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Tag: Tobi Lütke

  • Tobi Lütke on Uncapped Episode 50, Building Shopify in the AI Era, The Net Impact Memo, Six Week Cycles, and Why Software Was the Hidden Infrastructure of Our Time

    Tobi Lütke, the founder and CEO of Shopify, sits down with Jack Altman for Episode 50 of the Uncapped podcast for one of the most useful hours of operating wisdom you will hear from a sitting public company founder. The conversation moves from why Tobi still loves the work after twenty years, through the practical mechanics of running Shopify on six week review cycles, into the now famous AI memo he sent to the entire company, the rise of Claude Code style agents, what it means to spend tens of percent of revenue on AI tokens, why the modern web browser is a wonder of the world, and where small businesses actually fit in a world where the next Turing test might be “build me a million dollar business.” This is essential listening for any founder, operator, or investor trying to make sense of what 2026 actually requires.

    TLDW

    Tobi Lütke explains how he keeps loving his life’s work by pursuing what Paul Kapoa called “beautiful problems,” why “different” must always be the starting position because anything copied can only be marginally better, and why Silicon Valley’s last decade of orthodoxy has been bad for originality. He walks through his decision to send Shopify’s company wide AI memo and codify it into net impact performance reviews, the unlimited token policy for employees, why small three to five person teams are his bet, and how Parkinson’s Law and a six week review cycle force pace. He calls the doomer permanent underclass narrative completely absent from Shopify’s data, citing one new merchant getting their first sale every 36 seconds, and proposes “build me a million dollar business” as the real successor to the Turing test. He argues humanity has not stopped building wonders, we just built them all in software for thirty years, that the web browser is one of the most impressive engineering achievements ever made and could never get approved by a modern app store, and that the freed talent leaving software will rebuild the physical world. He shares his hiring philosophy, why he restarted the Shopify intern program at scale with Waterloo, his preference for public over private status, and ends with a short reading list anchored by Parkinson’s Law, Lessons of History, and a book called What Is Intelligence.

    Key Takeaways

    • Tobi’s recipe for life’s work is to find a beautiful problem worth occupying you for life, and accept that the solved problem will spawn delightful problem children to keep you engaged.
    • His simple model of success, “figure out what it costs and be willing to pay it,” with the price almost always being time, commitment, and discomfort rather than money.
    • He warns CEOs against collecting “barnacles” of aesthetic expectation, the statesman travel and baby kissing pattern, calling that lifestyle inefficient and personally miserable.
    • He invokes Kathy Sierra’s line “don’t make better cameras, make better photographers” as his core product philosophy, beautiful tools that induce more ambition and skill in the user.
    • Mediocre products feel like room temperature. Great products are forged in a furnace and require sustained heat from the team.
    • Shopify builds its own HR software internally because the available options are not what they want to use. Toolmaking is a stated cultural identity.
    • Originality is axiomatic. If you build the same thing as everyone else, you can only be marginally better. The starting position has to be “different,” and if you converge on the consensus answer through that path you have actually learned something.
    • Shopify has tried to eliminate the word “failure” internally, replacing it with “the successful discovery of something that didn’t work.”
    • Tobi says Silicon Valley spent the last decade declaring war on distinction, that the diversity push as practiced eradicated eccentricity, and that the inversion is now beginning. Companies should resemble islands of misfit toys, not convergence on a pre-ordained truth.
    • One of his most surprising career insights, when he visited the Valley as a Canadian outsider and asked founders how they ran their companies, he only ever received the highlight reel. Trying to clone what those founders described led him to invent practices the originals had never actually implemented.
    • The Shopify AI memo, sent company wide, made it explicit that two equally good engineers fifteen minutes earlier are no longer equivalent if one is fluent with AI tools and the other is not. This was codified into the company’s “net impact” performance review framework.
    • Tobi describes the “founder credibility bank” as the most underrated asset in a founder led company. Every onboarding deposits a little credibility, and the founder can spend it on hard change management that would otherwise take years of incremental culture work.
    • Shopify gives every employee an unlimited token policy for AI tools and displays token usage and departmental percentile on internal profiles. Token spend is tracked because it has to be allocated to opex, not because it is the target.
    • He confirms Shopify’s AI token spend is “extremely high” relative to revenue and notes that some private companies are now running token spend at many tens of percent of revenue, a level he thinks cannot persist at every stage but makes sense right now because the tokens are buying so much leverage.
    • Shopify is on track to 10x its annual token consumption and 3x its GPU footprint, and those two curves do not converge anywhere good for price relief.
    • His bet on team design is small, three to five people, which has always been Shopify’s bias. AI agents now handle the customer research summarization role that previously required a dedicated team member, raising every individual to a “seven out of ten on every scale.”
    • Parkinson’s Law (the book, 60 pages, 1960s edition) is his single most recommended management book. He owns multiple original print runs and gives copies to executives. “Work expands to the time allocated.”
    • Shopify runs on a six week review cycle. The first warning sign that a team has slipped into quarterly pacing is seeing “H1” or “H2” used in a PowerPoint. He now thinks six weeks is too slow and is actively trying to figure out what replaces it.
    • The “permanent underclass” doom narrative simply does not appear anywhere in Shopify’s data. New entrepreneurs are reporting that AI has finally fixed computers for them, expanding their businesses and letting them hire.
    • A new merchant gets their first Shopify sale every 36 seconds. Every reduction in onboarding friction produces a measurable jump in completed businesses.
    • Tobi proposes “go make me a million dollars” as the natural successor to the Turing test, an end to end test of acting in the real world, marketing, prioritizing, shipping, and producing something people will pay for.
    • Shopify Collective lets aspiring entrepreneurs sell other manufacturers’ products if their skill is marketing rather than making. Print on demand, additive manufacturing, contract manufacturing, CNC, 3D printing, and humanoid robotics are all pulling the cost of “make the product yourself” toward the floor.
    • The reason American infrastructure feels stagnant for thirty years is that the infrastructure humanity actually needed was digital. The web browser, Linux, Google, social networks, and Shopify itself are wonders that dwarf a refinery in complexity but are invisible by nature.
    • Tobi calls the modern web browser one of the wonders of the world. Font rendering alone is a Turing complete system. No app store on earth would approve the browser today if it did not already exist, because the pitch (“we download untrusted code from strangers and run it on your machine to reconfigure your computer for them”) sounds insane.
    • The next chapter is the brightest software engineers being freed by AI to build the physical infrastructure that has been deferred for a generation.
    • He prefers to predict the future by collecting many data points and matching them to super linear, linear, or sublinear curves. The current AI horizon is the hardest period of his career to forecast because the time horizons are so short.
    • Programming is overhyped as the locus of AI value. The bigger story is using the programming harness, the file system, tools, and memory files of products like Claude Code, to drag every other domain into the programming domain where the models are strongest.
    • The underhyped frontier is enterprise deployment. Most companies are still asking “help me do the thing I already did, slightly better,” instead of “if AI had existed since Alan Turing, how would I have designed this job from scratch.”
    • Tobi restarted the Shopify intern program at scale, partnered closely with the University of Waterloo, and explicitly frames interns as both students and teachers because they are AI native in a way the rest of the company is still catching up to.
    • He briefly believed AI would tilt the value of work toward early career talent with maximum fluid intelligence, then revised when he watched how much creative “steering” the best programmers were quietly contributing inside the AI loop. Good people are still good.
    • His recruiting philosophy is “build a company worth looking for” rather than selling candidates. Better to actually be healthier than to look healthier in photographs.
    • Tobi is a vocal defender of being a public company. Shopify IPO’d at a $1.5 billion valuation and has roughly 100x’d in public markets, which means an enormous number of retail investors have shared in the upside that recent unicorns reserve for insiders.
    • His framing of money, “money is how you vote for what you want.” Buying a product or buying a share is a vote for the thing existing.
    • His current reading recommendations, Parkinson’s Law, Lessons of History, and a book called What Is Intelligence that reframes biology around prediction.
    • He reads at night because his wife sleeps early and he does not need much sleep. He loves the Kindle precisely because it cannot do anything else, a “wonderful single purpose device.”

    Detailed Summary

    Why Tobi Still Loves the Work After Twenty Years

    The interview opens with Jack Altman asking how Tobi avoids the founder fade that hits most public company CEOs after a decade. Tobi answers from a place that is half psychology and half pedagogy. He has a hard time learning anything he has not first experienced as a problem worth solving, which is why he could not internalize school mathematics until he discovered that Wolfenstein 3D was essentially live trigonometry. That pattern, find a beautiful problem and let it drag you into the discipline, has carried him through twenty years of Shopify. He quotes Paul Kapoa on the idea that the luckiest people find a problem that occupies them for a lifetime and, if they are unfortunate enough to solve it, get rewarded with “delightful problem children” that keep the work alive.

    Barnacles, Statesmen, and the Aesthetic Trap of Being a CEO

    He admits he is not naturally calm, and that he initially fell into the trap of trying to perform the CEO aesthetic, the statesman, the global travel, the baby kissing. He found it inefficient and personally miserable. The shift came from reading Kathy Sierra and adopting her line about not making better cameras but making better photographers. Shopify exists, in his framing, to be a beautiful tool that induces ambition in the merchant. Mediocre products feel like room temperature, and great products are forged in a furnace. The job of leadership is to keep supplying the heat.

    Different First, Convergence Second, Failure as Successful Discovery

    Asked whether he prefers originality or quality, Tobi is unequivocal. The starting position must be different. If you copy the consensus answer, you are bounded to a few percentage points of variance from it. If you start different and converge on the consensus, you have learned something. If you start different and the experiment gets worse, you have learned something even more valuable, which is that one of your assumptions about the world was wrong. He calls null results in science “massively underrated” and notes that Shopify has tried to remove the word “failure” from the internal vocabulary, substituting “the successful discovery of something that didn’t work.”

    Why Silicon Valley Lost Its Originality

    Jack pushes on the herd mentality he has felt in the Bay Area, and Tobi is direct. He thinks Silicon Valley “declared war on distinction” for a decade, with the diversity conversation as practiced effectively eradicating eccentricity. He prefers the metaphor of “an island of misfit toys,” and says the inversion is now beginning. He also relays one of the most useful career lessons he has shared, that during his visits to the Valley as an outsider asking founders how they ran their companies, he only ever received the highlight reel. He went home and engineered a “Shopify version” of what he thought he had heard, and only years later realized that he had often built more rigorous versions of things the originals had never actually implemented.

    The AI Memo, Net Impact Reviews, and the Founder Credibility Bank

    Tobi was one of the first Fortune class CEOs to send a company wide memo saying that AI fluency was now a baseline expectation. He walks through the decision. Two engineers who were equally productive fifteen minutes ago are no longer equivalent the moment one of them adopts the new tools. The kind thing to do is to make that explicit. Shopify codified it into “net impact” performance reviews, where the question is not how much code you wrote but how much net impact you produced for the company and the mission. He gives every employee an unlimited token policy and tracks usage at the profile level, including percentile within department. The spend is tracked because it has to be allocated to opex, not because the number itself is the target.

    He introduces the concept of the “founder credibility bank,” which may be the single most quotable idea in the interview. Every time a new employee onboards and hears how the company was created, a small deposit of credibility is made into a virtual account that only the founder can draw on. Founders can spend that balance on hard change management, the kind of pace step change that would otherwise require years of small cultural nudging. The AI memo was a deliberate withdrawal from that account, and the speed of adoption that followed has been, in his telling, remarkable.

    Tokens, Opex, and the Limits of Spend as Revenue

    Jack presses on the financial reality of AI tokens. Tobi confirms that Shopify’s token spend is “extremely high” relative to revenue, and that the leverage they are buying makes the spend a no brainer at the current stage of the curve. He concedes that private companies running token spend at “many tens of percent of revenue” cannot sustain that ratio forever, but he is not worried for Shopify because the tokens are clearly productive and Shopify is a profitable public company with the balance sheet to lean in. He expects to 10x token consumption and 3x GPUs every year for now, and notes that the curves do not converge in a direction that lowers prices. He has high faith in markets to find clearing prices.

    Small Teams, Parkinson’s Law, and the Six Week Cycle

    On team architecture, Tobi has always preferred three to five person teams and says AI has finally made that feasible across the board. Roles that previously required a dedicated specialist, customer research summarization being the canonical example, are now handled by the “agentic harness” routing summarized customer feedback into every team. Everyone is a “seven out of ten on every scale” by default. He spends serious time on pace, which he treats as the single most important variable to control. His most recommended book is Parkinson’s Law, a 60 page volume from the 1960s that he gives to every executive. “Work expands to the time allocated.” He runs the company on a six week review cycle and treats the appearance of “H1” or “H2” in a PowerPoint as a hard warning sign that a team has drifted into quarterly thinking. He now believes six weeks is too long and is actively redesigning the cycle.

    There Is No Permanent Underclass in the Shopify Data

    Jack raises the cultural fear that AI is creating a permanent young underclass with no career ladder. Tobi simply does not see it in Shopify’s data. The merchants are reporting the opposite, that AI has finally fixed computers for non technical small business owners and is unlocking hiring. He cites the statistic that a new merchant gets their first sale on Shopify every 36 seconds, and that every reduction in onboarding friction produces a measurable jump in completed businesses. Every form of friction is a hurdle that someone considers giving up at. AI has removed more of those hurdles in two years than any platform shift before it.

    A New Turing Test, “Build Me a Million Dollar Business”

    Tobi nominates a successor to the Turing test, which he points out the field already sailed past with surprisingly little fanfare. The real test is “go make me a million dollars.” It requires acting in the real world, marketing, prioritization, shipping, sourcing, building inventory, and convincing strangers to vote for the product with a real million dollars of their own. He believes we are getting there. Shopify already supports the path through Shopify Collective, the discovery layer for manufacturers willing to white label their products, and print on demand, contract manufacturing, additive manufacturing, CNC, 3D printing, and humanoid robotics are all collapsing the cost of physically producing a product. Shopify’s stated ambition is to be the vessel for AI to run all of the non product parts of the business so that the only thing the human needs to show up with is the product itself.

    Software Was the Hidden Infrastructure of the Last Thirty Years

    The most original argument in the episode is about why American infrastructure has appeared to stagnate for a generation. Tobi rejects the standard story. Humanity has not stopped building wonders, it has built every one of them in software. The web browser, Linux, Google, the social networks, and Shopify itself are projects whose complexity dwarfs a refinery or a dam, and they were built by global volunteer networks and by companies the public underestimates because the work is invisible. The browser in particular he calls a wonder of the world. He notes that font rendering alone is a Turing complete system, that no modern app store would approve the browser if it did not already exist, and that the basic pitch of “we will download untrusted code from strangers and reconfigure your computer for them” should sound insane but does not because we are used to it. The implication for the next twenty years is that all of the talent that flowed into software is now being freed by AI to rebuild the physical infrastructure that has been quietly deferred.

    Predicting AI Two Years Out, Overhype and Underhype

    Jack asks whether a CEO should try to forecast AI two years ahead or operate six months at a time. Tobi is firmly in the forecasting camp and admits his friends would laugh because predicting the future from many data points and curve types is his predominant obsession. He says the AI memo was slightly too early, and that is exactly the point, because a memo that arrives late costs the company its head start. He flags two specific market level mis estimations. The first is that the labs over invest in programming because programming is their internal problem, and people then over generalize a model’s coding ability to other domains where it is not yet as strong. The second is that almost everyone is under deploying AI in their actual companies, still asking “help me do my old job better” instead of “if AI had existed since Alan Turing, how would I have designed this job from scratch.” That second framing is, in his view, where the next decade of value lives.

    Hiring, Interns as Teachers, and Why Good People Are Still Good

    Tobi briefly believed AI would tilt the value of labor toward early career fluid intelligence, since interns adopted the new tools faster than veterans. He revised that view once the coding harnesses matured. The best programmers, it turned out, were quietly contributing enormous amounts of creative steering inside the AI loop, work that does not show up in the diff but that no junior with no domain pattern matching can replicate. Good people are still good. Shopify has massively scaled its intern program with the University of Waterloo, and explicitly treats interns as both students and teachers because they bring AI nativeness the rest of the company still has to catch up to. On recruiting, Tobi’s philosophy is to build a company worth looking for. The metaphor he uses is health, that companies waste energy trying to look healthy in photos when they should be doing the work to actually be healthier.

    Public Company Defense and the Reading List

    Tobi pushes back on the modern preference for staying private. Shopify went public at $1.5 billion and is now over $100 billion, which means an enormous number of retail investors got to participate in the upside. He treats money as a voting mechanism. Buying a product is a vote for the product. Buying a share is a vote for the company. He is comfortable with the diligence and quarterly scrutiny of public markets because both make him a better operator. He closes with a short reading list, Parkinson’s Law (60 pages, 1960s edition, owned in original print runs and gifted to executives), Lessons of History, and a book called What Is Intelligence that reexplains biology from a prediction first perspective. He reads at night while his wife sleeps, on a Kindle, which he loves precisely because it cannot do anything else.

    Thoughts

    The single most useful idea Tobi puts on the table is the “founder credibility bank.” It explains, in one clean image, why founder led companies move so much faster than the same company would after a transition. The credibility is not personal magnetism, it is the structural slot the founder occupies in the org chart, and every onboarded employee makes a small deposit into it as they hear the founding story. Most founders never realize the account exists, or spend it on cosmetic decisions, and then are surprised when the well runs dry. Tobi’s discipline is the opposite. He saves the balance for moments of forced change and spends it confidently when the moment arrives, the AI memo being the obvious recent case. Any CEO reading this transcript should be making a list of the changes they have been postponing and asking whether they are operating with a fuller credibility account than they have been willing to admit.

    The token spend conversation is the most interesting strategic disclosure. A profitable public company at scale openly says it likes the tokens it is buying, is on track to 10x annual token consumption and 3x GPU footprint, and is comfortable with private peers spending tens of percent of revenue on inference. That is not the language of a market that is about to compress. It is the language of a leverage trade that is still in its early innings, and it is one of the cleanest statements you will get from a public CEO about why the AI capex story is not a bubble for the buyer. Whether it is a bubble for the seller is a separate question, but on the demand side, this interview is a load bearing data point.

    The argument that “software was the hidden infrastructure of the last thirty years” is the kind of reframe that should make policy people uncomfortable. The standard narrative that America stopped building anything ambitious since the Hoover Dam is true only if you refuse to count Chrome, Linux, AWS, Shopify, and every social graph that connects three billion people in real time. Tobi’s claim that the browser would not be approved by a modern app store is a particularly sharp gut check. The implication is not nostalgic. It is forward looking. The same talent that built the digital wonders is being freed by AI to redirect toward houses, transport, energy, and care, and the next decade will be measured by how much of that redirection actually lands.

    The “build me a million dollar business” framing as a Turing test successor is the kind of measurable goal that AI labs and policy makers should be writing down. It is end to end. It includes physical world action, marketing, sourcing, prioritization, and customer validation that no in domain benchmark can fake. Shopify is the obvious substrate for the first crossing of that threshold, and the existence of Shopify Collective, print on demand pipelines, and contract manufacturing networks means a credible attempt is already much closer than the public conversation acknowledges. The first end to end autonomous Shopify business that clears a million dollars will be a more legible AGI moment than any benchmark a lab can publish.

    The smaller thread on Silicon Valley orthodoxy is worth pulling on. Tobi’s claim that the diversity conversation as practiced eradicated distinction is unfashionable but observable inside many tech companies, where the people most likely to do unusual work are the most likely to leave. His preferred metaphor of “an island of misfit toys” is closer to what made the Valley work in earlier decades than the current consensus aesthetic. The fact that a Canadian outsider, geographically removed from the dominant social pressure, runs the most valuable Canadian technology company in history is probably not a coincidence.

    Watch the full conversation here on YouTube.

  • Shopify CEO Tobi Lütke: AI Is the Perfect Scapegoat for Layoffs, Canada Has Trump Derangement Syndrome, and 50% of Shopify Code Is Now AI-Generated

    TLDW

    Shopify CEO Tobi Lütke sat down with Harry Stebbings on 20VC for one of the most candid and controversial conversations of his career. Lütke 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. Lütke 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. Lütke 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 Lütke seeks them out.
    • Canada has “Trump Derangement Syndrome.” Over 60% of Canadians believe the United States is a bigger threat than Russia or China. Lütke 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. Lütke 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.” Lütke 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. Lütke 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. Lütke 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. Lütke initially defended the decision based on what he knew at the time, but later admitted he was probably just wrong.
    • Lütke 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. Lütke 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. Lütke 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 Lütke 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 Lütke whether entrepreneurs are motivated by fear of losing or hunger to win. Lütke 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

    Lütke 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. Lütke 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. Lütke 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. Lütke walks through the IPO mechanics: investment bankers serve the buy side, not the company, and Lütke 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. Lütke 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. Lütke 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

    Lütke 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. Lütke 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: Lütke 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

    Lütke 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, Lütke 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 Lütke is proud of Canadian Prime Minister Mark Carney for standing up to Trump. Lütke 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 Lütke 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

    Lütke 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, Lütke 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

    Lütke 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

    Lütke 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. Lütke runs on product instincts.

    Senior Engineers Don’t Write Code Anymore

    Lütke 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

    Lütke 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. Lütke 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?

    Lütke 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

    Lütke 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 Lütke 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.

    Lütke’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.

    Lütke’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.