PJFP.com

Pursuit of Joy, Fulfillment, and Purpose

Tag: AI startups

  • 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.

  • Paul Graham and Jessica Livingston on Resilience at Y Combinator: Founder Mode, Cockroaches, Sticking to Your North Star, and Why AI and Climate Keep Them Up at Night

    For the very first episode of Disaster Proof, the conversation goes to a garage in Palo Alto to sit down with Paul Graham and Jessica Livingston, the founders of Y Combinator. They have backed thousands of companies, including many now working in the resilience space, and the discussion covers what makes startups durable, why adaptability beats expertise, how Brian Chesky stumbled into founder mode at Airbnb, why the best ideas grow out of a founder’s own life, and the two specific risks (AI and climate change) that Paul says are the only ones he treats as genuinely game over. You can watch the full conversation on YouTube here.

    TLDW

    Paul Graham and Jessica Livingston explain why constant change favors young, flexible founders, and why Y Combinator picks people over ideas precisely so its judgment never goes obsolete. They unpack adaptability as the trait they hunt for in interviews, the “founder mode” story behind Brian Chesky steering Airbnb through COVID, and the 2008 strategy of funding tough, close-to-revenue “cockroaches.” Paul argues a company survives turbulence by sticking to a North Star instead of acting as a weather vane in shifting moral fashions, using the biosphere tree that collapses without wind as his metaphor for resilience. They turn to climate and energy as the next great market, the difficulty of selling into utilities, the Gridware success story, fusion no longer being thirty years away, and the trap of guilt-based business models versus the reliable assumption that users are selfish, greedy, and lazy. The personal-resilience half covers surviving Twitter mobs, Paul’s obsessive essay process, raising kids by indulging curiosity and picking your battles, prepping by living among reasonable people, political polarization, and why AI and climate are the two things that keep them up at night.

    Thoughts

    The most useful idea in this conversation is also the most counterintuitive: a world that feels like it is ending is structurally good for the people least invested in how it used to work. Paul’s point to terrified founders is that change is only a threat if you have sunk costs in the old order. A young founder has been doing the current plan for two weeks, so a step-function shift in the landscape costs them almost nothing to abandon. The incumbents with elaborate machinery and a decade of assumptions are the ones who should be afraid. That reframes resilience away from defense and toward optionality. The resilient party is not the one with the thickest walls, it is the one with the least to unlearn.

    The founder mode discussion is worth sitting with because it quietly overturns a generation of management orthodoxy. The old rule was that a good CEO hires executives and gets out of their way, and that getting into the details is micromanaging. Brian Chesky’s COVID experience at Airbnb broke that rule under maximum pressure. With bankruptcy on the table and a travel company facing a world that stopped traveling, he went line by line through the business and told people what good looked like, then gave them freedom to execute against that standard while still demanding visibility. The interesting nuance is the permission structure. A crisis granted Chesky the license to be involved that normal operating conditions would have framed as meddling. The lesson is not “always be in the weeds,” it is that the founder’s deep understanding and disproportionate caring are assets you are wasting if you reflexively delegate them away.

    Paul’s North Star argument is the part most likely to age well. His claim is that companies fail at resilience when they behave like weather vanes, swinging with each gust of public moral fashion. He pairs it with the biosphere tree that grows weak and topples because it was never exposed to wind. Both metaphors point at the same thing: resilience is built by surviving stress while holding your shape, not by avoiding stress and not by reshaping yourself to whatever the crowd currently rewards. The carbon-credit companies he mentions are the cautionary case. They built their entire premise on a fashion (customer guilt about carbon) and went out of business when the wind changed direction. Durable businesses convert a permanent human motive into value, which is why he prefers the brutally honest assumption that the user is selfish, greedy, and lazy, and that your job is to build something that produces good outcomes anyway.

    The climate and energy section reframes a worthy cause as a market-timing bet rather than a moral appeal, and that is the more powerful version. The comparison to fintech in 2008 is the tell. Banking technology was a sleepy, unglamorous sector that venture investors avoided until a crisis cracked it open and made it one of the best categories of the following decade. The argument is that energy and the physical world are sitting at a similar precipice, made newly viable because hardware is starting to behave more like software (order components, assemble, do not build everything from scratch) and because AI’s hunger for power has made energy the binding constraint on the whole industry. The Gridware story crystallizes the founder lesson underneath all of it. The best founder for a hard physical problem was a lineman who worked the electric lines and lived through the fires. The idea grew authentically out of his life, which is the same pattern Jessica keeps returning to and the same advice they give for raising kids.

    Finally, the personal-resilience material is more practical than it first appears. Paul’s method for surviving a Twitter mob is pattern recognition: once it has happened twenty times, you know it ends in two days and they move on to the next target, so you wait it out instead of capitulating. His essay process is the same conviction-building engine applied to ideas. He goes sentence by sentence until there is no false statement left to attack, which is why his challenge to angry readers (“point out the incorrect statement”) almost never gets answered. The throughline across the company advice, the parenting advice, and the personal advice is identical. You build durable conviction not by sitting in a room thinking, but by working the problem until it is right, then refusing to be blown off course by people who never actually engaged with the substance.

    Key Takeaways

    • Experts are frequently wrong because they are experts in a previous version of the world, so Paul deliberately avoids permanent beliefs about the current state of technology.
    • Y Combinator picks startups by picking founders, not ideas, because the founders know more about the ideas than the investors do.
    • Living in England and visiting for each batch lets Paul arrive every quarter expecting the world to be different, which keeps his mind open instead of anchored.
    • A world of constant change feels bad but is actually good for a young, flexible founder who has only been on the current plan for two weeks and can switch easily.
    • Vibe coding went from kind-of-works to reliably works, and even experienced programmers now generate huge volumes of code with AI.
    • There is still a software business even with AI, because someone has to know what to tell the AI to write, and no company is going to write its own database from scratch.
    • The scenario Paul worries about is model companies spinning up agents to start all the startups themselves, removing the need for human founders.
    • The founder traits Jessica looks for are unchanged over the years: determined, flexible-minded, and willing to adapt.
    • In interviews you can spot rigid founders because they answer the question they prepared rather than the one they were asked, and the gears visibly grind when you redirect them.
    • A good adaptability signal is a founder who says “I haven’t thought about that, but here is how I would think about it” instead of freezing.
    • Founder mode, the term, came from Brian Chesky’s experience steering Airbnb through COVID, when bankruptcy was openly discussed in board meetings.
    • Ken Chenault, the former American Express CEO on Airbnb’s board, told Chesky the moment was ten times worse than 9/11 and could define the company.
    • Founder mode meant Chesky understood every line item, told people what good looked like, then gave them freedom to execute while still wanting to see it.
    • Founders see through the fog because they understand the company better than anyone and they care more than anyone, and combining understanding with caring lets them see more.
    • There is always some disaster at Y Combinator, the way a hospital always has someone coding, so a crisis is the normal operating environment, not an exception.
    • During the 2008 crash, YC kept funding because it is always a good time to start a startup, but focused on people close to making money and very tough founders they called cockroaches.
    • Airbnb was the ultimate cockroach, seemingly indestructible, which is exactly why they liked it during the meltdown.
    • YC rests on two axioms: startups matter, and founders are the most important ingredient in startups. As long as those hold, YC has room to exist.
    • Company values are usually written down a few years in, documenting principles that already existed rather than inventing new ones.
    • You cannot move with fashion; you have to stick to your North Star, especially during turbulent, noisy times.
    • Trees grown inside a biosphere fell over because they were never exposed to wind, so being blown around is a necessary part of becoming strong enough to stand.
    • What preserves YC most is that it is a fundamentally good idea: it gives lonely founders money, the right peers, and colleagues they would never otherwise have.
    • The measure of a good startup idea is revenue, and any other metric you care about matters only because it predicts revenue.
    • At the early stage you can afford to be virtuous and even tell founders to go back to college, because the power law means one startup in the batch will carry the returns.
    • Every startup has to find early adopters, who decide quickly, usually do not have much money, and tend to be sophisticated, which means utilities are rarely your first customer.
    • A company that ultimately sells to utilities should start by selling to something that says yes faster, like running a pilot on a single corporate campus.
    • Utilities are under so much stress from wildfire liability, renewables, EV charging, and AI demand that they are unusually willing to try new things out of necessity.
    • Gridware, founded by a former lineman who lived through major fires, is now backed by Sequoia with PG&E as a huge customer, an example of an idea growing out of the founder’s life.
    • The second-biggest chunk of YC startups after AI is hard tech and physical products, not because software is dead but because building physical things is getting more possible.
    • Energy is one of AI’s fundamental constraints; if Sam Altman could have two things for Christmas, they would be energy and GPUs.
    • Nobody says fusion is thirty years away anymore, and the old thirty-year number existed because it was far enough out to avoid demands for results but close enough to keep attention.
    • Energy and physical markets may be where fintech was in 2008, a sleepy sector about to be cracked open by crisis into a great decade.
    • Guilt is a fragile business model because fashions change what people feel guilty about, which is why carbon-credit companies collapsed when the winds shifted.
    • Assume the user is selfish, greedy, and lazy, then build something that causes good things to happen anyway, like clean power that is simply cheaper and more reliable.
    • To survive Twitter mobs, remember they move on in about two days, half are bots or people you would never talk to in real life, and you cannot become a weather vane for moral fashions.
    • You build conviction by working on and developing an idea, not by sitting in a room thinking, unless it is pure thought like math.
    • Paul writes essays sentence by sentence until nothing in them is false, which is why his challenge to point out an incorrect statement almost never gets answered.
    • The best startup ideas, and the best projects in life generally, grow authentically out of the founder’s own interests and experiences.
    • Their parenting philosophy is to give kids confidence and a stable base, indulge their curiosity, and encourage projects nobody told them to do.
    • You pick your battles with kids: put your foot down on cruelty, but accept defeat on things like food and screen time.
    • A useful interview question for anyone with an unusual experience is not “what was it like” but “how was it different than you expected,” which surfaces the genuinely novel detail.
    • In a time of turbulence, bet on an island full of reasonable people; the English may not be very dynamic, but they are reasonable.
    • The hope on political polarization is to build resilient institutions that act as a cage around any single leader, so that throwing the rattle makes no difference.
    • AI and climate change are the two things Paul worries about most because they are both potentially game over, like the Gulf Stream reversing and turning Europe into a frozen wasteland.

    Detailed Summary

    Staying an expert when the world keeps changing

    The conversation opens on Paul Graham’s essay “How to Be an Expert in a Changing World,” whose core point is that experts are often wrong because they are experts in a previous version of the world. Asked how he keeps his own beliefs from going obsolete when the landscape can shift in ninety days, Paul says he focuses on people. YC picks founders rather than ideas because the founders know the ideas better than any investor could. He deliberately holds no permanent beliefs about the current state of technology, and the rhythm of flying in from England for each batch helps: he arrives every quarter already expecting everything to be different. One quarter the story is everyone training open-source models, the next quarter it is Claude code and nobody bothers with open-source models because the frontier versions are better anyway. He comes in with a completely open mind. Jessica and Paul note that today’s founders are more frightened, asking what is even still true, but the message Paul gives them is that constant change favors the young and flexible. If you have only been executing a plan for two weeks, a disruption costs you nothing; you just switch.

    What adaptability looks like in a founder

    Jessica describes the founders she funds as determined, flexible-minded, and willing to adapt, and calls adaptability a key trait always, but especially in uncertain times. In interviews, the rigid applicants reveal themselves by answering the question they planned to answer rather than the one they were asked, and you can almost hear the gears grind when you redirect them. Paul does not let that slide; if they dodge, he just asks again. The positive signal is a founder who, faced with a question they have not considered, says “here is how I would think about it” and reasons live. Both point out that YC itself had to adapt, and that the company they funded the interviewer’s startup as in 2009 looked very different by the end. They funded him in May 2009, in the thick of the financial crisis, after he had quit his job in August 2008 and briefly felt he had made a terrible mistake.

    Founder mode and seeing through the fog

    Paul points to Brian Chesky as the defining example of weathering disaster, a story he explored on This Week in Startups. When COVID hit a travel company like Airbnb, the word bankruptcy was being used in board meetings, and Ken Chenault, the former American Express CEO on the board, warned it was ten times worse than 9/11. Chesky went into what would later be named founder mode, getting into every line item, understanding exactly what was needed, telling people what good looked like, and then giving them freedom to execute while still insisting on visibility. The crisis gave him permission to be the involved CEO he had always wanted to be, the kind of involvement that normal operating conditions would have labeled micromanaging. Paul argues founders see through fog that blinds everyone else for a simple, rational reason: they understand the company better than anyone because they have been there longest and thought of most of it, and they also care more than anyone. Combine deep understanding with deep caring and of course they see more.

    Cockroaches, the North Star, and the biosphere tree

    Returning to 2008, when YC was self-funded and unsure whether anyone would invest by March, they decided to keep going on the principle that it is always a good time to start a startup, but to fund people close to making money and very tough founders they called cockroaches, after the creatures that survive nuclear war. Airbnb was the ultimate cockroach. Paul frames YC’s longevity around two axioms (startups matter, founders are the most important ingredient) and around resilience built through stress. He tells the story of trees grown inside a biosphere that fell over because they were never exposed to wind, since being blown about is a necessary part of a tree becoming strong enough to support its own weight. YC has been blown around and is still standing, which is exactly what gave it practice. The companion idea is the North Star: you cannot move with fashion or act as a weather vane swinging with other people’s moral fashions, you have to hold your founding principles, which Paul eventually wrote down rather than let a 23-year-old new hire do it.

    Climate, energy, and selling into hard markets

    The interviewer’s own path (a curiosity about wildfire that grew from living in California, watching PG&E go bankrupt, a fire on his Mendocino property, volunteering as a firefighter) becomes the case for ideas that grow authentically out of a founder’s life. Climate is framed broadly as energy, the built environment, and transportation, essentially the physical world, and those are hard markets where the buyers are utilities, governments, real estate, and insurance. The advice is to find early adopters who decide quickly, which usually means not starting with a utility but with something like a single corporate campus that will say yes faster. Utilities, though, are under so much stress from wildfire liability, renewables, EV charging, and AI demand that they are increasingly willing to try new things. Gridware, founded by a former lineman who lived through major fires, is the proof point: backed by Sequoia, with PG&E as a major customer. Paul notes the second-biggest chunk of YC startups after AI is hard tech, not because software died but because building physical things is getting more possible, more like ordering and assembling components. Energy is the binding constraint on AI, fusion no longer feels thirty years away, and the bet is that energy and physical markets are where fintech was in 2008, about to be cracked open.

    Guilt versus greed as a business model

    On the question of whether climate companies should sell on guilt (recycle, pay more because it is sustainable), Paul is blunt that guilt is fragile because fashions change what you are supposed to feel guilty about. The carbon-credit companies thrived until buying carbon credits stopped being cool, then went out of business. A founder’s own concern for the world can drive great companies, but depending on a customer’s guilt is shallow. The durable move is to assume the user is selfish, greedy, and lazy, someone who just wants to eat pizza and watch Netflix, and to build something that produces good outcomes despite that. Clean power is the perfect example: nobody watching Netflix is upset that fusion powers their television, and if it is cheaper and more reliable, that is simply more Netflix and more money for pizza.

    Personal resilience, Twitter mobs, and the essay process

    On surviving public criticism, Paul’s method is pattern recognition: after twenty mobs you stop counting and know it will be over in two days when they move to the next topic, so you wait it out even though it genuinely feels miserable. Half of them are bots or people you would never talk to in real life, but the deeper point is that companies and people stay resilient by not succumbing to mobs and not becoming weather vanes for moral fashions. Conviction is built by working on an idea, not sitting in a room thinking about it, unless it is pure thought like math. His essays are the engine: he writes a version one, notices everything wrong, and fixes it sentence by sentence until there is no false statement left. He will read an entire book for a single sentence because he would be mortified to publish something false and, having no deadlines, has no excuse. That is why his standing challenge to angry readers, to point out one incorrect statement, almost never gets answered.

    Raising kids, prepping, and the things that keep them up at night

    Their parenting philosophy is to give kids confidence and a stable base, indulge curiosity, and encourage projects nobody assigned, like the living room overrun by one son’s Lego. They pick their battles: they put their foot down on cruelty but admit total defeat on food, devices, and screen time. Paul’s favorite question for anyone with an unusual experience is not “what was it like” but “how was it different than you expected,” which surfaces the genuinely novel detail, and the meta-version of that became the show’s recurring question to all guests. On prepping, they joke that living in the English countryside is itself a form of preparation, and that in turbulent times you should bet on an island full of reasonable people. The episode closes on what keeps them up at night: AI and climate change, the two things Paul treats as uniquely game over, illustrated by the prospect of the Gulf Stream reversing and leaving Europe, which sits as far north as Alaska, a frozen wasteland. Jessica notes her YC superhero name was Panic, and the conversation ends, after a detour through political polarization and a child who insisted for six months on being called SR-71 forecast 80 leaping leopard, on the admission that they manage screen time by being utterly defeated.

    Notable Quotes

    “If you’re a startup founder, a world where things are constantly changing is actually good for you. It feels bad, but you’re better off than anybody else.”

    Paul Graham, on why turbulence favors young, flexible founders

    “You can’t move with fashion. You have to stick to your North Star.”

    Paul Graham, on holding founding principles during noisy, turbulent times

    “There’s always some kind of disaster. It’s almost a rule of thumb at Y Combinator that there’s always some disaster going on, just like in a hospital. There’s always somebody who’s coding.”

    Paul Graham, on crisis as the normal operating environment for startups

    “The measure of a good startup idea is revenue, sure. Let’s not pretend companies are supposed to do something else.”

    Paul Graham, on how to judge whether an idea is actually good

    “Assume that the user is selfish and lazy, and make something. Selfish, greedy, and lazy. And make something that causes good things to happen despite that.”

    Paul Graham, on why guilt is a weak business model and greed is a source of energy

    “This is where the best startup ideas come from. They grow authentically out of the founders’ lives.”

    Jessica Livingston, on a wildfire curiosity turning into a company

    “Please point out the incorrect statement I’ve made in this essay. And no one ever does that.”

    Paul Graham, on writing essays sentence by sentence until nothing in them is false

    “AI and climate change have something in common. They’re the two big things I worry about the most, because they’re both game overs.”

    Paul Graham, on what keeps him up at night

    This is the first episode of Disaster Proof, a series exploring the people and technologies building resilience in an increasingly volatile world. You can watch the full conversation with Paul Graham and Jessica Livingston on YouTube here.

    Related Reading

  • 5 Ways to Profit from the AI Gold Rush: Tips and Strategies for Success

    The AI gold rush is upon us, and it’s no secret that the potential for profit in the field of artificial intelligence is huge. With the rapid advancement of technology and the increasing demand for AI-powered products and services, now is the time to get in on the action and start profiting from this exciting industry.

    But how exactly can you profit from the AI gold rush? Here are a few ideas to get you started:

    1. Develop your own AI products or services.

    One of the most obvious ways to profit from the AI gold rush is to develop your own AI products or services. This can include anything from creating a new AI-powered software application to building a machine learning algorithm that can be used by other companies.

    To get started, it’s important to have a strong understanding of the underlying technologies and techniques that are used in artificial intelligence. This might include learning about machine learning, natural language processing, and computer vision. You’ll also want to familiarize yourself with the various tools and platforms that are available for building and deploying AI-powered products and services.

    1. Invest in AI-focused startups.

    Another way to profit from the AI gold rush is to invest in AI-focused startups. These companies are often at the forefront of the latest AI technologies and are well positioned to capitalize on the growing demand for AI products and services.

    To find potential investment opportunities, you can keep an eye on industry news and events, attend startup pitch events, and network with other investors and entrepreneurs in the AI space. It’s also a good idea to do your homework and thoroughly research any potential investments before committing any capital.

    1. Offer AI consulting services.

    If you have a strong background in artificial intelligence and are looking for a way to profit from the AI gold rush, you might consider offering AI consulting services. Many companies are looking to incorporate AI into their operations, but they may not have the in-house expertise to do so. As an AI consultant, you can help these companies understand the potential benefits of AI and guide them through the process of implementing AI-powered solutions.

    To get started as an AI consultant, you’ll need to build up your knowledge and expertise in the field. This might include earning a degree in a related field or gaining practical experience through internships or projects. You’ll also want to establish a strong network of contacts and connections within the AI industry to help you find consulting opportunities.

    1. Participate in AI-focused hackathons and competitions.

    Another way to profit from the AI gold rush is to participate in AI-focused hackathons and competitions. These events bring together developers, engineers, and data scientists to work on solving real-world problems using artificial intelligence.

    By participating in these events, you’ll have the opportunity to showcase your skills and expertise, network with other professionals in the AI field, and potentially win cash prizes or other awards. Many hackathons and competitions are sponsored by companies that are looking to find new talent and ideas, so this can also be a great way to get your foot in the door with potential employers or investors.

    1. Educate yourself and stay up-to-date on the latest AI trends.

    Finally, one of the most important things you can do to profit from the AI gold rush is to educate yourself and stay up-to-date on the latest trends and developments in the field. This might involve taking online courses or earning a degree in a related field, attending industry conferences and events, or simply staying abreast of the latest news and insights through blogs and online publications.

    By staying informed and keeping your skills sharp, you’ll be better positioned to take advantage of opportunities as

    they arise and make informed decisions about how to best profit from the AI gold rush. This could mean staying on top of emerging technologies and techniques, such as deep learning or natural language generation, or staying aware of new markets and industries that are adopting AI-powered solutions.

    In addition to staying current on the latest trends, it’s also important to continually develop and enhance your skills in the field. This might involve learning new programming languages or frameworks, taking online courses or earning certifications, or collaborating with others on AI-focused projects.

    As you continue to educate yourself and stay up-to-date on the latest AI trends, you’ll be better equipped to identify and seize opportunities to profit from the AI gold rush. Whether you’re developing your own AI products or services, investing in AI-focused startups, offering AI consulting services, participating in hackathons and competitions, or simply staying informed and current on the latest trends, there are plenty of ways to profit from the AI gold rush. With a little bit of effort and the right approach, you can position yourself to take advantage of this exciting and rapidly-evolving industry.