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  • Can the AI Industry Regulate Itself? All-In on Demis Hassabis’s SRO Proposal, Stripe’s PayPal Bid, Apple vs OpenAI, and New York’s Data Center Ban

    The besties open on the biggest live question in artificial intelligence policy: can the AI industry regulate itself before the government does it for them? Jason Calacanis, Chamath Palihapitiya, David Sacks, and David Friedberg dig into DeepMind co-founder Demis Hassabis’s proposal for a FINRA-style self-regulatory organization for frontier models, then work through a packed docket that runs from Stripe’s audacious bid for PayPal to Apple’s trade-secrets lawsuit against OpenAI, the xAI Grok Build data leak, the economics of token spend, New York’s first-in-the-nation data center moratorium, foreign influence campaigns shaping American attitudes toward AI, and a science corner on an enzyme that reverses skin aging. You can watch the full episode here.

    TLDW

    Demis Hassabis proposed a US-led international AI standards body modeled on FINRA: federally overseen, industry funded, run by independent technical experts, with frontier labs submitting models 30 days before release, voluntary at first and mandatory later. The proposal drew broad endorsement across the industry, and the besties debate whether an SRO beats the alternatives. Sacks says he could get on board only under five strict conditions (broad representation including startups and open source, frontier-only review, catastrophic-risk-only scope, voluntary-first, and substitution for rather than addition to new agencies), and warns the plan is an opening bid that Anthropic will use as a stepping stone toward Dario Amodei’s “FAA for AI.” The show then turns to Stripe, Block, and Advent bidding roughly $53 billion for PayPal and what it means for Visa and Mastercard, a wave of AI-native operators reviving stale digital businesses (Bending Spoons, Ryan Cohen), Apple’s lawsuit accusing OpenAI of stealing trade secrets, xAI’s Grok Build silently uploading entire codebases despite a privacy setting, the enormous spread in token costs and Ramp’s new spend controls, Apple’s local-model opportunity with M7 Ultra silicon, America’s looming energy deficit and behind-the-meter power, New York’s hyperscale data center moratorium, alleged Russian and PRC influence operations shaping anti-GMO and anti-data-center sentiment, and a science corner on a Calico enzyme that degrades glycation products to reverse skin aging.

    Thoughts

    The most important idea in this episode is not the SRO itself but Sacks’s framing of it as an opening bid. His five conditions are a genuinely useful blueprint for how self-regulation could work without curdling into regulatory capture, and his instinct that catastrophic-risk-only scope (cyber and CBRN, not disinformation or “microaggressions”) is the only defensible mandate is the right line to draw. But the deeper point is structural: when an industry walks into government and says “please regulate me,” almost no one in government answers “we’re not qualified.” They say thank you and come back for more. That asymmetry, not any specific rule, is what makes voluntary concessions dangerous. If the SRO is offered for free rather than traded for hard federal preemption written into law, it becomes the floor of a ratchet, not the ceiling of a compromise.

    The Anthropic critique running through the segment deserves to be taken on its merits rather than dismissed as a grudge. The claim is specific and falsifiable: that a company now valued in the trillions is funding a state-by-state strategy of one-upmanship, where each new bill is tougher than the last, deliberately producing a patchwork rather than the single national framework everyone claims to want. Whether or not you accept the motive, the mechanism is real and the incentives are legible. If your cost per million tokens is fifty to a hundred times your competitor’s, and cheaper open models plus fine-tuning can cover the vast majority of tasks, then the fastest way to protect a premium price is to make the cheap alternatives legally or practically harder to ship. That is the ladder-pulling thesis, and the token-cost numbers cited on the show are the reason it is not paranoid.

    The PayPal bid is the clearest signal of a new operating logic in the capital markets. The interesting question Chamath poses is not “what synergies does PayPal have” but “what is the only thing Advent, Stripe, and Block could build together,” and the answer is a genuine competitor to Visa and Mastercard: hundreds of millions of consumer accounts, Stripe’s merchant relationships and risk infrastructure, Block’s point-of-sale and Cash App, and stablecoin rails from Bridge and PYUSD that can push transactions on-us and bypass the card networks. The antitrust twist is elegant. Define the market as merchant APIs and it looks like consolidation; define it as the card duopoly and the same deal is pro-competitive. This deal would have been dead on arrival two years ago, and the fact that it is live now tells you as much about the regulatory climate as it does about payments.

    Underneath the payments story is a broader thesis worth naming: AI-native operators buying mature, founder-less, “stale” digital businesses and modernizing them. Bending Spoons rolling up AOL, Vimeo, Evernote, WeTransfer, and Eventbrite is the template, and Ryan Cohen’s eBay interest is the second dot on the line. The claim is that a modern operator can diagnose where a legacy business overspends, underinvests, and fails to use AI, then fix it with a small team of AI-first executives rather than a McKinsey engagement. It is a persuasive pattern, though PayPal is a harder case than the show admits: a 25-year-old interaction model growing 7% a year is not obviously revived by efficiency alone. Buying 400 million consumer accounts is buying distribution, not a product vision, and the open question is whether anyone can resuscitate the consumer experience rather than just milk it.

    The data center segment is where policy, energy, and information warfare collide, and Friedberg’s anti-GMO analogy is the sharpest thing in it. His argument is that manufactured public sentiment, traceable in one case to a foreign media push, can override the scientific and economic merits of a technology for years, and that the anti-data-center movement rhymes with it: closed-loop cooling that uses trivial amounts of water, land-use efficiency that dwarfs almonds and golf courses, and natural gas that burns clean, all drowned out by a moral panic. Whether or not you buy the specific foreign-influence attribution, the underlying tension is real and unresolved. America is staring at a structural electricity deficit while individual blue states treat data centers as a luxury they can refuse, and behind-the-meter power plus edge compute chasing cheap electrons is emerging as the workaround. The moratorium framing matters most here: a “pause” on data centers is not a few months, it is five years once you count ramp-up, and that is long enough to lose a race that may only be measured in months of lead.

    Key Takeaways

    • Demis Hassabis proposed a US-led international AI standards body modeled on FINRA: federally overseen, industry funded, and run by independent technical experts rather than a new government agency.
    • Under the proposal, frontier labs would submit models roughly 30 days before release; the body would assess risk to cybersecurity, national security, and biological threats, update benchmarks quarterly, and could coordinate a development slowdown if the situation demanded it.
    • The plan would be voluntary at first and mandatory later, and drew endorsement from a broad set of industry figures including Elon Musk, Sam Altman, Anthropic’s Jack Clark, Sundar Pichai, Satya Nadella, and Jack Dorsey.
    • A self-regulatory organization (SRO) like FINRA or the National Futures Association lets the industry set its own testing rules under federal oversight, adjusting faster than a government agency could as the technology changes.
    • Sacks laid out five conditions for supporting an SRO: broad representation including startups and open source; review of true frontier models only; scope limited to catastrophic risk (cyber and CBRN); voluntary before mandatory; and a substitute for, not an addition to, new regulatory agencies.
    • Sacks argued a government “FAA for AI” would be extreme: type certification for a new aircraft design takes 5 to 9 years, and applying that permission-based model to AI would push release timelines from months to years and lose the race to China.
    • He characterized the SRO as an “opening bid” that Anthropic and others would use as a stepping stone toward Dario Amodei’s repeatedly stated goal of an FAA-style regulator, unless it is traded for hard federal preemption written into law.
    • The besties cited a Politico report on Anthropic’s alleged state-by-state strategy of one-upmanship, using California’s SB 53 as a model and then ratcheting each subsequent state’s rules tougher, producing a patchwork rather than a single national framework.
    • Chamath warned of a “torrent of money” trying to influence both political parties toward some form of regulatory capture, and urged establishing industry rules quickly to supersede the need for a federal agency.
    • Stripe and private equity firm Advent, joined by Jack Dorsey’s Block contributing about $17 billion in equity, are jointly bidding roughly $53 billion (about $60 per share) for PayPal, with many expecting the final clearing price closer to $70.
    • The strategic logic is a new competitor to Visa and Mastercard: PayPal’s 400-plus million consumer accounts, Stripe’s merchants and risk infrastructure, Block’s point-of-sale and Cash App, and stablecoin rails from Stripe’s Bridge and PayPal’s PYUSD.
    • The antitrust outcome hinges on market definition: framed as merchant APIs (Stripe vs. Braintree) it looks anti-competitive, but framed against the Visa/Mastercard duopoly it is pro-competitive, and a deal like this would have been blocked two years ago.
    • PayPal peaked around a $322 billion market cap and fell to roughly $30 to 40 billion, which is precisely why it is now attracting bids; Stripe now processes more annual volume than PayPal, but lacks PayPal’s consumer relationship.
    • Sacks traced PayPal’s long stagnation to its 2002 eBay acquisition under Meg Whitman, when the founding team was pushed out; the “PayPal mafia” (which Sacks prefers to call the “PayPal diaspora”) formed as a result.
    • The deal is framed as part of a wave of AI-native operators reviving mature, founder-less digital businesses, with Bending Spoons (AOL, Vimeo, Evernote, WeTransfer, Eventbrite) as the roll-up template and Ryan Cohen’s eBay interest as another data point.
    • M&A is broadly “back on the menu” post-Lina Khan, with deals like Uber acquiring Delivery Hero, driving liquidity and renewed LP appetite for venture alongside SpaceX distributions.
    • Apple filed a 41-page lawsuit against OpenAI on July 10th alleging stolen trade secrets tied to OpenAI’s consumer hardware device; OpenAI’s chief hardware officer Tang Tan is a former Apple VP of iPhone design.
    • The complaint alleges Apple job candidates were directed to bring actual parts to OpenAI interviews for “show and tell,” and cites a text about accessing network storage; OpenAI has reportedly poached over 400 Apple employees.
    • The besties’ rule of thumb: when leaving a company, the only thing you can take is what is in your head; no documents, thumb drives, or files, because Apple rarely litigates and doing so signals something egregious.
    • xAI’s Grok Build, powered by Grok 4.5 and running inside Cursor, was reportedly sending users’ entire codebases (potentially including passwords and API keys) to servers despite a privacy setting meant to prevent it; xAI disabled the upload on July 13th and open-sourced the harness.
    • Chamath’s takeaway: privacy in AI is fragile and brittle, “zero data retention” cannot be guaranteed, and there are non-obvious data-leak vectors and “trap doors” everywhere, arguing for a stratified ecosystem with independent third-party layers between enterprises and models.
    • The “reverse information paradox” (building on Palantir’s Alex Karp) holds that technically capable enterprises want control over their compute, models, weights, data, and “alpha,” via real trust boundaries, private evals, in-tenant learning loops, decoupled orchestration, and the right to fine-tune.
    • Cited token costs per million showed a huge spread: roughly $56 on a premium frontier model, about $26 on another, roughly $1.50 for Grok input, around $1 for Elon’s, and about 50 cents for Chinese models, with a claim that 95 to 98% of tasks could run one tier cheaper.
    • Ramp CEO Eric Glyman launched token spend management because CFOs cannot see or control AI spend; Ramp customers’ token spend has grown 21x in a year, and someone will eventually miss an earnings quarter on runaway AI opex.
    • Engineers optimize for the latest, greatest model while CFOs bear the cost, a misalignment that platforms fine-tuning cheaper open models (like Mira Murati’s Thinking Machines effort) are positioned to exploit.
    • Calacanis called Apple a “screaming buy” on local models: rumored M7 Ultra silicon supporting up to 1.5 terabytes of memory could run last-generation frontier-class models locally on a Mac Studio, putting downward pressure on cloud AI pricing.
    • Edge compute is fragmenting outward: Sunrun announced distributed data center blocks for homes, and Span partnered with Nvidia, with compute increasingly “chasing energy” like cheap solar and battery power.
    • Chamath projected the US will be short 2.5 Californias’ worth of energy by 2050; a recent PJM auction that needed 7 to 8 gigawatts reportedly saw only a fraction show up, underscoring the electricity crunch.
    • “Behind the meter” power lets data centers generate their own electricity on owned property, but clean-air permitting is a major obstacle; Elon reportedly used clustered mobile engines and solutions like Bloom Energy to keep projects under personal-use permits (as with Colossus in Memphis).
    • New York Governor Kathy Hochul announced the nation’s first statewide moratorium on hyperscale data centers; the besties rebutted her claims on power, land, noise, water, and pollution point by point.
    • Modern data centers use closed-loop cooling (one claim compared a typical facility’s water use to a couple of In-N-Out restaurants), occupy trivial land relative to their economic value, generate tax revenue and construction jobs, and are largely powered by clean-burning natural gas.
    • Sacks argued the same political forces slowing domestic data centers are also behind chip export controls that would block data centers in allied countries, raising the question of where the buildout can happen at all.
    • Friedberg drew an anti-GMO analogy: he argued anti-GMO sentiment tracked the US presence of Russia Today (2010 to 2022) rather than the science, and worried a similar manufactured sentiment is now driving anti-data-center attitudes.
    • Sacks cited an OpenAI blog post on PRC-linked influence operations targeting US AI debates, with a congressional investigation reportedly coming, noting China has a clear incentive to slow American AI infrastructure.
    • Sacks framed the moment as a “moral panic”: the catastrophes people fear from AI (cyber, job loss) have not materialized, yet the US risks damaging its crown jewel of free-market innovation with premature regulation over hypothetical risks.
    • The panel questioned Dario Amodei’s prediction that 50% of entry-level knowledge-worker jobs could disappear within one to five years, arguing the harms have not shown up and only a handful of frontier labs (which already do safety testing and red-teaming) even matter.
    • A cited framing of the alleged Anthropic strategy: brand yourself as the safe AI company, ban unsafe AI, then profit; a fresh Chinese model (Kimi K2) was noted as very close to the frontier, suggesting a US lead of only months.
    • Science corner: a paper from Google’s Calico and partner Retro-style researchers used AlphaFold plus directed evolution to engineer a novel enzyme that degrades CML, a key advanced glycation end product in the extracellular matrix that drives aging.
    • The engineered enzyme cleared 52 to 97% of CML from body proteins in vitro and eliminated 55% of CML from donated elderly human skin, effectively reversing that skin’s biological age toward that of a 31-year-old, pointing first toward a potentially trillion-dollar cosmetic market.

    Detailed Summary

    Demis Hassabis’s FINRA-Style SRO for AI

    DeepMind’s Demis Hassabis published a proposal for a US-led international AI standards body modeled on FINRA, the Financial Industry Regulatory Authority. The design is federally overseen but industry funded and run by independent technical experts. Frontier labs would submit models about 30 days before release, and models would be assessed for risk across cybersecurity, national security, biological threats, and other high-risk domains. Benchmarks would update quarterly, the body could coordinate a development slowdown if warranted, and participation would be voluntary at first and mandatory later. The proposal drew endorsements across the industry, including Elon Musk (who called it thoughtful), Sam Altman, Anthropic’s Jack Clark, Sundar Pichai, Satya Nadella, and Jack Dorsey.

    Friedberg explained the SRO concept: bodies like FINRA and the National Futures Association let financial institutions set their own regulatory rules and check one another, under federal oversight but not federal control, reporting up to Senate and House committees. The AI analogy is that many players are all advancing the technology and none wants a single outside regulator dictating tests, especially after California’s earlier AI legislation was, in his telling, outdated by the time it would have taken effect. An SRO can bring in industry experts, adjust tests over time, and operate faster than a new agency. Chamath endorsed it strongly, warning that a “torrent of money” will try to influence both political parties toward regulatory capture, and that establishing rules quickly is the way to avoid that off-ramp while retaining ultimate federal oversight through Commerce and the DOJ.

    Sacks’s Five Conditions and the “FAA for AI” Warning

    Sacks said he could personally get on board with an SRO because it is “infinitely better” than a new government agency that would become a “DMV for AI,” or worse, Dario Amodei’s “FAA for AI.” He laid out five conditions: the SRO must have broad industry representation including startups and open source (to avoid the three biggest labs capturing it); it should review only true frontier models that represent a step change in capability, not hold up lesser models; its scope should be catastrophic risk only, meaning cyber and CBRN (chemical, biological, radiological, nuclear), not disinformation or speech; it should be voluntary before mandatory, proving it works first; and it must substitute for, not add to, new regulatory structures.

    He then explained why an FAA model is extreme: the FAA approves new airplane designs through type certification, which takes 5 to 9 years for a new aircraft and 3 to 5 years for major amendments. Applying permission-based regulation to AI, where new model versions ship every couple of months, would push timelines from months to years and lose the race to a China that will not abide by those rules. His conclusion: if the choice is FAA for AI, DMV for AI, or Hassabis’s SRO, the SRO wins, but it has to be kept “honest and pure,” because otherwise it becomes the opening bid in a coming wave of regulation and a vehicle for massive regulatory capture. He argued that companies making concessions to buy off politicians will only invite the government to come back for more, and that at some point these companies have to grow a spine, draw a line, and demand preemption in exchange.

    The Anthropic Regulatory-Capture Debate

    Sacks revisited his October claim that Anthropic was running a “sophisticated regulatory capture strategy based on fear-mongering,” arguing that what looked like beating up on a startup now looks different given Anthropic’s trillion-dollar valuation and industry-leading revenue. He cited a Politico piece, “Inside Anthropic’s state-by-state plan to ratchet up AI rules,” describing a strategy of one-upmanship: pass a model bill like California’s SB 53, then make each subsequent state’s rules stricter, deliberately producing a patchwork instead of a single national framework. The panel noted states have strong sovereignty rights (as with self-driving cars) and Anthropic is “winning” in California, Illinois, New York, and other blue states, because government officials rarely refuse an invitation to regulate.

    Stripe, Block, and Advent Bid for PayPal

    Stripe and private equity firm Advent, joined by Jack Dorsey’s Block contributing about $17 billion in equity, are jointly bidding roughly $53 billion (about $60 per share) for PayPal, with many expecting a final price closer to $70. PayPal still has more than 400 million consumer accounts and processes about $1.7 trillion a year, but its 25-year-old product is growing only about 7% and is seen as legacy. Chamath’s key question was what unique thing this trio could build: a competitor to Visa and Mastercard. Combining PayPal’s consumer accounts, Stripe’s merchant relationships and risk infrastructure, Block’s point-of-sale and Cash App, and stablecoin rails from Stripe’s Bridge and PayPal’s PYUSD would allow far more on-us transactions that bypass the card networks, potentially passing large discounts to merchants and consumers.

    Friedberg walked through the deal structure: the $17 billion equity contribution effectively means Stripe and Block sell equity to cash investors, that cash buys PayPal, and the parties end up cross-owning pieces of each other, with the Stripe team the likely operator post-close. The antitrust question turns on market definition: framed as merchant APIs, it is Stripe versus Braintree and looks like consolidation; framed against the Visa/Mastercard duopoly, adding competition is pro-competitive. Sacks noted the deal would have been “the antitrust equivalent of a colonoscopy” two years ago. He also recounted PayPal’s history: acquired by eBay in 2002 under the corporate-minded Meg Whitman, the founding team was pushed out, creating what he prefers to call the “PayPal diaspora” rather than the “PayPal mafia.”

    AI-Native Operators and the M&A Wave

    Freeberg framed the PayPal and eBay stories as part of an emerging line: AI-native operators buying first-generation digital-native businesses that have gone mature, stale, and founder-less, and that have not yet realized their AI potential or are overspending. Bending Spoons is the roll-up template, having acquired AOL, Vimeo, Evernote, WeTransfer, and Eventbrite and revitalized them from Milan with young, AI-first executives. The panel connected this to Josh Kushner’s and General Catalyst’s roll-ups of traditional services businesses. Calacanis added the macro backdrop: after venture was “on the ropes” under Lina Khan, M&A is “back on the menu,” with deals like Uber acquiring Delivery Hero, renewed LP appetite, and liquidity from SpaceX distributions.

    Apple Sues OpenAI Over Trade Secrets

    Apple filed a 41-page lawsuit against OpenAI on July 10th alleging stolen trade secrets used to develop OpenAI’s consumer hardware device. OpenAI’s chief hardware officer, Tang Tan, is Apple’s former VP of iPhone design; the complaint alleges he directed Apple job candidates interviewing at OpenAI to bring “actual parts” for “show and tell,” and cites a text from a former Apple engineer about accessing network storage. OpenAI has reportedly poached over 400 Apple employees. Chamath noted Apple rarely litigates, so the suit signals something they found egregious, while cautioning that the facts are alleged and unproven. Sacks declined to opine on the specifics but offered a simple rule: when changing jobs, take nothing but what is in your head, no documents, thumb drives, or files.

    The Grok Build Data Leak and AI Privacy

    xAI’s Grok Build, powered by Grok 4.5 and running inside Cursor, was reportedly sending users’ entire codebases (not just the files needed for a task, but potentially passwords, API keys, and change logs) to servers, despite a privacy setting meant to stop it. xAI disabled the upload on July 13th, Elon said previously uploaded data was deleted, and xAI open-sourced the harness. Chamath used it to make a larger point tied to his CNBC comments and Alex Karp’s remarks: privacy in AI is fragile and brittle, “zero data retention” cannot truly be guaranteed, and there are non-obvious leak vectors and “trap doors” everywhere. His conclusion is that enterprises need a stratified ecosystem with independent third-party layers between them and the models to manage exposure (a model his firm 8090 uses in its “software factory”).

    Sacks connected this to a blog post on the “reverse information paradox,” building on Karp’s point that technically capable enterprises want control over their compute, models, weights, data, and “alpha.” The recipe: establish a real trust boundary with private evals, proprietary learning loops inside the tenant, decoupled orchestration, and the explicit right to fine-tune their own outputs. He described an emerging ecosystem forming alternatives to the monolithic closed model stacks that Anthropic and, to some extent, OpenAI want customers locked into.

    Token Economics and Ramp’s Spend Controls

    The panel cited a wide spread in cost per million tokens: roughly $56 on a premium frontier model, about $26 on another (similar to a Claude tier), around $1.50 for Grok input, about $1 for Elon’s, and roughly 50 cents for Chinese models. Calacanis said he built a deep-linking podcast player across models on Perplexity and that the new Grok run cost only $11. Ramp CEO Eric Glyman appeared on Squawk Box to launch token spend management, noting Ramp customers’ token spend has grown 21x in a year and that CFOs struggle to see or control spend on an open-ended tab where rates rise with each new model. The takeaway: engineers optimize for the newest model while CFOs bear the cost, and unless that misalignment is controlled, runaway opex becomes a “money-burning furnace” that will eventually cause a public company to miss earnings. The panel argued 95 to 98% of tasks could run one tier cheaper, which is exactly the opportunity platforms fine-tuning cheaper open models (like Mira Murati’s Thinking Machines) are chasing.

    Apple’s Local-Model Opportunity and Edge Compute

    Calacanis called Apple a “screaming buy,” citing Mark Gurman’s report that a rumored M7 Ultra chip could support up to 1.5 terabytes of memory, double the current ceiling. That would let a Mac Studio run last-generation frontier-class models locally, giving users effectively unlimited tokens on the desktop and putting downward pressure on cloud AI pricing from the likes of Anthropic and OpenAI. Freeberg added that edge compute is fragmenting outward: solar company Sunrun announced distributed data center blocks for homes, and Span partnered with Nvidia. The theme is compute chasing cheap energy, whether excess solar or battery power charged at night.

    The Energy Deficit and Behind-the-Meter Power

    Chamath warned the US will be short about 2.5 Californias’ worth of energy by 2050, and pointed to a recent PJM auction (serving Pennsylvania, New Jersey, Maryland and other states) that needed 7 to 8 gigawatts but reportedly saw only a fraction show up. He explained “behind the meter” power: rather than drawing grid power from a utility line, a data center generates its own electricity on owned property. The obstacle is clean-air permitting. Solar takes too much space and batteries still need a generation source, so operators use gas. He described Elon clustering mobile 18-wheeler-style engines to keep them under personal-use permits, and newer solutions like Bloom Energy that allow large installations under similar rules, which is how projects like Colossus in Memphis got off the ground.

    New York’s Data Center Moratorium

    New York Governor Kathy Hochul announced the nation’s first statewide moratorium on hyperscale data centers, citing power draw, land use, water, and noise pollution. The besties rebutted each claim: behind-the-meter power means facilities bring their own electricity rather than competing with residential ratepayers; data centers are highly land-efficient, and New York State is roughly 70 to 80% undeveloped outside the city; noise can be managed with distance; modern facilities use closed-loop cooling (one comparison put a typical facility’s water use at a couple of In-N-Out restaurants, far less than almonds or golf courses); and natural gas is a clean-burning power source. They noted the tax revenue, construction boom, and ongoing jobs data centers create. Sacks cited a theory that Democrats intend the “moratorium” as leverage: pause construction until they can dictate terms, then lift it under a future administration in exchange for a new regulatory agency and speech controls ported from the social-media trust-and-safety agenda. He stressed a moratorium is effectively a five-year pause once ramp-up is counted, and that the same forces slowing domestic builds are pushing chip export controls that would block data centers in allied countries too.

    Foreign Influence, Anti-GMO, and the AI Moral Panic

    Freeberg drew an extended analogy between anti-data-center sentiment and anti-GMO sentiment. He argued that GMOs were prevalent and uncontroversial from their 1996 launch until anti-GMO sentiment rose in tandem with Russia Today’s US presence (2010 to 2022) and fell after RT was pushed out, and that similar KGB-era “directed measures” influence campaigns can be traced to opposition to nuclear energy in Germany. He cited a poll showing over 50% of Americans believe data centers increase water and electricity costs even where facilities recycle water and generate their own power. Sacks pointed to an OpenAI blog post on PRC-linked influence operations targeting US AI debates, with a congressional investigation reportedly coming, arguing China has a clear incentive to slow US AI infrastructure, kill open source, and constrain cheaper models. Sacks then broadened it to a “moral panic”: the feared catastrophes (cyber, job loss) have not materialized, yet the US risks damaging its crown jewel of free-market innovation over hypothetical risks, questioning Dario Amodei’s prediction that 50% of entry-level knowledge-worker jobs could vanish within one to five years and noting the fresh Chinese model Kimi K2 is close to the frontier.

    Science Corner: An Enzyme That Reverses Skin Aging

    Freeberg closed with a paper from Google’s secretive longevity startup Calico and a pharma partner focused on the extracellular matrix, the space between cells. Over time, sugars and fats bind to proteins there in a process called glycation, accumulating as advanced glycation end products (chiefly a molecule called CML) that stiffen tissue, cause wrinkles and immobility, and drive inflammation, with nothing in the body to break them down. The researchers used AlphaFold to find a protein that could bind and degrade CML, then applied directed evolution across five recursive cycles, DNA-programming thousands of variants to maximize activity. The engineered enzyme cleared 52 to 97% of CML from body proteins like collagen, casein, and hemoglobin in vitro, and eliminated 55% of CML from donated elderly human skin, effectively reversing that skin’s biological age toward a 31-year-old’s. Open questions remain about delivery (cream, shot, supplement, or an RNA therapy that makes the enzyme inside the body), but the panel expects the first market to be a trillion-dollar cosmetic one, and hailed it as a profound demonstration of AI-driven protein engineering.

    Notable Quotes

    “The whole industry is going to need to be regulated and I think the industry needs to regulate themselves. That’s the key to this.”

    Jason Calacanis, replaying his earlier call for AI self-certification

    “If my choices are between FAA for AI or what I would call the DMV for AI, I would much rather go for Demis’ SRO for AI.”

    David Sacks, on why self-regulation beats a new government agency

    “There’s hardly anyone in government who will ever say, oh no no no, we’re not qualified. Most people in the government will say thank you very much, what else can we take.”

    David Sacks, on the asymmetry that makes voluntary concessions dangerous

    “What it prevents is a handful of actors using their balance sheets and their capital to essentially pull the ladder up.”

    Chamath Palihapitiya, on the point of establishing industry rules quickly

    “You are creating a competitor to Visa and Mastercard.”

    Chamath Palihapitiya, on the only thing Stripe, Block, and Advent could build together with PayPal

    “The only thing you can bring to your new job is what’s in your head. Your memories. But never leave with anything else.”

    David Sacks, on avoiding trade-secret disputes when changing employers

    “Privacy in AI is very fragile and it’s very brittle. You are leaking information where you don’t know it.”

    Chamath Palihapitiya, on the limits of zero-data-retention promises

    “Unless you get a control of this and you can directly say how much money you’re making, this is a bridge to nowhere. It is a money burning furnace.”

    Chamath Palihapitiya, on uncontrolled enterprise token spend

    “We’re on the threshold of destroying the crown jewel of our economy, which is the system of free market innovation that we have.”

    David Sacks, on the risk of a premature AI regulatory apparatus

    “Number one, brand yourself as a safe AI company. Number two, ban unsafe AI. Three, profit.”

    David Sacks, summarizing the strategy he attributes to the “safe AI” positioning

    Watch the full conversation here: Can the AI Industry Regulate Itself? on the All-In Podcast.

    Related Reading

    • FINRA the financial-industry self-regulatory organization that Demis Hassabis’s AI proposal is modeled on.
    • AlphaFold (Wikipedia) the protein-structure prediction system behind the age-reversal enzyme discovery in the science corner.
    • PayPal Mafia (Wikipedia) background on the founders Sacks calls the “PayPal diaspora.”
    • The Founders by Jimmy Soni, the definitive history of PayPal’s founding team and its diaspora.
    • Advanced glycation end-products (Wikipedia) the biochemistry of CML and the extracellular-matrix aging the Calico enzyme targets.

  • Ken Griffin on AI, the Golden Age of Entrepreneurs, and the Taiwan Chip Risk That Would Cut US GDP 8 Percent: Inside the Citadel Founder’s Goldman Sachs Great Investors Interview

    Ken Griffin, founder and CEO of Citadel, sat down with Goldman Sachs’ Raj Mahajan at the firm’s Apex Symposium (recorded June 2, 2026) for this episode of Goldman Sachs Exchanges: Great Investors. It is their third public conversation in seven years, and Griffin is unusually candid: about the Friday he went home “shocked and depressed” over AI, the agentic system inside Citadel that compresses six weeks of PhD-level work into two hours, why a Chinese move on Taiwan would throw the US into a depression within six months, and the one question every hedge fund investor should ask their GP.

    TLDW

    Griffin names his two proudest leadership calls: dragging Citadel back to the office five days a week before it was acceptable (citing Fed research that remote work has hurt young Americans’ employment more than AI has), and Citadel’s pandemic role, from getting the FDA to approve experimental COVID drug trials in 72 hours to shaping the incentive design behind Operation Warp Speed, which he credits with saving roughly half a million American lives. On markets, he explains why the S&P sits at all-time highs despite wars in the Middle East and Europe: US energy insulation, stunning Chinese oil demand destruction, and record corporate earnings. On AI, he distinguishes hype from reality (a dinner of multinational CEOs gave him five stories of “AI transformation,” none of which were actually AI), then describes the internal breakthrough that changed his mind: an agentic system that reads, reproduces, and out-of-sample-tests academic finance papers in 2 to 3 hours instead of 6 to 8 weeks. The consequences: no layoffs at Citadel, but competitive moats across the economy are being filled in at lightning speed, setting up a golden age of entrepreneurship. He covers the compute market (all available compute is utilized all the time; market makers now spend hundreds of millions a year), China’s lead in roughly 67 of 74 critical technologies, the Taiwan scenario in which losing TSMC chips cuts US GDP 8 percent in six months, an energy doctrine built on nuclear, natural gas, and building data centers (with their own generation) in America, his stress-test approach to tail risk (definable, tolerable, still in business), and hedge fund economics: the industry’s cost of capital is roughly risk-free plus 4 percent, which is why Citadel has returned $25 to 30 billion to its LPs.

    Thoughts

    The most useful thing in this conversation is Griffin’s two-sided read on AI, because he refuses to pick a lane. The paper-replication story is the cleanest documented example yet of AI eating not just white-collar work but masters-and-PhD-level work, from the man whose firm profits from that labor. Yet in the same breath he reports zero headcount reduction, because Citadel has more problems to attack than people to attack them. Both things are true at once, and he names the synthesis honestly: the individual firm gets more productive while every firm’s moat gets shallower. Most commentary picks either the doom frame or the productivity frame. Griffin holds both, and his conclusion (a golden age of entrepreneurship, startups running on a few AI systems instead of 30 to 40 employees) is the actionable part.

    His dinner-party anecdote deserves to be a standard reference. Five global CEOs effusing about AI transformation, and every single story was actually machine learning, optimization, or plain digitization. The C-suite cannot tell AI from technology at large, which means a meaningful slice of the “AI is transforming our business” narrative priced into the S&P is really a decade-old digital revolution wearing a new label. That is not a bearish observation, since the earnings are real either way, but it matters for anyone trying to figure out which companies actually have AI leverage and which have rebranded their IT budget.

    The Taiwan section is the starkest risk framing you will hear from someone who runs both a hedge fund and one of the world’s largest market makers. An 8 percent GDP contraction in six months is not a market correction, it is Boeing halting production, new cars stopping, and consumer electronics freezing simultaneously, because TSMC chips are in every high-end product made. What makes his version distinctive is the second-order point: in a Taiwan blockade, he does not expect unified Western sanctions. Europe’s membership on “team USA” is less clear than it was two years ago, and the Middle East will play Switzerland because China buys its oil. Investors should notice that his answer to “how do you hedge this?” is not clever derivatives, it is his stress-test doctrine: know the worst case, size exposures so the loss is definable and tolerable, and stay in business to fight back.

    Finally, the small structural details are where the conversation earns its Great Investors billing. Compute has become a commodity input like jet fuel, fully utilized at all times and allocated purely by willingness to pay, which quietly favors high-margin businesses and squeezes everyone else. Alternative data made the present transparent, so the remaining edge in stock picking is multi-year vision about which companies are building transformative products. And the hedge fund test he closes with is one any allocator can use tomorrow: is your GP in the asset management business or the performance business? Citadel returning $25 to 30 billion to LPs is what the performance answer looks like in practice.

    Key Takeaways

    • Griffin’s proudest leadership call was bringing everyone back to the office five days a week, extremely early and against the culture, because humans are social creatures who learn through apprenticeship and mentorship.
    • He cites a Fed paper on reduced employment among workers under 30: remote work turns out to be a more important factor in diminished opportunities for young Americans than AI.
    • At the start of the pandemic, a hospital-system CEO called Griffin because he could not get FDA approval for drug trials on ventilated COVID patients; Citadel’s team got experimental trials approved in about 72 hours.
    • The key insight behind Operation Warp Speed, which Griffin discussed at length with Jared Kushner, was an incentives fix: the US government paid pharma to manufacture vaccines before FDA results existed, collapsing time-to-market from months to days.
    • By his math, the country spent a few billion dollars on that risk, saved a few trillion dollars of GDP, and saved roughly half a million American lives.
    • The S&P is at all-time highs despite a Middle East war, a still-raging war in Europe, and a potential skirmish over Cuba, because the US is relatively shielded from the energy shock.
    • China’s oil demand elasticity stunned even Citadel’s commodities business, one of the largest in the world; that demand destruction plus episodic oil flows out of the region has kept crude near the low $100s instead of the nearly $200 most models predicted if the straits closed.
    • Citadel has been a huge user of machine learning since TensorFlow arrived roughly a decade ago; the current wave is an acceleration of a digital revolution already underway, not a clean break.
    • At a dinner two years ago, Griffin asked global multinational leaders to share how AI was transforming their businesses: he got four or five great productivity stories and not one actually involved AI. They were machine learning, optimization, and digitization.
    • In the C-suite the nuance between AI and technology at large gets lost, but bigger budgets and CEO enthusiasm are pushing through real projects with real bottom-line impact; US corporate earnings are at all-time highs and multiples have actually come down as a result.
    • The use case that sent Griffin home shocked and depressed: a Citadel team member built an agentic AI system that reads an academic finance paper, reproduces it, verifies the published results, and tests them out of sample in 2 to 3 hours on average.
    • That same replication work previously took a legion of young masters and PhD hires roughly six to eight weeks per paper; Citadel finds a few tradeable ideas a year this way, and a few ideas can be worth a lot of money.
    • The point he stresses: this is not just a white-collar job being automated, it is a master’s or PhD-level job, and AI is now cracking problems (like the 80-year-old math problem OpenAI solved) that seemed beyond its reach two or three years ago.
    • Despite the breakthrough there has been no reduction in headcount at Citadel: the firm has more problems to attack than people, so Griffin takes every productivity gain he can get.
    • The flip side is that competitive moats across corporate America are being filled in at breathtaking speed, which Griffin expects to produce a golden age of entrepreneurial activity.
    • His example: a startup that would traditionally need 30 or 40 employees now runs with just a few AI systems, letting entrepreneurs take on incumbents in ways impossible 5, 10, or 20 years ago.
    • Some workers face genuinely hard transitions (his example is English-to-German translators), and the country needs to figure out how higher education can retrain these people quickly.
    • Stock picking remains a timeless business with a similar skill set, but the market will increasingly reward multi-year vision about which companies are creating transformative products rather than skill at calling quarterly earnings beats.
    • Alternative data (Citadel has access to the credit card spending of millions of Americans) made the here-and-now transparent a decade ago; AI plus bright people now triage the present almost instantly, so relative value accrues to those who can see years ahead.
    • At Citadel Securities, transformer models continue a decade of ML-driven improvement in pricing and risk management, and the same is true at other leading market-making firms.
    • For all intents and purposes, all available compute in the world is utilized all the time; access is decided by who will pay the most, and the per-unit price has risen beyond what anyone reasonably projected two or three years ago.
    • Large market-making firms now spend hundreds of millions of dollars a year on compute; Griffin compares compute inflation to jet fuel and egg prices, a cost that high-margin businesses can bear and low-margin businesses cannot.
    • China leads in roughly 67 or 68 of the 74 or 75 most important technologies in the world, including solar, EV batteries, and multiple quantum fields, and has pulled ahead in published academic papers.
    • The drivers are structural: 1.4 billion people, an extraordinarily strong educational culture, and far more STEM graduates, producing exactly the human talent needed to win in a high-IP world.
    • China is no longer relegated to producing low-margin products designed in America, and Griffin calls that shift a threat to the American way of life; the answer is not tariffs but educating US youth to out-compete, out-innovate, and out-problem-solve.
    • If China takes Taiwan and the US loses access to Taiwanese semiconductors, the rough estimate is US GDP falls 8 percent in six months: a great depression in the blink of an eye, unlike any before.
    • The mechanism is concrete: Boeing stops making planes within six months, most new cars stop being manufactured, consumer electronics production freezes, because TSMC chips are in every high-end product made.
    • There are no winners in a Taiwan escalation: tanking the US economy would have draconian knock-on effects for China given America’s importance as an export market.
    • In a Taiwan blockade Griffin does not expect unified global sanctions against China: where you sit determines your exposure, Europe’s place on team USA is less clear than two years ago, and the oil-exporting Middle East will play Switzerland.
    • On energy, the US must re-embrace nuclear, with small modular reactors a big part of the story: nuclear has effectively no carbon footprint and one of the lowest mortality rates of any energy source ever used (hydro has killed magnitudes more people).
    • He punctures the clean-energy veneer: solar cells are often made in western China by burning coal, with roughly a seven-year energy payback, and carbon fiber wind turbine blades last 20 years then fill landfills because they do not break down. No truly clean solution exists until fusion or broader nuclear.
    • Until then, natural gas is America’s huge asset: decades of cheap supply, and one of the few things that has actually brought down US carbon emissions.
    • Data centers are going to get built somewhere, and Griffin argues it would be inane for America to end up dependent on foreign countries for them; his fix for NIMBY politics is to require data center builders to construct corresponding power generation, tied to the grid for reliability, rather than pushing costs onto consumers.
    • His hedging doctrine for complicated risks: run stress tests, know exactly how much you lose and where in the worst case, and keep exposures sized so the loss is definable, tolerable, and leaves you still in business and able to fight back. You will never hedge every tail event.
    • Hedge fund industry economics: the long-run cost of capital is roughly the risk-free rate plus 4 percent; underperform and capital flows out, outperform and it flows in, and inflows dilute alpha because alpha capacity is finite.
    • Citadel has returned $25 to 30 billion to its limited partners to keep return on equity high: Griffin’s job is to grow annual alpha capacity, and any capital beyond what the portfolio needs goes back to LPs.
    • The alignment test for allocators: the biggest investor in Citadel’s funds is Griffin and his partners, and every LP should ask whether their GP is in the asset management business or the performance business.

    Detailed Summary

    Return to Office and the Cost of Remote Work

    Asked what he is most proud of beyond the numbers, Griffin starts with Citadel’s early, countercultural demand that everyone return to the office five days a week. He frames it as a human capital decision, not a control decision: people learn through apprenticeship, mentors are critical to development, and the underdevelopment of talent from remote work has damaged the broader economy. He points to recent Fed research on falling employment among under-30s: remote work turns out to matter more than AI in diminishing opportunities for young Americans. Citadel not only brought its team back but publicly extolled the virtues of doing so, and Griffin believes history will be on his side.

    72 Hours to FDA Approval and the Warp Speed Incentive Design

    His second point of pride is Citadel’s pandemic chapter. As the first US COVID cases appeared, a former partner running a major New York hospital system called: he could not get FDA approval for experimental drug trials on ventilated patients facing imminent death, and believed only Griffin could make it happen. Citadel’s team, with decades of government experience, got approvals moving in about 72 hours. The second act was Operation Warp Speed, whose core idea Griffin discussed at length with Jared Kushner: pay pharmaceutical companies to manufacture vaccines before FDA results, so a positive result means days to market instead of the standard sequence losing three to six months. No company would spend billions producing vaccines that might be flushed down the sewer, so the US government took the manufacturing risk on unproven efficacy. A few billion dollars spent, a few trillion in GDP saved, and roughly half a million American lives.

    All-Time Highs in a World at War

    Griffin’s market picture is unsentimental: there is a war in the Middle East, a still-raging war in Europe, potential trouble in Cuba, and the peace both men grew up with is off the table. Yet the S&P sits at record highs. His explanation: America is relatively shielded from the war-driven energy crisis. China has curtailed oil demand with an elasticity that stunned even Citadel’s commodity desk, and episodic oil and LNG flows keep leaving the region, holding crude around the low $100s when most estimates had a strait closure producing nearly $200 a barrel. Meanwhile corporate earnings are at all-time highs, enough that multiples have actually compressed over the last 12 months.

    The AI Story CEOs Tell Versus the One That Is True

    Citadel has used machine learning heavily since TensorFlow arrived a decade ago, powering everything from radiology reads to self-driving cars across the economy, so Griffin sees today’s AI wave as an acceleration of an ongoing digital revolution. His favorite corrective: at a dinner with global multinational leaders two years ago, everyone was effusive about AI transforming their businesses, so he asked them to go around the table with specifics. Four or five genuinely impressive productivity stories emerged, and not one involved AI: they were machine learning, optimization, digitization, technology at large. The C-suite blurs the distinction, but the enthusiasm has unlocked bigger technology budgets and real bottom-line projects, which is part of why earnings are at records.

    The Agentic System That Shocked Him

    Then comes the story behind the famous “shocked and depressed” Friday. Citadel employs legions of young masters and PhD graduates to replicate academic finance papers: read the hypothesis, judge the work, reproduce results, and test whether the effect persists out of sample (does buyback activity predict outperformance, for example). Each paper takes six to eight weeks, and the process surfaces a few valuable ideas a year. A colleague built an agentic AI system that does the entire pipeline (read, reproduce, verify, out-of-sample test) in two to three hours on average. Griffin’s emphasis: this is not routine white-collar work, it is master’s and PhD-level work, and paired with OpenAI solving a math problem open for 80 years, it shows AI cracking problems considered out of reach two or three years ago. Notably, Citadel cut zero headcount on the back of the breakthrough; the firm has more problems worth attacking than people to attack them, so every productivity gain gets absorbed.

    Filled-In Moats and a Golden Age of Entrepreneurs

    The macro consequence Griffin draws is double-edged. Hold two thoughts at once: AI is reaching very high-level work in the job market, with some workers (translators, for instance) facing hard transitions that demand fast retraining through higher education. And simultaneously, the competitive moats of corporate America are being filled in at breathtaking rates. That means entrepreneurs can launch businesses at speeds impossible 5, 10, or 20 years ago: he mentions a startup running on a few AI systems where 30 or 40 employees would once have been required. He expects a wave of these stories over the next couple of years as founders use the technology to take on incumbents.

    The Future of the Stock Picker

    Griffin has called stock picking a timeless business, and he still sees a similar skill set for the portfolio manager of the future, with one shift in emphasis. Predicting quarterly earnings beats has gotten far harder over a decade as alternative data (credit card panels covering millions of Americans, telegraphing Starbucks and McDonald’s revenues) made the present transparent. Now bright people plus good AI triage the here-and-now almost instantly. The scarce, rewarded skill becomes vision: identifying which companies are building genuinely transformative products years before the market fully prices it.

    Compute Is the New Jet Fuel

    At Citadel Securities, which holds double-digit market share across equities, futures, and treasuries, transformer models extend a decade of machine learning gains in pricing and risk. The compute market backdrop is what Griffin calls breathtaking: essentially all available compute on Earth is utilized all the time, so access reduces to who will pay the most. Per-unit compute prices exceed what anyone reasonably projected two or three years ago, and large market makers now spend hundreds of millions of dollars annually. He treats it as straightforward input inflation, like jet fuel or eggs: high-margin businesses can bear it, low-margin ones cannot.

    China’s Technology Lead and the Taiwan Equilibrium

    Griffin states the cold reality: China is one of the most innovative, fastest-growing economies in the world, leading in roughly 67 or 68 of the 74 or 75 most important technologies (solar, EV batteries, several quantum fields) and now ahead in published academic papers. The foundation is 1.4 billion people, a culture with an extraordinary emphasis on education, and far more STEM graduates. China is no longer relegated to manufacturing low-margin products designed in America, and Griffin calls that a threat to the American way of life. His prescription is pointed: not tariffs, but educating American youth to out-compete, out-innovate, and out-problem-solve. Taiwan is the painful pressure point with no winner. If China takes Taiwan and the US loses TSMC chips, GDP falls an estimated 8 percent in six months: Boeing stops making planes, most new car production halts, consumer electronics freeze, a great depression in the blink of an eye. China would suffer draconian knock-on effects too. As an investor he thinks about position: sanctions in a Taiwan blockade would not be unified, Europe’s place on team USA is a genuine question mark now, and the oil-exporting Middle East would play Switzerland since China is its biggest customer.

    Energy Realism: Nuclear, Gas, and American Data Centers

    On powering AI, Griffin wants America to lead again in nuclear, with small modular reactors central: no meaningful carbon footprint and one of the lowest mortality rates of any energy source ever deployed (hydro has killed magnitudes more people). He challenges the superficial cleanliness of renewables: solar cells are often made in western China with coal power, requiring about seven years of energy capture to break even against the coal burned making them, and 20-year-old carbon fiber wind turbine blades do not break down and are already filling landfills. Until fusion or expanded nuclear, America’s real asset is natural gas: decades of cheap supply that has actually driven US emissions down. His data center position is blunt: they will get built somewhere, and depending on foreign countries for them would be inane, so build them in America. His answer to NIMBY politics: require data center developers to build corresponding power generation, tied to the grid for reliability, so the cost never lands on the American consumer.

    Tail Risk, Tolerable Losses, and Hedge Fund Alignment

    On hedging complicated risks, Griffin’s method is stress testing: if this happens, how much do we lose and where, and is that loss tolerable? You can never manage a portfolio for every possible tail event, but you can keep exposures sized so the worst case is definable and tolerable, leaving you still in business and positioned to fight back. On industry returns, he pegs the hedge fund cost of capital at roughly the risk-free rate plus 4 percent as the long-run equilibrium: underperformance drains capital, outperformance attracts it, and since recent outperformance keeps pulling money in, growing assets dilute alpha. That is why Citadel has returned $25 to 30 billion to LPs: alpha capacity is finite, Griffin’s job is to grow it, and excess capital goes back to investors to keep return on equity high. The closing advice is an alignment test: Citadel’s biggest investor is Griffin and his partners, and every allocator should ask whether their GP is in the asset management business or the performance business.

    Notable Quotes

    “Turns out that remote working is a more important factor to diminished employment opportunities for young Americans than AI.”

    Ken Griffin, citing Fed research on under-30 employment

    “We spent a few billion dollars as a country. We saved a few trillion dollars in GDP. We saved roughly half a million American lives.”

    Ken Griffin, on Operation Warp Speed’s incentive design

    “I got four or five incredible stories of how companies were achieving meaningful productivity gains. Not one involved AI.”

    Ken Griffin, on his dinner with global multinational CEOs

    “My colleague built an agentic AI system that would read a paper, reproduce it, verify the results that were published in the paper, produce the results out of sample, and do all this work in about on average 2 to three hours.”

    Ken Griffin, on the breakthrough that replaced six to eight weeks of PhD-level work

    “We’re likely to see a golden age of entrepreneur activity. Like entrepreneurs will be able to launch new businesses at breathtaking speeds and will be able to take on incumbents in ways that you just couldn’t do 5, 10, 15, 20 years ago.”

    Ken Griffin, on AI filling in competitive moats

    “All the available compute today is more or less utilized all the time. So the question is who’s willing to pay the most for it?”

    Ken Griffin, on the global compute market

    “The US loses access to Taiwanese semiconductor chips, our GDP falls by 8% in 6 months. Simply put, we go into a great depression in the blink of an eye unlike any we’ve seen before.”

    Ken Griffin, on the Taiwan scenario

    “We better damn well build the data centers in America because they’re going to get built somewhere in the world.”

    Ken Griffin, on energy policy and AI infrastructure

    “Definable, tolerable, still in business, still in a position to fight back from that point.”

    Ken Griffin, summarizing his approach to hedging tail risk

    “Are they in the asset management business or are they in the performance business?”

    Ken Griffin, on the question every hedge fund investor should ask their GP

    Watch the full conversation here: Ken Griffin on Goldman Sachs Exchanges: Great Investors.

    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.

  • Charles Koch and Chase Koch on Koch Industries: 130K Employees, 60 Countries, and a $150B Private Empire Built on Principle-Based Management

    Charles Koch and his son Chase Koch sat down with David Friedberg for a long, candid Forbes/All-In conversation about how a small crude-oil gathering operation in southern Oklahoma became Koch Industries, a privately held company with more than 130,000 employees across 60 countries and revenue that would land it comfortably in the top 25 of the Fortune 500 if it were public. They walked through the founding story, the management principles that drove a 9,000x increase in value since the early 1960s, the failures that almost wiped out the company, and the philanthropic and political work being done through Stand Together. Watch the full conversation on YouTube.

    TLDW

    Charles Koch took over a roughly 300-person family business in 1961 at age 25, fired the bureaucratic president, and built it into one of the most profitable private companies in the world by applying what he calls Principle-Based Management. The core insight is to be capability bounded rather than industry bounded, to run an internal “republic of science” that rewards contribution over credentials, and to treat failure as the price of experimental discovery. Koch grew through both organic capability extension and large acquisitions like Georgia Pacific in 2005 and Molex in 2013, mostly by replacing top-down hierarchies with bottom-up empowerment. The conversation covers the founding by Fred Koch, the near-death failures of the late 1990s “gas to bread spread,” the Pine Bend Minnesota refinery turnaround, the role of Wichita as a competitive advantage, Chase Koch’s path from feed-yard laborer to leader of Koch Disruptive Technologies, the launch of Stand Together as a long-running social-change platform, the rejection of single-party politics, the case against entitlements and occupational licensing, and the principles for using AI as a permissionless empowerment tool rather than a top-down control system. The throughline is Viktor Frankl: more people have the means to live and less meaning to live for, and the remedy is helping every individual find a gift and apply it in a way that creates value for others.

    Key Takeaways

    • Koch Industries today has more than 130,000 employees across 60 countries and has increased in value roughly 9,000 times since Charles took over in the early 1960s, when headcount was about 300.
    • Founded in 1940 by Fred Koch in Wichita, Kansas. The two starting businesses were designing fractionating trays (separating liquids by boiling point) and crude oil gathering in Oklahoma.
    • Charles got three engineering degrees at MIT, worked at Arthur D. Little, and reluctantly came back at 25 only after his father said he would otherwise sell the company. His father gave him full autonomy over every decision except selling.
    • His first move was firing the controlling, memo-driven president and replacing protectionism with three pillars: create value for customers, empower employees, and own end-to-end execution. They built their own plant in Italy instead of stitching together European subcontractors.
    • The defining mental model is “capability bounded, not industry bounded.” You expand into adjacent industries where the capabilities you have already proven (operations, logistics, trading, refining, branding) create more value than incumbents, not because the new industry is in the same SIC code.
    • Wholly owned business platforms today include engineered projects and construction, solar plants, commodity trading and distribution, fertilizers, refined products, chemicals and polymers, glass, forest and consumer products, electrical products (Molex), and management software, plus four distinct investment firms.
    • Koch is explicitly not a Berkshire-style conglomerate of independent silos. Chase frames it as an integrated republic of science, an integrated set of capabilities that share knowledge and people across business lines.
    • “If you are not failing at anything, you are not doing anything new.” Failure is treated as the cost of experimental discovery, but only when the learning value exceeds the cost.
    • The worst failures came from violating the hiring rule. Hire on values first, talent second. People with destructive motivation (power and control over contribution) hide failures and invent successes, and the damage compounds when those people get promoted into leadership.
    • The 1973 trading blowup nearly bankrupted the company. The late 1990s “gas to bread spread” strategy, an attempt to vertically integrate from natural gas through fertilizer to pizza crust, nearly wiped out all of Koch’s earnings. Lesson repeated, then internalized.
    • One acquisition shipped hundreds of millions of dollars in out-of-the-money hog feed contracts that nobody bothered to read before closing. Apply the scientific method: try as hard to disprove your hypothesis as to prove it.
    • Georgia Pacific was acquired in 2005 for roughly $20 billion when Koch was much smaller. They originally tried to buy only the commodity pulp piece so GP could re-rate as a pure consumer-products company at a higher P/E. When legal blockers killed that path, they bought the whole thing.
    • The Georgia Pacific culture change started with sending Joe Moeller in as CEO. He gutted the 51st-floor coat-and-tie executive suite, fired the most bureaucratic managers, moved everyone to working floors, and converted the executive floor into open meeting rooms. Signals like that drive culture more than memos do.
    • The Pine Bend, Minnesota refinery, bought in 1969, was one of the hardest cultural turnarounds. The union strike was violent (rifles fired, switch engines used to ram units), Charles ran it nine months without union labor on his honeymoon, the work rules finally changed, and once empowered, the workforce built its own machine shop, cut spare-part costs, and grew capacity tenfold. It is now one of the best refineries in the country.
    • Molex, bought in 2013, took years to transform. The dominant paradigm was top-line growth rather than bottom-line value creation, partly because it had been public for 30 years and the market rewarded the wrong things. Almost every successful turnaround required swapping in leadership with a bottom-up empowerment paradigm.
    • Sheep-dipping does not work. Pushing 130,000 people through the same seminar will not rewire habits. Coaching one struggling team until it succeeds creates social mimicry. Other teams ask to be next. Demand for Principle-Based Management coaches now exceeds supply inside the company.
    • The talent doctrine is values first, skills second, credentials last. Wichita and the farm-team labor pool are deliberate competitive advantages because farm kids tend to show up contribution-motivated rather than entitlement-motivated.
    • The current Koch CIO, Jared Benson, joined as a contractor striping lines in the parking lot and has no college degree. He learned data science, built the cyber-security capability, and ran circles around credentialed peers.
    • Public-company pressure to IPO was the biggest external threat. Charles refused. Staying private was the only way to keep reinvesting roughly 90 percent of profits, to maintain the capability-bounded model that no analyst would underwrite, and to keep accepting low P/E optics on commodity businesses inside the portfolio.
    • Three things any lasting partnership requires (marriage, business, employment): shared vision, shared values, and complementary capabilities. Miss any one and it does not last.
    • Chase Koch started at age 15 throwing tennis matches to escape practice, got shipped to a feed yard the next morning, shared a single-wide trailer with his boss, shoveled manure, and discovered the “glorious feeling of accomplishment” that his grandfather Fred had written about in his famous letter to the next generation.
    • At one point Chase was promoted to president of Koch Fertilizer, realized after nine months he was a builder and not an optimization operator, walked into his boss’s office, and fired himself. The role went to someone with the right comparative advantage and the business grew faster. Chase went on to launch Koch Disruptive Technologies (KDT).
    • KDT would have been shut down on a normal three-to-four-year venture timeline. Koch kept investing through the losses because of two principles: experimental discovery and creative destruction. They also valued the knowledge inflow about disruptive technologies that might one day eat the core business.
    • Comparative advantage applies to careers. The job of 20,000 plus Koch supervisors is to keep moving people into roles where they can actually contribute. Beating people up in the wrong seat is destructive.
    • Viktor Frankl frames the moral problem of the era: ever more people have the means to live and no meaning to live for. Without meaning, people default to either power or pleasure. Both lead, at scale, to totalitarianism, authoritarianism, or socialism.
    • Charles credits Maslow’s Eupsychian Management, Polanyi’s Personal Knowledge, Hayek’s price-signal work, and Frankl’s logotherapy as the intellectual foundations of Principle-Based Management. The five dimensions: vision, virtue and talents, knowledge processes, decision rights, and incentives.
    • Stand Together, founded in 2003, is a community of close to a thousand business leaders pooling effort on social change rather than working in philanthropic silos. The thesis: every human has a gift and the institutions are putting up barriers (broken schools, broken criminal justice, bad policy, occupational licensing).
    • Education is one of Stand Together’s biggest fronts. Pre-COVID, around 20 percent of families were open to a new model. Post-COVID, it is 70 to 80 percent. They back Alpha School (Joe Liemandt), Khan Academy (Sal Khan), and the VELA Education Fund alongside the Walton family. Roughly 5,000 micro-schools have been seeded.
    • The model for social change mirrors the business model: bet on the person closest to the problem who already shows results. Scott Strode and The Phoenix gym went from a couple of Colorado locations to one million people overcoming addiction, with relapse rates under 10 percent, by combining community and exercise rather than top-down treatment programs.
    • Charles says the biggest mistake of the first 50 years was trying to drive social change through a single political party, first the Libertarians and later just the Republicans. The current rule, from Frederick Douglass, is “I will unite with anybody to do right and with nobody to do wrong.”
    • His policy critique cuts in every direction: occupational licensing locks out newcomers, the treatment of working illegal immigrants is wrong, tariffs undermine division of labor by comparative advantage and raise prices, and entitlements once created are nearly impossible to dismantle.
    • Asked whether capitalism inevitably compounds into monopoly, Charles answers that the fix is removing barriers to others realizing their potential, not capping the winners.
    • On AI: the principle is permissionless innovation. Cost is collapsing, access is widening, and the right use is empowering individuals to learn 1000x faster, not concentrating power.
    • Koch backs Cosmos and other AI efforts that apply market-based management principles. Internally, they launched an AI app called Principal Companion that uses the Socratic method to walk users through problems using the book’s principles, from business to parenting.
    • Writing the new book (Charles’s fifth, Chase’s first) was the most important project Chase has worked on. They went through 27 versions of the stewardship chapter. Charles still corrects Koch leaders who say “the proof is in the pudding” instead of “the proof of the pudding is in the eating.”
    • When asked about legacy, Charles answered in one sentence: he wants the country to more fully live up to the promise in the Declaration of Independence.

    Detailed Summary

    From 300 Employees to 130,000 Across 60 Countries

    Koch Industries was founded in 1940 by Fred Koch in Wichita, Kansas. When Charles took over full-time in 1961, the company had about 300 employees and two main businesses: designing fractionating trays for separating liquids by boiling point, and a crude oil gathering system in Oklahoma. Today the company has more than 130,000 employees in 60 countries and has grown in value roughly 9,000 times over that period. If Koch were public, revenue would put it easily in the top 25 of the Fortune 500. The portfolio spans engineered projects and construction, solar plants, commodity trading and distribution, fertilizers, refined products, chemicals and polymers, glass, forest and consumer products, electrical products through Molex, management software, and four distinct investment vehicles. Roughly 90 percent of profits are reinvested.

    Charles Coming In at 25

    Charles describes himself as a poor engineer who happened to be good at math, science, and theory and bad at making or operating things. After three MIT degrees and a stint at Arthur D. Little doing what he calls “absurd” management consulting at 25, his father called and said the company was struggling and his health was failing. Either Charles came back or it would be sold. He came back. The condition was full autonomy: Charles could run it any way he wanted, the only decision requiring approval was selling. Within a short time he fired the previous president, a top-down memo-writer obsessed with controlling spending, and rewrote the operating philosophy around three things: create value for customers, empower employees, and own the value chain end to end. Instead of farming European fractionating trays out to multiple subcontractors and then re-assembling, Koch built its own plant in Italy.

    Capability Bounded, Not Industry Bounded

    This is the single most important strategic idea in the interview. Conventional advice told Koch to become an integrated oil major because they were in crude oil gathering. Charles rejected that and ran on Hayek and Adam Smith instead: division of labor by comparative advantage. Be in the part of any value chain where you can create more value than anyone else. From crude oil gathering, Koch leveraged operations, logistics, and trading into pipelines, refineries, natural gas, chemicals, fertilizers. Georgia Pacific looked like a non sequitur, wood products, but the underlying capability set transferred, and the acquisition also added branding as a new capability that fed back into the system. Chase calls the result not a Berkshire-style conglomerate of independent businesses but a republic of science: an integrated set of capabilities that share talent, knowledge, and laboratories.

    The Failures That Almost Killed the Company

    Charles spends a long stretch on failures, because he says the strength is in them. The 1973 trading blowup tied to the Middle East war could have bankrupted the company. The late 1990s “gas to bread spread” was an attempt to control the entire chain from natural gas to nitrogen fertilizer to grain to pizza crust. It violated almost every principle in the book at once and wiped out most of Koch Industries earnings for the decade. One acquisition closed before anyone read the hog-feed contracts, and on closing day they discovered hundreds of millions of dollars of out-of-the-money positions. Every failure traced back to two violations: hiring leaders with destructive motivation (power and control instead of contribution), and skipping the scientific method (trying to prove a hypothesis instead of disprove it). Charles says “repetition penetrates even the dullest of minds,” and he had to be punished enough times before the lesson took.

    Georgia Pacific, Molex, and the Pine Bend Refinery

    Three acquisition stories show how Koch transfers culture into businesses ten times larger than the corporate playbook would normally allow. Georgia Pacific in 2005 was a $20 billion bet on a company much larger than Koch at the time. Joe Moeller, sent in as CEO, immediately fired the most bureaucratic managers, gutted the 51st-floor private-elevator executive suite (coat and tie required to visit), moved everyone to working floors, and turned the old executive floor into open meeting rooms. Molex, bought in 2013, had been public for 30 years and ran on top-line growth thinking because that is what the market rewarded. Changing the paradigm to bottom-up empowerment and bottom-line value creation took years and required new leadership. Pine Bend, Minnesota, bought in 1969, was the hardest. The union ran the refinery, ignored work rules, and went on a violent strike when Koch tried to change them, firing rifles and ramming switch engines into units. Charles ran the refinery nine months without union labor (during his honeymoon), eventually got the work rules changed, then spent years rebuilding the culture. The empowered workforce designed and built its own machine shop, cut spare-part costs, and grew capacity tenfold. Pine Bend is now one of the best refineries in the country.

    How Principle-Based Management Actually Diffuses

    Charles is blunt that they tried “sheep dipping” first, hauling everyone through a seminar. It did not work, because changing a habit means rewiring the brain through work at intensity over time, the way a weightlifter has to retrain to become a marathoner. The model that did work was small. Find one team that is struggling, coach them with principles, let them succeed, and the rest of the company asks to be next. Social mimicry replaces top-down rollout. Internally the Principle-Based Management group is now in higher demand than any other function.

    Talent: Values First, Skills Second, Credentials Last

    Koch deliberately stayed in Wichita partly to access a “farm team” labor pool of people who grew up contribution-motivated. Chase tells the story of Jared Benson, who started as a contractor striping lines in the Koch parking lot, taught himself data science, built the company’s cyber-security capability, and is now CIO with no college degree. The lesson runs against the prestige-school default of most large companies. Contribution motivation, not credentials, predicts long-run output, and Charles is willing to “hire slow and stupid” for anyone with bad values so the company can flush them quickly. Aligning incentives matters as much as hiring: reward people on overall long-run contribution to Koch’s future, including the value of what was learned from a failed experiment, not on near-term P&L.

    Why Koch Stayed Private

    Multiple parties pushed hard for an IPO over the decades. Charles refused. Going public would have made the capability-bounded model impossible to communicate to analysts, would have forced a higher payout ratio and broken the reinvestment compounding, and would have introduced the short-termism that wrecks bottom-up empowerment. Buffett gets credit, but Berkshire does not try to integrate its businesses the way Koch does. Asked whether a non-owner public CEO could ever apply the principles, Charles allows it is possible if they can sell a different durable story (as Buffett did), but it is much harder.

    Chase Koch’s Path

    Chase tells two formative stories. The first is being shipped to a feed yard at 15, sharing a single-wide trailer with his boss, shoveling manure for minimum wage, and finding, for the first time, what his grandfather Fred had called “the glorious feeling of accomplishment.” The second is firing himself as president of Koch Fertilizer after nine months because he realized he was a builder, not an operator. The business outgrew where he would have taken it, and he went on to launch Koch Disruptive Technologies, the venture and innovation arm that now feeds technological insight back into every Koch business line. The comparative-advantage principle applied to a career, in public, by the boss’s son.

    Stand Together and Social Change

    Stand Together, founded in 2003, is the Koch family’s social-change platform. It now includes close to a thousand aligned business leaders. The animating belief is that every human has a gift and institutional barriers (broken schools, broken criminal justice, occupational licensing, bad policy) prevent most people from finding and applying it. The Phoenix gym founded by Scott Strode is the canonical Stand Together bet: a person closest to the problem, with results (relapse rates under 10 percent), funded to scale. In seven or eight years it has gone from a couple of Colorado locations to one million people. On education, post-COVID openness to new models jumped from roughly 20 percent of families to 70 to 80 percent. Stand Together backs Alpha School, Khan Academy, and the VELA Education Fund alongside the Walton family, and has helped seed roughly 5,000 micro-schools.

    Politics: The Single-Party Mistake

    Charles says for the first 50 of his 60 years in this work he avoided major-party politics, then concluded the country needed principle-based policies badly enough that engagement was required. The mistake was trying to do it through one party. The Libertarian Party turned into purity tests reminiscent of the early Communist Party. Doing it through Republicans blew up too. The rule going forward is Frederick Douglass’s: unite with anybody to do right and with nobody to do wrong. He is openly critical of both parties on occupational licensing, immigration policy, tariffs, entitlements, and the treatment of working illegal immigrants. He invokes Jefferson on slavery to describe his current mood: “If God is just, I despair for the future of our country.”

    Capitalism, Compounding, and AI

    Asked whether capitalism inevitably ends in monopoly because successful operators compound, Charles flips the framing. The remedy is not to cap the winners, it is to remove the barriers preventing everyone else from realizing their potential. Occupational licensing, immigration restriction on contributors, tariffs that undermine comparative advantage. On AI, Koch’s principle is permissionless innovation: cost is collapsing, access is widening, and the right outcome is individual empowerment and 1000x faster learning, not power concentration. Internally they launched Principal Companion, an AI app built on the principles in the book that uses the Socratic method to walk users through problems rather than handing out answers. Koch backs Cosmos and other AI ventures applying market-based management.

    The Philosophical Spine

    Charles cites four foundational thinkers. Polanyi’s Personal Knowledge gave him the model for how habits encode knowledge in the brain and why retraining is bodily work. Maslow’s Eupsychian Management supplied the empirical link between self-actualization and organizational performance. Hayek supplied the price system and the case against central planning. Frankl supplied the diagnosis: more means to live, less meaning to live for, and in that vacuum people drift to either power or pleasure, both paths to the slippery slope of authoritarianism and socialism. The Principle-Based Management answer is to design the company (and the country) so that everyone can find a gift and apply it to help others succeed.

    Thoughts

    The most useful concept in the conversation, the one worth stealing for any operator regardless of industry, is “capability bounded, not industry bounded.” Most companies define their addressable market by SIC code or competitive set. Koch defines it by the actual transferable skills they have demonstrated: operations, logistics, trading, refining, branding, cyber-security. Each acquisition is a probe to see whether the capability set creates more value than incumbents, and each acquisition that works hands back new capabilities (branding from Georgia Pacific, electronic-components engineering from Molex) that compound the option space. This is the same logic that makes Amazon’s AWS, advertising, and logistics businesses adjacent rather than diversifications. Industry conglomerates collapse. Capability conglomerates do not, because the capabilities reinforce each other.

    The honest treatment of failure is rarer than it sounds. Most CEOs who say “we celebrate failure” mean something performative. Charles’s version has teeth because the failures he names (the 1973 trade, the late 1990s vertical-integration push, the unread hog contracts) were almost terminal, and the lesson he draws is not “fail fast” but a specific causal claim about hiring leaders with destructive motivation. The asymmetry between contribution-motivated and destructively motivated employees, with the latter capable of hiding losses and inventing successes until the damage compounds, is the kind of insight that only comes from forty years of post-mortems. The remedy, hire slow and dumb if values are bad so you can purge fast, is uncomfortable enough to be real advice.

    The case for staying private is also harder than the founder-flex version usually heard from private operators. Charles is not arguing that private is better for everyone. He is arguing that a specific operating model (high reinvestment, cross-business capability sharing, willingness to take long P/E hits on commodity legs, leadership succession over decades) cannot be communicated to public markets without distortion. If you do not run that model, going public is fine. If you do, going public would have killed the system. That distinction is worth holding on to when reading the founder-control discourse in tech, because most “stay private forever” arguments do not actually meet that bar.

    The political reflection is the most surprising part of the conversation, particularly given the public reputation. Charles plainly says the biggest mistake of his life in social change was trying to do it through one party, that the Libertarians collapsed into purity-test factionalism, that the Republican approach failed in similar ways, and that the current operating rule is the one Frederick Douglass actually wrote down. He criticizes the current administration’s treatment of working illegal immigrants and the tariff regime by name. Whether one agrees or disagrees on policy, the willingness to grade your own past work in public, decades after the bets were placed, is rare at this level.

    Finally, the Frankl framing deserves a longer hearing than a podcast can give it. “Ever more people have the means to live and no meaning to live for” is the most economical statement of the malaise running through politics, addiction, education, and labor data right now. Koch’s bet is that the answer is not policy alone but a design problem: build institutions (companies, schools, philanthropies, AI tools) that let each individual find a gift and apply it in a way that creates value for others. That is the through-line connecting Principle-Based Management, Stand Together, the Alpha School partnership, The Phoenix gym, and Principal Companion. Whether it scales is an open question. The fact that one family business has spent 60 years pressure-testing it makes the experiment worth paying attention to.

    Watch the full Charles Koch and Chase Koch conversation on All-In and Forbes.

  • The Untapped Potential of LNG Cold Energy: A Chilling Opportunity

    tl;dw

    Asianometry’s video discusses the underutilized “cold energy” produced during LNG regasification (the process of turning liquefied natural gas back into its gaseous state). This cold energy, usually wasted, has potential applications in power generation (using methods like the Rankine cycle), industrial processes (like air separation and carbon capture), desalination, and even cooling data centers. The video highlights examples of countries already using this technology and emphasizes the vast potential of this currently wasted resource as the LNG industry grows.

    The video from Asianometry explores the potential of LNG (Liquefied Natural Gas) cold energy.

    • LNG Transportation: LNG is transported in liquid form, requiring significant energy to cool the gas and then reheat it (regasification) at the destination.
    • Cold Energy as a Byproduct: The regasification process generates a large amount of cold energy, which is often wasted.
    • Potential Applications: The video discusses various applications for this cold energy, including:
      • Power Generation: Using the temperature difference to drive turbines and generate electricity.
      • Industrial Processes: Improving the efficiency of air separation and carbon capture.
      • Desalination: Enhancing desalination processes by using the cold energy to cool the system.
    • Examples: The video highlights examples of countries like Japan and Thailand that are already utilizing LNG cold energy for industrial purposes, such as air separation.

    The video concludes by emphasizing the significant potential of LNG cold energy as a valuable resource and encourages further exploration of its applications to improve energy efficiency and sustainability.

    The global demand for energy is constantly increasing, driving the search for more efficient and sustainable solutions. While Liquefied Natural Gas (LNG) has emerged as a crucial energy source, a significant byproduct of its processing – cold energy – remains largely untapped. This article delves into the potential of LNG cold energy, exploring its origins, promising applications, and the challenges that need to be addressed to fully realize its potential.

    The Rise of LNG and the Cold Energy Byproduct:

    Natural gas, a relatively clean-burning fossil fuel, plays a vital role in the global energy mix. However, transporting natural gas over long distances via pipelines can be economically challenging. LNG provides a solution by cooling natural gas to approximately -162°C (-260°F), condensing it into a liquid that occupies 600 times less volume. This liquefaction process enables efficient transportation by specialized tankers across oceans.

    Upon arrival at import terminals, LNG undergoes regasification, the process of converting it back into its gaseous state for distribution through pipelines. This regasification requires adding heat to the extremely cold LNG, resulting in a significant amount of “cold energy” – a temperature differential between the LNG and the surrounding environment. This cold energy, often around -15°C (5°F), is typically dissipated as waste, representing a substantial loss of potential energy.

    Understanding the Regasification Process:

    The regasification process relies on specialized equipment called vaporizers. Several types exist, each with its own advantages and disadvantages:

    • Direct Fired Vaporizers: An older technology using burners to directly heat the LNG. These are less common today due to corrosion and efficiency concerns.
    • Submerged Combustion Vaporizers (SCVs): These vaporizers pass cold LNG through pipes submerged in hot water heated by submerged combustion. While widely used, particularly in the United States, SCVs can suffer from corrosion caused by acidic byproducts of combustion.
    • Open Rack Vaporizers (ORVs): ORVs utilize the temperature difference between LNG and warmer seawater. LNG flows through pipes exposed to seawater, facilitating heat exchange. This method is highly efficient where suitable seawater temperatures are available.

    Regardless of the method used, the regasification process inevitably generates a significant amount of cold energy.

    Unlocking the Potential: Applications of LNG Cold Energy:

    The potential applications of LNG cold energy are diverse and offer significant opportunities for energy efficiency and sustainability:

    • Power Generation: Utilizing the temperature differential between the cold LNG and the ambient environment can drive power generation systems.
      • Direct Expansion: This method directly uses the pressure change during regasification to drive a turbine and generate electricity.
      • Organic Rankine Cycle (ORC): ORC systems employ a working fluid with a low boiling point. The cold LNG cools the working fluid, creating a temperature gradient that drives a turbine and generates power. Cascading ORC systems can further enhance efficiency.
    • Industrial Applications:
      • Air Separation: The cryogenic temperatures of LNG can significantly reduce the energy required for separating air into its constituent components, such as nitrogen and oxygen, valuable for various industries.
      • Cryogenic Carbon Capture: Cooling flue gas from industrial processes to very low temperatures can facilitate the separation and capture of CO2, mitigating greenhouse gas emissions.
    • Desalination:
      • Thermal Desalination Enhancement: Integrating LNG cold energy into thermal desalination processes, such as Multi-Stage Flash (MSF), can improve efficiency by cooling the condensing steam.
      • Freezing Desalination: This method uses cold energy to freeze seawater into an ice slurry, separating the ice (freshwater) from the brine. While technically challenging, it offers the potential for high energy efficiency.
    • Data Center Cooling: Data centers consume vast amounts of energy for cooling. Utilizing LNG cold energy can provide a sustainable and efficient cooling solution, reducing their environmental impact.
    • Cold Storage and Food Preservation: The cold energy can be directly used for cooling warehouses, cold storage facilities, and other applications requiring low temperatures, such as food preservation and pharmaceutical storage.

    Challenges and Future Outlook:

    Despite the significant potential, several challenges hinder the widespread adoption of LNG cold energy utilization:

    • Location Constraints: LNG import terminals are often located far from potential end-users of the cold energy, requiring infrastructure for transport.
    • Economic Viability: The capital costs associated with implementing cold energy utilization technologies need to be carefully evaluated against the potential energy savings.
    • Matching Supply and Demand: The continuous availability of cold energy from regasification needs to be matched with consistent demand for its applications.

    However, growing awareness of energy efficiency and sustainability is driving increased interest in LNG cold energy utilization. Technological advancements, policy support, and innovative business models are paving the way for greater adoption of these technologies.

    Overlooked

    LNG cold energy represents a significant, yet often overlooked, opportunity to improve energy efficiency and sustainability. By strategically implementing various applications, we can transform this waste stream into a valuable resource, contributing to a cleaner and more sustainable energy future. As the LNG industry continues to grow, so too does the potential for harnessing this chilling opportunity.