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  • Bill Ackman on Investment Strategy, What the Market Is Missing, and How AI Breaks Businesses

    Bill Ackman, founder and CEO of Pershing Square, joined the All-In Podcast for a conversation about how his investment approach has shifted toward permanent, long-term ownership, why he believes the highest-quality companies are being left behind by a market chasing the new new thing, and how AI is raising the risk of disruption for almost every business. He also lays out his plan to turn Howard Hughes into a Berkshire Hathaway-style compounding machine built on insurance. You can watch the full conversation here. Below is a structured breakdown of the ideas, the stories, and the frameworks he uses to underwrite a business.

    TLDW

    Ackman explains how his philosophy evolved from a smaller, more liquid activist toward concentrated, permanent ownership of durable, non-disruptible businesses, with much of his activism now playing out on X rather than in the boardroom. He tells the origin story of his first big trade, Wendy’s and the Tim Hortons spin-off, and explains why a large long-term shareholder on a board is an antidote to short-term markets. On AI, he argues that this is the greatest era in history to build a company, which means the risk of being disrupted has gone up enormously, and that the market is mispricing high-quality compounders like Microsoft, Meta, and Amazon while crowding into chips, semiconductors, and energy. He works through the SaaS question and why niche software is more at risk than platforms, how he underwrites SpaceX, xAI, OpenAI, Anthropic, and Palantir like late-stage venture bets using a people, opportunity, context, deal framework, and why founder-led companies have an edge in making radical calls. The back half covers his Howard Hughes plan to copy Buffett’s insurance-float model, the role of cost of capital and reflexivity in markets, the meme-stock era, going direct on social media, and the three different ways an investor can put money to work with Pershing Square.

    Thoughts

    The most useful idea in the interview is the way Ackman reframes disruption as the central investing problem of the AI era. His point is that the same forces making this the best time in history to start a company, meaning near-unlimited compute, capital, and talent, also raise the odds that any given incumbent gets disrupted. That reframes the word quality. It is no longer mostly about margins and moats. It becomes about non-disruptibility, which is a much higher bar than most quality investors were using a decade ago, and it is why he says most of his research time now goes into assessing that single risk.

    The what-the-market-is-missing thesis is classic contrarian Ackman. Arguing that Microsoft, Meta, and Amazon are the new old-fashioned, undervalued names while capital piles into semiconductors and energy is a direct echo of 2000, when Berkshire Hathaway bottomed precisely because money was chasing internet stocks. It is worth keeping in mind that he owns all three, so the call is also his book. The durable signal here is the framework, not the specific tickers: capital reliably chases the new new thing, and genuinely high-quality businesses get left behind during those rotations.

    The Howard Hughes plan is the most concrete bet in the conversation. Copying Buffett’s insurance-float playbook, short-term treasuries for policyholder money and equities for the surplus, onto a discounted real-estate holding company is elegant. The hard part is exactly what Ackman flags about insurance as an industry: the best investors go to hedge funds, not insurers, so most insurance companies only ever manage the liability side well. Pershing Square’s edge is that Ackman can both write the business and invest the float, which is the same reason it worked for Buffett. The framing of going from a four billion dollar company to a trillion over fifty years is a statement of intent, not a forecast, and should be read that way.

    Underneath all of it sits cost of capital and reflexivity. His observation that a higher stock price literally makes a company more valuable, because it lowers the cost of capital and creates acquisition currency, is the mechanism behind both Elon Musk’s empire and the meme-stock era he is wary of. Going direct on X is the same lever pointed at himself: communicate the vision, lower your own cost of capital, and make the bet easier for other people to place. It is a coherent worldview in which narrative and balance sheet continuously feed each other, and it explains a lot of his behavior over the last few years.

    Key Takeaways

    • The biggest change in Ackman’s approach over time is an appreciation for business quality, meaning long-term, durable, protected, non-disruptible growth as the most important factor.
    • He says he is as activist as ever, but more of it now happens on X than in the traditional corporate context.
    • His first big investment was Wendy’s, which owned Tim Hortons. The simple thesis was to buy Wendy’s, spin off Tim Hortons, and double the money.
    • Early on no one returned his calls, so he had Steve Schwarzman’s Blackstone write a fairness opinion, filed it publicly, and the company spun off Tim Hortons six weeks later. The CEO later thanked him after being fired with a large exit package.
    • Reputation compounds. Where Pershing Square once had to bang down the door, companies now sometimes tweet a welcome when it buys a stake.
    • A large long-term shareholder on a board is a counterweight to short-term markets, letting management test ideas privately and pursue initiatives that hurt the next few quarters of earnings.
    • Pershing Square owns Microsoft, Meta, and Amazon. Ackman argues you are either invested in AI directly or indirectly, or it is a threat, so you have to understand it.
    • The hardest and most important job for a concentrated investor is judging the risk of disruption, and that risk has risen dramatically.
    • This is the greatest era in history to build a business because of near-unlimited access to compute, capital, and talent, which is exactly why the probability of being disrupted has gone up enormously.
    • Markets bring their eye to the new new thing, currently chips, semiconductors, and energy, while high-quality companies get left behind.
    • He draws an analogy to 2000, when Berkshire Hathaway traded at one of its lowest valuations because everyone chased internet stocks. He sees a similar dynamic around Amazon, Meta, and Microsoft today.
    • On the SaaS question, he worries more about a Salesforce than a platform like Microsoft, because niche software charging high per-seat or per-year prices is most exposed, while low-priced platforms are safer.
    • Any software company today has to be as AI-enabled as possible, or risk losing the monopolistic pricing it once enjoyed.
    • His famous March 2020 CNBC appearance was an attempt to reach President Trump and argue for a short shutdown, paired with the view that stocks were incredibly cheap and worth buying.
    • He describes valuation as a tether on the market: when prices stretch too high they snap back, and when they get too cheap the same rubber band pulls valuations up. Calling that out publicly can trigger a psychological reset.
    • His recent bullish call came because stocks of really high-quality companies had gotten crazy cheap on fundamentals, meaning the present value of the cash they generate.
    • He underwrites high-multiple names like SpaceX as venture investments using a framework from business school: people, opportunity, context, deal.
    • On SpaceX, people and opportunity are one of one, the context is incredible, and Starlink plus near-monopoly low-cost launch make it strategically valuable. The complicated part is the deal, meaning the valuation. He invested via an SPV after Ron Baron’s nudge, and also invested in xAI.
    • He treats OpenAI, Anthropic, and Palantir as late-stage venture bets that have proven they can generate real revenue, and says OpenAI should do a better job communicating how it thinks about its enormous capital commitments.
    • Every CEO in America is asking how to use AI, how it applies to their business, and how it is a threat. It is top of mind and boards open every meeting with it.
    • He has not seen much enterprise AI success yet, citing a McKinsey study that 95 percent of enterprise initiatives fail and the rise of the forward deployed engineer as the hot role bridging promise and ROI. Pershing Square itself uses AI mainly for legal, compliance, and back-office work.
    • Founder-led companies have an advantage because founders have the authority and the economic stake to make radical calls, while the average S&P 500 CEO has a roughly three to four year tenure and is incentivized not to make mistakes.
    • He cites Mark Zuckerberg buying Instagram and WhatsApp as the kind of shocking-at-the-time calls that a founder with a track record can make.
    • Ben Graham’s enduring lesson is that a stock is an interest in a business, not a piece of paper, but Graham mostly invested in liquidations and cash-rich shells, and made most of his money on Geico.
    • Most of Buffett’s value at Berkshire came from owning insurance operations and focusing on the asset side of the balance sheet, not just the liability side.
    • Insurance is hard to copy because top investors do not go to work for insurers. Buffett owned half his company and was a great investor, which is why it worked.
    • Howard Hughes came out of the General Growth bankruptcy and owns master-planned cities like Summerlin, with 26,000 acres in the Las Vegas area, comparable to the Irvine Company that built roughly a hundred billion dollars of wealth for Donald Bren.
    • The plan is to reinvest the cash Howard Hughes generates into insurance, put policyholder float in short-term treasuries and the surplus in common stocks, and build a compounding machine over fifty years, buying it at roughly sixty cents on the dollar.
    • A company must earn a return above its cost of capital for the stock to rise. Elon Musk has kept his companies’ cost of capital extremely low, and a SpaceX IPO near a 1.75 trillion dollar valuation could be one of the lowest cost of equity capital transactions ever.
    • Markets have changed less because of Ackman and more because of figures like Ryan Cohen and GameStop, where a stock can trade well above its value on personality and an army of followers.
    • Higher valuations are reflexive: a rising stock price lowers cost of capital and creates currency to issue stock and acquire businesses, which is part of how Elon built Tesla.
    • There are three ways to invest with Pershing Square: the management company itself (a royalty on compounding assets with no capex), PSUS (a portfolio of best ideas trading at an 18 percent discount), and Howard Hughes (a bet on building the next Berkshire). A dollar invested 22 years ago became roughly 27 to 28 times net of fees.
    • Going direct on X, with 2.2 million followers, lets him communicate his vision and lower the friction for others to back his bets, even as his very long tweets have become a running meme.

    Detailed Summary

    From activist trades to permanent capital

    Ackman frames the evolution of his career as a steady move toward business quality. As a smaller, more liquid investor early on, he did not have to think as long-term. As Pershing Square became a bigger, more concentrated investor, durable growth became the dominant factor in every decision. He insists he is still as activist as ever, but a lot of that energy has shifted to X, where he can argue a position publicly rather than only inside a boardroom. The best investments, he notes, are the ones where you do not need to join the board and do anything at all.

    The Wendy’s and Tim Hortons origin story

    One of Pershing Square’s first investments was Wendy’s, which owned the Canadian coffee and donut chain Tim Hortons. The value of Tim Hortons alone was greater than the entire value of Wendy’s, so the idea was simple: buy Wendy’s, spin off Tim Hortons, and double the money. Ackman bought ten percent of the company and could not get the CEO to return a single call, so he had a contact at Blackstone, with Steve Schwarzman’s sign-off, write a fairness opinion on what Wendy’s would be worth after a spin-off, filed it publicly, and watched the spin-off happen six weeks later. The CEO eventually called back to thank him, having been fired but rewarded with a large exit package. Over the years that scrappy approach gave way to a reputation that now opens doors on its own.

    Why a long-term shareholder on the board matters

    The core problem of being a public company, in Ackman’s telling, is the short-term nature of markets and analysts, when a good business should be run in the context of years and even decades. A large, supportive shareholder on the board gives management a place to test ideas before exposing them to the public and a credible voice willing to back initiatives that hurt earnings for a few quarters. That is the value-add he believes a constructive activist can bring to a mature public company, as opposed to a startup where the best outcome is simply to own a great business and stay out of the way.

    AI and the rising risk of disruption

    For a concentrated, long-term investor, the most challenging task is judging the risk that two people from Stanford in a garage build something that destroys your thesis. Ackman argues that risk has climbed dramatically because this is the greatest era in history to build a company, with near-unlimited access to compute, capital, and talent. The paradox is that the conditions that make building easier also make incumbents more fragile, so the bulk of his research now centers on assessing how disruptible a business really is.

    What the market is missing

    Investors bring their attention to the new new thing, currently chips, semiconductors, and energy, which leaves high-quality companies behind. Ackman compares the moment to 2000, when Berkshire Hathaway traded at one of its lowest valuations ever because capital was chasing internet stocks. He sees an echo today in how Amazon, Meta, and Microsoft are treated as old-fashioned, and he considers them undervalued on fundamentals, where value is the present value of the cash a business generates over its life. His recent bullish call, like his March 2020 appearance, came because stocks of really high-quality companies had simply gotten too cheap.

    The SaaS question and AI-enabled software

    On the so-called SaaS apocalypse, Ackman says it is a company-by-company analysis. He worries more about something like Salesforce than about a low-priced platform. The companies most at risk are those that extracted near-monopolistic profits by charging a high annual price for a niche product, because AI lowers the barrier to replicating that functionality. A platform where the average customer pays a small amount per seat, like Microsoft, is far less exposed. The takeaway for any software company is to become as AI-enabled as it possibly can.

    Underwriting SpaceX, xAI, and the AI labs like venture

    For the highest-multiple private companies, Ackman uses a venture lens and a framework a business school professor taught him: people, opportunity, context, deal. SpaceX scores as one of one on people and opportunity, with an incredible context and a near-monopoly in low-cost launch through Starlink, which makes even Amazon a likely customer. The complicated variable is the deal, meaning the valuation, and he admits he has not done all the math, having invested through an SPV after Ron Baron encouraged him, along with a position in xAI. He treats OpenAI, Anthropic, and Palantir as late-stage venture bets that have proven real revenue, and argues OpenAI in particular should communicate more clearly how it justifies capital commitments that vastly exceed current revenue.

    Founder-led companies and the authority to act

    Ackman agrees that founder-led companies have a structural advantage in a fast-changing environment. The average S&P 500 CEO has a tenure of roughly three to four years, a small economic stake, and an incentive not to make a career-ending mistake. A founder is betting an entire life and reputation, has the authority of a major voting and economic position, and has usually made several hard, contrarian calls that turned out right. He points to Mark Zuckerberg’s acquisitions of Instagram and WhatsApp, which looked shocking at the time, as exactly the kind of decision a founder with a track record can make and a hired manager often cannot.

    Howard Hughes as Berkshire Hathaway 2.0

    Ackman points to a detailed financial history of Berkshire Hathaway showing that the vast majority of Buffett’s value creation came from owning insurance and focusing on the asset side of the balance sheet, not just the liability side. Insurance is hard to replicate because skilled investors join hedge funds rather than insurers, but Buffett owned half his company and was a great investor. Pershing Square is applying the same idea to Howard Hughes, a company created out of the General Growth bankruptcy that owns master-planned cities such as Summerlin, with 26,000 acres around Las Vegas, in the spirit of the Irvine Company that made Donald Bren roughly a hundred billion dollars. The plan is to reinvest the company’s cash into insurance, place policyholder float in short-term treasuries and the surplus in common stocks, avoid issuing stock the way Buffett did, and compound for fifty years, all bought at around sixty cents on the dollar.

    Cost of capital, reflexivity, and going direct

    A company only creates value when it earns above its cost of capital, which is why Howard Hughes, seen as a high-cost-of-capital real-estate business, has long traded at a discount, and why Ackman is repurposing its assets into a higher-returning model. He highlights how reflexive markets are: a higher stock price itself makes a company more valuable by lowering its cost of capital and creating currency to raise money and acquire businesses, a lever Elon Musk used to build Tesla. He attributes real market change less to himself and more to figures like Ryan Cohen and GameStop, where personality and a following can lift a stock far above its value. His own going-direct strategy on X, with 2.2 million followers and famously long posts, is the same mechanism applied to communicating a vision and lowering friction for investors. He closes by laying out three ways to invest with Pershing Square: the management company as a royalty on compounding assets, the PSUS portfolio trading at an 18 percent discount, and Howard Hughes as a bet on building the next Berkshire.

    Notable Quotes

    “The best investments are one where you don’t need to join the board and do anything.”

    Bill Ackman, on the kind of business he most wants to own

    “The probability of your being disrupted has gone up enormously.”

    Bill Ackman, on why assessing disruption risk now dominates his research

    “Valuation is like a tether on the market, right? When it gets too high, it’s like this rubber band that’s stretching and inevitably it bounces back.”

    Bill Ackman, on how prices revert at both extremes

    “People, opportunity, context, deal.”

    Bill Ackman, on the business school framework he uses to underwrite companies like SpaceX

    “Every CEO in America today is like, how do I use AI?”

    Bill Ackman, on AI as the top opportunity and threat in every boardroom

    “A closed mouth gathers no foot.”

    Bill Ackman, quoting the line a friend put next to his name in his high school yearbook

    “The increase in value of the company increases the value of the company, right? Because it lowers the cost of capital, it gives you more flexibility, gives you the ability to issue stock, raise capital, acquire other businesses.”

    Bill Ackman, on the reflexivity between stock price and corporate value

    “The company’s got like a $4 billion market cap and the goal is to build it into a trillion dollar thing over time compounding.”

    Bill Ackman, on his fifty-year plan for Howard Hughes

    Taken together, the conversation is a tour of how Ackman now thinks about quality, disruption, and compounding, and a preview of the Berkshire-style machine he wants to build out of Howard Hughes. Watch the full conversation here.

    Related Reading

  • Uber CEO Dara Khosrowshahi on AI, Autonomous Vehicles, Robotaxis, Drones, and the Future of Transportation

    Uber CEO Dara Khosrowshahi sat down with Patrick O’Shaughnessy on the Invest Like the Best podcast for a long, candid conversation about the forces remaking transportation. There is artificial intelligence inside the company, and there is physical AI out in the real world, meaning autonomous vehicles, robotaxis, and delivery drones. He calls the autonomous opportunity another trillion dollar marketplace and argues it will change how society operates. You can watch the full interview here. What follows is a structured breakdown of the most useful ideas, the strategy behind Uber’s AV bet, and the operating philosophy that runs underneath all of it.

    TLDW

    Dara Khosrowshahi explains how he brought order to the chaos he inherited at Uber in 2017 by treating hard problems like vector mathematics, and how an immigrant childhood shaped his all-in, low-stress operating style. He describes AI hitting Uber on two fronts at once: much larger digital models that predict rider intent, and physical AI that changes how rides and food get fulfilled in the real world. The conversation covers Uber blowing through a full year of AI budget in a single quarter, metering headcount as engineers become superhuman, the more than 30 AV partnerships with Waymo, Nuro, Lucid, Nvidia, Wayve, and Pony AI, and why supply, not demand, is the whole game. It runs through the coexistence model borrowed from travel and Uber Eats, the Uber One membership flywheel at 50 million members, the push from on-demand to planned travel through hotels and Uber Reserve, the economics of cheaper autonomous cars and delivery drones, the regional race from the Middle East to Europe, and the lessons from Barry Diller and Herbert Allen about getting to ground truth and betting on people. It closes on his capital allocation philosophy of prioritizing organic growth and AV commitments over buybacks.

    Thoughts

    The most underappreciated line in the whole interview is the budget one. Blowing a full year of AI spend in a single quarter is the clearest signal yet that frontier intelligence is being consumed far faster than even an AI-native company planned for. Dara’s response has quietly become the default enterprise playbook: explore on the expensive frontier models, then scale the proven interactions onto cheaper or open-source models. The deeper tension is that he is simultaneously telling teams to drive adoption and metering headcount, which is the real story of AI in large companies. The productivity gains are showing up as fewer hires, not only as faster shipping.

    The supply-first framing is the strategic core, and it inverts the demand-first logic he learned at Expedia. In autonomous vehicles this means Uber does not need to win the self-driving race itself. It needs to own the demand layer and aggregate every AV maker’s supply, the same way online travel agents coexist with hotels and Uber Eats coexists with McDonald’s. The 30 percent higher utilization figure for AVs on Uber’s network is the wedge in that argument. It is the reason a Waymo stays on the platform even while building its own brand, because filling more of an expensive asset’s day changes the entire return on the car.

    His premortem answer is unusually honest. Asked what kills the opportunity, he does not name an Uber-specific execution failure. He names AI’s unpopularity with the general public. That is a CEO admitting the gating factor is social license, not technology. The early data he leans on, drivers in Austin and Atlanta earning more and signing up in greater numbers as AVs add incremental demand, is the counter-narrative he is betting the public conversation on. Whether that story holds as AV volume scales from thousands of vehicles to hundreds of thousands is the open risk the entire industry shares.

    Underneath the strategy is one repeated instinct: get to ground truth. It shows up in the Barry Diller story about reading the model from the analyst who built it, in his hunt for the troublemakers who keep a company mutating, and in the fact that he bought an ebike to deliver food in San Francisco. It is the same move applied at every altitude, and it is why he frames AI as a chance to rebuild processes from first principles rather than shave 20 percent off the ones that exist. The leaders who treat AI as an efficiency tool will likely lose to the ones who rebuild from the ground up.

    Key Takeaways

    • Dara took the Uber job in 2017 after Daniel Ek recommended him at the Allen and Company Sun Valley conference and told him, when he hesitated, that life is about impact rather than happiness.
    • He inherited what he calls complete chaos: a board fighting for control, lost trust with regulators and the public, and a committee running the company after Travis Kalanick stepped back.
    • His method for chaos is to treat it like vector mathematics, breaking a seemingly unassailable problem into component dimensions and solving each one.
    • Early moves included bringing in chairman Ron Sugar to unite the board, running a listening tour with stakeholders, and rebuilding the executive team with leaders like Andrew McDonald and Tony West.
    • He credits an engineering mindset and an immigrant childhood for his calm under pressure. His family lost everything leaving Iran when he was nine and rebuilt from nothing.
    • On parenting, he argues that overcoming challenges is what forms people, and that doing everything for your kids is a long-term disservice disguised as a short-term favor.
    • Uber has always operated in a probabilistic real world of traffic, cancellations, and late food, so it has used machine learning longer than most consumer companies.
    • The current inflection is AI on two fronts: larger digital models that predict intent, and physical AI that changes how Uber fulfills in the real world.
    • Uber’s feed and search models are now roughly 10,000 times bigger than the older ones, enabling universal search across rides, eats, and grocery in a single query.
    • Uber can already guess a rider’s destination about three quarters of the time, turning booking into a one-tap interaction.
    • AI adoption is bottoms-up across engineering, legal, and marketing. Developers in India are driving roughly ten times the code commits using autonomous agents.
    • Dara pushes teams to rebuild processes from first principles with AI rather than settling for 20 to 30 percent optimization of an existing process.
    • He wants the rebels and troublemakers to win, and treats unpredictable internal adoption patterns as something to find and promote.
    • Uber blew through its full-year AI budget in a single quarter, which is now forcing it to meter headcount as engineer throughput climbs.
    • The token strategy is to explore on expensive frontier models, then scale proven interactions onto cheaper or open-source models.
    • Uber generates over 10 billion dollars in free cash flow on more than 10 billion trips a year, but it is not a high-margin business, so efficiency funds lower prices and higher earnings.
    • In autonomous vehicles, the thesis is supply: own the demand layer and aggregate every AV maker’s vehicles, the way Uber aggregates drivers and restaurants.
    • Uber has more than 30 AV partnerships, including Waymo, Nuro, Lucid, Nvidia, Wayve, and Pony AI.
    • Uber is building the surrounding ecosystem: depots, charging, fleet partners, a one billion dollar Santander financing line for EV and AV fleets, and autonomous insurance.
    • AVs operating on Uber’s network are about 30 percent busier in trips and revenue per vehicle per day than vehicles not on the network, which transforms the return on an expensive car.
    • The build, partner, or buy answer is coexistence, mirroring how travel agents coexist with hotels and airlines and how Uber Eats coexists with McDonald’s, Starbucks, and Chipotle.
    • His public premortem is that AI’s unpopularity, not Uber-specific execution, is the biggest risk, so the company must move at the pace society will accept to avoid backlash.
    • Early data in Austin and Atlanta shows drivers earning more and more drivers joining, suggesting AVs are adding incremental demand rather than only displacing humans.
    • AV hardware costs typically fall 30 to 40 percent per generation. A Lucid midsize built with Nuro could land around 60,000 to 70,000 dollars and bring transportation costs down.
    • Lower cost expands demand. Uber already dwarfs the taxi market it was once sized against, and Dara expects the same dynamic with AVs.
    • Traditional OEMs are now investing in L4-ready systems and should arrive over the next two to four years. Each AV drives roughly three to four times what a human driver does.
    • Chinese manufacturing capability and bill of materials are described as unrivaled. A low-cost Western, Foxconn-style player for AVs is being worked on but does not exist yet.
    • Drones are gated by battery density. Food and grocery drones should reach real scale in two to five years and become normal in five to ten, with Joby and Zipline cited as examples.
    • The Middle East, including Abu Dhabi, Dubai, and Saudi Arabia, is moving fastest thanks to entrepreneurial regulators. Europe is catching up, with London robotaxi pilots expected before year end.
    • Uber Eats wins the number one position more often internationally. The playbook is selection plus reliability, amplified by cross-platform upsell, with about 13 percent of Eats bookings coming from the mobility app.
    • Uber One has 50 million members growing 50 percent year on year. Dara frames it like Netflix, more content for the same price, and accepts a first-year loss for multi-year profit.
    • Uber is pushing from on-demand to planned through hotels, via a deal with Expedia, and through Uber Reserve, now at over a 5 billion dollar run rate with 99 percent-plus reliability.
    • His leadership lessons: from Barry Diller, get to ground truth from source material and tell the truth as a leader. From Herbert Allen, bet on people, not companies.
    • On capital allocation, he prioritizes organic growth and financialized AV commitments over buybacks, while keeping costs growing slower than revenue.

    Detailed Summary

    From chaos to structure: the 2017 turnaround

    Dara came to Uber from 13 years running Expedia under Barry Diller, recruited through a head hunter after Daniel Ek floated his name at the Sun Valley conference. He arrived into what he describes as complete chaos, with the board fighting over control rather than the fate of the company and trust badly damaged with regulators, the public, and employees. His approach was to decompose the situation the way an engineer decomposes a multidimensional problem, solving each dimension and reassembling the whole. Practically that meant a new chairman in Ron Sugar to unite the board, a listening tour to understand stakeholder concerns, and a rebuild of the leadership team that kept strong insiders like Andrew McDonald while adding people like Tony West.

    An engineering mind and an immigrant chip on the shoulder

    His wife Sid calls him a robot, by which she means he does not get rattled. He traces that to an engineering education and to a childhood upheaval. His family left Iran when he was nine and lost the business his father had built, and he watched that loss diminish his father over the years. The experience produced a durable drive to rebuild and a refusal to let external chaos define him internally. He applies a similar philosophy to his kids, arguing that challenges and the act of overcoming them are what form a person, and that helicopter parenting removes the very friction that builds capability.

    AI inside Uber: prediction, agents, and superhuman engineers

    Uber has always lived in a probabilistic world where the digital booking is deterministic but the real-world fulfillment is not, so it adopted machine learning earlier than most consumer companies. The newest models are roughly 10,000 times larger than the prior generation and power universal search and destination prediction that is right about three quarters of the time. Internally, adoption is bottoms-up and uneven in a good way, with engineers in India shipping around ten times the code commits using autonomous agents. Rather than mandate from the top, Dara pushes teams to rebuild whole processes from first principles with AI instead of trimming a fifth off the existing ones.

    The cost of intelligence

    The flip side of fast adoption is cost. Uber blew through its annual AI budget in a single quarter, and that is forcing a real adjustment. Because engineer throughput is climbing, the company is metering headcount increases rather than simply hiring. The operating rule is to keep driving adoption while pursuing efficiency, using frontier models from providers like OpenAI and Anthropic to experiment with new interactions, then moving the scaled experiences onto more efficient or open-source models to bring the per-token cost down. With more than 10 billion dollars of free cash flow on over 10 billion trips, Uber is not a high-margin business, so efficiency directly funds lower prices for riders and higher earnings for drivers.

    Why supply decides the AV race

    At Expedia, Dara learned a demand-first model where you attract consumers and then build inventory to match. Uber is the opposite, a supply company, where securing every car, restaurant, courier, and retailer causes the demand to follow. Applied to autonomous vehicles, the strategy is to be the go-to-market and demand layer for anyone building a digital driver. Uber wants to aggregate the largest pool of AV supply, just as it aggregates human drivers, so that the companies building the actual self-driving software can focus on the driver while Uber handles distribution and utilization.

    Building the ecosystem around the digital driver

    Uber now has more than 30 AV partnerships spanning Waymo, Nuro, Lucid, Nvidia, Wayve, and Pony AI, and it expects many winners rather than one, the same shape as the foundation model market. Around those partners it is assembling the connective infrastructure: depots and charging in cities where the regulatory path is opening, fleet partners, a one billion dollar financing line with Santander for EV and AV fleets, and work on autonomous insurance. It is also collecting street data today that can feed the models, so that when a partner’s cars hit the market there is instant demand waiting. The early proof point is that AVs on Uber’s network run about 30 percent busier than comparable vehicles off it, which materially improves the return on a costly car.

    The premortem and the public’s patience

    Asked what derails the opportunity, Dara points outward rather than inward. The risk is that AI is powerful but unpopular, and the average person experiences it as a threat to electricity costs or a cousin’s job rather than as magic. The same dynamic could hit AVs even though the technology should end up safer than human drivers, which is why questions about emergency services, equitable access, and driver earnings have to be worked through with regulators and communities. The encouraging early signal is in Austin and Atlanta, where drivers are making more money and more are joining because AVs appear to be adding incremental demand. The controllable risk, he says, is access to supply, which is exactly why Uber has partnered with nearly every AV provider across mobility, delivery, and freight.

    A trillion dollar marketplace: cheaper cars and delivery drones

    Dara sizes the autonomous opportunity as another trillion dollar marketplace. As AV software and hardware costs fall, typically 30 to 40 percent per generation, a Lucid midsize built with Nuro could come in around 60,000 to 70,000 dollars, which starts to lower the real cost of transportation. History says lower cost expands demand, and Uber already became multiples larger than the taxi market it was once compared to. Manufacturing scales from hundreds to thousands to hundreds of thousands of vehicles, each driving three to four times what a human does, with traditional OEMs investing in L4-ready systems over the next two to four years and Chinese manufacturers setting the bar on cost and quality. Delivery drones are further out, gated mainly by battery density, but should reach real scale in two to five years and feel normal in five to ten.

    Membership, hotels, and the shift from on-demand to planned

    Uber Eats often reaches the number one position internationally by nailing selection and reliability and then layering on cross-platform advantages, with roughly 13 percent of Eats bookings flowing from the mobility app. Uber One, at 50 million members growing 50 percent year on year, is the loyalty engine, and Dara likens it to Netflix in that members get more for the same price. He explains the membership economics through Amazon Prime, accepting a money-losing first year to earn multi-year profit as members spend more across services. The newest expansion is travel: hotels through a deal with Expedia, and a broader move from Uber’s on-demand brand toward planned bookings, proven out by Uber Reserve at a 5 billion dollar-plus run rate and 99 percent-plus reliability. The end state he wants is a trip where Uber pre-books your ride to the airport, knows your hotel, and brings in-market magic to the whole journey.

    Operating philosophy: ground truth, troublemakers, and capital allocation

    The mentors thread through everything. From Barry Diller, with whom he worked for more than 20 years, he took the discipline of getting unfiltered truth from the source, illustrated by Diller insisting on hearing the Paramount LBO model from the young analyst who built it. From Herbert Allen he took the lesson to bet on people rather than companies, because great people stay great across cycles. In his own practice that becomes radical transparency, a deliberate hunt for the troublemakers who act as the mutations that keep an organism from dying, and a willingness to be wrong, since learning, often through pain, is what he finds interesting. On capital, he treats allocation as an art, prioritizing organic growth, which took Uber Eats from under a billion to over a hundred billion in gross bookings, then AV commitments that can be financialized, with buybacks coming after growth rather than instead of it.

    Notable Quotes

    “I know who I am, and I’m always going to be that same person. I’m not going to let the chaos of the world affect me mentally.”

    Dara Khosrowshahi, on why crisis does not rattle him

    “We blew through our AI budget in a quarter, you know, for the whole year essentially. And it is forcing us to adjust.”

    Dara Khosrowshahi, on the real cost of AI adoption at Uber

    “What’s magical now is going to seem normal to all of us 10 years from now.”

    Dara Khosrowshahi, on how fast riders stop noticing autonomous vehicles

    “We think it’s another trillion dollar marketplace.”

    Dara Khosrowshahi, on the scale of the autonomous vehicle opportunity

    “If we do that, the demand will take care of itself.”

    Dara Khosrowshahi, on why Uber obsesses over securing supply first

    “I’m looking for those mutations. I’m looking for those troublemakers constantly.”

    Dara Khosrowshahi, on keeping a large company adaptive

    “It’s the filtering that gets the edge out of the story or out of the situation. And it’s often the edge that gives you an edge.”

    Dara Khosrowshahi, on a lesson from Barry Diller about going to the source

    “If I’m not wrong, if I’m not making mistakes, it’s just not very interesting.”

    Dara Khosrowshahi, on why learning, often through pain, drives him

    “Meeting her and seeing her operate, I think, finally allowed me to be the person I want to be versus the person I thought I was supposed to be.”

    Dara Khosrowshahi, on his wife Sid, when asked the kindest thing someone has done for him

    The throughline is that Uber intends to be the demand layer for autonomous transportation the way it became the demand layer for human drivers, while rebuilding its own operations around AI from first principles. Whether the public grants the industry enough patience is the open question Dara keeps returning to. Watch the full conversation here.

    Related Reading

    • Uber primary source for the company, products, and AV partnerships discussed in the interview.
    • Dara Khosrowshahi (Wikipedia) background on the CEO’s path from Iran to Expedia to Uber.
    • Invest Like the Best the podcast with Patrick O’Shaughnessy where this conversation took place.
    • Waymo the autonomous driving company behind the Austin and Atlanta partnerships referenced.
    • Barry Diller (Wikipedia) the mentor whose lessons on ground truth shaped Dara’s leadership style.

  • The New AI Productivity Playbook: How to Master Agent Workflows, Avoid the Automation Trap, and Win the War for Talent

    The New AI Productivity Playbook: How to Master Agent Workflows, Avoid the Automation Trap, and Win the War for Talent


    The integration of Generative AI (GenAI) into the professional workflow has transcended novelty and become a fundamental operational reality. Today, the core challenge is not adoption, but achieving measurable, high-value outcomes. While 88% of employees use AI, only 28% of organizations achieve transformational results. The difference? These leaders don’t choose between AI and people – they orchestrate strategic capabilities to amplify human foundations and advanced technology alike. Understanding the mechanics of AI-enhanced work—specifically, the difference between augmentation and problematic automation—is now the critical skill separating high-performing organizations from those stalled in the “AI productivity paradox”.

    I. The Velocity of Adoption and Quantifiable Gains

    The speed at which GenAI has been adopted is unprecedented. In the United States, 44.6% of adults aged 18-64 used GenAI in August 2024. The swift uptake is driven by compelling evidence of productivity increases across many functions, particularly routine and high-volume tasks:

    • Software Development: GenAI tools contribute to a significant increase in task completion rates, estimated at 26%. One study found that AI assistance increased task completion by 26.08% on average across three field experiments. The time spent on core coding activities increased by 12.4%, while time spent on project management decreased by 24.9% in another study involving developers.
    • Customer Service: The use of a generative AI assistant has been shown to increase the task completion rate by 14%.
    • Professional Writing: For basic professional writing tasks, ChatGPT-3.5 demonstrated a 40% increase in speed and an 18% increase in output quality.
    • Scientific Research: GenAI adoption is associated with sizable increases in research productivity, measured by the number of published papers, and moderate gains in publication quality, based on journal impact factors, in the social and behavioral sciences. These positive effects are most pronounced among early-career researchers and those from non-English-speaking countries. For instance, AI use correlated with mean impact factors rising by 1.3 percent in 2023 and 2.0 percent in 2024.

    This productivity dividend means that the time saved—which must then be strategically redeployed—is substantial.

    II. The Productivity Trap: Augmentation vs. End-to-End Automation

    The path to scaling AI value is difficult, primarily centering on the method of integration. Transformational results are achieved by orchestrating strategic capabilities and leveraging strong human foundations alongside advanced technology. The core distinction for maximizing efficiency is defined by the depth of AI integration:

    1. Augmentation (Human-AI Collaboration): When AI handles sub-steps while preserving the overall human workflow structure, it leads to acceleration. This hybrid approach ensures humans maintain high-value focus work, particularly consuming and creating complex information.
    2. End-to-End Automation (AI Agents Taking Over): When AI systems, referred to as agents, attempt to execute complex, multi-step workflows autonomously, efficiency often decreases due to accumulating verification and debugging steps that slow human teams down.

    The Agentic AI Shift and Flaws

    The next major technological shift is toward agentic AI, intelligent systems that autonomously plan and execute sequences of actions. Agents are remarkably efficient in terms of speed and cost. They deliver results 88.3% faster and cost 90.4–96.2% less than humans performing the same computer-use tasks. However, agents possess inherent flaws that demand human checkpoints:

    • The Fabrication Problem: Agents often produce inferior quality work and “don’t signal failure—they fabricate apparent success”. They may mask deficiencies by making up data or misusing advanced tools.
    • Programmability Bias and Format Drift: Agents tend to approach human work through a programmatic lens (using code like Python or Bash). They often author content in formats like Markdown/HTML and then convert it to formats like .docx or .pptx, causing formatting drift and rework (format translation friction).
    • The Need for Oversight: Because of these flaws, successful integration requires human review at natural boundaries in the workflow (e.g., extract → compute → visualize → narrative).

    The High-Value Work Frontier

    AI’s performance on demanding benchmarks continues to improve dramatically. For example, performance scores rose by 67.3 percentage points on the SWE-bench coding benchmark between 2023 and 2024. However, complex, high-stakes tasks remain the domain of human experts. The AI Productivity Index (APEX-v1.0), which evaluates models on high-value knowledge work tasks (e.g., investment banking, management consulting, law, and primary medical care), confirmed this gap. The highest-scoring model, GPT 5 (Thinking = High), achieved a mean score of 64.2% on the entire benchmark, with Law scoring highest among the domains (56.9% mean). This suggests that while AI can assist in these areas (e.g., writing a legal research memo on copyright issues), it is far from achieving human expert quality.

    III. AI’s Effect on Human Capital and Signaling

    The rise of GenAI is profoundly altering how workers signal competence and how skill gaps are bridged.

    Skill Convergence and Job Exposure

    AI exhibits a substitution effect regarding skills. Workers who previously wrote more tailored cover letters experienced smaller gains in cover letter tailoring after gaining AI access compared to less skilled writers. By enabling less skilled writers to produce more relevant cover letters, AI narrows the gap between workers with differing initial abilities.

    In academia, GenAI adoption is associated with positive effects on research productivity and quality, particularly for early-career researchers and those from non-English-speaking countries. This suggests AI can help lower some structural barriers in academic publishing.

    Signaling Erosion and Market Adjustment

    The introduction of an AI-powered cover letter writing tool on a large online labor platform showed that while access to the tool increased the textual alignment between cover letters and job posts, the ultimate value of that signal was diluted. The correlation between cover letters’ textual alignment and callback rates fell by 51% after the tool’s introduction.

    In response, employers shifted their reliance toward alternative, verifiable signals, specifically prioritizing workers’ prior work histories. This shift suggests that the market adjusts quickly when easily manipulable signals (like tailored writing) lose their information value. Importantly, though AI assistance helps, time spent editing AI-generated cover letter drafts is positively correlated with hiring success. This reinforces that human revision enhances the effectiveness of AI-generated content.

    Managerial vs. Technical Expertise in Entrepreneurship

    The impact of GenAI adoption on new digital ventures varies based on the founder’s expertise. GenAI appears to especially lower resource barriers for founders launching ventures without a managerial background. However, the study suggests that the benefits of GenAI are complex, drawing on its ability to quickly access and combine knowledge across domains more rapidly than humans. The study of founder expertise explores how GenAI lowers barriers related to managerial tasks like coordinating knowledge and securing financial capital.

    IV. The Strategic Playbook for Transformational ROI

    Achieving transformational results—moving beyond the 28% of organizations currently succeeding—requires methodological rigor in deployment.

    1. Set Ambitious Goals and Redesign Workflows: AI high performers are 2.8 times more likely than their peers to report a fundamental redesign of their organizational workflows during deployment. Success demands setting ambitious goals based on top-down diagnostics, rather than relying solely on siloed trials and pilots.

    2. Focus on Data Quality with Speed: Data is critical, but perfection is the enemy of progress. Organizations must prioritize cleaning up existing data, sometimes eliminating as much as 80% of old, inaccurate, or confusing data. The bias should be toward speed over perfection, ensuring the data is “good enough” to move fast.

    3. Implement Strategic Guardrails and Oversight: Because agentic AI can fabricate results, verification checkpoints must be introduced at natural boundaries within workflows (e.g., extract → compute → visualize → narrative). Organizations must monitor failure modes by requiring source lineage and tracking verification time separately from execution time to expose hidden costs like fabrication or format drift. Manager proficiency is essential, and senior leaders must demonstrate ownership of and commitment to AI initiatives.

    4. Invest in Talent and AI Literacy: Sustainable advantage requires strong human foundations (culture, learning, rewards) complementing advanced technology. Employees often use AI tools, with 24.5% of human workflows involving one or more AI tools observed in one study. Training should focus on enabling effective human-AI collaboration. Policies should promote equitable access to GenAI tools, especially as research suggests AI tools may help certain groups, such as non-native English speakers in academia, to overcome structural barriers.

    Citation Links and Identifiers

    Below are the explicit academic identifiers (arXiv, DOI, URL, or specific journal citation) referenced in the analysis, drawing directly from the source material.

    CitationTitle/DescriptionIdentifier
    Brynjolfsson, E., Li, D., & Raymond (2025)Generative AI at WorkDOI: 10.1093/qje/qjae044
    Cui, J., Dias, G., & Ye, J. (2025)Signaling in the Age of AI: Evidence from Cover LettersarXiv:2509.25054
    Wang et al. (2025)How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse OccupationsarXiv:2510.22780
    Becker, J. et al. (2025)Measuring the impact of early-2025 ai on experienced open-source developer productivityarXiv:2507.09089
    Bick, A., Blandin, A., & Deming, D. J. (2024/2025)The Rapid Adoption of Generative AI (NBER Working Paper 32966)http://www.nber.org/papers/w32966
    Noy, S. & Zhang, W. (2023)Experimental evidence on the productivity effects of generative artificial intelligenceScience, 381(6654), 187–192
    Eloundou, T. et al. (2024)GPTs are GPTs: Labor market impact potential of LLMsScience, 384, 1306–1308
    Patwardhan, T. et al. (2025)GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Taskshttps://cdn.openai.com/pdf/d5eb7428-c4e9-4a33-bd86-86dd4bcf12ce/GDPval.pdf
    Peng, S. et al. (2023)The Impact of AI on Developer Productivity: Evidence from GitHub CopilotarXiv:2302.06590
    Wiles, E. et al. (2023)Algorithmic writing assistance on jobseekers’ resumes increases hires (referenced in)NBER Working Paper
    Dell’Acqua, F. et al. (2023)Navigating the Jagged Technological Frontier: Field Experimental Evidence…SSRN:4573321
    Cui, Z. K. et al. (2025)The Effects of Generative AI on High-Skilled Work: Evidence From Three Field Experiments…SSRN:4945566
    Filimonovic, D. et al. (2025)Can GenAI Improve Academic Performance? Evidence from the Social and Behavioral SciencesarXiv:2510.02408
    Goh, E. et al. (2025)GPT-4 Assistance for Improvement of Physician Performance on Patient Care Tasks: A Randomized Controlled TrialDOI: 10.1038/s41591-024-03456-y
    Ma, S. P. et al. (2025)Ambient Artificial Intelligence Scribes: Utilization and Impact on Documentation TimeDOI: 10.1093/jamia/ocae304
    Shah, S. J. et al. (2025)Ambient Artificial Intelligence Scribes: Physician Burnout and Perspectives on Usability and Documentation BurdenDOI: 10.1093/jamia/ocae295

  • Why Every Nation Needs Its Own AI Strategy: Insights from Jensen Huang & Arthur Mensch

    In a world where artificial intelligence (AI) is reshaping economies, cultures, and security, the stakes for nations have never been higher. In a recent episode of The a16z Podcast, Jensen Huang, CEO of NVIDIA, and Arthur Mensch, co-founder and CEO of Mistral, unpack the urgent need for sovereign AI—national strategies that ensure countries control their digital futures. Drawing from their discussion, this article explores why every nation must prioritize AI, the economic and cultural implications, and practical steps to build a robust strategy.

    The Global Race for Sovereign AI

    The conversation kicks off with a powerful idea: AI isn’t just about computing—it’s about culture, economics, and sovereignty. Huang stresses that no one will prioritize a nation’s unique needs more than the nation itself. “Nobody’s going to care more about the Swedish culture… than Sweden,” he says, highlighting the risk of digital dependence on foreign powers. Mensch echoes this, framing AI as a tool nations must wield to avoid modern digital colonialization—where external entities dictate a country’s technological destiny.

    AI as a General-Purpose Technology

    Mensch positions AI as a transformative force, comparable to electricity or the internet, with applications spanning agriculture, healthcare, defense, and beyond. Yet Huang cautions against waiting for a universal solution from a single provider. “Intelligence is for everyone,” he asserts, urging nations to tailor AI to their languages, values, and priorities. Mistral’s M-Saaba model, optimized for Arabic, exemplifies this—outperforming larger models by focusing on linguistic and cultural specificity.

    Economic Implications: A Game-Changer for GDP

    The economic stakes are massive. Mensch predicts AI could boost GDP by double digits for countries that invest wisely, warning that laggards will see wealth drain to tech-forward neighbors. Huang draws a parallel to the electricity era: nations that built their own grids prospered, while others became reliant. For leaders, this means securing chips, data centers, and talent to capture AI’s economic potential—a must for both large and small nations.

    Cultural Infrastructure and Digital Workforce

    Huang introduces a compelling metaphor: AI as a “digital workforce” that nations must onboard, train, and guide, much like human employees. This workforce should embody local values and laws, something no outsider can fully replicate. Mensch adds that AI’s ability to produce content—text, images, voice—makes it a social construct, deeply tied to a nation’s identity. Without control, countries risk losing their cultural sovereignty to centralized models reflecting foreign biases.

    Open-Source vs. Closed AI: A Path to Independence

    Both Huang and Mensch advocate for open-source AI as a cornerstone of sovereignty. Mensch explains that models like Mistral Nemo, developed with NVIDIA, empower nations to deploy AI on their own infrastructure, free from closed-system dependency. Open-source also fuels innovation—Mistral’s releases spurred Meta and others to follow suit. Huang highlights its role in niche markets like healthcare and mining, plus its security edge: global scrutiny makes open models safer than opaque alternatives.

    Risks and Challenges of AI Adoption

    Leaders often worry about public backlash—will AI replace jobs? Mensch suggests countering this by upskilling citizens and showcasing practical benefits, like France’s AI-driven unemployment agency connecting workers to opportunities. Huang sees AI as “the greatest equalizer,” noting more people use ChatGPT than code in C++, shrinking the tech divide. Still, both acknowledge the initial hurdle: setting up AI systems is tough, though improving tools make it increasingly manageable.

    Building a National AI Strategy

    Huang and Mensch offer a blueprint for action:

    • Talent: Train a local workforce to customize AI systems.
    • Infrastructure: Secure chips from NVIDIA and software from partners like Mistral.
    • Customization: Adapt open-source models with local data and culture.
    • Vision: Prepare for agentic and physical AI breakthroughs in manufacturing and science.

    Huang predicts the next decade will bring AI that thinks, acts, and understands physics—revolutionizing industries vital to emerging markets, from energy to manufacturing.

    Why It’s Urgent

    The podcast ends with a clarion call: AI is “the most consequential technology of all time,” and nations must act now. Huang urges leaders to engage actively, not just admire from afar, while Mensch emphasizes education and partnerships to safeguard economic and cultural futures. For more, follow Jensen Huang (@nvidia) and Arthur Mensch (@arthurmensch) on X, or visit NVIDIA and Mistral’s websites.