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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

  • Dan Loeb on Building Third Point’s $25 Billion Investment Empire: AI, Activism, Credit, and the FTX Mistake

    Dan Loeb has spent three decades turning a $3 million fund into Third Point, a roughly $25 billion collection of hedge fund, credit, insurance, and venture businesses. In this Invest Like the Best conversation with Patrick O’Shaughnessy, Loeb walks through how he reinvented his strategy from deep value and event-driven trades into quality and thematic investing, why he now believes every serious investor has to be a technology investor, how he reads the AI cycle and the semiconductor melt-up, where activism and corporate governance still pay, and the single mistake that taught him the most. It is a rare, unhurried look at how a famously sharp-elbowed activist actually thinks about markets, businesses, and people.

    TLDW

    Loeb covers an enormous amount of ground: his daily process for staying ahead of the information firehose, Jensen Huang’s AI stack as a mental model, and why Nvidia, Anthropic, and Elon Musk’s companies are the three most consequential firms he tracks. He traces Third Point’s roots in credit and event-driven investing at Jefferies, the influence of Joel Greenblatt’s “You Can Be a Stock Market Genius,” and his later pivot to quality investing shaped by “The Outsiders” and Lawrence Cunningham’s “Quality Investing.” He argues the AI rally is not a dot-com-style valuation bubble because the leaders generate enormous cash, explains why human judgment and structural market quirks still create alpha, and makes the case that AI will never fully run a capital system. He digs into corporate governance and his father’s influence, the Sotheby’s and Sony activism campaigns, the hard reality of activism in Japan, and what investing in Danaher’s operating system taught him. He names FTX as his hardest lesson, breaks down Third Point’s evolution into a 60-percent-credit platform spanning CLOs, structured credit, reinsurance and annuities, describes how he is pushing his analysts to use AI and Claude daily, and closes on kindness and the friend who let him sleep on a couch before he made it.

    Thoughts

    The most striking thing about Loeb is that he treats his own strategy as a thing to be disrupted rather than defended. He built his reputation on Greenblatt-style special situations, spin-offs, demutualizations, and post-reorg equities bought cheap because of forced selling and sandbagged guidance. Most investors who win that way spend the rest of their careers protecting the formula. Loeb instead watched the people who stayed rigid about deep value and low multiples underperform or disappear, and deliberately retrained himself and his team around business quality and thematic conviction. The willingness to abandon a winning identity is the actual edge here, more than any single trade. It is the rare investor who can say his current strategy would not fit cleanly on a PowerPoint deck and treat that as a feature.

    His AI framing deserves attention because it is unfashionably calm. The bear case on AI is usually about valuation, and Loeb dismantles it on the leaders’ own numbers: these are companies investing off their balance sheets, generating enormous cash, trading at multiples that do not resemble 1999. He was short the dot-com bubble, so he is not a permabull cheering from the sidelines. His real point is subtler, that the danger is expectations, not valuations. The semiconductor index ran up 40 percent on genuinely strong fundamentals, but Micron and Nvidia both put up monster quarters and saw their stocks fall because expectations had simply outrun even great results. That gap between fundamentals and price is where he thinks the human investor still earns a living, precisely because quant strategies, CTAs, and risk-managed pods are forced to sell into weakness rather than buy it.

    The governance material is the most quietly radical part of the conversation. Loeb defends shareholder primacy against the Business Roundtable’s softer stakeholder language, but his argument is not the cartoon version where shareholder value means strip-mining a company. It is that boards have one job, accountability for capital allocation and management, and that vague multi-stakeholder mandates become an excuse for directors to avoid the hard work. His read on bad governance is almost always relational: directors who let loyalty to an underperforming CEO override their duty, or who sit on boards for status and income. The Sotheby’s story is the clean illustration, a centuries-old, high-status business run unprofitably because nobody treated it like a business. Loeb’s pattern is to find the gap between claimed status and actual performance and to raise the social cost of coasting.

    What is genuinely new in Loeb’s posture is how he talks about AI inside his own firm. He is not pitching it as a moat or a headcount-reduction story. He frames Claude and AI tools as a way to make each person a more autonomous self-improver, something that gives back whatever you put into it, with some analysts running agents overnight and burning tokens while he personally uses it more for queries. Coming from a 30-year fundamental investor, the absence of defensiveness is the signal. He pairs it with Brad Gerstner’s nod to “Essentialism”: the firehose is now infinite, so the scarce skill is deciding what is actually relevant. That is a more honest answer to the AI question than either doom or hype.

    Finally, the FTX confession is worth sitting with because of how he frames it. He does not retreat into cynicism about venture or crypto. He notes that Sam Bankman-Fried, fraud aside, had a real nose for value, with stakes in Anthropic, Cursor, and Solana that would have made him a top venture investor of the era. The lesson Loeb extracts is procedural, not philosophical: their due diligence now includes checking bank balances, the most basic verification that would have surfaced the problem. It is a useful reminder that even sophisticated capital can skip boring fundamentals when a company is growing fast and the cap table looks good. The discipline is not in having a grand theory of fraud, it is in never skipping the unglamorous checks.

    Key Takeaways

    • Loeb’s macro focus right now collapses to two variables: where oil goes, dictated by war and geopolitics, and what AI does on the spending and infrastructure front and its impact on society and the economy.
    • He argues you can no longer punt on technology and focus on industrials or consumer; tech is a big, growing, compounding part of the economy that affects everything else, so every investor has to become a tech investor.
    • He uses Jensen Huang’s AI stack as a mental model: power and energy at the bottom, then chips and infrastructure, up through large language models, software, and applications.
    • The three most consequential companies he tracks are Nvidia, Anthropic, and Elon Musk’s companies collectively.
    • Third Point’s roots are in credit and event-driven investing, shaped by his time at Jefferies watching investors like David Tepper before he founded Appaloosa, Eric Mindich at Goldman, and firms like Angelo Gordon and Farallon.
    • Joel Greenblatt’s “You Can Be a Stock Market Genius” was his foundational framework: spin-offs, demutualizations, privatizations, and post-reorg equities where a new, illiquid security gets dumped by holders who will not do the work.
    • Spin-off managers often sandbag guidance because their incentive packages get set at the time of the spin-off, creating a predictable gap between conservative numbers and real value.
    • From 1995 to roughly 2013-2015, event-driven special situations were Third Point’s bread and butter; those opportunities still exist, but the real edge now is overlaying them with a business-quality lens.
    • The pivot to quality and thematic investing was influenced most by “The Outsiders” (capital allocation plus great operations) and Lawrence Cunningham’s “Quality Investing” (high-moat, high-return-on-capital businesses to own for years).
    • AI disruption made last year one of the worst for many apparently high-quality companies, as businesses that looked durable rapidly became less so.
    • Loeb sees the AI rally as fundamentally different from the dot-com bubble: the leaders invest off their balance sheets, generate enormous cash, and do not carry the valuation excess of 1999.
    • The danger in semis is expectations, not valuation: Nvidia and Micron posted spectacular quarters yet saw stocks fall because expectations had outrun even great numbers.
    • Structural forces still create alpha for fundamental investors: quants, CTAs, and multi-strategy pods have risk metrics that force selling on the way down, the opposite of what is rational for long-term holders.
    • He believes AI will not fully run a capital system; private equity, restructurings, creditor committees, and high-touch negotiation will always need humans.
    • His interest in governance came from his father, a securities lawyer and corporate governance expert who sat on the boards of Mattel and Williams-Sonoma and pushed ethical sourcing ahead of his time.
    • Loeb defends shareholder primacy, citing Milton Friedman and Warren Buffett, and criticizes the Business Roundtable’s move away from shareholder value as a distraction from the board’s real duty.
    • Bad governance usually comes from directors letting loyalty to a weak CEO override fiduciary duty, lacking the knowledge to do the job, or serving for status and income.
    • Writing is a core activism lever: great writing is clear thinking, and social pressure through writing and PR is one of the most effective ways to move a board, alongside financial and legal levers.
    • The Sotheby’s campaign targeted a high-status, centuries-old business run unprofitably; Third Point bought 9.9 percent, eventually brought in Tad Smith from MSG, who cleaned up operations and technology before the company sold.
    • Third Point increasingly prefers to back great companies with excellent management and cheer them on rather than hunt for mismanaged businesses, because bad management tends to cluster into a morass.
    • Third Point is a collection of businesses; the flagship hedge fund grew from $3 million to about $9 billion and is roughly 30 percent credit, with the broader firm closer to 60 percent credit.
    • The firm spans a roughly $7 billion CLO business, structured and corporate credit, an insurance company, asbestos liabilities, a small private credit unit, and a venture capital arm.
    • The unifying thread is valuing enterprises across early, mid, and mature stages and investing in whichever fulcrum security offers the best risk-reward, from equity to senior debt.
    • Loeb cites buying Twitter’s financing debt near 96-97 cents at a 12 percent yield when most credit investors were scared, and a difficult xAI debt financing, as examples of cross-discipline conviction.
    • He is the portfolio manager only of the hedge fund; the credit, CLO, structured credit, and high-yield businesses have their own PMs and investment committees he does not sit on.
    • The Sony campaign saw Third Point own up to 7 percent and push to separate the conglomerate; management resisted for years before spinning out the semiconductor and financial services businesses.
    • He learned that activism in Japan is hard, but the government often wants reform; he co-wrote a paper with Larry Lindsey and Niall Ferguson urging corporate governance and return on invested capital as a fourth arrow of Abenomics, picked up as a Wall Street Journal editorial.
    • Investing in Danaher was his most instructive experience, teaching him how the Danaher Business System drives continuous improvement (Kaizen) and how the company celebrates rather than shames underperformance because problems are fixable.
    • FTX was his hardest lesson; it looked great and was verifiable on the blockchain, but was not what it appeared, and now Third Point’s diligence includes checking bank balances.
    • He notes that, fraud aside, Sam Bankman-Fried had a strong nose for value with stakes in Anthropic, Cursor, and Solana.
    • Recent mistakes also include shorts where Third Point thought certain info-services businesses would resist AI disruption; he still expects a shakeout with some phoenixes rising from the ashes.
    • He is pushing his whole team to use AI daily, hiring native computer scientists and system integrators, and describes Claude as a tool that makes you autonomous and gives back whatever you put into it.
    • Third Point’s distinctive edge is optimism about AI creating net jobs and the ability to default into credit investing during stressed times, as it did with investment-grade credit in 2020.
    • Credit is hard to copy because it runs on relationships, not electronic trading; that is why Third Point built into CLOs and eyes the roughly $6 trillion structured credit market rather than treating it as tourism.
    • The great analyst has changed: 20 years ago it was someone who could model fast and crack a complex restructuring (Loeb made a career-defining bet on Drexel Burnham claims); today it is a Gavin Baker type who deeply understands an industry, like the analyst who flew to Texas and realized Casey’s General Stores was really a pizza chain.
    • Outside the US, Loeb is more bullish on Korea, Taiwan, and Japan as hunting grounds, finds Europe tough on regulation (though he owns Rolls-Royce and ASML), and finds the Middle East the most vibrant region.
    • What worries him most is not the business but running out of time for family, surfing, and reading; what excites him is incorporating everything relevant about the world and forming relationships with people building interesting things.
    • His closing reflection is on kindness as a top-tier value, and the friend, Carter, who let him sleep on a couch and seeded his early fund, echoing a Palmer Luckey line that money cannot buy friends who believed in you when you had nothing.

    Detailed Summary

    Staying ahead of the firehose and reading the macro

    Loeb opens by admitting he does not have a perfectly organized system for processing the modern flood of information. He checks the news for what is relevant to the economy and to Third Point’s positions, tries not to obsess over minute-to-minute moves, and leans more tactical than strategic. When people ask him about macro, he says the usual government-reported metrics (growth, unemployment, inflation, rates, currencies, gold, crypto) are trumped right now by two things: where oil goes, which depends on war and geopolitics, and what AI does on the spending and infrastructure side and its impact on society and the economy. To understand technology, he leans on Jensen Huang’s framing of the AI stack and talks to smart people regularly, and he watches three companies above all: Nvidia, Anthropic, and Elon Musk’s companies as a group.

    From event-driven roots to quality investing

    Third Point’s DNA comes from Loeb’s time as a credit investor at Jefferies, where he watched some of the best distressed, event-driven, and risk-arbitrage investors operate, from David Tepper to Eric Mindich to firms like Angelo Gordon and Farallon. His first lens was event-driven: spin-offs, demutualizations, privatizations, and post-reorg equities, where a newly created and illiquid security gets dumped by holders who will not do the work, and management sandbags guidance because incentive packages are set at the spin date. He barely thought about moats or returns on capital; he just wanted to buy something genuinely cheap with those characteristics. That was the firm’s bread and butter from 1995 until roughly 2013-2015. Those opportunities still exist, but Loeb describes deliberately evolving toward business quality and thematic investing, influenced by “The Outsiders” on capital allocation and Lawrence Cunningham’s “Quality Investing” on durable, high-return businesses. He organized the team around industry experts rather than generalists. The twist: AI disruption recently turned many apparently high-quality companies into much lower-quality ones, fast.

    The AI cycle, bubbles, and the human edge

    Loeb resists the bubble narrative. He was short the dot-com bubble and remembers the valuation excess; today’s AI leaders, by contrast, invest off their balance sheets and generate enormous cash, so unless you believe the capex yields no return, the earnings and multiples do not look like 1999. The real driver of volatility, he argues, is expectations: the semiconductor index ran up 40 percent on strong fundamentals, but Nvidia and Micron both delivered blowout quarters and still saw their stocks fall because expectations had run too high. That dynamic is exactly where a fundamental investor earns a living, because quants, CTAs, and risk-managed pods are structurally forced to sell into weakness. He also doubts AI will ever fully run a capital system, since private equity, restructurings, creditor committees, and high-touch credit always need humans. He cites “Reminiscences of a Stock Operator” and Ecclesiastes: there is nothing new under the sun, and human nature, with its bubbles, panics, and extremes, does not change.

    Governance, his father, and the duty of boards

    Loeb traces his governance interest to his father, a securities lawyer and corporate-governance expert who served on the boards of Mattel and Williams-Sonoma and championed ethical sourcing before it was common. He calls the American board system beautiful: directors are answerable to shareholders and accountable for strategy and key financial decisions. Governance breaks down when directors lose sight of their fiduciary duty, lack the knowledge or talent diversity to do the job, or prioritize things other than shareholders. He invokes Milton Friedman and Warren Buffett to argue that caring about communities, employees, and conduct is not inconsistent with shareholder value but part of it, and criticizes the Business Roundtable for muddying the board’s core duty. The most common failure he sees is directors letting loyalty to an underperforming CEO override their duty. Most of the time Third Point redirects existing boards without even taking a seat; the extreme proxy fights are the exception.

    Activism, writing, Sotheby’s, and Sony

    Great writing, Loeb says, is clear thinking and organizing your thoughts to get a desired outcome, and it is one of activism’s most effective levers alongside financial and legal pressure. Social pressure through writing and PR can move a board on its own. He sees a pattern in his campaigns: targets that hold themselves out as high status but are not living up to it. Sotheby’s is the clean example, a centuries-old, high-status business run unprofitably, where Third Point bought 9.9 percent, gave the existing CEO a year, then helped install Tad Smith from MSG, who modernized operations and technology before the company was sold. Sony was a two-act campaign in which Third Point owned up to 7 percent and pushed to break up the conglomerate; he recounts sharing the thesis with Andrew Ross Sorkin at the New York Times under embargo, the panic it caused, and how management resisted for years before spinning out the semiconductor and financial services units. The lesson: activism in Japan is genuinely hard, even though the government wanted reform. He co-authored a paper with Larry Lindsey and Niall Ferguson arguing corporate governance and return on invested capital should be a fourth arrow of Abenomics, which ran as a Wall Street Journal editorial.

    The Danaher operating system

    Loeb calls Danaher his most instructive investment. He and his partner persuaded the company to compress its five-day Danaher Business System training into a single day, and he came away with a deep appreciation for how a real operating system drives continuous improvement. The standout lesson was cultural: Danaher holds people individually accountable, but when it finds someone underperforming it celebrates rather than shames, because the problems are addressable and fixable, and it does this relentlessly across operations and working capital. He also points to the diaspora of Danaher executives, including Larry Culp and the leadership at Ingersoll Rand, as evidence of the system’s depth. The investment worked for about four years before COVID-era order surges and inventory swings turned tailwinds into headwinds; Third Point sold and has recently bought back in modestly.

    The structure of Third Point and the fulcrum security

    Third Point is not one fund but a collection of businesses. The flagship hedge fund grew from $3 million to about $9 billion and is roughly 30 percent credit, generically around 110 percent long and 30-40 percent short on the equity side. Across the firm the credit weight is closer to 60 percent, spanning a roughly $7 billion CLO business, several billion in structured and corporate credit, an insurance company, a couple billion in asbestos liabilities, a small new private credit unit, and a venture arm. The unifying thread is valuing enterprises at any stage and investing in whichever fulcrum security (the one with the best risk-reward) makes sense. Loeb illustrates with Credit Suisse’s takeover by UBS, where the holdco paper proved the fulcrum, and with buying Twitter’s resold financing debt near 96-97 cents at a 12 percent yield when other credit investors were scared, plus a difficult xAI debt financing that few credit people wanted. He pushes back on the idea that he sits atop everything: he is the PM only of the hedge fund, while the other businesses have their own PMs and committees he is not on.

    Insurance, the FTX lesson, and recent mistakes

    Loeb started a Bermuda reinsurance company in 2010, backed by himself, Kelso, and Pinebrook, on a barbell thesis of investing the float in Third Point and treasuries to defer taxes and lever capital. The reinsurance side soured, and about three years ago he concluded they had the right idea but the wrong vehicle, that plain-vanilla annuities (which can only invest in credit) would have fit better. Third Point merged the reinsurer into its UK closed-end fund, Third Point Offshore Investors, reincorporated from Guernsey to Cayman, and repurposed it into an insurance company managing private credit, structured credit, whole-loan mortgages, real estate lending, and investment-grade debt. His hardest lesson was FTX: it looked great, was verifiable on the blockchain, and had a strong cap table, but was not what it seemed; diligence now includes checking bank balances. He notes Sam Bankman-Fried, fraud aside, had a great nose for value (Anthropic, Cursor, Solana). Other recent mistakes were shorts where Third Point bet certain info-services businesses would resist AI disruption; he still expects a shakeout with some survivors rising from the ashes.

    AI inside the firm, the analyst of the future, and kindness

    Loeb is pushing his entire team to use AI daily, hiring native computer scientists and system integrators, and describes Claude as a tool that makes you an autonomous self-improver and gives back whatever you put into it, with some analysts running agents overnight while he uses it more for queries. He pairs this with Brad Gerstner’s recommendation of “Essentialism”: you cannot do it all, so you must decide what is most relevant. The great analyst has changed: 20 years ago it was someone who could model fast and crack a complex restructuring, as Loeb did with the Drexel Burnham bankruptcy claims early in his career; today it is a Gavin Baker type who deeply understands an industry and its technology, like the analyst who flew to Texas and realized Casey’s General Stores was really a pizza chain in disguise. On the rest of the world, he is more bullish on Korea, Taiwan, and Japan, finds Europe tough on regulation (while owning Rolls-Royce and ASML), and finds the Middle East the most vibrant region. He closes on what worries and excites him (time with family, surfing, and reading versus the joy of incorporating everything relevant about the world), and on kindness, crediting his friend Carter, who let him sleep on a couch and seeded his early fund, and echoing Palmer Luckey’s line that money cannot buy friends who believed in you when you had nothing.

    Notable Quotes

    “I think you have to be a tech person today. It’s a big and growing and compounding part of the economy. It affects everything else.”

    Dan Loeb, on why no serious investor can punt on technology anymore

    “Hold on to your seats because things are only going to accelerate from here.”

    Dan Loeb, recounting a 2013 Davos warning about technological change he now applies to AI

    “Maybe that’s where the human element comes in, to understand and to be able to make those tough trading decisions when fundamentals are going one way and stock prices are going the other way, and to be able to take the pain of losses in the short run.”

    Dan Loeb, on where a human investor still has an edge over machines

    “It’s very different from the dot-com bubble, which we were short going into. You don’t have the valuation bubble now on those companies that you had back in those days.”

    Dan Loeb, on why he does not see the AI rally as a 1999-style bubble

    “When they found someone that was underperforming, it was celebrated instead of shamed, because look at all these things you’re doing wrong, we can fix those. And they did.”

    Dan Loeb, on the accountability culture he learned from the Danaher Business System

    “I would have to say our investment in FTX. It looked great. The company was growing fast. We could verify it all on the blockchain.”

    Dan Loeb, naming his hardest investment lesson

    “Be kind to people you have no idea how it will ever benefit you. And sometimes it will and sometimes it won’t.”

    Dan Loeb, on elevating kindness in your hierarchy of values

    “The one thing money doesn’t buy you is friends that believed in you when you had nothing.”

    Dan Loeb, quoting Gavin Baker quoting Palmer Luckey, on the friend who seeded his early fund

    Watch the full conversation between Dan Loeb and Patrick O’Shaughnessy here.

    Related Reading

  • Gavin Baker on Orbital Compute, TSMC, Frontier AI Models, Anthropic’s Vertical Take Off, and the Coming Wafer Shortage

    Gavin Baker, founder and CIO of Atreides Management, returns to Patrick O’Shaughnessy’s Invest Like the Best for his sixth appearance. He calls the current AI moment the most extraordinary moment in the history of capitalism, walks through what Anthropic’s vertical takeoff in revenue actually means, lays out why orbital compute is closer than skeptics believe, dissects the TSMC bottleneck that may be the only thing standing between today’s market and a full-on AI bubble, and rates every hyperscaler on how they have positioned for a world where frontier model providers may stop selling API access altogether.

    TLDW

    Anthropic added eleven billion dollars of ARR in a single month, which is roughly the combined business of Palantir, Snowflake, and Databricks built over a decade. That is the setup. From there Gavin Baker covers the March and April selloff, the contrarian read that a closed Strait of Hormuz was actually bullish for American manufacturing competitiveness, why Anthropic and OpenAI multiples may be misleadingly cheap on an unconstrained run rate basis, why Elon Musk’s discipline on SpaceX valuation created a superpower of permanent access to capital, the practical engineering case for orbital compute as racks in space rather than Pentagon sized space stations, why TSMC’s capacity discipline is the single most important variable in whether the AI cycle becomes a bubble, what Terafab in Texas changes, why the Pareto frontier of AI models has flipped from Google dominance to Anthropic and OpenAI dominance in nine months, the shift from all you can eat AI subscriptions to usage based pricing and what that means for revenue scaling, Richard Sutton’s bitter lesson as the largest risk to the AI trade, why frontier tokens still capture an overwhelming share of economic value, the role of continual learning as the third great open question, why most new chip startups should not try to build a better GPU, why Cerebras did something different and hard, why disaggregated inference may extend GPU useful lives to ten or fifteen years and rescue the private credit industry, why being in the token path is the new venture filter, the new prisoner’s dilemma around releasing frontier models via API, an honest rating of Google, Meta, Amazon, and Microsoft, why personal safety is becoming a real AI era risk, and why he remains an AI optimist maximalist who believes this could be the next Pax Americana.

    Key Takeaways

    • Anthropic added eleven billion dollars of ARR in one month, more than the combined businesses of Palantir, Snowflake, and Databricks built across a decade. There is no precedent for this in the history of capitalism.
    • The SaaS and cloud revolution created between five and ten trillion dollars of value over twenty years. AI is replaying that compression on a timeline measured in months.
    • The March selloff was a drawdown driven by disagreement with price action, not invalidated thesis. That is the kind of drawdown an investor can lean into.
    • Deep Seek Monday in January 2025 was a similar setup. By the day of the selloff, AWS Asia GPU prices had already doubled, GPU availability had fallen, and it was obvious reasoning models would be vastly more compute hungry at inference. The market priced the opposite.
    • The Strait of Hormuz closing was actually positive for America. US natural gas (the primary input into US electricity, which feeds AI) fell twenty percent on Bloomberg while Asian and European natural gas doubled or tripled. American manufacturing competitiveness improved overnight.
    • The US is now the world’s largest producer and exporter of oil and gas. The economy is dramatically less energy intensive than in the 1970s. The shortage trauma comparison does not hold.
    • Tech as a sector traded as cheaply versus the rest of the market in early April as at any point in the last ten years, into the single most bullish moment for AI fundamentals on record.
    • Anthropic is dramatically more capital efficient than OpenAI, having burned roughly eighty percent less to reach a similar revenue scale. They have very different structural returns on invested capital.
    • Anthropic at roughly nine hundred billion for fifty billion of ARR (growing a thousand percent) is striking. Adjusted for compute constraint, the unconstrained run rate could be one hundred fifty to two hundred billion, putting the implied multiple closer to five times.
    • Claude Opus generates roughly seventy percent fewer tokens for the same question than previously, with token quantity tied to answer quality. Subscribers on flat-fee plans are getting a lobotomized model.
    • Elon Musk’s superpower is twenty years of making investors money. He never pushes valuation. SpaceX compounded low thirty percent per year for a decade because Musk treats fair pricing as a sacred covenant.
    • Capitalism will solve the watts shortage. The current bottleneck has shifted from chips and energy to zoning and political approval. Many capex decisions are paused until after the US midterms.
    • The watts shortage probably begins to alleviate in 2027 and 2028. Orbital compute solves it longer term.
    • Orbital compute is not Pentagon sized data centers in space. It is racks in space. A Blackwell rack is three thousand pounds, eight feet tall, four feet deep, three feet wide. SpaceX has shown a satellite roughly that size.
    • The satellites operate in sun synchronous orbit so solar wings (around five hundred feet per side) always face the sun and the radiator on the dark side always points to deep space.
    • Starlink V3 satellites already run at around twenty kilowatts. A Blackwell rack runs at one hundred kilowatts. SpaceX engineers express genuine confidence they have already solved cooling and radiator design at these scales.
    • Racks in space are connected with lasers traveling through vacuum, the same lasers already on every Starlink. SpaceX operates the world’s largest satellite fleet and, via xAI Colossus, the world’s largest data center on Earth.
    • Inference will move to orbit. Training will stay on Earth for a long time. Terrestrial data centers remain valuable for the rest of an investor’s career.
    • The wafer bottleneck is structural and political. TSMC is essentially Taiwan’s GDP, water, and electricity. The leaders see themselves as inheritors of Morris Chang’s sacred legacy and they do not behave like a Western public company.
    • Jensen Huang has never had a contract with TSMC. The relationship is run on handshakes and the assumption that things will be fair over time.
    • If TSMC did everything Jensen wanted, Nvidia could be selling two to three trillion dollars of GPUs in 2026 and 2027. TSMC’s discipline is the single largest factor preventing a true AI bubble.
    • Historically, foundational technologies always get a bubble. Railroads, canals, the internet. The current AI buildout is overwhelmingly funded out of operating cash flow, GPUs are running at one hundred percent utilization, and that is fundamentally different from the year 2000 fiber overbuild.
    • If one of Intel or Samsung Foundry catches up at the leading node, the other will follow, and TSMC’s discipline collapses. Watch TSMC capacity decisions to predict a bubble.
    • Terafab, the SpaceX and Tesla joint venture to build the world’s largest fab in America, has a partnership with Intel that grants access to fifty years of institutional foundry knowledge. The A teams at ASML, KLA, Lam Research, and Applied Materials will follow Elon’s reputation in hardware engineering.
    • The hiring playbook for Terafab includes building Taiwan Town, Japan Town, and Korea Town next to the fab. Recruit the engineers and import their families, their restaurants, and their staff.
    • Frontier tokens still capture an overwhelming share of all economic value created at the model layer. This is surprising and is one of the three big open questions for AI investing.
    • The Pareto frontier of intelligence versus cost has flipped. Nine months ago Google’s TPU dominated every point on the frontier. Today Anthropic and OpenAI dominate, with Grok 4.3 on the frontier and Gemini 3.1 hanging on.
    • Google’s conservative TPU V8 design (partly an attempt to reduce dependence on Broadcom and Nvidia) is the leading explanation for the loss of per token cost leadership.
    • AI pricing is shifting from all you can eat to usage based, mirroring the cellular and long distance industries. Cellular stopped being a great growth industry when it went all you can eat. AI just made the opposite move.
    • OpenAI and Anthropic together could exceed two hundred billion in ARR this year if compute keeps coming online and frontier token pricing holds.
    • The two hundred fifty dollar a month consumer AI plan is no longer enough to evaluate frontier capability. Enterprise plans with usage based billing are required because rate limits are now severe.
    • The three biggest open questions for AI investors are: violation of the bitter lesson via ASI or human ingenuity, whether frontier tokens keep commanding their premium, and when continual learning arrives.
    • Today’s continual learning is crude reinforcement learning during mid training on verifiable tasks. True continual learning means weights updating dynamically, like a human who learns the first time they touch fire.
    • Trying to build a better GPU is a losing strategy. Jensen will copy any one to three percent share design. Startups should target one percent share, do something different, and make it hard enough that Nvidia cannot fast follow.
    • Disaggregated inference (separating prefill and decode) opens new design canvases. Prefill is memory capacity bound. Decode is memory bandwidth bound. Each can be optimized independently.
    • Cerebras did something different and hard with wafer scale computing. Three generations of chips and real grit to get there.
    • Disaggregation of inference may stretch GPU useful lives to ten or fifteen years, dropping financing costs from low sevens to five or six percent, mathematically lowering the cost of the AI buildout and likely saving the private credit industry from its SaaS loan exposure.
    • Sellers of shortage outperform buyers of shortage. But owning the largest installed base of what is currently in shortage (hyperscaler CPU fleets, for example) is also a strong position.
    • Most of the economic value at the application layer of AI has been destroyed, not created. The exceptions are companies in the token path or in niches small enough that frontier labs ignore them.
    • Coding may be the shortest path to ASI. If you can write code, you can write code that does anything. Cursor, Cognition, and Anthropic correctly focused on it.
    • Jensen could probably get close to the frontier with his own Nemotron family of models whenever he wants. The fact that he chooses not to is a strategic decision about not commoditizing his customers.
    • The new prisoner’s dilemma in AI is whether frontier labs release their best model via API. If everyone agrees not to, Chinese open source falls behind. If anyone defects, the defector pulls ahead on revenue and resources, forcing everyone else to defect.
    • Google still owns the largest compute installed base. Without TPU’s prior cost advantage, this matters more. YouTube data has real value in a world of robotics. GCP is going crazy.
    • Meta deserves credit for becoming AI first internally faster than any other internet giant. Musa, their first MSL model, is impressively close to the Pareto frontier.
    • Amazon is strong because of Trainium and robotics driven retail P&L efficiency. Nova is better than it gets credit for.
    • Microsoft flinched on capex in early 2025 and lost position. Satya Nadella’s current decision to use Microsoft compute for Microsoft products rather than reselling to OpenAI is a courageous and probably correct call, even at the cost of an eight hundred dollar stock price.
    • The hyperscalers most engaged with startups are Amazon and Nvidia by a mile, followed by Google. Broadcom is the favorite ASIC partner. AMD, Microsoft, and Meta have minimal startup engagement and that will cost them as the best teams are now at startups.
    • Personal safety in an AI era requires a family or company safe word that cannot be socially engineered. Deepfake voice and video extortion at the speed of FaceTime is already feasible.
    • Ukraine is winning largely on the back of having the best battlefield AI outside America and Israel. Adversaries are starting to internalize what AI dominance means geopolitically.
    • An optimistic read is that this becomes a new Pax Americana, the way the post 1945 American nuclear monopoly was used to rebuild Germany and Japan rather than dominate.
    • AI cured a friend’s daughter’s rare disease by spinning up a research effort that identified a market drug capable of impacting her condition. That is the upside that keeps Gavin an AI optimist maximalist.

    Detailed Summary

    The most extraordinary moment in the history of capitalism

    Gavin’s framing of the current moment is unusually direct. Anthropic added eleven billion dollars of annual recurring revenue in a single month. The three highest profile SaaS companies of the last decade plus, Palantir, Snowflake, and Databricks, took a decade and tens of thousands of employees collectively to build the combined business that Anthropic added in thirty days. He has been investing through every major tech cycle and says there is no historical analog. Not the dotcom era, not the cloud transition, not mobile. This is its own thing.

    The market response, then, was peculiar. The NASDAQ sold off into the single most bullish moment for AI fundamentals on record. Tech traded at roughly its widest discount versus the rest of the market in a decade. Investors who said they wished they had bought into AI during 2022, during COVID, or during Deep Seek Monday got the same valuation setup again in early April, this time with an even clearer inflection.

    Why the Strait of Hormuz closing was secretly bullish for America

    One reason the macro fear in March may have been mispriced is that the same geopolitical event that drove the selloff was, in practice, a relative benefit to the United States. American natural gas, the input into American electricity, which is the input into American AI training and inference, fell roughly twenty percent. Asian and European natural gas prices doubled or tripled. The US emerged with sharply improved relative manufacturing competitiveness, which is exactly what the current administration cares about.

    The 1970s comparison does not hold. The US economy is dramatically less energy intensive, it is now the world’s largest producer and largest exporter of oil and gas, and there are no shortages, only price moves. That backdrop made it easier for disciplined investors to stay focused on AI fundamentals through the volatility.

    Anthropic and OpenAI valuations on an unconstrained run rate

    Anthropic at roughly nine hundred billion for fifty billion of ARR sounds rich until you adjust for the fact that the company is severely compute constrained. Gavin estimates that, unconstrained, Anthropic might be at one hundred fifty to two hundred billion in run rate revenue, putting the implied multiple closer to five times. He also points out that Claude Opus now generates roughly seventy percent fewer tokens for the same question than it used to. Token quantity correlates with answer quality, and Anthropic is rate limiting and shrinking outputs to ration capacity across its user base.

    Anthropic and OpenAI are also structurally very different. Anthropic has burned around eighty percent less cash than OpenAI to reach a comparable revenue scale. That implies very different long term returns on invested capital, though OpenAI has done a better job locking in compute and Sarah Friar is one of the most exceptional CFOs Gavin has worked with.

    Why neither lab is raising at a three trillion dollar valuation

    The answer Gavin gives is that both labs are deliberately leaving valuation on the table the way Elon has done for two decades. SpaceX compounded at low thirty percent annually for a decade because Elon never pushed price. The result is a permanent superpower of access to capital. Investors trust him because they have made money with him for twenty years. That is a moat that compounds with every round.

    Anthropic could probably raise at a one hundred percent premium to its rumored latest mark. They are choosing not to. In an uncertain world (Ukraine, Russia, Iran, Taiwan), preserving the ability to raise more capital later at fair prices is more valuable than maximizing this round.

    Watts and wafers, the two real constraints

    Capitalism is solving the watts problem. The leading PE infrastructure investors now say zoning and political approval, not chips or energy, are the gating factors. Companies are deferring big capex announcements until after the US midterms. Turbine capacity is being doubled at the manufacturers. Companies like Boom Aerospace are repurposing jet engines for grid use. Watts probably ease meaningfully in 2027 and 2028 and then orbital compute does the rest.

    Wafers are the harder problem because they live in Taiwan, run on handshakes, and depend on a corporate culture that does not respond to public market incentives. TSMC is essentially the GDP, water consumption, and electricity consumption of Taiwan. Its leadership treats the company as the legacy of Morris Chang. The Silicon Shield doctrine is real and internal.

    Orbital compute as racks in space

    The biggest mental update Gavin asks listeners to make is to stop picturing data centers in space as Pentagon sized space stations. A Blackwell rack is three thousand pounds and roughly the size of a refrigerator. SpaceX has shown a concept satellite of about that size. Solar wings extend five hundred feet to each side and the radiator extends hundreds of feet behind, both possible because the orbit is sun synchronous and the orientation is fixed relative to the sun.

    SpaceX engineers Gavin has spoken to at Starbase express genuine confidence that they have solved cooling at these power levels. They have. Starlink V3 satellites already operate at twenty kilowatts. A Blackwell rack is one hundred kilowatts. The same company operates the world’s largest satellite fleet and the world’s largest data center on Earth via xAI Colossus. The racks are connected to each other with lasers traveling through vacuum, technology already deployed in every Starlink. The naysayers, Gavin observes, are armchair skeptics and Larry Ellison’s response (he is out there landing rockets, no one else is) is the right frame.

    Terafab in Texas and the threat to TSMC’s discipline

    Terafab, the SpaceX and Tesla joint venture, intends to be the largest fab in the world. The partnership with Intel grants access to fifty years of foundry institutional knowledge, allowing Terafab to start three to five quarters behind the leading node rather than fifteen years behind. The A teams at the semicap equipment companies (ASML, KLA, Lam Research, Applied Materials) will follow Elon’s reputation in hardware engineering the same way they followed TSMC twenty years ago when Intel stumbled.

    The talent strategy is the part most observers underestimate. Recruit the best engineers globally, then import their families, their restaurants, their staff. Build Taiwan Town, Japan Town, and Korea Town next to the fab. Optimize the human experience for the people whose work matters. Intel and Samsung do not think that way.

    Bubble watch and the year 2000 comparison

    Every foundational technology in modern history has had a bubble. Railroads, canals, the internet. Carlota Perez documented why. Markets correctly identify the importance, diversity of opinion collapses, supply gets ahead of demand, the bubble crashes. The current cycle has two important differences. The buildout is overwhelmingly funded out of operating cash flow, not debt. Every GPU is running at one hundred percent utilization, while at the peak of the fiber bubble ninety nine percent of fiber was unused.

    TSMC discipline is the single largest reason a bubble has not formed. If Jensen could buy everything TSMC could theoretically make, Nvidia could sell two to three trillion dollars of GPUs in 2026 and 2027. At some point that becomes more than the market can absorb. If Intel or Samsung Foundry catches up at the leading node, the other will too. TSMC’s pricing discipline collapses and the bubble starts.

    The Pareto frontier and the loss of Google’s cost advantage

    The most important chart in AI is the Pareto frontier of model intelligence versus per token cost. Nine months ago, Google’s TPU based models dominated every point on it. OpenAI, Anthropic, and xAI sat inside the frontier. Today the frontier is dominated by Anthropic and OpenAI, with Grok 4.3 on the frontier and Gemini 3.1 hanging on by subsidization more than economics. The most likely cause is Google’s conservative TPU V8 design, an attempt to reduce dependence on Broadcom and Nvidia that sacrificed per token economics.

    The bitter lesson, frontier tokens, and continual learning

    Three open questions dominate AI investing. The first is whether Richard Sutton’s bitter lesson (more compute beats human algorithmic cleverness) gets violated by ASI itself optimizing for efficiency. Closer observers of AI are more skeptical of a violation. Gavin thinks ASI’s first move will be to make itself more efficient and more resourced, which is technically a temporary violation.

    The second is whether frontier tokens keep capturing the overwhelming share of economic value at the model layer. Today they do, surprisingly. Gemini 3.1 Pro was mindblowing nine months ago and is intolerable today. The third is when continual learning arrives. Today’s models need a million fire touches to learn what a human learns from one. True continual learning would mean dynamic weight updates in real time and would produce a fast takeoff.

    From all you can eat to usage based AI pricing

    AI is shifting from flat fee plans to usage based pricing. The historical analogy is cellular and long distance. Both stopped being great growth industries when they went all you can eat. AI just made the opposite move. The consequence is that flat fee subscribers, even on premium consumer plans, get a rate limited and token throttled version of the frontier model. Enterprise plans with usage based billing are now required to evaluate true capability. Gavin thinks the combination of new compute coming online and usage based pricing is what gets OpenAI and Anthropic past two hundred billion in combined ARR this year.

    Chip startups, prefill decode disaggregation, and Cerebras

    Trying to build a better GPU is the wrong move. The four scaled players (Nvidia, AMD, Trainium, TPU) have copy capability for any one to three percent share design that looks attractive. The good news for startups is that disaggregated inference (separating prefill and decode) opens a richer design canvas. Prefill is memory capacity bound. Decode is memory bandwidth bound. Each can be optimized independently. Andrew Fox’s analogy is a British naval ship of the eighteenth century. Prefill is loading the cannon. Decode is firing it.

    Cerebras is the model. Wafer scale computing is genuinely different and genuinely hard. It took three generations of chips to get right. Andrew Feldman and his team had the grit to keep going through chip one being a failure. The design has a high ratio of on chip compute and memory relative to shoreline IO, which is why Cerebras is now experimenting with putting an optical wafer on top of the compute wafer to solve scale out.

    GPU useful lives and the rescue of private credit

    One of the strongest claims in the conversation is that disaggregated inference will stretch GPU useful lives to ten or fifteen years. The skeptical narrative (GPUs are obsolete in two years, companies are cooking their depreciation books) is wrong. You can put a Cerebras system or Groq LPU in front of older Hopper or Ampere parts, use them only for prefill, and run them until they physically melt. Private credit, which is in pain from SaaS loans and which underwrote GPU loans on three to four year lives, may be saved by this.

    If GPU financing rates can come down from low sevens to five or six percent, the mathematics of the AI buildout improves materially. That is a structural tailwind that compounds for years.

    The application layer, the token path, and a new prisoner’s dilemma

    Trillions of dollars of value have been destroyed at the application layer, not created. Cursor and Cognition are the rare scaled exceptions, and they got there by focusing on coding very early. As Amjad Masad noted, coding is plausibly the shortest path to ASI because a coding agent can write itself into any new domain. Jamin Ball’s frame is that the new venture filter is whether the company is in the token path. Data Bricks is. Most application layer startups are not.

    Jensen could probably get close to the frontier with Nemotron whenever he wants, and the strategic question of whether to do that is a new prisoner’s dilemma. If every frontier lab agrees not to release best models via API, Chinese open source falls steadily behind. If anyone defects, the defector gains revenue and resources, and everyone else has to defect. The same dynamic exists between TSMC, Intel, and Samsung. If Nvidia or AMD ever truly used an alternative foundry, that foundry would catch up rapidly.

    Rating the hyperscalers

    Google has the largest compute installed base, the YouTube data that matters in a robotics world, and a search business that prints. Their loss of TPU cost leadership is the surprise of the year. If Google IO in five days does not produce a leapfrog model, the Nvidia centric narrative gets even stronger.

    Meta deserves real credit. Zuckerberg made Meta AI first internally faster than any other internet giant, paid up for the talent contracts when no one else would, and shipped Musa as a first model from MSL that is close to the Pareto frontier. Amazon is well positioned on Trainium, robotics in retail, and a Nova model line that is better than it gets credit for. Microsoft flinched on capex in early 2025 and lost position. Satya Nadella’s current decision to use Microsoft compute for Copilot rather than reselling to OpenAI is courageous and probably correct, even at the cost of stock price.

    The most interesting cross hyperscaler metric is startup engagement. Nvidia and Amazon engage deeply with startups. Google is next. Broadcom is the favored ASIC partner. AMD, Microsoft, and Meta have minimal startup engagement, which Gavin believes will cost them as the best teams now sit at startups.

    Personal safety, geopolitics, and the Pax Americana case

    The closing section turns darker. Personal safety in an AI era requires a family or company safe word that cannot be socially engineered. Deepfake voice and video extortion via something that looks exactly like your child calling on FaceTime is already feasible. Political violence against AI leaders is a real concern. Geopolitically, Ukraine is winning largely because it has the best battlefield AI outside America and Israel. How adversaries respond to that asymmetry is the next great variable.

    Gavin’s optimistic frame is the Pax Americana. After 1945 the US had a nuclear monopoly and could have controlled the world. Instead it rebuilt Germany and Japan, both of which became the most reliable American allies for the next eighty years. If AI dominance plays out similarly, this is a generationally positive story rather than a destabilizing one. The personal anecdote that closes the conversation is a friend whose daughter was diagnosed with a rare genetic condition. He spun up agents, identified a drug already on the market that addresses her mutation, and her life is immeasurably different because of AI. That is the upside.

    Thoughts

    The Anthropic eleven billion in a month framing is the kind of stat that resets priors. The right way to interpret it is not as a one off but as a measure of how fast value can compound when the underlying technology improves on a curve steeper than the ability of the rest of the economy to absorb it. The skeptical question is whether that ARR is durable or whether it is heavily tied to a customer base of other AI companies that are themselves on a single venture funded year of runway. The bullish answer is that frontier coding, frontier research, and frontier enterprise tasks are not going to stop being valuable, and Anthropic is the best at all three. Both can be true. The number is still extraordinary.

    The argument that TSMC discipline is the only thing preventing a bubble is the analytically tightest part of the conversation. The implied trade is to watch TSMC capacity additions like a hawk and to be more, not less, cautious if Intel Foundry or Samsung Foundry ever announce real share at the leading node. The Terafab thesis is more speculative but more interesting. If Elon’s talent recruiting playbook works and the Intel partnership gives Terafab a real seat at the table within five years, the geometry of the global semiconductor industry shifts in a way that is bullish for American manufacturing, bullish for power and water infrastructure in Texas, and ambiguous for TSMC itself.

    The Pareto frontier discussion deserves more attention than it usually gets. Pricing leadership in AI is not a vanity metric. It determines who can subsidize free tier usage, who can absorb compute shortages, who can ship cheaper enterprise plans, and ultimately whose model becomes the default for any given workload. Google losing per token leadership in nine months is one of the most under analyzed events in the sector and it explains a lot about why Anthropic and OpenAI are growing the way they are. If Google IO does not produce a leapfrog model, the implied verdict on TPU V8 design choices gets a lot harsher.

    The application layer destruction point is worth sitting with. Founders building on top of frontier models are competing in a world where the model itself moves faster than any moat they can build, where the model lab can absorb their niche if it gets interesting, and where the only protection is either deep token path integration or a niche so small the lab does not bother. That is a much harsher venture environment than the early SaaS era. The compensating opportunity is that one human can now run a hundred agents, so the ceiling on what a small team can build is correspondingly higher. The bet is that productivity per founder rises faster than competitive pressure from the labs. We will find out.

    The orbital compute pitch is the section that will polarize listeners. The naive read is that this is science fiction. The closer read is that every component (sun synchronous orbit, laser interconnect, twenty kilowatt satellite buses, ten thousand satellite manufacturing cadence, full rocket reusability) already exists. The remaining engineering problems are repair, maintenance, and radiator scale, all of which are real but tractable on a five to ten year horizon. The strategic implication is that the political and zoning ceiling on terrestrial data centers becomes less binding if orbital compute is a credible alternative for inference workloads. The investor implication is that being short the watts and cooling complex on a five year horizon is a real trade, not a meme.

    Watch the full conversation here.

  • Insights from Joe Rogan’s Interview with Mark Zuckerberg: Content Moderation, AI, and the Future of Meta Jan 10 2025

    Mark Zuckerberg, CEO of Meta, recently joined Joe Rogan on The Joe Rogan Experience (#2255). The two delved into pressing issues, including content moderation, government influence, artificial intelligence, and Zuckerberg’s personal interests like martial arts and hunting. Here’s a breakdown of their conversation.

    1. Content Moderation and Government Influence
    Zuckerberg reflected on Meta’s evolving content moderation policies, especially after events like the 2016 election and the COVID-19 pandemic. He admitted that government pressure, particularly during the Biden administration, influenced Meta’s approach to curbing misinformation. Notably, Meta resisted requests to suppress discussions about vaccine side effects, emphasizing the importance of free expression.

    Key Takeaway: Meta is recalibrating its policies to reduce over-censorship while fostering open discourse.

    2. Artificial Intelligence and Open Source Development
    Discussing AI, Zuckerberg highlighted Meta’s commitment to making AI widely accessible through open-source platforms. He cautioned against a single entity monopolizing AI, advocating instead for decentralized innovation. With AI advancing rapidly, Zuckerberg predicts it will soon handle mid-level engineering tasks, enabling greater creativity for human developers.

    3. Zuckerberg’s Passion for MMA and Personal Growth
    Zuckerberg shared his journey into martial arts, emphasizing how MMA and Jiu-Jitsu have brought balance to his life. He revealed his experience of competing in Jiu-Jitsu tournaments and how the discipline complements his role as a CEO. Despite an ACL injury, he remains committed to training and plans to compete again.

    4. Ethical Hunting and Conservation
    Zuckerberg also discussed managing invasive pig populations on his Kauai ranch. He uses hunting as a means of conservation and as a teaching tool for his children to respect nature and understand the circle of life.

    5. Augmented Reality (AR) and Meta’s Vision for the Future
    Meta’s AR advancements were showcased, including Ray-Ban smart glasses and neural interfaces. Zuckerberg envisions a world where AR seamlessly integrates digital and physical realities, fostering more immersive and efficient interactions.

    Wrap
    From redefining social media policies to pioneering AI and AR, Zuckerberg is navigating a complex technological and cultural landscape. His personal endeavors in martial arts and conservation reflect a balanced approach to leadership and innovation.

  • The Shocking Truth About Your Privacy on Meta’s Threads

    The Shocking Truth About Your Privacy on Meta's Threads

    Privacy has become a prominent concern for social media users recently. Understanding how platforms collect and use your data is crucial to maintaining your online privacy. We will examine several platforms’ privacy policies, specifically focusing on Threads, Bluesky, Mastodon, Spill, Hive Social, and Twitter.

    Threads

    Threads collects a significant amount of data linked to you. This includes Purchase History, Financial Information, Location (Precise and Coarse), Contact Info (Physical Address, Email Address, Name, Phone Number, Other User Contact Info), Search History, Browsing History, Identifiers (User ID, Device ID), Usage Data, Diagnostics, and Other Data. This is used for various purposes such as Third-Party Advertising, Developers Advertising or Marketing, Analytics, Product Personalization, App Functionality, and Other Purposes.

    Bluesky

    Bluesky, an app developed by Twitter’s founder Jack Dorsey, collects less personal data than Threads or Twitter. It primarily collects data for app functionality, including remembering your email and user ID, or accessing photos and videos on your device.

    Mastodon

    Mastodon is another social media app that values user privacy. In contrast to many other platforms, the Mastodon app for iOS does not collect any data from your device. However, for Android owners, the app may share your name and email address with other companies.

    Spill

    Spill, a Black-owned social media app, also gathers some sensitive information but does not collect as much data as Threads. Its data collection covers Location (Coarse Location), Contact Info (Email Address, Name, Phone Number), User Content (Emails or Text Messages, Photos or Videos, Audio Data), and Sensitive Info.

    Hive Social

    Hive Social, a smaller platform popular with gamers, collects information about you for functionality and analytics, but it’s not connected specifically to you. The data includes Contact Info (Email Address, Name, Phone Number), User Content (Photos or Videos, Customer Support, Other User Content), Identifiers (User ID), Usage Data, and Diagnostics.

    Twitter

    In comparison, Twitter collects data linked to you and uses it to track your actions. This includes your purchase history, browsing history, and precise location. However, it does not list “sensitive information” as one of the disclosed categories of data collection.

    Understanding how different platforms handle your data is a crucial part of maintaining online privacy. While Twitter and Threads collect extensive data, alternatives such as Bluesky, Mastodon, Spill, and Hive Social offer more privacy-focused policies. Users should always check and understand the privacy policies and data collection practices of the platforms they use to ensure their personal information is handled appropriately.

    Here are some practical steps users can take to protect their data:

    1. Limit App Permissions: Limit what information an app can access on your phone. Be wary of apps that require unnecessary permissions.
    2. Use VPNs: Virtual Private Networks (VPNs) can encrypt your data and make your online activities less traceable.
    3. Update Your Devices: Regularly update your devices and apps to the latest versions. Updates often include important security patches.
    4. Use Strong, Unique Passwords: Using a combination of letters, numbers, and symbols can help protect your accounts. Also, avoid using the same password across multiple platforms.
    5. Enable Two-Factor Authentication: Two-Factor Authentication (2FA) adds an additional layer of security to your accounts by requiring two types of identification.
    6. Be Mindful of Sharing Personal Information: Be cautious about what personal information you share online. Once it’s out there, it’s nearly impossible to take back.

    Despite the worrying trends in data collection by companies like Meta, users are not completely powerless. By being proactive in managing and protecting personal data, you can navigate the digital world with a greater sense of control and security. If one thing is clear, it’s that user privacy should never be an afterthought in our increasingly interconnected world.

  • Instagram Threads: An Ambitious Attempt to Rattle Twitter’s Dominance – Screenshots Live on the App Store Now

    Instagram has thrown down the gauntlet to Twitter with the launch of its new application, Threads, designed to facilitate text-based conversations within online communities. Although a bold move from the social media giant, industry experts are questioning if Threads can overcome Twitter’s extensive network effect to secure a sizable market share.

    Launched recently, Instagram Threads invites communities to engage in discussions about a wide array of topics, from the most trending to niche interest. It empowers users to follow their preferred creators, interact with like-minded individuals, or cultivate their follower base by sharing unique ideas, viewpoints, and creativity.

    The screenshots of the app, now available in the App Store, depict an intuitive, user-friendly design aligned with Instagram’s hallmark aesthetic. The interface seems to emphasize ease of use and enhanced connectivity, as Instagram attempts to differentiate itself from Twitter’s robust platform.

    However, Twitter’s immense network effect presents a formidable challenge for Instagram Threads. Network effect, a phenomenon where increased numbers of participants improve the value of a product or service, is arguably Twitter’s most significant asset. With a diverse user base spanning across various demographics and regions globally, Twitter’s massive network effect has been instrumental in its sustained success and resilience against competition.

    While Instagram is a force to reckon with in the realm of photo and video sharing, breaking into the space dominated by Twitter is a completely different ballgame. Twitter’s interface, characterized by its concise, fast-paced posting format, has attracted millions of users globally who actively engage in conversations about trending topics, making it an important source of breaking news, public opinion, and more.

    That said, competition is always beneficial for the end-users. Instagram Threads might not dethrone Twitter anytime soon, but it certainly pushes the envelope in terms of how social media platforms facilitate text-based conversations. It will also drive Twitter to innovate and improve, ensuring that the platform doesn’t rest on its laurels.

    Instagram’s attempt to crack into Twitter’s market should be seen as a positive sign for the industry, with increased competition usually leading to enhanced user experience and innovative solutions. Users can now download Instagram Threads from the App Store and see if it provides a compelling alternative to Twitter’s long-standing platform.

  • Musk vs Zuckerberg: Battle of the Tech Titans in the Vegas Octagon – Reality or Meme Goldmine?

    The tech world is bracing itself for an unprecedented show of force, and we’re not talking about the next big software update. Enter “The Walrus,” also known as Elon Musk, and “The Eye of Sauron,” or Mark Zuckerberg if you prefer. These two titans of tech have agreed to swap keyboards for boxing gloves in a no-holds-barred cage match.

    It all started when Musk tweeted, “I’m up for a cage fight,” to which Zuckerberg, kingpin of Meta, responded with a screenshot captioned, “send me location”. The internet exploded faster than a SpaceX rocket launch, and a Meta spokesperson said, “The story speaks for itself,” which is corporate speak for, “We can’t believe it either.” Musk then suggested the “Vegas Octagon” as the battleground.

    For those who aren’t MMA aficionados, the Octagon is the UFC’s version of a gladiator arena, based in the not-so-quiet Las Vegas, Nevada. But before you imagine Musk and Zuckerberg throwing punches, you need to know about Musk’s secret weapon: “The Walrus.” He described this as lying on top of his opponent and doing… well, nothing. This comical strategy might be the tech mogul’s way of saying, “Hey, I’m not taking this too seriously,” or maybe he’s just really into walruses.

    But let’s not forget about The Eye of Sauron. Zuckerberg may not have a legion of orcs at his disposal, but he’s been secretly training in mixed martial arts and winning jiu-jitsu tournaments. Musk, on the other hand, has admitted his main workout is tossing his kids into the air, which we’re not sure is UFC approved.

    As you can imagine, this news sent social media into overdrive, with meme creators having a field day. One business consultant even encouraged users to “choose your fight” with pictures of the tech bosses. Like it or not, the Musk vs. Zuckerberg face-off is now the internet’s favourite meme.

    Nick Peet, a fight sports journalist, stated that UFC president Dana White must be “licking his lips at the possibility” of this fight. He also believes that Musk’s unpredictable nature could indeed mean the fight happens, despite the absurdity of it all.

    But who would win this geeky gladiator bout? Peet places his bets on Zuckerberg. While Musk has the height and weight advantage, Zuckerberg’s jiu-jitsu training might allow him to “give him a good old cuddle and choke him out”.

    It’s important to remember that Musk has a knack for making wild statements that sometimes don’t come to fruition. Remember when he said he made his dog the CEO of Twitter? Or when he promised a hyperloop that is yet to materialize? On the other hand, he did step down as Twitter CEO after users voted for his resignation. So who knows? This fight might just happen.

    Meanwhile, Meta has been cooking up its own Twitter competitor, a text-based social network, potentially taking the Musk-Zuckerberg rivalry from the Octagon to the online arena.

    In the end, whether this tech titans’ tussle happens or not, it’s given us a good laugh and some amazing memes. So grab some popcorn and stay tuned, because the Musk vs. Zuckerberg saga is far from over.

  • Leveraging Efficiency: The Promise of Compact Language Models

    Leveraging Efficiency: The Promise of Compact Language Models

    In the world of artificial intelligence chatbots, the common mantra is “the bigger, the better.”

    Large language models such as ChatGPT and Bard, renowned for generating authentic, interactive text, progressively enhance their capabilities as they ingest more data. Daily, online pundits illustrate how recent developments – an app for article summaries, AI-driven podcasts, or a specialized model proficient in professional basketball questions – stand to revolutionize our world.

    However, developing such advanced AI demands a level of computational prowess only a handful of companies, including Google, Meta, OpenAI, and Microsoft, can provide. This prompts concern that these tech giants could potentially monopolize control over this potent technology.

    Further, larger language models present the challenge of transparency. Often termed “black boxes” even by their creators, these systems are complicated to decipher. This lack of clarity combined with the fear of misalignment between AI’s objectives and our own needs, casts a shadow over the “bigger is better” notion, underscoring it as not just obscure but exclusive.

    In response to this situation, a group of burgeoning academics from the natural language processing domain of AI – responsible for linguistic comprehension – initiated a challenge in January to reassess this trend. The challenge urged teams to construct effective language models utilizing data sets that are less than one-ten-thousandth of the size employed by the top-tier large language models. This mini-model endeavor, aptly named the BabyLM Challenge, aims to generate a system nearly as competent as its large-scale counterparts but significantly smaller, more user-friendly, and better synchronized with human interaction.

    Aaron Mueller, a computer scientist at Johns Hopkins University and one of BabyLM’s organizers, emphasized, “We’re encouraging people to prioritize efficiency and build systems that can be utilized by a broader audience.”

    Alex Warstadt, another organizer and computer scientist at ETH Zurich, expressed that the challenge redirects attention towards human language learning, instead of just focusing on model size.

    Large language models are neural networks designed to predict the upcoming word in a given sentence or phrase. Trained on an extensive corpus of words collected from transcripts, websites, novels, and newspapers, they make educated guesses and self-correct based on their proximity to the correct answer.

    The constant repetition of this process enables the model to create networks of word relationships. Generally, the larger the training dataset, the better the model performs, as every phrase provides the model with context, resulting in a more intricate understanding of each word’s implications. To illustrate, OpenAI’s GPT-3, launched in 2020, was trained on 200 billion words, while DeepMind’s Chinchilla, released in 2022, was trained on a staggering trillion words.

    Ethan Wilcox, a linguist at ETH Zurich, proposed a thought-provoking question: Could these AI language models aid our understanding of human language acquisition?

    Traditional theories, like Noam Chomsky’s influential nativism, argue that humans acquire language quickly and effectively due to an inherent comprehension of linguistic rules. However, language models also learn quickly, seemingly without this innate understanding, suggesting that these established theories may need to be reevaluated.

    Wilcox admits, though, that language models and humans learn in fundamentally different ways. Humans are socially engaged beings with tactile experiences, exposed to various spoken words and syntaxes not typically found in written form. This difference means that a computer trained on a myriad of written words can only offer limited insights into our own linguistic abilities.

    However, if a language model were trained only on the vocabulary a young human encounters, it might interact with language in a way that could shed light on our own cognitive abilities.

    With this in mind, Wilcox, Mueller, Warstadt, and a team of colleagues launched the BabyLM Challenge, aiming to inch language models towards a more human-like understanding. They invited teams to train models on roughly the same amount of words a 13-year-old human encounters – around 100 million. These models would be evaluated on their ability to generate and grasp language nuances.

    Eva Portelance, a linguist at McGill University, views the challenge as a pivot from the escalating race for bigger language models towards more accessible, intuitive AI.

    Large industry labs have also acknowledged the potential of this approach. Sam Altman, the CEO of OpenAI, recently stated that simply increasing the size of language models wouldn’t yield the same level of progress seen in recent years. Tech giants like Google and Meta have also been researching more efficient language models, taking cues from human cognitive structures. After all, a model that can generate meaningful language with less training data could potentially scale up too.

    Despite the commercial potential of a successful BabyLM, the challenge’s organizers emphasize that their goals are primarily academic. And instead of a monetary prize, the reward lies in the intellectual accomplishment. As Wilcox puts it, the prize is “Just pride.”

  • Apple Delays Release of AR Glasses, to Focus on Cheaper Mixed Reality Headset

    Apple Delays Release of AR Glasses, to Focus on Cheaper Mixed Reality Headset

    Apple’s foray into the world of virtual and augmented reality has been a long time coming, and the tech giant is finally ready to enter the market. However, the company’s initial plans have changed, with Apple now postponing its first pair of augmented-reality glasses and instead focusing on a cheaper mixed-reality headset.

    The mixed-reality headset is expected to be released sometime in 2024 or early 2025, and will offer users a blend of virtual reality and augmented reality experiences. It will be powered by a Mac-grade M2 processor and a dedicated chip for handling AR and VR visuals, and will cost around $3,000. The goal is to eventually reduce the price of the headset to be competitive with other mixed-reality headsets on the market, such as Meta Platforms Inc.’s Quest Pro VR headset, which is currently priced at $1,500.

    Apple’s initial plan was to release the AR glasses after the debut of the mixed-reality headset, but the company has since postponed the launch due to technical challenges. AR glasses are designed to overlay visuals and information on real-world views, and earlier attempts at the concept such as Google Glass haven’t been successful. Additionally, the cost and weight of the device are big factors in its potential success, and Apple has yet to find the right chips, batteries, software, and manufacturing to make a lightweight device that can last all day.

    The company is still exploring the possibilities of AR glasses, with some teams continuing to look into the technologies for a standalone device. However, with the current state of technology, many within Apple are skeptical that the company will ever ship AR glasses. Other tech companies, such as Meta and Alphabet Inc.’s Google, have also announced their own plans for AR glasses, but their products remain in early stages.

    In the meantime, Apple is continuing to work on its mixed-reality headset, and has trademarked the names “Reality Pro” and “Reality One”. The Pro name is likely for the initial model, while the “One” suffix could be under consideration for the cheaper version. The company is also working on a dedicated chip for the headset, which will be called “Reality Processor”.

    For now, Apple’s mixed-reality headset will be the company’s first foray into the world of virtual and augmented reality. It will be interesting to see how the product is received, and whether it will be the precursor to the eventual launch of Apple’s long-awaited AR glasses.