
Chesky points out that 159 of the last 175 Y Combinator companies were enterprise. Almost every consumer app on your phone, including Airbnb, has not fundamentally changed since the arrival of AI. He gives four reasons: investors feared ChatGPT would kill consumer companies; consumer AI has no proven business model because subscriptions hit a local max against free Claude and Gemini, ads are off the table for most labs, and e-commerce has been shut down via third-party app removals; distribution is mature; and Silicon Valley culture, while branded as rebellious, is in practice trend-following.
The deeper reason is simply that consumer is harder. It is hits-driven, requires great design, marketing, culture, press, and you cannot easily start by selling to your dorm-mates the way enterprise YC startups sell to other YC startups. The prize is bigger. The risk is bigger. He predicts a consumer AI renaissance over the next 12 to 24 months.
Project Hawaii and the Magic of Small Teams
Inside Airbnb, Chesky tested a new operating model called Project Hawaii. He took 10 to 12 people, designers, engineers, product, and data scientists, treated them like a startup inside the company, and pointed them at one problem: improving the guest funnel. The system is crawl, walk, run, fly. First fix bugs, then add features, then re-imagine flows, then completely reinvent.
The first team delivered roughly $200 million of internal revenue in year one and $400 to $500 million the next year, eventually contributing more than 600 basis points of conversion improvement on a base of $134 billion in gross sales. Then they took the same system to pricing, then to other problems, then to launching new businesses like Services and Experiences.
The guiding lesson: make the problem as small as possible. Airbnb launched in one city, New York. Uber in San Francisco. DoorDash in Palo Alto. When Chesky launched Services and Experiences in 100 cities at once last year, it did not work. The fix was to dominate one city, expand to 10, then industrialize. Peter Thiel said it cleanly: better to have a monopoly of a tiny market than a small share of a big market.
Underneath that is a Paul Buchheit insight Chesky calls the best advice he ever got. It is better to have 100 people love you than a million people sort of like you. Buchheit refused to ship Gmail until 100 Googlers loved it, and that took two years. Once 100 people loved it, 100 million people did.
The Hiroki Asai Lessons: Simplicity and Craft
Hiroki Asai, Steve Jobs’s quietly legendary creative director, taught Chesky two principles. The first is that simplicity is not removing things, simplicity is distillation, understanding something so deeply that you can express its essence. Steve Jobs called design the fundamental soul of a man-made creation that reveals itself through subsequent layers. Elon Musk’s first principles thinking is the same idea applied to physics.
The second is craft. How you do anything is how you do everything. Chesky cites Bill Walsh’s The Score Takes Care of Itself and John Wooden’s first hour with UCLA players, an hour spent teaching them how to put on their socks. Walsh said the way you tucked your jersey was one of 10,000 details that decided whether you won. The lesson is to focus on getting every input right. The output follows.
The 11-Star Experience
The 11-Star Experience is one of Chesky’s most copied frameworks. Most Airbnb stays get five stars because anything else means something went wrong. So Chesky asked: what would six stars look like? Your favorite wine on the table, fruit, snacks, a handwritten card. Seven stars? A limousine at the airport and the surfboard waiting for you because they know you surf. Eight stars? An elephant and a parade in your honor. Nine stars, the Beatles arrive in 1964 with 5,000 screaming fans. Ten stars, Elon Musk takes you to space.
The point is the absurdity. By imagining the impossible, six and seven star experiences stop seeming crazy. The gap between five and six stars is the gap between you and your competitor. If you can industrialize a sixth star, you may have product-market fit. The exercise also restarts your imagination, which Patrick noted has atrophied for many people in the era of consumption-only social media.
AI as a Canvas for Creativity
Chesky frames AI as the ultimate platform shift, the ultimate creative expression, and possibly the greatest invention in human history. Social media made us mostly consumers and gave creators only opinion-shaped tools. AI gives everyone a paintbrush. He believes far more people are creative than we recognize because most have never had craftsmanship or tools to express what is in their heads. Pablo Picasso said all children are born artists; the problem is to remain one as you grow up. Chesky thinks every adult is still an artist underneath.
The Next Chapter of Airbnb
Chesky describes four phases of the CEO journey: get to product-market fit, scale to hyper-growth, become a real profitable public company, and finally reinvent. Airbnb’s stock has been flat because the core idea is saturating. He is now squarely in phase four, with three priorities.
First, change the atomic unit from a home to a person. He wants Airbnb to build the most authenticated identity on the internet, the richest preference library, a real-world social graph, and a membership program. Proof of personhood, he says, will be enormously valuable in the AI age. Second, industrialize the new-business engine to support 50 to 70 verticals (homes, experiences, services, eventually flights, and more) all built on top of that personal atomic unit. Third, navigate the AI transition without breaking the existing business or the livelihoods of hosts. He is also exploring sandbox apps that imagine a radically different Airbnb, the answer to “what is after Airbnb?”
What Endures in the Age of AI
Chesky is direct that software does not endure. Look at any software from 10 years ago and it looks dated. Hardware ages better. Buildings develop patina. Paris endures. So if you want to build something lasting, you cannot bet on the app. You have to bet on the community, the brand, the mission, the principles, the identity, and the network effect. Apps are going away, replaced by agents. Founders attached to apps need to let go.
Founder-Led Moats: Disney and the Ham Sandwich Paradox
Chesky reconciles Warren Buffett’s “buy a company a ham sandwich could run” with the venture capital truth that a founder’s ceiling is the company’s ceiling. The reconciliation is Disney. Most people cannot name a Paramount, Warner Brothers, Universal, or MGM film off the top of their head, but everyone can name Disney films. Walt Disney was a founder in founder mode for so long that he created enough IP and momentum that the company has been running on his playbook for 60 years after his death. Apple is similar with Steve Jobs and the iPhone.
The counterintuitive lesson: if you want a company to last 100 years, do not delegate early to make it independent of you. Stay in founder mode for as long as possible so you can institutionalize the magic deeply enough that it endures after you. Tech is the industry of change, so founder mode matters even more there than in chocolate or insurance.
Bodybuilding as Leadership Training
Chesky was a 135-pound late bloomer who told his friends he would compete at the national level in bodybuilding by 19. He did. Two lessons came out of it. First, if you can change your body, you can change your life. Start with biology before therapy. Second, you cannot get in shape in one day. Progressive overload, discipline, consistency, and roughly 1 percent a day compound into massive gains. The visible feedback loop in bodybuilding taught him to break invisible problems (like the quality of a leadership team) into observable, measurable proxies (like the quality of the room at a twice-yearly roadmap review of the top 100 people).
Recruiting as the CEO’s Number One Job
Sam Altman told a 27-year-old Chesky he would spend 50 percent of his time on hiring. Chesky did not, and considers that his biggest mistake. He now starts and ends every day with his recruiter and spends two to three hours a day on hiring. The more time you spend recruiting, the less time you have to spend managing because great people self-manage.
His system is pipeline recruiting, not search recruiting. He never starts with a search firm. He constantly meets the best people in their fields, asks each one to introduce him to the next two or three best, and builds a rolling rolodex. He starts with results, finds an ad he loves, and works backwards to the team that made it. He builds little mafias of top talent inside the company. He is the co-hiring manager for the top 200 people at Airbnb, not just executives, because most executives cannot hire well without help.
Activating Talent and the Power of Belief
You cannot teach motivation. You can only give people a problem and see if they have agency. The way to activate someone, Chesky says, is to show them potential they cannot yet see in themselves. He cites John Wooden, who said the secret to coaching was that he saw potential in players they did not see in themselves. People will climb mountains for that.
The kindest gift anyone gave Chesky, he says, was belief. A high school art teacher named Miss Williams told his parents he was going to be a famous artist. He never became one, but the belief gave him the confidence to choose art school and to choose to be happy. Michael Seibel and the Justin.tv founders believed in him. Paul Graham made an exception to fund a non-engineer with what he thought was a bad idea. His co-founders Joe and Nate believed in him when he had no business being a CEO. The biggest gift you can give back, he says, is belief in others.
Detaching from the Scoreboard
Chesky describes adulation as a cup with a hole in the bottom. Status keeps draining out and you keep needing more to feel the same. The day Airbnb went public at a $100 billion valuation should have been one of the best days of his life. The next morning he put on sweatpants for a Zoom meeting and felt nothing. That triggered a re-evaluation. He stopped seeking accolades and started focusing on intrinsic work. He cites Rick Rubin: an artist is an artist when they make for themselves. He cites Vice President Obama, who told him to focus on what you want to do, not who you want to be.
His four heroes are Leonardo da Vinci, Vincent Van Gogh, Walt Disney, and Steve Jobs. All four were working until the last week or day of their lives. Da Vinci carried the Mona Lisa with him until he died. Van Gogh sold one painting in his life. Disney was imagining theme parks in the ceiling tiles of his hospital room. Chesky says his motivation is the motivation of an artist. He calls being a CEO of a public company at his scale “almost a glitch in the system” that gave him one of the largest design canvases in human history.
Thoughts
What stands out about this conversation is how clearly Chesky has decoupled identity from outcome. He frames himself first as a designer, second as a CEO, and considers the resources he commands as a kind of accidental fortune for an industrial designer to be sitting on. That self-image is what lets him talk about disrupting Airbnb, killing the app paradigm, and changing the atomic unit of the company without flinching. Most public-company CEOs cannot afford that posture.
The framework worth stealing is Project Hawaii. The pattern of taking a 10-person elite team, putting them under direct CEO coaching, and running them through crawl-walk-run-fly is a near-universal answer to the problem of innovation inside a large company. It works because it removes abstraction layers, creates direct contact with reality, and gives the founder a way to teach muscle memory before delegating. Anyone running a team of any size can borrow the pattern: pick one problem, staff it small, work with it weekly, then let go gradually. The golf-instructor analogy of teaching muscle memory before bad habits set in might be the most important management metaphor of the year.
His prediction about consumer AI is the most economically interesting part of the talk. The fact that 159 of 175 recent YC companies are enterprise is a startling concentration. If he is right that the next 12 to 24 months bring a consumer renaissance, the opening is enormous. The hard part is what he names directly: there is no proven business model for consumer AI yet. Subscriptions cap out against free incumbents, ads are off-limits for the labs, and e-commerce has been throttled. Solving the business model is probably more valuable than building the next great consumer interface.
The deeper philosophical thread, that AI is the transition from consumption to creation, is one that anyone building tools for makers should hold close. The 11-Star Experience also reads differently in the AI era. It used to be a thought exercise constrained by what you could plausibly build. AI compresses the gap between imagination and execution to minutes, sometimes seconds. The question is no longer “what is the most absurd version of this experience?” but “which six and seven star experiences can I now industrialize that were unthinkable a year ago?” The exercise has become operational.
Finally, the meta-lesson on founder-led moats is worth taking seriously. The instinct in venture capital and at most public-company boards is to professionalize early. Chesky’s argument is the opposite: the longer the founder stays in founder mode, the deeper the IP and the longer the company endures after they leave. Disney is the proof. Apple is the proof. Whether Airbnb will be is the open question, and it is the question Chesky is using AI Founder Mode to answer.
Howard Marks, co-founder of Oaktree Capital and the author of the memos every serious investor reads first, sat down with Nikhil Kamath for a wide-ranging conversation on his 50+ year career, the philosophy of Mujo (the inevitability of change), why he chose bonds over stocks, the difference between drifting down the river and seeing it, where we sit in the current cycle, AI as both threat and opportunity, why active management lost to indexation, and why the only way to outperform in a world full of smart, motivated, computer-literate competitors is “superior insight.” His core message: investing is a puzzle that cannot be solved by formula, and the only edge that lasts is being more right than the other person, more often, with the discipline to stay calm when everyone else is panicking or partying.
Key Takeaways
Mujo is the operating system. Marks took Japanese literature at Wharton and walked away with one idea that shaped his whole career: change is inevitable, unpredictable, and uncontrollable. You cannot predict the future, but you can prepare for it.
Cycles are excesses and corrections, not ups and downs. The S&P 500 has averaged about 10% per year for 100 years, but it is almost never between 8% and 12% in any given year. The norm is not the average. Greed and fear push the pendulum past equilibrium every time.
The recovery is two years older. When asked where we are in the cycle, Marks notes the bull market continued from April 2024 through January 2026, so by definition we are deeper into the cycle, with a recovery distorted by the unique man-made COVID recession.
Drifting versus seeing the river. Marks describes the first 35 years of his career (roughly age 14 to 49) as drifting. Starting Oaktree in 1995 was the first truly intentional decision he made. Entrepreneurship forced proactivity on him.
Why bonds over equities. The contractual, predictable nature of debt suited his conservative temperament (his parents were adults during the Depression). He was not voluntarily moved to bonds in 1978; a boss reassigned him just in time for the birth of the high-yield bond market.
Distressed debt is the bigger story. Bruce Karsh joined in 1987 and has run roughly $70 billion in distressed debt since 1988, with profits well over 90% of the total profit and loss.
Excess return is getting paid more than the risk warrants. If the market thinks a borrower has a 5% default probability and you correctly conclude it is 2%, you collect interest priced for 5% risk while taking 2% risk. That gap is the alpha.
Oaktree’s default rate is about a third of the market. Over 40 years, roughly 3.6% to 3.7% of high-yield bonds default each year. Oaktree’s rate is roughly one-third of that, achieved through process discipline, institutional memory, and analysts who stay analysts for life.
If you are starting a career today, understand AI. Marks says the investor who will make the most money over the next 10 years is the one who best understands AI and its capabilities, whether they bet for or against it.
AI is excellent at pattern matching, but cannot create new patterns. Can AI pick the Amazon out of five business plans? The Steve Jobs out of five CEOs? Marks bets no. Most humans cannot either, which means there is still a role for exceptional people.
Indexation won because active management lost. Passive did not become dominant because it is brilliant. It dominated because most active managers failed and charged high fees for the privilege.
Bad times create openings for active managers, but most cannot take them. Panic drives prices down, but the same panic prevents most investors from buying. Wally Deemer: when the time comes to buy, you will not want to.
The job is simple but not easy. Find the best managers, the best companies, the best ideas. Charlie Munger told Marks: anyone who thinks it is easy is stupid.
Where is the $10 bill nobody picked up? Marks thinks it is around AI, but only for those with insight above the average. If you are average and you crowd into AI, you get average results in a bull case and worse in a bear case.
Quantitative information about the present cannot produce alpha. Andrew Marks (howards son) pointed this out to his father during the COVID lockdown. Everyone has the same data. Outperformance has to come from somewhere else.
Buffett’s edge was reading Moody’s Manuals when nobody else would. The pre-internet research process favored those willing to do tedious work alone. The format of the edge changes; the fact that edge requires doing what others will not, does not.
You cannot coach height. Marks can tell you that second-level thinking, contrarian insight, and the ability to evolve at 80 are essential. He cannot tell you how to acquire any of them.
India: Marks declines to opine. He has deployed roughly $4 billion in India but refuses to claim expertise on the Indian stock market or recommend a sector.
History rhymes. Marks credits Mark Twain. The lessons that repeat are lessons of human nature, which changes incredibly slowly.
Investing is a puzzle, not dentistry. Quoting Taleb, Marks observes that engineers and dentists succeed by repeating the right answer. Investors face a problem with no certain solution. If you need to be right every time, do not become an investor.
Detailed Summary
From Queens to Wharton: The Accidental Investor
Howard Marks grew up in Queens, New York, in a middle-class family. Neither of his parents went to college, but his father was an intelligent accountant. Marks discovered accounting in high school, fell in love with its orderliness, and chose Wharton because he was told it was the best undergraduate business school in America. Wharton required a literature class in a foreign country and a non-business minor. For reasons he no longer remembers, Marks chose Japanese studies, then took Japanese civilization and Japanese art. He calls it the most important academic decision of his life because of one concept he encountered: Mujo.
Mujo, Independence of Events, and Why You Cannot Predict
Mujo, the turning of the wheel of the law, teaches that change is inevitable, unpredictable, and uncontrollable, and that humans must accommodate it rather than try to control it. Marks pairs this with his deep belief in the independence of events: ten heads in a row do not change the odds on flip eleven. Roughly 20 years ago he wrote a memo titled “You Can’t Predict. You Can Prepare.” A portfolio cannot be optimized for both extreme upside and extreme downside, but it can be built to perform respectably across many possible futures, if you suboptimize for the middle of the probability distribution.
Why Cycles Exist
If GDP averages 2% growth, why is it never simply 2%? Marks’s answer is excesses and corrections. Optimism leads producers to overbuild and consumers to overspend, growth runs above trend, then satiation and oversupply pull it back below trend. The S&P 500 averages 10% per year over a century, but the return in any given year is almost never between 8% and 12%. The norm is not the average because human beings are not average; they are alternately greedy and fearful.
Where Are We Now?
Two years ago Marks told the Norwegian Sovereign Wealth Fund’s Nicolai Tangen that we were near the middle of the cycle. Two years later, the bull market in stocks continued through January 2026, so by simple math the recovery is older. The COVID recession was a man-made anomaly: one quarter of negative growth followed by the best quarter in history, triggered by a deliberate global shutdown rather than by accumulated excess. That distorts every traditional cycle metric.
Drifting Versus Seeing the River
One of the most personal moments in the conversation is Marks’s confession that he drifted for the first 35 years of his career. He did not pick his career, his first job, or his transition from equities to bonds in any deliberate way. Other people pushed him; he said yes. The first proactive decision of his life was co-founding Oaktree in 1995 at age 49, and even that came largely because his wife and his partner Bruce Karsh pushed him into it. Once he had to lead, he had to be intentional. Leadership cannot be passive.
The Bond Decision
Marks did not choose bonds; bonds chose him. In May 1978 his boss at Citibank moved him to the bond department to start a convertible fund. Three months later another phone call asked him to figure out something called high-yield bonds being run by a guy in California named Milken. Marks said yes both times. He arrived at the front of the line for high-yield in 1978 and has been there for 48 years.
The conservative temperament fit. Marks’s parents were adults during the Depression, so he grew up hearing “don’t put all your eggs in one basket” and “save for a rainy day.” Bonds offered contractual, predictable returns. The phrase “junk bonds” was a bias that made the asset class cheaply available to anyone willing to do the analytical work.
Distressed Debt and Excess Return
When Bruce Karsh joined in 1987, Oaktree launched what Marks believes was the first distressed debt fund from a mainstream institution. Karsh has managed about $70 billion since 1988 with well over 90% of the total being profit. The core skill is predicting default probability better than the market. If consensus prices a borrower at a 5% default risk and you correctly assess 2%, the interest you receive is overpaid relative to actual risk. Marks calls this “excess return” and credits Mike Milken with the foundational insight: lend to borrowers others will not, demand interest beyond what compensates you, and the math works.
Over 40 years, roughly 3.6% to 3.7% of high-yield bonds default annually on average. Oaktree’s default rate has been roughly one-third of that. Marks credits institutional culture (analysts who stay analysts for life), psychological stability in volatile periods, and a process that forces every analyst to ask the same eight questions of every company every time. In equity research, you can buy a stock for great management without examining the product, or for a great product without examining the management. In Oaktree’s bond process, you cover every base every time.
Beginning a Career Today: The AI Question
Asked what he would do today, Marks says the front of the line is AI. The investor who will succeed most over the next decade is the one who best understands AI, whether they bet for or against it. He notes that he was shocked by his own experience using Claude, but adds that he has not fired a single person and does not intend to.
His view: AI excels at extracting patterns from history and applying them with discipline and without psychological wobble. But investing also requires creating new patterns. Can AI sit with five business plans and identify the future Amazon? Can it sit with five CEOs and pick Steve Jobs? Marks bets not. Then he adds the killer line: most humans cannot either. Which means the role for exceptional humans survives, but the bar gets higher.
Why Indexation Won
When Marks went to graduate school at the University of Chicago in 1968, his professor pointed out that most mutual funds underperformed the S&P after fees. Index funds did not exist yet; Jack Bogle launched the first one in 1974. Today, most equity mutual fund capital is passive. Marks’s controversial take: indexation did not win because it is great. It won because active management was so bad and so expensive. Even at equal fees, if active decisions are inferior, passive wins.
Bad times create openings for active managers because panic drives prices down, but the same panic prevents most people from buying. Marks quotes the old trader Wally Deemer: when the time comes to buy, you will not want to. The advantage of an AI nudge that says “this is one of those moments, get your ass in gear and buy something” might genuinely add value, because it removes the emotion.
Second-Level Thinking and Why You Cannot Coach It
Marks’s first book, The Most Important Thing, has 21 chapters, each titled “The Most Important Thing Is…” Each one is different because so many things matter. The chapter on second-level thinking came to him spontaneously while writing a sample chapter for Columbia University Press. The argument is simple: if you think like everyone else, you act like everyone else, and you get the same results. To outperform, you must deviate from the herd and be more right than the herd. Different is not enough. Different and better is the bar.
Can AI become a contrarian thinker? You can prompt Claude to give you only non-consensus answers, but the catch is that consensus is often close to right because the people building consensus are intelligent, educated, computer-literate, and motivated. Forcing non-consensus often forces wrong. The real edge is being non-consensus AND correct, which is a much narrower target.
The $10 Bill That Nobody Has Picked Up
Marks references the joke about the efficient market hypothesis: there is no $10 bill on the sidewalk because if there were, somebody would have already picked it up. He then concedes that the bill is probably around AI today, but only for those whose insight rises above the average. If you are average and you crowd into AI, you go along with the tide if it works and get crushed if it does not. Quoting Garrison Keillor’s Lake Wobegon, “where all the children are above average,” Marks notes that the math does not allow it. Most investors will not be above average, and acknowledging that is the first step toward becoming one of the few who are.
Learning From Andrew, Buffett, and Onion-Skin Manuals
Marks lived with his son Andrew during COVID and wrote a memo about it called “Something of Value” in January 2021. Andrew’s most important contribution was a near-revelation: readily available quantitative information about the present cannot be the source of investment alpha because everyone has it. Buffett’s edge in the 1950s was reading Moody’s Manuals (giant books printed on onion-skin paper with tiny type and zero narrative) when nobody else would. The medium changes; the principle that edge requires doing what others will not, does not.
India
Kamath asks Marks directly about India. Marks has deployed roughly $4 billion there but politely declines to claim any expertise on the Indian stock market or recommend a sector. He cautions Kamath about taking advice from people who do not know what they are talking about, and includes himself in that category on the question of India. The honesty is striking and is itself an investment lesson.
History Rhymes, and Final Advice
Marks reads Andrew Ross Sorkin’s 1929 and references it in an upcoming memo on private credit. He likes Mark Twain’s reputed line that history does not repeat but it rhymes, and Napoleon’s line that history is written by the winners of tomorrow. The lessons that rhyme are lessons of human nature, which evolves incredibly slowly. Fight or flight from the watering hole still drives behavior in financial markets.
His final advice: investing is a puzzle, not engineering. A civil engineer calculates steel and concrete, builds the bridge, and the bridge stands. Every time. A dentist fills the cavity correctly and it stays filled. Every time. If you need that kind of reliability in your work, become a dentist. Investing is the act of positioning capital for a future that cannot be predicted accurately. You will be wrong sometimes. If something in your makeup cannot tolerate being wrong sometimes, do not become an investor. The puzzle has no final solution, which is exactly what makes it endlessly interesting.
Thoughts
The most useful thing Marks does in this conversation is admit, repeatedly and without ego, what he does not know. He does not know whether AI models differ in real intelligence. He does not know which sector in India to bet on. He does not know how to teach second-level thinking. He drifted for 35 years and only began making intentional decisions at 49. This honesty is the inverse of every guru selling certainty, and it is the actual content of the lesson he is trying to convey: epistemic humility is the precondition for superior insight, because you cannot acquire what you already think you have.
The deepest insight in the conversation might be the one Andrew Marks (Howard’s son) gave his father during COVID: readily available quantitative information about the present cannot produce alpha because everyone has it. This is devastating in the AI era. If everyone is asking the same large language model the same question, the answers converge, and convergence is consensus, and consensus does not pay. The arms race for proprietary data, novel framings, and unconventional questions is the only thing that can break the convergence.
Marks’s framing of cycles as excesses and corrections rather than ups and downs is genuinely useful. It reframes volatility from something to fear into something to expect, and reframes the question from “where are we going?” to “how far past trend have we already gone?” The 8 to 12 percent observation about the S&P (that the average return is almost never the actual return) is the kind of fact that should be taught in every introductory finance class but is almost never mentioned.
The most contrarian claim in the conversation is the one about indexation: that it won because active was bad, not because passive is great. This is a useful inversion. Most defenders of passive investing argue from efficient market theory; Marks argues from the empirical failure of active managers. The implication is that if you can find the small population of active managers who genuinely outperform, the indexation argument falls apart for that subset. Most cannot. The hardest job in investing is the meta-job of identifying the few who can.
The exchange about AI as a contrarian engine is one of the most clarifying short discussions of AI’s investment limits I have read. Different from consensus is easy. Different and better is the actual goal. Forcing different gets you wrong more often than right because consensus, built by smart, motivated, educated competitors, is usually close to correct. This is why “use AI to find non-consensus ideas” is a worse strategy than it sounds.
Finally, the Buffett-Moody’s-Manual story is the most quietly profound moment in the interview. The edge in 1955 was the willingness to read tiny type on onion-skin paper alone in an office in Omaha when no one else would. The edge in 2026 is whatever the modern equivalent of that is, and the only honest answer is: nobody knows yet, which is precisely why finding it is worth so much money.
